  We consider the problem of online learning in Linear Quadratic Control
systems whose state transition and state-action transition matrices $A$ and $B$
may be initially unknown. We devise an online learning algorithm and provide
guarantees on its expected regret. This regret at time $T$ is upper bounded (i)
by $\widetilde{O}((d_u+d_x)\sqrt{d_xT})$ when $A$ and $B$ are unknown, (ii) by
$\widetilde{O}(d_x^2\log(T))$ if only $A$ is unknown, and (iii) by
$\widetilde{O}(d_x(d_u+d_x)\log(T))$ if only $B$ is unknown and under some mild
non-degeneracy condition ($d_x$ and $d_u$ denote the dimensions of the state
and of the control input, respectively). These regret scalings are minimal in
$T$, $d_x$ and $d_u$ as they match existing lower bounds in scenario (i) when
$d_x\le d_u$ [SF20], and in scenario (ii) [lai1986]. We conjecture that our
upper bounds are also optimal in scenario (iii) (there is no known lower bound
in this setting).
  Existing online algorithms proceed in epochs of (typically exponentially)
growing durations. The control policy is fixed within each epoch, which
considerably simplifies the analysis of the estimation error on $A$ and $B$ and
hence of the regret. Our algorithm departs from this design choice: it is a
simple variant of certainty-equivalence regulators, where the estimates of $A$
and $B$ and the resulting control policy can be updated as frequently as we
wish, possibly at every step. Quantifying the impact of such a
constantly-varying control policy on the performance of these estimates and on
the regret constitutes one of the technical challenges tackled in this paper.


  The authors present full wave simulations and experimental results of
propagation of electromagnetic waves in shallow seawaters. Transmitter and
receiver antennas are ten-turns loops placed on the seabed. Some propagation
frameworks are presented and simulated. Finally, simulation results are
compared with experimental ones.


  We suggest to include the density of electron charge explicitly in the
electron potential of density functional theory, rather than implicitly via
exchange-correlation functionals. The advantages of the approach are conceptual
and numerical. Conceptually, it allows to formulate a physical principle for
the development of quantum mechanical systems: it is the principle of energetic
equilibrium, because the energy principle, in this case, applies not only
globally, but also on a local level. The method is an order-$N$ method, scaling
linearly with the number of atoms. It is used to calculate the electronic
groundstate of a metallic surface, where we find good agreement with
experimental values.


  The existence of supermassive collapsed objects in the cores of most galaxies
poses still-unanswered questions. First, how did they form, and how does their
mass depend on the properties of the host galaxy? Second, can observations
probe the metric in the strong-field domain, testing whether it indeed agrees
with the Kerr geometry predicted by general relativity (and, if so, what the
spin is)?


  The Dvali-Gabadadze-Porrati brane-world model provides a possible approach to
address the late-time cosmic acceleration. However, it has subsequently been
pointed out that a ghost instability will arise on the self-accelerating
branch. Here, we carefully investigate whether this ghost problem could be
possibly cured by introducing the Gauss-Bonnet term in the five-dimensional
bulk action, a natural generalization to the Dvali-Gabadadze-Porrati model. Our
analysis is carried out for a background where a de Sitter brane is embedded in
an anti-de Sitter bulk. Our result shows that the ghost excitations cannot be
avoided even in this modified model.


  Resilience is a key driver for planning adaptation strategies to mitigate
risks due to both natural and anthropogenic hazards. The effectiveness of a
resilience-driven decision-making strategy for adapting systems against
stressors depends on how resilience is mapped to decision variables. This
requires a functional resilience metric, without which it would not be possible
to identify the priority needs for improvement, assess changes, or show
improvement in post-adaptation resilience. In this paper, we aim to contribute
to existing methodologies by proposing a novel model-based resilience
assessment strategy while building on the notion of composite resilience
indicators. Our proposed indicator is functional, reproducible, and is mapped
to adaptation decisions. We propose a framework for integrating the developed
resilience functional form into an adaptation decision-making model. To
illustrate our approach, we use a case study on assessing resilience of On-Site
Wastewater Treatment and Disposal Systems (OSTDS) exposed to risks due to
sea-level rise.


  A new Monte Carlo move for polymer simulations is presented. The ``wormhole''
move is build out of reptation steps and allows a polymer to reptate through a
hole in space; it is able to completely displace a polymer in time N^2 (with N
the polymer length) even at high density. This move can be used in a similar
way to configurational bias, in particular it allows grand canonical moves, it
is applicable to copolymers and can be extended to branched polymers. The main
advantage is speed since it is exponentially faster in N than configurational
bias, but is also easier to program.


