  We study a coronal mass ejection (CME) associated with an X-class flare,
whose initiation is clearly observed in low corona with high-cadence,
high-resolution EUV images, providing us a rare opportunity to witness the
early evolution of an energetic CME in detail. The eruption starts with a slow
expansion of cool overlying loops (~1 MK) following a jet-like event in the
periphery of the active region. Underneath the expanding loop system a reverse
S-shaped dimming is seen immediately above the brightening active region in hot
EUV passbands. The dimming is associated with a rising diffuse arch (~6 MK),
which we interpret as a preexistent, high-lying flux rope. This is followed by
the arising of a double hot channel (~10 MK) from the core of the active
region. The higher structures rise earlier and faster than lower ones, with the
leading front undergoing extremely rapid acceleration up to 35 km s$^2$. This
suggests that the torus instability is the major eruption mechanism and that it
is the high-lying flux rope rather than the hot channels that drives the
eruption. The compression of coronal plasmas skirting and overlying the
expanding loop system, whose aspect ratio $h/r$ increases with time as a result
of the rapid upward acceleration, plays a significant role in driving an
outward-propagating global EUV wave and a sunward-propagating local EUV wave,
respectively.


  We present exact analytical results for the Caputo fractional derivative of a
wide class of elementary functions, including trigonometric and inverse
trigonometric, hyperbolic and inverse hyperbolic, Gaussian, quartic Gaussian,
and Lorentzian functions. These results are especially important for
multi-scale physical systems, such as porous materials, disordered media, and
turbulent fluids, in which transport is described by fractional partial
differential equations. The exact results for the Caputo fractional derivative
are obtained from a single generalized Euler's integral transform of the
generalized hyper-geometric function with a power-law argument. We present a
proof of the generalized Euler's integral transform and directly apply it to
the exact evaluation of the Caputo fractional derivative of a broad spectrum of
functions, provided that these functions can be expressed in terms of a
generalized hyper-geometric function with a power-law argument. We determine
that the Caputo fractional derivative of elementary functions is given by the
generalized hyper-geometric function. Moreover, we show that in the most
general case the final result cannot be reduced to elementary functions, in
contrast to both the Liouville-Caputo and Fourier fractional derivatives.
However, we establish that in the infinite limit of the argument of elementary
functions, all three definitions of a fractional derivative - the Caputo,
Liouville-Caputo, and Fourier- converge to the same result given by the
elementary functions. Finally, we prove the equivalence between
Liouville-Caputo and Fourier fractional derivatives.


  The kernel of the evolution equation is used to build a mathematical theory
of thermal phenomena of gaseous and condensed matter. The group velocity of
sound and the molar density are proposed to be its two thermal variables that
replace the over-complete set of temperature, pressure and volume. The defining
constants of the New SI (2019) of physical units are used to form a
dimensionless variable of the new geometrical thermal theory. The dimensionless
thermal sum generates, by the variation principle, equations on physical
observables. Energy and entropy are neither defined nor needed in the present
formalism. The molar specific heat is derived and its temperature dependence is
verified with the experimental data for mono-atomic gases. Critique of the
postulates and notions of traditional statistical thermodynamics is given.


  Distributed optimization has gained significant attention in recent years,
primarily fueled by the availability of a large amount of data and
privacy-preserving requirements. This paper presents a fixed-time convergent
optimization algorithm for solving a potentially non-convex optimization
problem using a first-order multi-agent system. Each agent in the network can
access only its private objective function, while local information exchange is
permitted between the neighbors. The proposed optimization algorithm combines a
fixed-time convergent distributed parameter estimation scheme with a fixed-time
distributed consensus scheme as its solution methodology. The results are
presented under the assumption that the team objective function is strongly
convex, as opposed to the common assumptions in the literature requiring each
of the local objective functions to be strongly convex. The results extend to
the class of possibly non-convex team objective functions satisfying only the
Polyak-\L ojasiewicz (PL) inequality. It is also shown that the proposed
continuous-time scheme, when discretized using Euler's method, leads to
consistent discretization, i.e., the fixed-time convergence behavior is
preserved under discretization. Numerical examples comprising large-scale
distributed linear regression and training of neural networks corroborate our
theoretical analysis.


  This paper addresses cooperation in a multicell environment where base
stations (BSs) wish to jointly serve multiple users, under a
constrained-capacity backhaul. We point out that for finite backhaul capacity a
trade-off between sharing user data, which allows for full MIMO cooperation,
and not doing so, which reduces the setup to an interference channel but also
requires less overhead, emerges. We optimize this trade-off by formulating a
rate splitting approach in which non-shared data (private to each transmitter)
and shared data are superposed. We derive the corresponding achievable rate
region and obtain the optimal beamforming design for both shared and private
symbols. We show how the capacity of the backhaul can be used to determine how
much of the user data is worth sharing across multiple BSs, particularly
depending on how strong the interference is.


  A quantum simulator based on ultracold optically trapped atoms for simulating
the physics of atoms and molecules in ultrashort intense laser fields is
introduced. The slowing down by about 13 orders of magnitude allows to watch in
slow motion the tunneling and recollision processes that form the heart of
attosecond science. The extreme flexibility of the simulator promises a deeper
understanding of strong-field physics, especially for many-body systems beyond
the reach of classical computers. The quantum simulator can experimentally
straightforwardly be realized and is shown to recover the ionization
characteristics of atoms in the different regimes of laser-matter interaction.


  In this work, we study flavor-violating scalars (flavons) in a range of large
masses that have not been explored previously. We model the interactions with
an effective field theory formulation where the flavon is heavier than the top
quark. In addition, we assume that the flavon only couples to fermions of the
Standard Model in a flavor-changing way. As the flavon couples strongly to top
quarks, same-sign and opposite-sign top quark pair signals can be explored in
the search for those particles. Using parametrized neural networks, we show
that it is possible to probe flavons with masses in the 200-1600 GeV range
through their interactions with a top quark plus up and charm quarks for
effective couplings of order 10^-2 TeV^-1 at the 14 TeV High-Luminosity LHC.


  In this work, we present BanglaParaphrase, a high-quality synthetic Bangla
Paraphrase dataset curated by a novel filtering pipeline. We aim to take a step
towards alleviating the low resource status of the Bangla language in the NLP
domain through the introduction of BanglaParaphrase, which ensures quality by
preserving both semantics and diversity, making it particularly useful to
enhance other Bangla datasets. We show a detailed comparative analysis between
our dataset and models trained on it with other existing works to establish the
viability of our synthetic paraphrase data generation pipeline. We are making
the dataset and models publicly available at
https://github.com/csebuetnlp/banglaparaphrase to further the state of Bangla
NLP.


  In this work, we develop Gaussian process regression (GPR) models of
hyperelastic material behavior. First, we consider the direct approach of
modeling the components of the Cauchy stress tensor as a function of the
components of the Finger stretch tensor in a Gaussian process. We then consider
an improvement on this approach that embeds rotational invariance of the
stress-stretch constitutive relation in the GPR representation. This approach
requires fewer training examples and achieves higher accuracy while maintaining
invariance to rotations exactly. Finally, we consider an approach that recovers
the strain-energy density function and derives the stress tensor from this
potential. Although the error of this model for predicting the stress tensor is
higher, the strain-energy density is recovered with high accuracy from limited
training data. The approaches presented here are examples of physics-informed
machine learning. They go beyond purely data-driven approaches by embedding the
physical system constraints directly into the Gaussian process representation
of materials models.


  We consider the permutation group algebra defined by Cameron and show that if
the permutation group has no finite orbits, then no homogeneous element of
degree one is a zero-divisor of the algebra. We proceed to make a conjecture
which would show that the algebra is an integral domain if, in addition, the
group is oligomorphic. We go on to show that this conjecture is true in certain
special cases, including those of the form H Wr S and H Wr A, and show that in
the oligormorphic case, the algebras corresponding to these special groups are
polynomial algebras. In the H Wr A case, the algebra is related to the shuffle
algebra of free Lie algebra theory. We finish by considering some integer
sequences which arise from certain of these groups.


  We report a synchrotron x-ray scattering study of the diluted spin-Peierls
(SP) material Cu_{1-x}Mg_xGeO_3. In a recent paper we have shown that the SP
dimerization attains long-range order only for x < x_c = 0.022(0.001). Here we
report that the SP transition is reentrant in the vicinity of the critical
concentration x_c. This is manifested by broadening of the SP dimerization
superlattice peaks below the reentrance temperature, T_r, which may mean either
the complete loss of the long-range SP order or the development of a
short-range ordered component within the long-range ordered SP state. Marked
hysteresis and very large relaxation times are found in the samples with Mg
concentrations in the vicinity of x_c. The reentrant transition is likely
related to the competing Neel transition which occurs at a temperature similar
to T_r. We argue that impurity-induced competing interchain interactions play
an essential role in these phenomena.


  Let $G$ be a multiplicatively written finite group. We denote by $\mathsf
E(G)$ the smallest integer $t$ such that every sequence of $t$ elements in $G$
contains a product-one subsequence of length $|G|$. In 1961, Erd\H{o}s,
Ginzburg and Ziv proved that $\mathsf E(G)\leq 2|G|-1$ for every finite
solvable group $G$ and this result is well known as the Erd\H{o}s-Ginzburg-Ziv
Theorem. In 2010, Gao and Li improved this result to $\mathsf
E(G)\leq\frac{7|G|}{4}-1$ and they conjectured that $\mathsf E(G)\leq
\frac{3|G|}{2}$ holds for any finite non-cyclic group. In this paper, we
confirm the conjecture for all finite non-cyclic groups of odd order.