  We consider the Stochastic Matching problem, which is motivated by
applications in kidney exchange and online dating. In this problem, we are
given an undirected graph. Each edge is assigned a known, independent
probability of existence and a positive weight (or profit). We must probe an
edge to discover whether or not it exists. Each node is assigned a positive
integer called a timeout (or a patience). On this random graph we are executing
a process, which probes the edges one-by-one and gradually constructs a
matching. The process is constrained in two ways. First, if a probed edge
exists, it must be added irrevocably to the matching (the query-commit model).
Second, the timeout of a node $v$ upper-bounds the number of edges incident to
$v$ that can be probed. The goal is to maximize the expected weight of the
constructed matching.
  For this problem, Bansal et al. (Algorithmica 2012) provided a
$0.33$-approximation algorithm for bipartite graphs and a $0.25$-approximation
for general graphs. We improve the approximation factors to $0.39$ and $0.269$,
respectively.
  The main technical ingredient in our result is a novel way of probing edges
according to a not-uniformly-random permutation. Patching this method with an
algorithm that works best for large-probability edges (plus additional ideas)
leads to our improved approximation factors.


  Accretion disc turbulence is investigated in the framework of the shearing
box approximation. The turbulence is either driven by the magneto-rotational
instability or, in the non-magnetic case, by an explicit and artificial forcing
term in the momentum equation. Unlike the magnetic case, where most of the
dissipation occurs in the disc corona, in the forced hydrodynamic case most of
the dissipation occurs near the midplane. In the hydrodynamic case evidence is
presented for the stochastic excitation of epicycles. When the vertical and
radial epicyclic frequencies are different (modeling the properties around
rotating black holes), the beat frequency between these two frequencies appear
to show up as a peak in the temporal power spectrum in some cases. Finally, the
full turbulent resistivity tensor is determined and it is found that, if the
turbulence is driven by a forcing term, the signs of its off-diagonal
components are such that this effect would not be capable of dynamo action by
the shear--current effect.


  Many applications require optimizing an unknown, noisy function that is
expensive to evaluate. We formalize this task as a multi-armed bandit problem,
where the payoff function is either sampled from a Gaussian process (GP) or has
low RKHS norm. We resolve the important open problem of deriving regret bounds
for this setting, which imply novel convergence rates for GP optimization. We
analyze GP-UCB, an intuitive upper-confidence based algorithm, and bound its
cumulative regret in terms of maximal information gain, establishing a novel
connection between GP optimization and experimental design. Moreover, by
bounding the latter in terms of operator spectra, we obtain explicit sublinear
regret bounds for many commonly used covariance functions. In some important
cases, our bounds have surprisingly weak dependence on the dimensionality. In
our experiments on real sensor data, GP-UCB compares favorably with other
heuristical GP optimization approaches.


  We establish a family of sharp Sobolev trace inequalities involving the
$W^{k,2}(\mathbb{R}_+^{n+1},y^a)$-norm. These inequalities are closely related
to the realization of fractional powers of the Laplacian on
$\mathbb{R}^n=\partial\mathbb{R}_+^{n+1}$ as generalized Dirichlet-to-Neumann
operators associated to powers of the weighted Laplacian in upper half space,
generalizing observations of Caffarelli--Silvestre and of Yang.


  A general formulation for describing odd-harmonic cosmic strings is developed
and used to determine the self-intersection properties of high-harmonic loops.
This is important because loop formation mechanisms produce high-harmonic
components (kinks) which can only be eliminated very slowly by gravitational
radiation, damping by the dense surrounding plasma in the era of string
formation, or by the expansion of the Universe. For the class of loops examined
it has been found that in the high-harmonic limit, essentially all cosmic loops
self-intersect.


  Since Cocke and Minsky proved 2-tag systems universal, they have been
extensively used to prove the universality of numerous computational models.
Unfortunately, all known algorithms give universal 2-tag systems that have a
large number of symbols. In this work, tag systems with only 2 symbols (the
minimum possible) are proved universal via an intricate construction showing
that they simulate cyclic tag systems. Our simulation algorithm has a
polynomial time overhead, and thus shows that binary tag systems simulate
Turing machines in polynomial time.
  We immediately find applications of our result. We reduce the halting problem
for binary tag systems to the Post correspondence problem for 4 pairs of words.
This improves on 7 pairs, the previous bound for undecidability in this
problem. Following our result, only the case for 3 pairs of words remains open,
as the problem is known to be decidable for 2 pairs. As a further application,
we find that the matrix mortality problem is undecidable for sets with five
$3\times 3$ matrices and for sets with two $15\times 15$ matrices. The previous
bounds for the undecidability in this problem was seven $3\times 3$ matrices
and two $ 21\times 21$ matrices.