  Battery-hybrid power source architectures can reduce fuel consumption and
emissions for ships with diverse operation profiles. However, conventional
control strategies may fail to improve performance if the future operation
profile is unknown to the controller. This paper proposes a guidance,
navigation, and control (GNC) function that integrates trajectory generation
and hybrid power source supervisory control. We focus on time and fuel optimal
path-constrained trajectory planning. This problem is a nonlinear and nonconvex
optimal control problem, which means that it is not readily amenable to
efficient and reliable solution onboard. We propose a nonlinear change of
variables and constraint relaxations that transform the nonconvex planning
problem into a convex optimal control problem. The nonconvex
three-degree-of-freedom dynamics, hydrodynamic forces, fixed pitch propeller,
battery, and general energy converter (e.g., fuel cell or generating set)
dissipation constraints are expressed in convex functional form. A condition
derived from Pontryagin's Minimum Principle guarantees that, when satisfied,
the solution of the relaxed problem provides the solution to the original
problem. The validity and effectiveness of this approach are numerically
illustrated for a battery-hybrid vessel in model scale. First, the convex
hydrodynamic hull and rudder force models are validated with towing tank test
data. Second, optimal trajectories and supervisory control schemes are
evaluated under varying mission requirements. The convexification scheme in
this work lays the path for the employment of mature, computationally robust
convex optimization methods and creates a novel possibility for real-time
optimization onboard future smart and unmanned surface vehicles.


  We study the problem of model selection type aggregation with respect to the
Kullback-Leibler divergence for various probabilistic models. Rather than
considering a convex combination of the initial estimators $f_1, \ldots, f_N$,
our aggregation procedures rely on the convex combination of the logarithms of
these functions. The first method is designed for probability density
estimation as it gives an aggregate estimator that is also a proper density
function, whereas the second method concerns spectral density estimation and
has no such mass-conserving feature. We select the aggregation weights based on
a penalized maximum likelihood criterion. We give sharp oracle inequalities
that hold with high probability, with a remainder term that is decomposed into
a bias and a variance part. We also show the optimality of the remainder terms
by providing the corresponding lower bound results.


  This paper treats boundary conditions on black hole horizons for the full
3+1D Einstein equations. Following a number of authors, the apparent horizon is
employed as the inner boundary on a space slice. It is emphasized that a
further condition is necessary for the system to be well posed; the
``prescribed curvature conditions" are therefore proposed to complete the
coordinate conditions at the black hole. These conditions lead to a system of
two 2D elliptic differential equations on the inner boundary surface, which
coexist nicely to the 3D equation for maximal slicing (or related slicing
conditions). The overall 2D/3D system is argued to be well posed and globally
well behaved. The importance of ``boundary conditions without boundary values"
is emphasized. This paper is the first of a series. This revised version makes
minor additions and corrections to the previous version.


  Asteroseismology is playing an increasingly important role in the
characterization of red-giant host stars and their planetary systems. Here, we
conduct detailed asteroseismic modeling of the evolved red-giant branch (RGB)
hosts KOI-3886 and $\iota$ Draconis, making use of end-of-mission Kepler
(KOI-3886) and multi-sector TESS ($\iota$ Draconis) time-series photometry. We
also model the benchmark star KIC 8410637, a member of an eclipsing binary,
thus providing a direct test to the seismic determination. We test the impact
of adopting different sets of observed modes as seismic constraints. Inclusion
of $\ell=1$ and 2 modes improves the precision on the stellar parameters,
albeit marginally, compared to adopting radial modes alone, with $1.9$-$3.0\%$
(radius), $5$-$9\%$ (mass), and $19$-$25\%$ (age) reached when using all
p-dominated modes as constraints. Given the very small spacing of adjacent
dipole mixed modes in evolved RGB stars, the sparse set of observed g-dominated
modes is not able to provide extra constraints, further leading to highly
multimodal posteriors. Access to multi-year time-series photometry does not
improve matters, with detailed modeling of evolved RGB stars based on
(lower-resolution) TESS data sets attaining a precision commensurate with that
based on end-of-mission Kepler data. Furthermore, we test the impact of varying
the atmospheric boundary condition in our stellar models. We find mass and
radius estimates to be insensitive to the description of the near-surface
layers, at the expense of substantially changing both the near-surface
structure of the best-fitting models and the values of associated parameters
like the initial helium abundance, $Y_{\rm i}$. Attempts to measure $Y_{\rm i}$
from seismic modeling of red giants may thus be systematically dependent on the
choice of atmospheric physics.


  Modern software systems are usually highly configurable, providing users with
customized functionality through various configuration options. Understanding
how system performance varies with different option combinations is important
to determine optimal configurations that meet specific requirements. Due to the
complex interactions among multiple options and the high cost of performance
measurement under a huge configuration space, it is challenging to study how
different configurations influence the system performance. To address these
challenges, we propose HINNPerf, a novel hierarchical interaction neural
network for performance prediction of configurable systems. HINNPerf employs
the embedding method and hierarchic network blocks to model the complicated
interplay between configuration options, which improves the prediction accuracy
of the method. Besides, we devise a hierarchical regularization strategy to
enhance the model robustness. Empirical results on 10 real-world configurable
systems show that our method statistically significantly outperforms
state-of-the-art approaches by achieving average 22.67% improvement in
prediction accuracy. In addition, combined with the Integrated Gradients
method, the designed hierarchical architecture provides some insights about the
interaction complexity and the significance of configuration options, which
might help users and developers better understand how the configurable system
works and efficiently identify significant options affecting the performance.


  We investigate the interior hyperbolic region of axisymmetric and stationary
black holes surrounded by a matter distribution. First, we treat the
corresponding initial value problem of the hyperbolic Einstein equations
numerically in terms of a single-domain fully pseudo-spectral scheme.
Thereafter, a rigorous mathematical approach is given, in which soliton methods
are utilized to derive an explicit relation between the event horizon and an
inner Cauchy horizon. This horizon arises as the boundary of the future domain
of dependence of the event horizon. Our numerical studies provide strong
evidence for the validity of the universal relation $\Ap\Am = (8\pi J)^2$ where
$\Ap$ and $\Am$ are the areas of event and inner Cauchy horizon respectively,
and $J$ denotes the angular momentum. With our analytical considerations we are
able to prove this relation rigorously.


  In equilibrium, the number of conduction electrons in a solid substance
depends on the conformation of the atoms in the substance. When a magnetic
field is applied, it takes time for the system to come to a new equilibrium
with a new conformation. During such times, current may flow. But, unless
superconduction is involved, the new equilibrium contains no current. It is
shown that the transitory currents are consistent with experiments involving
magnetization of small rings at low temperatures.


  Ap Librae is one out of a handful of low-frequency peaked blazars to be
detected at TeV $\gamma$-rays and the only one with an identified X-ray jet.
Combined observations of Fermi-LAT at high energies (HE) and of H.E.S.S at very
high energies (VHE) revealed a striking spectral property of Ap Librae; the
presence of a broad high-energy component that extends more than nine orders of
magnitude in energy and is, therefore, hard to be explained by the usual
single-zone synchrotron self-Compton model. We show that the superposition of
different emission components related to photohadronic interactions can explain
the $\gamma$-ray emission of Ap Librae without invoking external radiation
fields. We present two indicative model fits to the spectral energy
distribution of Ap Librae where the VHE emission is assumed to originate from a
compact, sub-pc scale region of the jet. A robust prediction of our model is
VHE flux variability on timescales similar to those observed at X-rays and HE
$\gamma$-rays, which can be further used to distinguish between a sub-pc or kpc
scale origin of the TeV emission. We thus calculate the expected variability
signatures at X-rays, HE and VHE $\gamma$-rays and show that quasi-simultaneous
flares are expected, with larger amplitude flares appearing at $\gamma$-rays.
We assess the detectability of VHE variability from Ap Librae with CTA, next
generation of IACTs. We show that $\sim$hr timescale variability at
$E_{\gamma}>0.1$ TeV could be detectable at high significance with shorter
exposure times than current Cherenkov telescopes.


  In this paper, we address the problem of hidden common variables discovery
from multimodal data sets of nonlinear high-dimensional observations. We
present a metric based on local applications of canonical correlation analysis
(CCA) and incorporate it in a kernel-based manifold learning technique.We show
that this metric discovers the hidden common variables underlying the
multimodal observations by estimating the Euclidean distance between them. Our
approach can be viewed both as an extension of CCA to a nonlinear setting as
well as an extension of manifold learning to multiple data sets. Experimental
results show that our method indeed discovers the common variables underlying
high-dimensional nonlinear observations without assuming prior rigid model
assumptions.


  Existing deep hashing approaches fail to fully explore semantic correlations
and neglect the effect of linguistic context on visual attention learning,
leading to inferior performance. This paper proposes a dual-stream learning
framework, dubbed Deep Collaborative Discrete Hashing (DCDH), which constructs
a discriminative common discrete space by collaboratively incorporating the
shared and individual semantics deduced from visual features and semantic
labels. Specifically, the context-aware representations are generated by
employing the outer product of visual embeddings and semantic encodings.
Moreover, we reconstruct the labels and introduce the focal loss to take
advantage of frequent and rare concepts. The common binary code space is built
on the joint learning of the visual representations attended by language, the
semantic-invariant structure construction and the label distribution
correction. Extensive experiments demonstrate the superiority of our method.


  Conventional wisdom dictates that $\mathbb{Z}_N$ factors in the integral
cohomology group $H^p(X_n, \mathbb{Z})$ of a compact manifold $X_n$ cannot be
computed via smooth $p$-forms. We revisit this lore in light of the dimensional
reduction of string theory on $X_n$, endowed with a $G$-structure metric that
leads to a supersymmetric EFT. If massive $p$-form eigenmodes of the Laplacian
enter the EFT, then torsion cycles coupling to them will have a non-trivial
smeared delta form, that is an EFT long-wavelength description of $p$-form
currents of the $(n-p)$-cycles of $X_n$. We conjecture that, whenever torsion
cycles are calibrated, their linking number can be computed via their smeared
delta forms. From the EFT viewpoint, a torsion factor in cohomology corresponds
to a $\mathbb{Z}_N$ gauge symmetry realised by a St\"uckelberg-like action, and
calibrated torsion cycles to BPS objects that source the massive fields
involved in it.


  Radio sources have traditionally provided convenient beacons for probing the
early Universe. Hy Spinrad was among the first of the tenacious breed of
observers who would attempt to obtain optical identifications and spectra of
the faintest possible `radio galaxies' to investigate the formation and
evolution of galaxies at hy redshift. Modern telescopes and instruments have
made these tasks much simpler, although not easy, and here we summarize the
current status of our hunts for hy redshift radio galaxies (HyZRGs) using radio
spectral and near-IR selection.