  A traditional assumption underlying most data converters is that the signal
should be sampled at a rate exceeding twice the highest frequency. This
statement is based on a worst-case scenario in which the signal occupies the
entire available bandwidth. In practice, many signals are sparse so that only
part of the bandwidth is used. In this paper, we develop methods for low-rate
sampling of continuous-time sparse signals in shift-invariant (SI) spaces,
generated by m kernels with period T. We model sparsity by treating the case in
which only k out of the m generators are active, however, we do not know which
k are chosen. We show how to sample such signals at a rate much lower than m/T,
which is the minimal sampling rate without exploiting sparsity. Our approach
combines ideas from analog sampling in a subspace with a recently developed
block diagram that converts an infinite set of sparse equations to a finite
counterpart. Using these two components we formulate our problem within the
framework of finite compressed sensing (CS) and then rely on algorithms
developed in that context. The distinguishing feature of our results is that in
contrast to standard CS, which treats finite-length vectors, we consider
sampling of analog signals for which no underlying finite-dimensional model
exists. The proposed framework allows to extend much of the recent literature
on CS to the analog domain.


  This paper is withdrawn by the authors. We theoretically examine graphene
plasmon polaritons (GPP) supported by a sheet of graphene sandwiched between
two dielectric media. The influence of the dielectric environment on GPP's is
studied within local and nonlocal descriptions of graphene's conductivity. We
demonstrate that a local-response approximation invariably leads,
counter-intuitively, to a substrate-independent surface plasmon frequency. On
the other hand, the nonlocal description of the electron gas allows the
presence of non-vertical inter-band transitions which changes the results
qualitatively: the GPP's now depend on the surroundings, and the singularity in
the local GPP dispersion relation is removed.


  Building a high quality automatic speech recognition (ASR) system with
limited training data has been a challenging task particularly for a narrow
target population. Open-sourced ASR systems, trained on sufficient data from
adults, are susceptible on seniors' speech due to acoustic mismatch between
adults and seniors. With 12 hours of training data, we attempt to develop an
ASR system for socially isolated seniors (80+ years old) with possible
cognitive impairments. We experimentally identify that ASR for the adult
population performs poorly on our target population and transfer learning (TL)
can boost the system's performance. Standing on the fundamental idea of TL,
tuning model parameters, we further improve the system by leveraging an
attention mechanism to utilize the model's intermediate information. Our
approach achieves 1.58% absolute improvements over the TL model.


  Let X be a real projective algebraic manifold, s numerates connected
components of X(R) and _2Br(X) the subgroup of elements of order 2 of the
cohomological Brauer group Br(X). We study the natural homomorphism \xi :
_2Br(X) \to (Z/2)^s and prove that \xi is epimorphic if H^3(X(C)/G;Z/2) \to
H^3(X(R);Z/2) is injective. Here G=Gal(C/R). For an algebraic surface X with
H^3(X(C)/G;Z/2)=0 and X(R)\not=\emptyset, we give a formula for dim _2Br(X). As
a corollary, for a real Enriques surfaces Y, the \xi is epimorphic and dim
_2Br(Y)=2s-1 if both liftings of the antiholomorphic involution of Y to the
universal covering K3- surface X have non-empty sets of real points (this is
the general case). For this case, we also give a formula for the number s_{nor}
of non-orientable components of Y which is very important for the topological
classification of real Enriques surfaces.


  Maximum regularized likelihood estimators (MRLEs) are arguably the most
established class of estimators in high-dimensional statistics. In this paper,
we derive guarantees for MRLEs in Kullback-Leibler divergence, a general
measure of prediction accuracy. We assume only that the densities have a convex
parametrization and that the regularization is definite and positive
homogenous. The results thus apply to a very large variety of models and
estimators, such as tensor regression and graphical models with convex and
non-convex regularized methods. A main conclusion is that MRLEs are broadly
consistent in prediction - regardless of whether restricted eigenvalues or
similar conditions hold.


  Video editing is a creative and complex endeavor and we believe that there is
potential for reimagining a new video editing interface to better support the
creative and exploratory nature of video editing. We take inspiration from
latent space exploration tools that help users find patterns and connections
within complex datasets. We present VideoMap, a proof-of-concept video editing
interface that operates on video frames projected onto a latent space. We
support intuitive navigation through map-inspired navigational elements and
facilitate transitioning between different latent spaces through swappable
lenses. We built three VideoMap components to support editors in three common
video tasks. In a user study with both professionals and non-professionals,
editors found that VideoMap helps reduce grunt work, offers a user-friendly
experience, provides an inspirational way of editing, and effectively supports
the exploratory nature of video editing. We further demonstrate the versatility
of VideoMap by implementing three extended applications. For interactive
examples, we invite you to visit our project page:
https://humanvideointeraction.github.io/videomap.