  The use of the unmanned aerial vehicle (UAV) has been foreseen as a promising
technology for the next generation communication networks. Since there are no
regulations for UAVs deployment yet, most likely they form a network in
coexistence with an already existed network. In this work, we consider a
transmission mechanism that aims to improve the data rate between a terrestrial
base station (BS) and user equipment (UE) through deploying multiple UAVs
relaying the desired data flow. Considering the coexistence of this network
with other established communication networks, we take into account the effect
of interference, which is incurred by the existing nodes. Our primary goal is
to optimize the three-dimensional (3D) trajectories and power allocation for
the relaying UAVs to maximize the data flow while keeping the interference to
existing nodes below a predefined threshold. An alternating-maximization
strategy is proposed to solve the joint 3D trajectory design and power
allocation for the relaying UAVs. To this end, we handle the information
exchange within the network by resorting to spectral graph theory and
subsequently address the power allocation through convex optimization
techniques. Simulation results show that our approach can considerably improve
the information flow while the interference threshold constraint is met.


  ZnO nanoparticles (NP) of size less than around 20 nm inside a chamber
exhibits complete reduction in their visible photoluminescence (PL) intensity
when partial pressure of oxygen in the surrounding gaseous environment is
decreased by evacuation. However the visible PL from ZnO nanoparticles are
insensitive to the gases like N2, He and Ar. The rate of change of PL intensity
with pressure (or the sensitivity) is inversely proportional to the ambient air
pressure. Sensitivity increases when surface to volume ratio increases for
lower particle size. The charged oxygen vacancies are attributed as the
responsible candidates for surface related visible PL from ZnO NP.


  A strong-coupling series expansion for the Green's function and the
extremely-correlated Fermi liquid (ECFL) theory are used to calculate the
moments of the electronic spectral functions of the infinite-U Hubbard model.
Results from these two complementary methods agree very well at both, low
densities, where the ECFL solution is the most accurate, and at high to
intermediate temperatures, where the series converge. We find that a modified
first moment, which underestimates the contributions from the occupied states
and is accessible in the series through the time-dependent Green's function,
best describes the quasiparticle peak location in the strongly-correlated
regime. This is examined by the ECFL results at low temperatures, where it is
shown that the spectral function is largely skewed towards the occupied states.


  We develop a theory of quasiparticle localization in superconductors in
situations without spin rotation invariance. We discuss the existence, and
properties of superconducting phases with localized/delocalized quasiparticle
excitations in such systems in various dimensionalities. Implications for a
variety of experimental systems, and to the properties of random Ising models
in two dimensions, are briefly discussed.


  There are demographic biases present in current facial recognition (FR)
models. To measure these biases across different ethnic and gender subgroups,
we introduce our Balanced Faces in the Wild (BFW) dataset. This dataset allows
for the characterization of FR performance per subgroup. We found that relying
on a single score threshold to differentiate between genuine and imposters
sample pairs leads to suboptimal results. Additionally, performance within
subgroups often varies significantly from the global average. Therefore,
specific error rates only hold for populations that match the validation data.
To mitigate imbalanced performances, we propose a novel domain adaptation
learning scheme that uses facial features extracted from state-of-the-art
neural networks. This scheme boosts the average performance and preserves
identity information while removing demographic knowledge. Removing demographic
knowledge prevents potential biases from affecting decision-making and protects
privacy by eliminating demographic information. We explore the proposed method
and demonstrate that subgroup classifiers can no longer learn from features
projected using our domain adaptation scheme. For access to the source code and
data, please visit https://github.com/visionjo/facerec-bias-bfw.


  Vortices are topological objects formed in coherent nonlinear systems. As
such they are studied in a wide number of physical systems and promise
applications in information storage, processing, and communication. In
semiconductor microcavities, vortices in polariton condensates can be
conveniently created, studied, and manipulated using solely optical means. For
non-resonant excitation with a ring-shaped pump a stable vortex can be formed,
leading to bistability with left- and righthanded vorticity. In the present
work we report on a much richer vortex multistability, with optically
addressable vortices with topological charges |m|=1, 2, and 3, all stable for
the same system and excitation parameters. This unusual multistable behavior is
rooted in the inherent nonlinear feedback between reservoir excitations and
condensate in the microcavity. For larger radius of the ring shaped pump we
also find a Bessel vortex with its characteristic spiralling phase in the high
density region and pronounced self-stabilization ability. Our theoretical
results open up exciting possibilities for optical manipulation of vortex
multiplets in a compact semiconductor system.


  The evidence for neutrino oscillations leads to small neutrino masses, which
can be realized by means of the seesaw mechanism. In this framework,
baryogenesis may be achieved from leptogenesis.


  It is known that a single mapping defined on one term of a differential
graded vector space extends to a strongly homotopy Lie algebra structure on the
graded space when that mapping satisfies two conditions. This strongly homotopy
Lie algebra is nontrivial (it is not a Lie algebra); however we show that one
can obtain an sh-Lie algebra where the only nonzero mappings defining it are
the lower order mappings. This structure applies to a significant class of
examples. Moreover in this case the graded space can be replaced by another
graded space, with only three nonzero terms, on which the same sh-Lie structure
exists.


  We report in this paper a thorough study on the the dynamical mechanics of
the fractional Brownian motion systems. Where several non-trivial properties
are revealed such as the abundant non-Markovian effects resulted from the
fractional characters of the system. In general, the dynamics of the fBm system
is found to be of a purely Newton's type, despite of the anomalous fractional
properties of the system.


  The large forward-backward asymmetry of the top quark pair production
measured by the hadron colliders shed light on new physics signals beyond the
Standard Model. In the Randall-Sundrum model with an additional SU(3) group in
the strong sector, we compare the total cross section and forward-backward
asymmetry of the top quark pair production with the newest data obtained by the
CDF and the D0 collaborations. Our numerical analysis shows that the parameter
$c_q \gtrsim 0.5$, $c_t \sim -0.6\sim-0.8$, $\tan\phi \gtrsim 20$ or $\tan\phi
\leq 1/20$ and the first excitation of axial gluon with a mass about $5
\thicksim 6 \mathrm{TeV}$ can accommodate this large anomaly without violating
other experimental constraints.


  We numerically study the symmetry breaking instabilities of vortex patterns
in a rotating harmonic potential using a type of Ginzburg-Landau equation. The
configurations of vortex lattices change markedly by the symmetry-breaking
instabilities, and then, some vortices move away from the confinement
potential, which leads to the annihilation of vortices. The symmetry-breaking
instabilities and the instabilities of vortex nucleation determine the
parameter region of stable vortex patterns. We verify that the
symmetry-breaking instabilities also occur in a type of complex Ginzburg-Landau
equation.


  This list of 10 problems and their subsequent discussion by prominent
scholars at the workshop in Geneva Observatory, 1993, were published in Lecture
Notes in Physics, 1994, not too reachable now. The problems cover Theory,
Computer Simulations (including regarding Lyapunov exponents and the search of
codes radically differing from standard N-body ones) and Observations. The
elapsed 20 year period enables to reveal the occurred developments, if any, on
each problem and the validity of predictions made by experts.


  Many interesting and fundamentally practical optimization problems, ranging
from optics, to signal processing, to radar and acoustics, involve constraints
on the Fourier transform of a function. It is well-known that the {\em fast
Fourier transform} (fft) is a recursive algorithm that can dramatically improve
the efficiency for computing the discrete Fourier transform. However, because
it is recursive, it is difficult to embed into a linear optimization problem.
In this paper, we explain the main idea behind the fast Fourier transform and
show how to adapt it in such a manner as to make it encodable as constraints in
an optimization problem. We demonstrate a real-world problem from the field of
high-contrast imaging. On this problem, dramatic improvements are translated to
an ability to solve problems with a much finer grid of discretized points. As
we shall show, in general, the "fast Fourier" version of the optimization
constraints produces a larger but sparser constraint matrix and therefore one
can think of the fast Fourier transform as a method of sparsifying the
constraints in an optimization problem, which is usually a good thing.


  In this work, we explore the use of an iterative Bayesian Monte Carlo (IBM)
procedure for nuclear data evaluation within a Talys Evaluated Nuclear data
Library (TENDL) framework. In order to identify the model and parameter
combinations that reproduce selected experimental data, different physical
models implemented within the TALYS code, were sampled and varied
simultaneously to produce random input files with unique model combinations.
All the models considered were assumed to be equal a priori. Parameters to
these models were then varied simultaneously using the TALYS code system to
produce a set of random ENDF files which were processed into x-y tables for
comparison with selected experimental data from the EXFOR database within a
Bayesian framework. To improve our fit to experimental data, we iteratively
update our 'best' file - the file that maximises the likelihood function - by
re-sampling model parameters around this file. The method proposed has been
applied for the evaluation of p+Cd-111 and Co-59 between 1 - 100 MeV incident
energy region. Finally, the adjusted files were compared with experimental data
from the EXFOR database as well as with evaluations from the TENDL-2017 and
JENDL-4.0/HE nuclear data libraries.


  Records of time-stamped social interactions between pairs of individuals
(e.g., face-to-face conversations, e-mail exchanges, and phone calls)
constitute a so-called temporal network. A remarkable difference between
temporal networks and conventional static networks is that time-stamped events
rather than links are the unit elements generating the collective behavior of
nodes. We propose an importance measure for single interaction events. By
generalizing the concept of the advance of event proposed by [Kossinets G,
Kleinberg J, and Watts D J (2008) Proceeding of the 14th ACM SIGKDD
International conference on knowledge discovery and data mining, p 435], we
propose that an event is central when it carries new information about others
to the two nodes involved in the event. We find that the proposed measure
properly quantifies the importance of events in connecting nodes along
time-ordered paths. Because of strong heterogeneity in the importance of events
present in real data, a small fraction of highly important events is necessary
and sufficient to sustain the connectivity of temporal networks. Nevertheless,
in contrast to the behavior of scale-free networks against link removal, this
property mainly results from bursty activity patterns and not heterogeneous
degree distributions.


  Potential based no-regret dynamics are shown to be related to fictitious
play. Roughly, these are epsilon-best reply dynamics where epsilon is the
maximal regret, which vanishes with time. This allows for alternative and
sometimes much shorter proofs of known results on convergence of no-regret
dynamics to the set of Nash equilibria.


  Fourth-derivative gravity has two free parameters, $\alpha$ and $\beta$,
which couple the curvature-squared terms $R^2$ and $R_{\mu\nu}^2$. Relativistic
effects and short-range laboratory experiments can be used to provide upper
limits to these constants. In this work we briefly review both types of
experimental results in the context of higher-derivative gravity. The strictest
limit follows from the second kind of test. Interestingly enough, the bound on
$\beta$ due to semiclassical light deflection at the solar limb is only one
order of magnitude larger.


  We prove the weight-monodromy conjecture for varieties which are p-adically
uniformized by a product of the Drinfeld upper half spaces. It is an easy
consequence of Dat's work on the cohomology complex of the Drinfeld upper half
space.


  Given a link of a normal surface singularity with its canonical contact
structure, we compare the collection of its Stein fillings to its Milnor
fillings (that is, Milnor fibers of possible smoothings). We prove that, unlike
Stein fillings, Milnor fillings of a given link have bounded topology; for
links of sandwiched singularities, we further establish that there are only
finitely many Milnor fillings. We discuss some other obstructions for a Stein
filling to be represented by a Milnor fiber, and for various types of
singularities, including simple classes like cusps and triangle singularities,
we produce Stein fillings that do not come from Milnor fibers or resolutions.
Meanwhile, we discover that there are many contact 3-manifolds that admit
infinitely many Stein fillings but do not admit arbitrarily large ones.


  Decision trees have been a very popular class of predictive models for
decades due to their interpretability and good performance on categorical
features. However, they are not always robust and tend to overfit the data.
Additionally, if allowed to grow large, they lose interpretability. In this
paper, we present a mixed integer programming formulation to construct optimal
decision trees of a prespecified size. We take the special structure of
categorical features into account and allow combinatorial decisions (based on
subsets of values of features) at each node. Our approach can also handle
numerical features via thresholding. We show that very good accuracy can be
achieved with small trees using moderately-sized training sets. The
optimization problems we solve are tractable with modern solvers.


  We present modeling of the long-term optical light curve and radial velocity
curve of the binary stellar system CXOGBS J175553.2-281633, first detected in
X-rays in the \textit{Chandra} Galactic Bulge Survey. We analyzed 7 years of
optical I-band photometry from OGLE and found long-term variations from year to
year. These long-term variations can most likely be explained with by either
variations in the luminosity of the accretion disk or a spotted secondary star.
The phased light curve has a sinusoidal shape, which we interpret as being due
to ellipsoidal modulations. We improve the orbital period to be $P = 10.34488
\pm 0.00006$ h with a time of inferior conjunction of the secondary star $T_0 =
{\rm HJD\ } 2455260.8204 \pm 0.0008$. Moreover, we collected 37 spectra over 6
non-consecutive nights. The spectra show evidence for an evolved K7 secondary
donor star, from which we obtain a semi-amplitude for the radial velocity curve
of $K_2 = 161 \pm 6 $ km s$^{-1}$. Using the light curve synthesis code {\tt
XRbinary}, we derive the most likely orbital inclination for the binary of $i =
63.0\pm0.7$ deg, a primary mass of $M_1 = 0.83 \pm 0.06$ M$_\odot$, consistent
with a white dwarf accretor, and a secondary donor mass of $M_2 = 0.65 \pm
0.07$ M$_\odot$, consistent with the spectral classification. Therefore, we
identify the source as a long orbital period cataclysmic variable star.


  The bright-rimmed cloud IC 1396N is believed to host one of the few known
cases where two bipolar CO outflows driven by young stellar objects actually
collide. The CO outflows are traced by chains of knots of H_2 emission, with
enhanced emission at the position of the possible collision.
  The aim of this work is to use the proper motions of the H_2 knots to confirm
the collision scenario.
  A second epoch H_2 image was obtained, and the proper motions of the knots
were determined with a time baseline of ~11 years. We also performed
differential photometry on the images to check the flux variability of the
knots.
  For each outflow (N and S) we classified the knots as pre-collision or
post-collision. The axes of the pre-collision knots, the position of the
possible collision point, and the axes of the post-collision knots were
estimated. The difference between the proper motion direction of the
post-collision knots and the position angle from the collision point was also
calculated. For some of the knots we obtained the 3D velocity by using the
radial velocity derived from H_2 spectra.
  The velocity pattern of the H_2 knots in the area of interaction
(post-collision knots) shows a deviation from that of the pre-collision knots,
consistent with being a consequence of the interaction between the two
outflows. This favours the interpretation of the IC 1396N outflows as a true
collision between two protostellar jets instead of a projection effect.


  We identify a set of asteroids among Centaurs and Damocloids, that orbit
contrary to the common direction of motion in the Solar System and that enter
into resonance with Jupiter and Saturn. Their orbits have inclinations I >= 140
deg and semi-major axes a < 15 AU. Two objects are currently in retrograde
resonance with Jupiter: 2006 BZ8 in the 2/-5 resonance and 2008 SO218 in the
1/-2 resonance. One object, 2009 QY6, is currently in the 2/-3 retrograde
resonance with Saturn. These are the first examples of Solar System objects in
retrograde resonance. The present resonant configurations last for several
thousand years. Brief captures in retrograde resonance with Saturn are also
possible during the 20,000 years integration timespan, particularly in the 1/-1
resonance (2006 BZ8) and the 9/-7 resonance (1999 LE31).


  This paper presents a novel method, called Modular Grammatical Evolution
(MGE), towards validating the hypothesis that restricting the solution space of
NeuroEvolution to modular and simple neural networks enables the efficient
generation of smaller and more structured neural networks while providing
acceptable (and in some cases superior) accuracy on large data sets. MGE also
enhances the state-of-the-art Grammatical Evolution (GE) methods in two
directions. First, MGE's representation is modular in that each individual has
a set of genes, and each gene is mapped to a neuron by grammatical rules.
Second, the proposed representation mitigates two important drawbacks of GE,
namely the low scalability and weak locality of representation, towards
generating modular and multi-layer networks with a high number of neurons. We
define and evaluate five different forms of structures with and without
modularity using MGE and find single-layer modules with no coupling more
productive. Our experiments demonstrate that modularity helps in finding better
neural networks faster. We have validated the proposed method using ten
well-known classification benchmarks with different sizes, feature counts, and
output class count. Our experimental results indicate that MGE provides
superior accuracy with respect to existing NeuroEvolution methods and returns
classifiers that are significantly simpler than other machine learning
generated classifiers. Finally, we empirically demonstrate that MGE outperforms
other GE methods in terms of locality and scalability properties.


  Consider a discrete-time quantum walk on the $N$-cycle governed by the
following condition: at every time step of the walk, the option persists, with
probability $p$, of exercising a projective measurement on the coin degree of
freedom. For a bipartite quantum system of this kind, we prove that the von
Neumann entropy of the total density operator converges to its maximum value.
Thus, when influenced by decoherence, the mutual information between the two
subsystems, corresponding respectively to the space of the coin and the space
of the walker, eventually must diminish to zero. To put it plainly, any level
of decoherence greater than zero forces the system eventually to become
completely "disentangled".}


  In this paper, we prove that a Thue equation F(x,y) = h, where h is an
integer and F is a polynomial of degree n with integer coefficients and without
repeated roots, has at most 2n^3 - 2n - 3 solutions provided that the
Mordell-Weil rank of the Jacboian of the corresponding curve is less than
(n-1)(n-2)/2. The proof uses the method of Coleman-Chabauty, extended here to
apply to arbitrary regular models of curves, along with an explicit
construction of a portion of a regular model for the curve corresponding to the
Thue equation.


  One of the most challenging problems in applied mathematics is the
approximate solution of nonlinear partial differential equations (PDEs) in high
dimensions. Standard deterministic approximation methods like finite
differences or finite elements suffer from the curse of dimensionality in the
sense that the computational effort grows exponentially in the dimension. In
this work we overcome this difficulty in the case of reaction-diffusion type
PDEs with a locally Lipschitz continuous coervice nonlinearity (such as
Allen-Cahn PDEs) by introducing and analyzing truncated variants of the
recently introduced full-history recursive multilevel Picard approximation
schemes.


  We consider general difference equations $u_{n+1} = F(u)_n$ for $n \in
\mathbb{Z}$ on exponentially weighted $\ell_2$ spaces of two-sided Hilbert
space valued sequences $u$ and discuss initial value problems. As an
application of the Hilbert space approach, we characterize exponential
stability of linear equations and prove a stable manifold theorem for causal
nonlinear difference equations.


  Designing artificial cyber-agents able to interact with human safely, smartly
and in a natural way is a current open problem in control. Solving such an
issue will allow the design of cyber-agents capable of co-operatively
interacting with people in order to fulfil common joint tasks in a multitude of
different applications. This is particularly relevant in the context of
healthcare applications. Indeed, the use has been proposed of artificial agents
interacting and coordinating their movements with those of a patient suffering
from social or motor disorders. Specifically, it has been shown that an
artificial agent exhibiting certain kinematic properties could provide
innovative and efficient rehabilitation strategies for these patients.
Moreover, it has also been shown that the level of motor coordination is
enhanced if these kinematic properties are similar to those of the individual
it is interacting with. In this paper we discuss, first, a new method based on
Markov Chains to confer "human motor characteristics" on a virtual agent, so as
that it can coordinate its motion with that of a target individual while
exhibiting specific kinematic properties. Then, we embed such synthetic model
in a control architecture based on reinforcement learning to synthesize a
cyber-agent able to mimic the behaviour of a specific human performing a joint
motor task with one or more individuals.


  In this paper, we begin by introducing a well-known geometry concept: the
Fermat point in a triangle. Then, we generalize the problem and propose an
iterative algorithm based on gradient descent to the weighted form in Lp space.
We also build specific solutions to some special norms: one, two and infinity.
We show that the solution may not be unique in norm-one and infinity. Finally,
we provide our qualitative results in the PASCAL-based program.