  We discuss three exactly solvable spin models of geometric frustration.
First, we discuss a 1-parameter subfamily of the 16 vertex model, which can be
mapped to a planar Ising model and solved via Fisher-Dubed\'{a}t decorations.
We then consider a 1-parameter family generalization of the Villain's fully
frustrated model, which interpolates between Onsager's 2D Ising model and the
Villain one. We then discuss spin ice models on a tree, which can be solved
exactly using recursions \textit{a l\'{a} Bethe}.


  We present an online deliberation system using mutual evaluation in order to
collaboratively develop solutions. Participants submit their proposals and
evaluate each other's proposals; some of them may then be invited by the system
to rewrite 'problematic' proposals. Two cases are discussed: a proposal
supported by many, but not by a given person, who is then invited to rewrite it
for making yet more acceptable; and a poorly presented but presumably
interesting proposal. The first of these cases has been successfully
implemented. Proposals are evaluated along two axes-understandability (or
clarity, or, more generally, quality), and agreement. The latter is used by the
system to cluster proposals according to their ideas, while the former is used
both to present the best proposals on top of their clusters, and to find poorly
written proposals candidates for rewriting. These functionalities may be
considered as important components of a large scale online deliberation system.


  The Gumbel-Softmax is a continuous distribution over the simplex that is
often used as a relaxation of discrete distributions. Because it can be readily
interpreted and easily reparameterized, it enjoys widespread use. We propose a
modular and more flexible family of reparameterizable distributions where
Gaussian noise is transformed into a one-hot approximation through an
invertible function. This invertible function is composed of a modified softmax
and can incorporate diverse transformations that serve different specific
purposes. For example, the stick-breaking procedure allows us to extend the
reparameterization trick to distributions with countably infinite support, thus
enabling the use of our distribution along nonparametric models, or normalizing
flows let us increase the flexibility of the distribution. Our construction
enjoys theoretical advantages over the Gumbel-Softmax, such as closed form KL,
and significantly outperforms it in a variety of experiments. Our code is
available at https://github.com/cunningham-lab/igr.


  Data on businesses collected by statistical agencies are challenging to
protect. Many businesses have unique characteristics, and distributions of
employment, sales, and profits are highly skewed. Attackers wishing to conduct
identification attacks often have access to much more information than for any
individual. As a consequence, most disclosure avoidance mechanisms fail to
strike an acceptable balance between usefulness and confidentiality protection.
Detailed aggregate statistics by geography or detailed industry classes are
rare, public-use microdata on businesses are virtually inexistant, and access
to confidential microdata can be burdensome. Synthetic microdata have been
proposed as a secure mechanism to publish microdata, as part of a broader
discussion of how to provide broader access to such data sets to researchers.
In this article, we document an experiment to create analytically valid
synthetic data, using the exact same model and methods previously employed for
the United States, for data from two different countries: Canada (LEAP) and
Germany (BHP). We assess utility and protection, and provide an assessment of
the feasibility of extending such an approach in a cost-effective way to other
data.


  Climate neutrality paradigms put electricity systems at the core of a clean
energy supply. At the same time, indirect electrification, with a potential
uptake of hydrogen or derived fuel economy, plays a crucial role in
decarbonising the energy supply and industrial processes. Besides energy
markets coordinating the transition, climate and energy policy targets require
fundamental changes and expansions in the energy transmission, import,
distribution, and storage infrastructures. While existing studies identify
relevant demands for hydrogen, critical decisions involve imports versus
domestic fuel production and investments in new or repurposing existing
pipeline and storage infrastructure. Linking the pan-European energy system
planning model SCOPE SD with the multiperiod European gas market model IMAGINE,
the case study analysis and its transformation pathway results indicate
extensive network development of hydrogen infrastructure, including expansion
beyond refurbished methane infrastructure. However, the ranges of future
hydrogen storage costs and market uptake restrictions expose and quantify the
uncertainty of its role in Europes transformation. The study finds that rapidly
planning the construction of hydrogen storage and pipeline infrastructure is
crucial to achieving the required capacity by 2050.