  IoT devices have become indispensable components of our lives, and the
advancement of AI technologies will make them even more pervasive, increasing
the vulnerability to malfunctions or cyberattacks and raising privacy concerns.
Encryption can mitigate these challenges; however, most existing anomaly
detection techniques decrypt the data to perform the analysis, potentially
undermining the encryption protection provided during transit or storage.
Homomorphic encryption schemes are promising solutions as they enable the
processing and execution of operations on IoT data while still encrypted,
however, these schemes offer only limited operations, which poses challenges to
their practical usage. In this paper, we propose a novel privacy-preserving
anomaly detection solution designed for homomorphically encrypted data
generated by IoT devices that efficiently detects abnormal values without
performing decryption. We have adapted the Histogram-based anomaly detection
technique for TFHE scheme to address limitations related to the input size and
the depth of computation by implementing vectorized support operations. These
operations include addition, value placement in buckets, labeling abnormal
buckets based on a threshold frequency, labeling abnormal values based on their
range, and bucket labels. Evaluation results show that the solution effectively
detects anomalies without requiring data decryption and achieves consistent
results comparable to the mechanism operating on plain data. Also, it shows
robustness and resilience against various challenges commonly encountered in
IoT environments, such as noisy sensor data, adversarial attacks, communication
failures, and device malfunctions. Moreover, the time and computational
overheads determined for several solution configurations, despite being large,
are reasonable compared to those reported in existing literature.


  Burn-UD is a hydrodynamic combustion code used to model the phase transition
of hadronic to quark matter with particular application to the interior of
neutron stars. Burn-UD models the flame micro-physics for different equations
of state (EoS) on both sides of the interface, i.e. for both the ash
(up-down-strange quark phase) and the fuel (up-down quark phase). It also
allows the user to explore strange quark seeding produced by different
processes including DM annihilation inside neutron stars. The simulations
provide a physical window to diagnose whether the combustion process will
simmer quietly and slowly, lead to a transition from deflagration to detonation
or a (quark) core-collapse explosion. Such an energetic phase transition (a
Quark-Nova) would have consequences in high-energy astrophysics and could aid
in our understanding of many still enigmatic astrophysical transients.
Furthermore, having a precise understanding of the phase transition dynamics
for different EoSs could aid further in constraining the nature of the
non-perturbative regimes of QCD in general. We hope that Burn-UD will evolve
into a platform/software to be used and shared by the QCD community exploring
the phases of Quark Matter and astrophysicists working on Compact Stars.


  I will present a simple, economic and predictive model of Yukawa structures
in the context of a renormalizable SO(10) grandunification. The righthanded
neutrino mass is generated radiatively. The fermions have Yukawa couplings with
one 10 and one 120 dimensional Higgses. In the approxiamate two generation
scenario the model predicts degenerate neutrinos, small quark mixing and b-tau
Yukawa unification.


  To investigate whether the pleurae, airways and vessels surrounding a nodule
on non-contrast computed tomography (CT) can discriminate benign and malignant
pulmonary nodules. The LIDC-IDRI dataset, one of the largest publicly available
CT database, was exploited for study. A total of 1556 nodules from 694 patients
were involved in statistical analysis, where nodules with average scorings <3
and >3 were respectively denoted as benign and malignant. Besides, 339 nodules
from 113 patients with diagnosis ground-truth were independently evaluated.
Computer algorithms were developed to segment pulmonary structures and quantify
the distances to pleural surface, airways and vessels, as well as the counting
number and normalized volume of airways and vessels near a nodule. Odds ratio
(OR) and Chi-square (\chi^2) testing were performed to demonstrate the
correlation between features of surrounding structures and nodule malignancy. A
non-parametric receiver operating characteristic (ROC) analysis was conducted
in logistic regression to evaluate discrimination ability of each structure.
For benign and malignant groups, the average distances from nodules to pleural
surface, airways and vessels are respectively (6.56, 5.19), (37.08, 26.43) and
(1.42, 1.07) mm. The correlation between nodules and the counting number of
airways and vessels that contact or project towards nodules are respectively
(OR=22.96, \chi^2=105.04) and (OR=7.06, \chi^2=290.11). The correlation between
nodules and the volume of airways and vessels are (OR=9.19, \chi^2=159.02) and
(OR=2.29, \chi^2=55.89). The areas-under-curves (AUCs) for pleurae, airways and
vessels are respectively 0.5202, 0.6943 and 0.6529. Our results show that
malignant nodules are often surrounded by more pulmonary structures compared
with benign ones, suggesting that features of these structures could be viewed
as lung cancer biomarkers.


  Here we report the experimental evidence of the interactions between the
excitations of the $\pi\to\pi^*$ optical transition and the image potential
states (IPS) of highly oriented pyrolitic graphite (HOPG). By using non-linear
angle resolved photoelectron spectroscopy (NL-ARPES) we show that the IPS
photoemission intensity, the effective mass, and the linewidth exhibit a strong
variation when the photon energy is tuned across the $\pi\to\pi^*$ saddle
points in the 3.1 - 4.5 eV photon energy range. A model based on the
self-energy formalism is proposed to correlate the effective mass and the
linewidth variations to transient many body effects, when a high carriers
density (in the $10^{20}$ cm$^{-3}$ range) is created by the absorption of a
coherent light pulse. This finding brings a clear evidence of a high IPS-bulk
coupling in graphite and opens the way for exploiting the IPS as a sensitive,
nonperturbing probe for the many-body dynamics in materials.


  One of the most important experimental results for proton-proton scattering
at the LHC is the observation of a so-called "ridge" structure in the two
particle correlation function versus the pseudorapidity difference $\Delta\eta$
and the azimuthal angle difference $\Delta\phi$. One finds a strong correlation
around $\Delta\phi=0$, extended over many units in $\Delta\eta$. We show that a
hydrodynamical expansion based on flux tube initial conditions leads in a
natural way to the observed structure. To get this result, we have to perform
an event-by-event calculation, because the effect is due to statistical
fluctuations of the initial conditions, together with a subsequent collective
expansion. This is a strong point in favour of a fluid-like behavior even in
$pp$ scattering, where we have to deal with length scales of the order of 0.1
fm.


  We study the reduction properties of low genus curves whose Jacobian has
complex multiplication. In the elliptic curve case, we classify the possible
Kodaira types of reduction that can occur. Moreover, we investigate the
possible Namikawa Ueno types that can occur for genus $2$ curves whose Jacobian
has complex multiplication which is defined over the base field. We also
produce bounds on the torsion subgroup of abelian varieties with complex
multiplication defined over local fields.


  We consider a class of positive linear operators which, among others,
constitute a link between the classical Bernstein operators and the genuine
Bernstein-Durrmeyer mappings. The focus is on their relation to certain
Lagrange-type interpolators associated to them, a well known feature in the
theory of Bernstein operators. Considerations concerning iterated Boolean sums
and the derivatives of the operator images are included. Our main tool is the
eigenstructure of the members of the class.


  Clustering is one of the most fundamental tools in data science and machine
learning, and k-means clustering is one of the most common such methods. There
is a variety of approximate algorithms for the k-means problem, but computing
the globally optimal solution is in general NP-hard. In this paper we consider
the k-means problem for instances with low dimensional data and formulate it as
a structured concave assignment problem. This allows us to exploit the low
dimensional structure and solve the problem to global optimality within
reasonable time for large data sets with several clusters. The method builds on
iteratively solving a small concave problem and a large linear programming
problem. This gives a sequence of feasible solutions along with bounds which we
show converges to zero optimality gap. The paper combines methods from global
optimization theory to accelerate the procedure, and we provide numerical
results on their performance.


  I will discuss the recent LHC and Planck results, which are completely
compatible with the Standard Model of particle physics, and the standard
cosmological model ($\Lambda$CDM), respectively. It turns out that the
extension of the Standard Model is, of course, required, but can be very
minimal. I will discuss also what future measurements may be important to test
this approach.


  Using detailed simulations of calorimeter showers as training data, we
investigate the use of deep learning algorithms for the simulation and
reconstruction of particles produced in high-energy physics collisions. We
train neural networks on shower data at the calorimeter-cell level, and show
significant improvements for simulation and reconstruction when using these
networks compared to methods which rely on currently-used state-of-the-art
algorithms. We define two models: an end-to-end reconstruction network which
performs simultaneous particle identification and energy regression of
particles when given calorimeter shower data, and a generative network which
can provide reasonable modeling of calorimeter showers for different particle
types at specified angles and energies. We investigate the optimization of our
models with hyperparameter scans. Furthermore, we demonstrate the applicability
of the reconstruction model to shower inputs from other detector geometries,
specifically ATLAS-like and CMS-like geometries. These networks can serve as
fast and computationally light methods for particle shower simulation and
reconstruction for current and future experiments at particle colliders.


  In this write-up we review and update our recent lattice QCD calculation of
$B \to K^*$, $B_s \to \phi$, and $B_s \to K^*$ form factors [arXiv:1310.3722].
These unquenched calculations, performed in the low-recoil kinematic regime,
provide a significant improvement over the use of extrapolated light cone sum
rule results. The fits presented here include further kinematic constraints and
estimates of additional correlations between the different form factor shape
parameters. We use these form factors along with Standard Model determinations
of Wilson coefficients to give Standard Model predictions for several
observables [arXiv:1310.3887]. The modest improvements to the form factor fits
lead to improved determinations of $F_L$, the fraction of longitudinally
polarized vector mesons, but have little effect on most other observables.


  The most stringent test of theoretical models of the first-order Fermi
mechanism at collisionless astrophysical shocks is a comparison of the
theoretical predictions with observational data on particle populations. Such
comparisons have yielded good agreement between observations at the
quasi-parallel portion of the Earth's bow shock and three theoretical
approaches, including Monte Carlo kinetic simulations. This paper extends such
model testing to the realm of oblique interplanetary shocks: here observations
of proton and alpha particle distributions made by the SWICS ion mass
spectrometer on Ulysses at nearby interplanetary shocks are compared with test
particle Monte Carlo simulation predictions of accelerated populations. The
plasma parameters used in the simulation are obtained from measurements of
solar wind particles and the magnetic field upstream of individual shocks. Good
agreement between downstream spectral measurements and the simulation
predictions are obtained for two shocks by allowing the the ratio of the
mean-free scattering length to the ionic gyroradius, to vary in an optimization
of the fit to the data. Generally small values of this ratio are obtained,
corresponding to the case of strong scattering. The acceleration process
appears to be roughly independent of the mass or charge of the species.


  Given a Brownian path $\beta(t)$ on $\mathbb{R}$, starting at $1$, a.s. there
is a singular time set $T_{\beta}$, such that the first hitting time of $\beta$
by an independent Brownian motion, starting at $0$, is in $T_{\beta}$ with
probability one. A couple of problems regarding hitting measure for random
processes are presented.


  The growth channel of massive galaxies involving mergers can be studied via
close pairs as putative merger progenitors, where the stellar populations of
the satellite galaxies will be eventually incorporated into the massive
primaries. We extend our recent analysis of the GAMA-based sample of close
pairs presented in Ferreras et al. to the general spectroscopic dataset of SDSS
galaxies (DR14), for which the high S/N of the data enables a detailed analysis
of the differences between satellite galaxies with respect to the mass of the
primary galaxy. A sample of approximately two thousand satellites of massive
galaxies is carefully selected within a relatively narrow redshift range
(0.07<z<0.14). Two main parameters are considered as major drivers of the star
formation history of these galaxies, namely: the stellar velocity dispersion of
the satellite ($\sigma$), as a proxy of "local" drivers, and the ratio between
the stellar mass of the satellite and the primary, $\mu=M_{\rm SAT}/M_{\rm
PRI}$, meant to serve as an indicator of environment. Consistently with the
independent, GAMA-based work, we find that satellites around the most massive
primaries appear older, at fixed velocity dispersion, with respect to
satellites of lower mass primaries. This trend is more marked in lower mass
satellites ($\sigma$~100 km/s), with SSP-equivalent age differences up to ~0.5
Gyr, and can be interpreted as a one-halo assembly bias, so that satellites
corresponding to smaller values of the mass ratio $\mu$ represent older
structures, akin to fossil groups.


  The generalization of Lorentz invariance to solvable two-dimensional lattice
fermion models has been formulated in terms of Baxter's corner transfer matrix.
In these models, the lattice Hamiltonian and boost operator are given by
fermionized nearest-neighbor Heisenberg spin chain operators. The
transformation properties of the local lattice fermion operators under a boost
provide a natural and precise way of generalizing the chiral structure of a
continuum Dirac field to the lattice. The resulting formulation differs from
both the Wilson and staggered (Kogut-Susskind) prescriptions. In particular, an
axial $Q_5$ rotation is sitewise local, while the vector charge rotation mixes
nearest neighbors on even and odd sublattices.


  Acceptor and donor doping is a standard for tailoring semiconductors. More
recently, doping was adapted to optimize the behavior at ferroelectric domain
walls. In contrast to more than a century of research on semiconductors, the
impact of chemical substitutions on the local electronic response at domain
walls is largely unexplored. Here, the hexagonal manganite ErMnO$_3$ is donor
doped with Ti$^{4+}$. Density functional theory calculations show that
Ti$^{4+}$ goes to the B-site, replacing Mn$^{3+}$. Scanning probe microscopy
measurements confirm the robustness of the ferroelectric domain template. The
electronic transport at both macro- and nanoscopic length scales is
characterized. The measurements demonstrate the intrinsic nature of emergent
domain wall currents and point towards Poole-Frenkel conductance as the
dominant transport mechanism. Aside from the new insight into the electronic
properties of hexagonal manganites, B-site doping adds an additional degree of
freedom for tuning the domain wall functionality.


  The quantum oscillations of the magnetoresistance under ambient and high
pressure have been studied for WTe$_2$ single crystals, in which extremely
large magnetoresistance was discovered recently. By analyzing the Shubnikov-de
Haas oscillations, four Fermi surfaces are identified, and two of them are
found to persist to high pressure. The sizes of these two pockets are
comparable, but show increasing difference with pressure. At 0.3 K and in 14.5
T, the magnetoresistance decreases drastically from 1.25 $\times$ $10^5$\%
under ambient pressure to 7.47 $\times$ $10^3$\% under 23.6 kbar, which is
likely caused by the relative change of Fermi surfaces. These results support
the scenario that the perfect balance between the electron and hole populations
is the origin of the extremely large magnetoresistance in WTe$_2$.


  Probabilistic State Space Models (SSMs) are essential for Reinforcement
Learning (RL) from high-dimensional, partial information as they provide
concise representations for control. Yet, they lack the computational
efficiency of their recent deterministic counterparts such as S4 or Mamba. We
propose KalMamba, an efficient architecture to learn representations for RL
that combines the strengths of probabilistic SSMs with the scalability of
deterministic SSMs. KalMamba leverages Mamba to learn the dynamics parameters
of a linear Gaussian SSM in a latent space. Inference in this latent space
amounts to standard Kalman filtering and smoothing. We realize these operations
using parallel associative scanning, similar to Mamba, to obtain a principled,
highly efficient, and scalable probabilistic SSM. Our experiments show that
KalMamba competes with state-of-the-art SSM approaches in RL while
significantly improving computational efficiency, especially on longer
interaction sequences.


  Within an Onsager-like density functional theory we explore the thermodynamic
and structural properties of an isotropic and nematic fluid of hard needle-like
colloids in contact with a hard substrate coated with a soft short-ranged
attractive or repulsive layer. As a function of the range and the strength of
the soft interactions we find wetting and drying transitions, a pre-drying
line, and a symmetry-breaking transition from uniaxial to biaxial in the
wetting and drying film.


  Efficient error-mitigation techniques demanding minimal resources is key to
quantum information processing. We propose a generic protocol to mitigate
quantum errors using detection-based quantum autoencoders. In our protocol, the
quantum data are compressed into a latent subspace while leaving errors
outside, the latter of which is then removed by a measurement and
post-selection. Compared to previously developed methods, our protocol on the
one hand requires no extra qubits, and on the other hand has a near-optimal
denoising power, in which under reasonable requirements all errors detected
outside of the latent subspace can be removed, while those inside the subspace
cannot be removed by any means. Our detection-based quantum autoencoders are
therefore particularly useful for near-term quantum devices in which
controllable qubits are limited while noise reduction is important.


  Heatmap-based anatomical landmark detection is still facing two unresolved
challenges: 1) inability to accurately evaluate the distribution of heatmap; 2)
inability to effectively exploit global spatial structure information. To
address the computational inability challenge, we propose a novel
position-aware and sample-aware central loss. Specifically, our central loss
can absorb position information, enabling accurate evaluation of the heatmap
distribution. More advanced is that our central loss is sample-aware, which can
adaptively distinguish easy and hard samples and make the model more focused on
hard samples while solving the challenge of extreme imbalance between landmarks
and non-landmarks. To address the challenge of ignoring structure information,
a Coordinated Transformer, called CoorTransformer, is proposed, which
establishes long-range dependencies under the guidance of landmark coordination
information, making the attention more focused on the sparse landmarks while
taking advantage of global spatial structure. Furthermore, CoorTransformer can
speed up convergence, effectively avoiding the defect that Transformers have
difficulty converging in sparse representation learning. Using the advanced
CoorTransformer and central loss, we propose a generalized detection model that
can handle various scenarios, inherently exploiting the underlying relationship
between landmarks and incorporating rich structural knowledge around the target
landmarks. We analyzed and evaluated CoorTransformer and central loss on three
challenging landmark detection tasks. The experimental results show that our
CoorTransformer outperforms state-of-the-art methods, and the central loss
significantly improves the performance of the model with p-values< 0.05.


  Time and its lack of play a central role in our everyday lives. Despite
increasing productivity, many people experience time stress, exhaustion and a
longing for time affluence, and at the same time, a fear of not being busy
enough. All this leads to a neglect of natural time, especially the patterns
and rhythms created by physiological processes, subsumed under the heading of
chronobiology. The present paper presents and evaluates a calendar application,
which uses chronobiological knowledge to support people s planning activities.
Participants found our calendar to be interesting and engaging. It especially
made them think more about their bodies and appropriate times for particular
activities. All in all, it supported participants in negotiating. external
demands and personal health and wellbeing. This shows that technology does not
necessarily has to be neutral or even further current (mal-)practices. Our
calendar cares about changing perspectives and thus about enhancing users
wellbeing.


  We present the design and performance of the GROWTH-India telescope, a 0.7 m
robotic telescope dedicated to time-domain astronomy. The telescope is equipped
with a 4k back-illuminated camera giving a 0.82-degree field of view and
sensitivity of m_g ~20.5 in 5-min exposures. Custom software handles
observatory operations: attaining high on-sky observing efficiencies (>~ 80%)
and allowing rapid response to targets of opportunity. The data processing
pipelines are capable of performing PSF photometry as well as image subtraction
for transient searches. We also present an overview of the GROWTH-India
telescope's contributions to the studies of Gamma-ray Bursts, the
electromagnetic counterparts to gravitational wave sources, supernovae, novae
and solar system objects.


  We present \emph{Chandra} monitoring data for six gravitationally lensed
quasars: QJ 0158$-$4325, HE 0435$-$1223, HE 1104$-$1805, SDSS 0924+0219, SDSS
1004+4112, and Q 2237+0305. We detect X-ray microlensing variability in all six
lenses with high confidence. We detect energy dependent microlensing in HE
0435$-$1223, SDSS 1004+4112, SDSS 0924+0219 and Q 2237+0305. We present a
detailed spectral analysis for each lens, and find that simple power-law models
plus Gaussian emission lines give good fits to the spectra. We detect intrinsic
spectral variability in two epochs of Q 2237+0305. We detect differential
absorption between images in four lenses. We also detect the \feka\ emission
line in all six lenses, and the Ni XXVII K$\alpha$ line in two images of Q
2237+0305. The rest frame equivalent widths of the \feka\ lines are measured to
be 0.4--1.2 keV, significantly higher than those measured in typical active
galactic nuclei of similar X-ray luminosities. This suggests that the \feka\
emission region is more compact or centrally concentrated than the continuum
emission region.