  We investigate the condensation phase transitions of conserved-mass
aggregation (CA) model on weighted scale-free networks (WSFNs). In WSFNs, the
weight $w_{ij}$ is assigned to the link between the nodes $i$ and $j$. We
consider the symmetric weight given as $w_{ij}=(k_i k_j)^\alpha$. In CA model,
the mass $m_i$ on the randomly chosen node $i$ diffuses to a linked neighbor of
$i$,$j$, with the rate $T_{ji}$ or an unit mass chips off from the node $i$ to
$j$ with the rate $\omega T_{ji}$. The hopping probability $T_{ji}$ is given as
$T_{ji}= w_{ji}/\sum_{<l>} w_{li}$, where the sum runs over the linked
neighbors of the node $i$. On the WSFNs, we numerically show that a certain
critical $\alpha_c$ exists below which CA model undergoes the same type of the
condensation transitions as those of CA model on regular lattices. However for
$\alpha \geq \alpha_c$, the condensation always occurs for any density $\rho$
and $\omega$. We analytically find $\alpha_c = (\gamma-3)/2$ on the WSFN with
the degree exponent $\gamma$. To obtain $\alpha_c$, we analytically derive the
scaling behavior of the stationary distribution $P^{\infty}_k$ of finding a
walker at nodes with degree $k$, and the probability $D(k)$ of finding two
walkers simultaneously at the same node with degree $k$. We find $P^{\infty}_k
\sim k^{\alpha+1-\gamma}$ and $D(k) \sim k^{2(\alpha+1)-\gamma}$ respectively.
With $P^{\infty}_k$, we also show analytically and numerically that the average
mass $m(k)$ on a node with degree $k$ scales as $k^{\alpha+1}$ without any
jumps at the maximal degree of the network for any $\rho$ as in the SFNs with
$\alpha=0$.


  Human awareness in robot motion planning is crucial for seamless interaction
with humans. Many existing techniques slow down, stop, or change the robot's
trajectory locally to avoid collisions with humans. Although using the
information on the human's state in the path planning phase could reduce future
interference with the human's movements and make safety stops less frequent,
such an approach is less widespread. This paper proposes a novel approach to
embedding a human model in the robot's path planner. The method explicitly
addresses the problem of minimizing the path execution time, including
slowdowns and stops owed to the proximity of humans. For this purpose, it
converts safety speed limits into configuration-space cost functions that drive
the path's optimization. The costmap can be updated based on the observed or
predicted state of the human. The method can handle deterministic and
probabilistic representations of the human state and is independent of the
prediction algorithm. Numerical and experimental results on an industrial
collaborative cell demonstrate that the proposed approach consistently reduces
the robot's execution time and avoids unnecessary safety speed reductions.


  Very faint X-ray transients (VFXTs) are a group of X-ray binaries with low
luminosities, the peak X-ray luminosities during their outbursts being only
10$^{34}$--10$^{36}$ erg\,s$^{-1}$. Using the $\gamma$-ray data obtained with
the Large Area Telescope (LAT) onboard {\it Fermi Gamma-Ray Space Telescope},
we investigate their possible nature of containing rotation-powered pulsars, or
more specifically being transitional millisecond pulsars (MSPs). Among more
than 40 known VFXTs, we select 12 neutron star systems. We analyze the LAT data
for the fields of the 12 VFXTs in 0.2--300 GeV energy range, but do not find
any counterparts likely detected by {\it Fermi}. We obtain the luminosity upper
limits for the 12 sources. While the distances to the sources are largely
uncertain, the upper limits are comparable to the luminosities of two
transitional systems PSR J1023$-$0038 and XSS J12270$-$4859. From our study, we
conclude that no evidence is found at $\gamma$-rays for the suggestion that
some of VFXTs could contain rotation-powered MSPs (or be transitional MSP
systems).


  Self modifying code is code that can modify its own instructions during the
execution of the program. It is extensively used by malware writers to
obfuscate their malicious code. Thus, analysing self modifying code is nowadays
a big challenge. In this paper, we consider the LTL model-checking problem of
self modifying code. We model such programs using self-modifying pushdown
systems (SM-PDS), an extension of pushdown systems that can modify its own set
of transitions during execution. We reduce the LTL model-checking problem to
the emptiness problem of self-modifying B\"uchi pushdown systems (SM-BPDS). We
implemented our techniques in a tool that we successfully applied for the
detection of several self-modifying malware. Our tool was also able to detect
several malwares that well-known antiviruses such as BitDefender, Kinsoft,
Avira, eScan, Kaspersky, Qihoo-360, Baidu, Avast, and Symantec failed to
detect.


  The impact of formation flying on interferometry is growing over the years
for the potential performance it could offer. However, it is still an open
field, and many studies are still required. This article presents the basic
principles behind interferometry focusing first on a single array and secondly
on a formation of satellites. A sensitivity analysis is carried out to evaluate
how the performance of the interferometry is affected by an error in the
relative position in the formation geometry. This is estimated by computing the
loss of the performance in terms of percentage deviation due to a non-nominal
relative trajectory, including two-dimensional errors and defining a payload
index. The main goal of this study is to estimate whether some errors in the
relative state are more impacting than others. The final objective is to
compute the link between a position error and a specific loss of performance,
to foresee the origin of the the error. Furthermore, a dynamical model is
developed to describe the relative motion in the Low Earth Orbit environment,
considering both the unperturbed and the J2 and drag contributions. A
Proportional, Integral and Derivative controller is implemented for the
position control of a multiple satellite formation flying, considering a low
thrust control profile. The Formation Flying L-band Aperture Synthesis study is
taken as the case scenario, analysing both nominal and non-nominal
configurations. This study serves as a starting point for the development of a
combined tool to assess the performance of the interferometry and the control
on the relative state for future remote sensing studies involving relative
motion.


  At the intersection of what I call uncomputable art and computational
epistemology, a form of experimental philosophy, we find an exciting and
promising area of science related to causation with an alternative, possibly
best possible, solution to the challenge of the inverse problem. That is the
problem of finding the possible causes, mechanistic origins, first principles,
and generative models of a piece of data from a physical phenomenon. Here we
explain how generating and exploring software space following the framework of
Algorithmic Information Dynamics, it is possible to find small models and learn
to navigate a sci-fi-looking space that can advance the field of scientific
discovery with complementary tools to offer an opportunity to advance science
itself.


  We calculate the new physics effects to the branching ratios of the lepton
flavor conserving decays Z -> l^+ l^- in the framework of the general two Higgs
Doublet model. We predict the upper limits for the couplings
|\bar{\xi}^{D}_{N,\mu\tau}| and |\bar{\xi}^{D}_{N,\tau\tau}| as 3\times 10^2
GeV and 1\times 10^2 GeV, respectively.


  Branching of bilayer membranes appear in the inverted hexagonal phase as well
as in metastable states of the lamellar phase such as membrane fusion
intermediates. A method for estimating the line tension of the branching
junction is proposed for molecular simulations. The line tension is calculated
from the pressure tensor of equiangularly branched membranes. The simulation
results agree very well with the theoretical prediction of Hamm and Kozlov's
tilt model. The transition between the lamellar and inverted hexagonal phases
is also investigated using the tilt model.


  The effect of the inclusion of As antisites in the diluted magnetic
semiconductor (Ga,Mn)As is studied within density functional theory in the
local spin density approximation. In the case of homogeneous distribution of Mn
ions we find that the ferromagnetism is largely weakened by the presence of the
antisites. This is due to compensation of the free holes which mediate the long
range ferromagnetic order. In contrast, when two Mn ions are coupled through
only one As ion, ferromagnetic and antiferromagnetic order compete. In this
case the magnetic ground state depends on: i) the position of the As antisites
relative to the Mn, and ii) the As antisite concentration. We explain our
results using a model of competing antiferromagnetic super-exchange and
ferromagnetic double-exchange via localized Zener carriers. The strong
dependence of the ferromagnetic order on the microscopic configuration accounts
for the large variation in experimental data


  This paper presents SIM-Sync, a certifiably optimal algorithm that estimates
camera trajectory and 3D scene structure directly from multiview image
keypoints. SIM-Sync fills the gap between pose graph optimization and bundle
adjustment; the former admits efficient global optimization but requires
relative pose measurements and the latter directly consumes image keypoints but
is difficult to optimize globally (due to camera projective geometry). The
bridge to this gap is a pretrained depth prediction network. Given a graph with
nodes representing monocular images taken at unknown camera poses and edges
containing pairwise image keypoint correspondences, SIM-Sync first uses a
pretrained depth prediction network to lift the 2D keypoints into 3D scaled
point clouds, where the scaling of the per-image point cloud is unknown due to
the scale ambiguity in monocular depth prediction. SIM-Sync then seeks to
synchronize jointly the unknown camera poses and scaling factors (i.e., over
the 3D similarity group). The SIM-Sync formulation, despite nonconvex, allows
designing an efficient certifiably optimal solver that is almost identical to
the SE-Sync algorithm. We demonstrate the tightness, robustness, and practical
usefulness of SIM-Sync in both simulated and real experiments. In simulation,
we show (i) SIM-Sync compares favorably with SE-Sync in scale-free
synchronization, and (ii) SIM-Sync can be used together with robust estimators
to tolerate a high amount of outliers. In real experiments, we show (a)
SIM-Sync achieves similar performance as Ceres on bundle adjustment datasets,
and (b) SIM-Sync performs on par with ORB-SLAM3 on the TUM dataset with
zero-shot depth prediction.


  We describe a novel method for modeling the global, steady solar wind using
photospheric magnetic fields as a driving boundary condition. Prior wind models
in this class include both rapid heuristic methods that use potential field
extrapolation and variants thereof, trading rigor for computation speed, and
detailed 3D magnetohydrodynamic (MHD) models that attempt to simulate the
entire solar corona with a degree of physical rigor, but require large amounts
of computation. The Field Line Universal relaXer (FLUX), an open-source
numerical code which implements the 'fluxon' semi-lagrangian approach to MHD
modeling, provides an intermediate approach between these two general classes.
In particular, the fluxon approach to MHD describes the magnetic field through
discrete analogues of magnetic field lines, relaxing these structures to a
stationary state of force balance. In this work we introduce a one-dimensional
solar wind solution along each fieldline, providing an ensemble of solutions
that are interpolated back onto a uniform grid at an outer boundary surface.
This provides advantages in physical rigor over heuristic semi-analytic
techniques, and in computational efficiency over full 3D MHD techniques. Here
we describe the underlying methodology and the FLUXPipe modeling pipeline
process.