  The arrival time prediction of Coronal mass ejections (CMEs) is an area of
active research. Many methods with varying levels of complexity have been
developed to predict CME arrival. However, the mean absolute error (MAE) of
predictions remains above 12 hours, even with the increasing complexity of
methods. In this work we develop a new method for CME arrival time prediction
that uses magnetohydrodynamic simulations involving data-constrained
flux-rope-based CMEs, which are introduced in a data-driven solar wind
background. We found that, for 6 CMEs studied in this work, the MAE in arrival
time was ~8 hours. We further improved our arrival time predictions by using
ensemble modeling and comparing the ensemble solutions with STEREO-A&B
heliospheric imager data. This was done by using our simulations to create
synthetic J-maps. A machine learning (ML) method called the lasso regression
was used for this comparison. Using this approach, we could reduce the MAE to
~4 hours. Another ML method based on the neural networks (NNs) made it possible
to reduce the MAE to ~5 hours for the cases when HI data from both STEREO-A&B
were available. NNs are capable of providing similar MAE when only the STEREO-A
data is used. Our methods also resulted in very encouraging values of standard
deviation (precision) of arrival time. The methods discussed in this paper
demonstrate significant improvements in the CME arrival time predictions. Our
work highlights the importance of using ML techniques in combination with
data-constrained magnetohydrodynamic modeling to improve space weather
predictions.


  Chemical reactions often proceed through the formation and the consumption of
intermediate species. An example is the creation and subsequent degradation of
the substrate-enzyme complexes in an enzymatic reaction. In this paper we
provide a setting, based on ordinary differential equations, in which the
presence of intermediate species has little effect on the overall dynamics of a
biological system. The result provides a method to perform model reduction by
elimination of intermediate species. We study the problem in a multiscale
setting, where the species abundances as well a the reaction rates scale to
different orders of magnitudes. The different time and concentration scales are
parameterised by a single parameter $N$. We show that a solution to the
original reaction system is uniformly approximated on compact time intervals to
a solution of a reduced reaction system without intermediates and to a solution
of a certain limiting reaction systems, which does not depend on $N$. Known
approximation techniques such as the theorems by Tikhonov and Fenichel cannot
readily be used in this framework.


  Properties of the 62,336,617 Steiner triple systems of order 21 with a
non-trivial automorphism group are examined. In particular, there are 28 which
have no parallel class, six that are 4-chromatic, five that are 3-balanced, 20
that avoid the mitre, 21 that avoid the crown, one that avoids the hexagon and
two that avoid the prism. All systems contain the grid. None have a block
intersection graph that is 3-existentially closed.


  The purpose of this paper is to deals with the problem of regional boundary
asymptotic gradient full order observer (regional boundary asymptotic gradient
full order observer) concept by using internal regional case. Thus, we study
the relation between this notion and the corresponding asymptotic detectability
and sensors. More precisely, various important results have been examined and
explored concern an extension of an approach which enables to reconstruct the
gradient of current state from internal region. In addition, it has been shown
that the characterization of regional boundary asymptotic gradient full order
observability under which conditions to be achieved. Finally, we have
illustrate that there is a dynamical system which does not represent the
observer in the usual sense, but it could be interpreted as a regional boundary
asymptotic gradient full order observer.


  A search for inelastic boosted dark matter (iBDM) using the COSINE-100
detector with 59.5 days of data is presented. This relativistic dark matter is
theorized to interact with the target material through inelastic scattering
with electrons, creating a heavier state that subsequently produces standard
model particles, such as an electron-positron pair. In this study, we search
for this electron-positron pair in coincidence with the initially scattered
electron as a signature for an iBDM interaction. No excess over the predicted
background event rate is observed. Therefore, we present limits on iBDM
interactions under various hypotheses, one of which allows us to explore an
area of the experimental search for iBDM using a terrestrial detector.


  We discuss gauge theories on D3 branes embedded in special non-tachyonic
orientifolds of the 0B string theory. In general, they correspond to
non-supersymmetric SU(N) gauge theories with scalars in the adjoint
representation and spinors in the (anti-)symmetric representation. We study
these theories via the AdS/CFT correspondence and present evidence of their
relation to N=4 SYM in the planar limit. We also discuss finite N properties,
focusing in particular on the renormalization group flow. Up to two loops, the
logarithmic running of the gauge coupling is described by the dilaton tadpole
and the cosmological constant that naturally emerge on the string theory side.


  We study the $\lambda\phi^4_{1+1}$ kink solion and the zero-mode contribution
to the Kink soliton mass in regions beyond the semiclassical regime. The
calculations are done in the non-trivial scaling region and where appropriate
the results are compared with the continuum, semiclassical values. We show, as
a function of parameter space, where the zero-mode contributions become
significant.


  Solving intractable mathematical problems in simulators composed of atoms,
ions, photons or electrons has recently emerged as a subject of intense
interest. Here we extend this concept to phonons that are localised in
spectrally pure resonances in an electromechanical system which enables their
interactions to be exquisitely fashioned via electrical means. We harness this
platform to emulate the Ising Hamiltonian whose spin 1/2 particles are
replicated by the phase bistable vibrations from a parametric resonance where
multiple resonances play the role of a spin bath. The coupling between the
mechanical pseudo spins is created by generating thermomechanical two-mode
squeezed states which impart correlations between resonances that can imitate a
ferromagnetic, random or an anti-ferromagnetic state on demand. These results
suggest an electromechanical simulator for the Ising Hamiltonian could be built
with a large number of spins with multiple degrees of coupling, a task that
would overwhelm a conventional computer.


  The search for first top-quark pair candidate events and the related
background studies using data-driven techniques are reported for about 300
inverse nb of 7 TeV proton-proton collision data delivered by the Large Hadron
Collider (LHC) and collected with the ATLAS detector. Selected events are
characterized by the presence of high-pT isolated charged lepton(s), high jet
multiplicity, jet(s) identified as originating from b-quark by a secondary
vertex tagger algorithm, and missing transverse energy. They reveal kinematics
properties consistent with top pair production.


  The standard approach for optimization of XPath queries by rewriting using
views techniques consists in navigating inside a view's output, thus allowing
the usage of only one view in the rewritten query. Algorithms for richer
classes of XPath rewritings, using intersection or joins on node identifiers,
have been proposed, but they either lack completeness guarantees, or require
additional information about the data. We identify the tightest restrictions
under which an XPath can be rewritten in polynomial time using an intersection
of views and propose an algorithm that works for any documents or type of
identifiers. As a side-effect, we analyze the complexity of the related problem
of deciding if an XPath with intersection can be equivalently rewritten as one
without intersection or union. We extend our formal study of the view-based
rewriting problem for XPath by describing also (i) algorithms for more complex
rewrite plans, with no limitations on the number of intersection and navigation
steps inside view outputs they employ, and (ii) adaptations of our techniques
to deal with XML documents without persistent node Ids, in the presence of XML
keys. Complementing our computational complexity study, we describe a
proof-of-concept implementation of our techniques and possible choices that may
speed up execution in practice, regarding how rewrite plans are built, tested
and executed. We also give a thorough experimental evaluation of these
techniques, focusing on scalability and the running time improvements achieved
by the execution of view-based plans.


  Objectives: Negative splitting (i.e., finishing the race faster) is a tactic
commonly employed by elite marathon athletes, even though research supporting
the strategy is scarce. The presence of pacers allows the main runner to run
behind a formation, preserving energy. Our aim is to show that, in the presence
of pacers, the most efficient pacing strategy is positive splitting. Methods:
We evaluated the performance of an elite marathon runner from an energetic
standpoint, including drag values obtained through Computational Fluid Dynamics
(CFD). In varying simulations for different pacing strategies, the energy for
both the main runner and his pacer were conserved and the total race time was
calculated. Results: In order to achieve minimum race time, the main runner
must start the race faster and run behind the pacers, and when the pacers drop
out, finish the race slower. Optimal race times are obtained when the protected
phase is run 2.4 to 2.6% faster than the unprotected phase. Conclusion: Our
results provide strong evidence that positive splitting is indeed the best
pacing strategy when at least one pacer is present, causing significant time
savings in official marathon events.


  In this article, we prove an asymptotic formula for mean values of long
Dirichlet polynomials with higher order shifted divisor functions, assuming a
smoothed additive divisor conjecture for higher order shifted divisor
functions. As a consequence of this work, we prove special cases of conjectures
of Conrey-Keating on mean values of long Dirichlet polynomials with higher
order shifted divisor functions as coefficients.


  The Chinese Space Station Telescope (abbreviated as CSST) is a future
advanced space telescope. Real-time identification of galaxy and nebula/star
cluster (abbreviated as NSC) images is of great value during CSST survey. While
recent research on celestial object recognition has progressed, the rapid and
efficient identification of high-resolution local celestial images remains
challenging. In this study, we conducted galaxy and NSC image classification
research using deep learning methods based on data from the Hubble Space
Telescope. We built a Local Celestial Image Dataset and designed a deep
learning model named HR-CelestialNet for classifying images of the galaxy and
NSC. HR-CelestialNet achieved an accuracy of 89.09% on the testing set,
outperforming models such as AlexNet, VGGNet and ResNet, while demonstrating
faster recognition speeds. Furthermore, we investigated the factors influencing
CSST image quality and evaluated the generalization ability of HR-CelestialNet
on the blurry image dataset, demonstrating its robustness to low image quality.
The proposed method can enable real-time identification of celestial images
during CSST survey mission.


  We calculate current (shot) noise in a metallic diffusive conductor generated
by spin imbalance in the absence of a net electric current. This situation is
modeled in an idealized three-terminal setup with two biased ferromagnetic
leads (F-leads) and one normal lead (N-lead). Parallel magnetization of the
F-leads gives rise in spin-imbalance and finite shot noise at the N-lead.
Finite spin relaxation results in an increase of the shot noise, which depends
on the ratio of the length of the conductor ($L$) and the spin relaxation
length ($l_s$). For $L\gg l_s$ the shot noise increases by a factor of two and
coincides with the case of the anti-parallel magnetization of the F-leads.