  In a XOR-based alternating block cipher the plaintext is masked by a sequence
of layers each performing distinct actions: a highly nonlinear permutation, a
linear transformation, and the bitwise key addition. When assessing resistance
against classical differential attacks (where differences are computed with
respect to XOR), the cryptanalysts must only take into account differential
probabilities introduced by the nonlinear layer, this being the only one whose
differential transitions are not deterministic. The temptation of computing
differentials with respect to another difference operation runs into the
difficulty of understanding how differentials propagate through the XOR-affine
levels of the cipher. In this paper we introduce a special family of braces
that enable the derivation of a set of differences whose interaction with every
layer of an XOR-based alternating block cipher can be understood. We show that
such braces can be described also in terms of alternating binary algebras of
nilpotency class two. Additionally, we present a method to compute the
automorphism group of these structures through an equivalence between bilinear
maps. By doing so, we characterise the XOR-linear permutations for which the
differential transitions with respect to the new difference are deterministic,
facilitating an alternative differential attack.


  Recent advances in methods and techniques enable us to develop an interactive
overlay to the global map of science based on aggregated citation relations
among the 9,162 journals contained in the Science Citation Index and Social
Science Citation Index 2009 combined. The resulting mapping is provided by
VOSViewer. We first discuss the pros and cons of the various options: cited
versus citing, multidimensional scaling versus spring-embedded algorithms,
VOSViewer versus Gephi, and the various clustering algorithms and similarity
criteria. Our approach focuses on the positions of journals in the
multidimensional space spanned by the aggregated journal-journal citations. A
number of choices can be left to the user, but we provide default options
reflecting our preferences. Some examples are also provided; for example, the
potential of using this technique to assess the interdisciplinarity of
organizations and/or document sets.


  The modularity of a graph is a parameter that measures its community
structure; the higher its value (between $0$ and $1$), the more clustered the
graph is.
  In this paper we show that the modularity of a random $3$-regular graph is at
least $0.667026$ asymptotically almost surely (a.a.s.), thereby proving a
conjecture of McDiarmid and Skerman. We also improve the a.a.s. upper bound
given therein to $0.789998$.
  For a uniformly chosen graph $G_n$ over a given bounded degree sequence with
average degree $d(G_n)$ and with $|CC(G_n)|$ many connected components, we
distinguish two regimes with respect to the existence of a giant component. In
the subcritical regime, we compute the second term of the modularity. In the
supercritical regime, we prove that there is $\varepsilon > 0$, for which the
modularity is a.a.s. at least \begin{equation*} \dfrac{2\left(1 -
\mu\right)}{d(G_n)}+\varepsilon, \end{equation*} where $\mu$ is the
asymptotically almost sure limit of $\dfrac{|CC(G_n)|}{n}$.


  For flexible non-blind image denoising, existing deep networks usually take
both noisy image and noise level map as the input to handle various noise
levels with a single model. However, in this kind of solution, the noise
variance (i.e., noise level) is only deployed to modulate the first layer of
convolution feature with channel-wise shifting, which is limited in balancing
noise removal and detail preservation. In this paper, we present a novel
flexible image enoising network (CFMNet) by equipping an U-Net backbone with
multi-layer conditional feature modulation (CFM) modules. In comparison to
channel-wise shifting only in the first layer, CFMNet can make better use of
noise level information by deploying multiple layers of CFM. Moreover, each CFM
module takes onvolutional features from both noisy image and noise level map as
input for better trade-off between noise removal and detail preservation.
Experimental results show that our CFMNet is effective in exploiting noise
level information for flexible non-blind denoising, and performs favorably
against the existing deep image denoising methods in terms of both quantitative
metrics and visual quality.


  Published during a severe economic crisis, this study presents the first
spatial microsimulation model for the analysis of income inequalities and
poverty in Greece. First, we present a brief overview of the method and discuss
its potential for the analysis of multidimensional poverty and income
inequality in Greece. We then present the SimAthens model, based on a
combination of small-area demographic and socioeconomic information available
from the Greek census of population with data from the European Union
Statistics on Income and Living Conditions (EU-SILC). The model is based on an
iterative proportional fitting (IPF) algorithm, and is used to reweigh EU-SILC
records to fit in small-area descriptions for Athens based on 2001 and 2011
censuses. This is achieved by using demographic and socioeconomic
characteristics as constraint variables. Finally, synthesis of the labor market
and occupations are chosen as the main variables for externally validating our
results, in order to verify the integrity of the model. Results of this
external validation process are found to be extremely satisfactory, indicating
a high goodness of fit between simulated and real values. Finally, the study
presents a number of model outputs, illustrating changes in social and economic
geography, during a severe economic crisis, offering a great opportunity for
discussing further potential of this model in policy analysis.