  Energy harvesting battery-free embedded devices rely only on ambient energy
harvesting that enables stand-alone and sustainable IoT applications. These
devices execute programs when the harvested ambient energy in their energy
reservoir is sufficient to operate and stop execution abruptly (and start
charging) otherwise. These intermittent programs have varying timing behavior
under different energy conditions, hardware configurations, and program
structures. This paper presents Energy-aware Timing Analysis of intermittent
Programs (ETAP), a probabilistic symbolic execution approach that analyzes the
timing and energy behavior of intermittent programs at compile time. ETAP
symbolically executes the given program while taking time and energy cost
models for ambient energy and dynamic energy consumption into account. We
evaluated ETAP on several intermittent programs and compared the compile-time
analysis results with executions on real hardware. The results show that ETAP's
normalized prediction accuracy is 99.5%, and it speeds up the timing analysis
by at least two orders of magnitude compared to manual testing.


  In this Letter, we demonstrate, to the best of our knowledge, the first
spatiotemporally mode-locked fiber laser with self-similar pulse evolution. The
multimode fiber oscillator generates parabolic amplifier similaritons at 1030
nm with 90 mW average power, 2.3 ps duration, and 37.9 MHz repetition rate.
Remarkably, we observe experimentally a near-Gaussian beam quality (M^2<1.4) at
the output of the highly multimode fiber. The output pulses are compressed to
192 fs via an external grating compressor. Numerical simulations are performed
to investigate the cavity dynamics which confirm experimental observations of
self-similar pulse propagation. The reported results open a new direction to
investigate new types of pulse besides beam shaping and nonlinear dynamics in
spatiotemporal mode-locked fiber lasers.


  By taking into account the radiation reaction force, we derive a set of
one-fluid relativistic magnetohydrody-namics (RMHD) equations with the
Landau-Lifshitz radiation reaction force based on a relativistic two-fluid
plasma. These equations could be used to situations where spatiotemporal scales
of plasma's motion are suffi-ciently large.


  We show that any closed spin manifold not diffeomorphic to the two-sphere
admits a sequence of volume-one-Riemannian metrics for which the smallest
non-zero Dirac eigenvalue tends to zero. As an application, we compare the
Dirac spectrum with the conformal volume.


  It is known that magnetic vortices in two dimensional Heisenberg models with
easy-plane anisotropy exhibit an instability depending on the anisotropy
strength. In this paper, we study the statistic behavior of the two-dimensional
easy-plane Heisenberg models with distance-dependent interactions, $J_{xy}(r)$
and $J_z(r)$ for in-plane and out-of-plane components. We develop analytical
and numerical methods for accurate determination of critical anisotropy, above
which out-of-plane vortices are stable. In particular, we explore the vortex
formation of the Gaussian-type interaction model and determine the critical
anisotropy accurately for square, hexagonal and triangular lattices.


  We describe a (first, to the best of our knowledge) essentially soluble
example of dynamical symmetry breaking phenomenon in a 3+1 dimensional gauge
theory without fundamental scalar fields: QED in a constant magnetic field.


  Measurements of atmospheric turbulence made over the Arctic pack ice during
the Surface Heat Budget of the Arctic Ocean experiment (SHEBA) are used to
determine the limits of applicability of Monin-Obukhov similarity theory (in
the local scaling formulation) in the stable atmospheric boundary layer. Based
on the spectral analysis of wind velocity and air temperature fluctuations, it
is shown that, when both of the gradient Richardson number, Ri, and the flux
Richardson number, Rf, exceed a 'critical value' of about 0.20 - 0.25, the
inertial subrange associated with the Richardson-Kolmogorov cascade dies out
and vertical turbulent fluxes become small. Some small-scale turbulence
survives even in this supercritical regime, but this is non-Kolmogorov
turbulence, and it decays rapidly with further increasing stability. Similarity
theory is based on the turbulent fluxes in the high-frequency part of the
spectra that are associated with energy-containing/flux-carrying eddies.
Spectral densities in this high-frequency band diminish as the
Richardson-Kolmogorov energy cascade weakens; therefore, the applicability of
local Monin-Obukhov similarity theory in stable conditions is limited by the
inequalities Ri < Ri_cr and Rf < Rf_cr. However, it is found that Rf_cr = 0.20
- 0.25 is a primary threshold for applicability. Applying this prerequisite
shows that the data follow classical Monin-Obukhov local z-less predictions
after the irrelevant cases (turbulence without the Richardson-Kolmogorov
cascade) have been filtered out.


  Modern wireless traffic demand pushes Internet Service Providers to develop
effective strategies to improve user experience. Since deploying dense Base
Stations (BSs) is not cost efficient, an alternative is to deploy autonomous
drones to supplement existing BSs. A street graph is adopted to represent the
area of interest. The outdoor User Equipments (UEs) to be served locate near
streets and the 2D projections of drones are restricted to streets to avoid
collision with buildings. We build up a UE density function based on a real
dataset, reflecting the traffic in the area. We study four problems: where to
deploy single drone to cover maximum UEs, where to deploy $k$ drones cover
maximum UEs subject to an inner drone distance constraint, where to deploy $k$
drones cover maximum UEs subject to inner drone distance constraint and drones'
battery constraints, and the minimum drones to cover a given percentage of UEs
subject to inner drone distance constraint. We prove that the latter three
problems are NP-hard and propose greedy algorithms with theoretical analysis.
To our best knowledge, this is the first paper to consider the battery
constraints for drone deployments. Extensive simulations have been conducted to
verify the effectiveness of our approaches.


  We present a derivation of the threshold resummation formula for the
Drell-Yan rapidity distribution. Our argument is valid for all values of
rapidity and to all orders in perturbative QCD and can be applied to all
Drell-Yan processes in a universal way, i.e. both for the production of a
virtual photon \gamma^{*} and the production of a vector boson W^{\pm}, Z^{0}.
We show that for the fixed-target experiment E866/NuSea used in current parton
fits, the NLL resummation corrections are comparable to NLO fixed-order
corrections and are crucial to obtain agreement with the data.


  Large-scale black-box models have become ubiquitous across numerous
applications. Understanding the influence of individual training data sources
on predictions made by these models is crucial for improving their
trustworthiness. Current influence estimation techniques involve computing
gradients for every training point or repeated training on different subsets.
These approaches face obvious computational challenges when scaled up to large
datasets and models.
  In this paper, we introduce and explore the Mirrored Influence Hypothesis,
highlighting a reciprocal nature of influence between training and test data.
Specifically, it suggests that evaluating the influence of training data on
test predictions can be reformulated as an equivalent, yet inverse problem:
assessing how the predictions for training samples would be altered if the
model were trained on specific test samples. Through both empirical and
theoretical validations, we demonstrate the wide applicability of our
hypothesis. Inspired by this, we introduce a new method for estimating the
influence of training data, which requires calculating gradients for specific
test samples, paired with a forward pass for each training point. This approach
can capitalize on the common asymmetry in scenarios where the number of test
samples under concurrent examination is much smaller than the scale of the
training dataset, thus gaining a significant improvement in efficiency compared
to existing approaches.
  We demonstrate the applicability of our method across a range of scenarios,
including data attribution in diffusion models, data leakage detection,
analysis of memorization, mislabeled data detection, and tracing behavior in
language models. Our code will be made available at
https://github.com/ruoxi-jia-group/Forward-INF.


  Robot design has traditionally been costly and labor-intensive. Despite
advancements in automated processes, it remains challenging to navigate a vast
design space while producing physically manufacturable robots. We introduce
Text2Robot, a framework that converts user text specifications and performance
preferences into physical quadrupedal robots. Within minutes, Text2Robot can
use text-to-3D models to provide strong initializations of diverse
morphologies. Within a day, our geometric processing algorithms and
body-control co-optimization produce a walking robot by explicitly considering
real-world electronics and manufacturability. Text2Robot enables rapid
prototyping and opens new opportunities for robot design with generative
models.


  We show how a difference in the correlation length longitudinally and
transversely, with respect to the jet axis in e+e- annihilation, arises
naturally in a model for Bose-Einstein correlations based on the Lund string
model. In genuine three-particle correlations the difference is even more
apparent and they provide therefore a good probe for the longitudinal
stretching of the string field. The correlation length between pion pairs is
found to be rather independent of the pion multiplicity and the kaon content of
the final state.


  In this work, we propose a simple but effective method to interpret black-box
machine learning models globally. That is, we use a compact binary tree, the
interpretation tree, to explicitly represent the most important decision rules
that are implicitly contained in the black-box machine learning models. This
tree is learned from the contribution matrix which consists of the
contributions of input variables to predicted scores for each single
prediction. To generate the interpretation tree, a unified process recursively
partitions the input variable space by maximizing the difference in the average
contribution of the split variable between the divided spaces. We demonstrate
the effectiveness of our method in diagnosing machine learning models on
multiple tasks. Also, it is useful for new knowledge discovery as such insights
are not easily identifiable when only looking at single predictions. In
general, our work makes it easier and more efficient for human beings to
understand machine learning models.


  As a general rule of thumb the resolution of a light microscope (i.e. the
ability to discern objects) is predominantly described by the full width at
half maximum (FWHM) of its point spread function (psf)---the diameter of the
blurring density at half of its maximum. Classical wave optics suggests a
linear relationship between FWHM and resolution also manifested in the well
known Abbe and Rayleigh criteria, dating back to the end of 19th century.
However, during the last two decades conventional light microscopy has
undergone a shift from microscopic scales to nanoscales. This increase in
resolution comes with the need to incorporate the random nature of observations
(light photons) and challenges the classical view of discernability, as we
argue in this paper. Instead, we suggest a statistical description of
resolution obtained from such random data. Our notion of discernability is
based on statistical testing whether one or two objects with the same total
intensity are present. For Poisson measurements we get linear dependence of the
(minimax) detection boundary on the FWHM, whereas for a homogeneous Gaussian
model the dependence of resolution is nonlinear. Hence, at small physical
scales modeling by homogeneous gaussians is inadequate, although often
implicitly assumed in many reconstruction algorithms. In contrast, the Poisson
model and its variance stabilized Gaussian approximation seem to provide a
statistically sound description of resolution at the nanoscale. Our theory is
also applicable to other imaging setups, such as telescopes.