  We study the fraction of stars in and around the {\delta} Scuti instability
strip that are pulsating, using {\em Gaia} DR2 parallaxes to derive precise
luminosities. We classify a sample of over 15,000 {\em Kepler} A and F stars
into {\delta} Sct and non-{\delta} Sct stars, paying close attention to
variability that could have other origins. We find that 18 per cent of the
{\delta} Sct stars have their dominant frequency above the Kepler long-cadence
Nyquist frequency (periods < 1 hr), and 30 per cent have some super-Nyquist
variability. We analyse the pulsator fraction as a function of effective
temperature and luminosity, finding that many stars in the {\delta} Sct
instability strip do not pulsate. The pulsator fraction peaks at just over 70
per cent in the middle of the instability strip. The results are insensitive to
the amplitude threshold used to identify the pulsators. We define a new
empirical instability strip based on the observed pulsator fraction that is
systematically hotter than theoretical strips currently in use. The stellar
temperatures, luminosities, and pulsation classifications are provided in an
online catalogue.


  Assistive robot arms have the potential to help disabled or elderly adults
eat everyday meals without relying on a caregiver. To provide meaningful
assistance, these robots must reach for food items, pick them up, and then
carry them to the human's mouth. Current work equips robot arms with standard
utensils (e.g., forks and spoons). But -- although these utensils are intuitive
for humans -- they are not easy for robots to control. If the robot arm does
not carefully and precisely orchestrate its motion, food items may fall out of
a spoon or slide off of the fork. Accordingly, in this paper we design, model,
and test Kiri-Spoon, a novel utensil specifically intended for robot-assisted
feeding. Kiri-Spoon combines the familiar shape of traditional utensils with
the capabilities of soft grippers. By actuating a kirigami structure the robot
can rapidly adjust the curvature of Kiri-Spoon: at one extreme the utensil
wraps around food items to make them easier for the robot to pick up and carry,
and at the other extreme the utensil returns to a typical spoon shape so that
human users can easily take a bite of food. Our studies with able-bodied human
operators suggest that robot arms equipped with Kiri-Spoon carry foods more
robustly than when leveraging traditional utensils. See videos here:
https://youtu.be/nddAniZLFPk


  We report the successful fabrication of lateral organic spin valves with a
channel length in the sub $100\,nm$ regime. The fabication process is based on
in-situ shadow evaporation under UHV conditions and therefore yields clean and
oxygen-free interfaces between the ferromagnetic metallic electrodes and the
organic semiconductor. The spin valve devices consist of Nickel and Cobalt-iron
electrodes and the high mobility \emph{n}-type organic semiconductor
$N,N'$-bis(heptafluorobutyl)-$3,4:9,10$-perylene diimide. Our studies comprise
fundamental investigations of the process' and materials' suitability for the
fabrication of lateral spin valve devices as well as magnetotransport
measurements at room temperature. The best devices exhibit a magnetoresistance
of up to $50\,%$, the largest value for room temperature reported so far.


  Recent advances in natural language processing and machine learning have led
to the development of chatbot models, such as ChatGPT, that can engage in
conversational dialogue with human users. However, the ability of these models
to generate toxic or harmful responses during a non-toxic multi-turn
conversation remains an open research question. Existing research focuses on
single-turn sentence testing, while we find that 82\% of the individual
non-toxic sentences that elicit toxic behaviors in a conversation are
considered safe by existing tools. In this paper, we design a new attack,
\toxicbot, by fine-tuning a chatbot to engage in conversation with a target
open-domain chatbot. The chatbot is fine-tuned with a collection of crafted
conversation sequences. Particularly, each conversation begins with a sentence
from a crafted prompt sentences dataset. Our extensive evaluation shows that
open-domain chatbot models can be triggered to generate toxic responses in a
multi-turn conversation. In the best scenario, \toxicbot achieves a 67\%
activation rate. The conversation sequences in the fine-tuning stage help
trigger the toxicity in a conversation, which allows the attack to bypass two
defense methods. Our findings suggest that further research is needed to
address chatbot toxicity in a dynamic interactive environment. The proposed
\toxicbot can be used by both industry and researchers to develop methods for
detecting and mitigating toxic responses in conversational dialogue and improve
the robustness of chatbots for end users.


  We determine the Cabibbo-Kobayashi-Maskawa matrix (CKM) element |Vus| in
several different ways using updated preliminary HFAG averages for the tau
lepton branching fractions and we compare the results with the determinations
obtained from the kaon decays and from the unitarity of the CKM matrix.


  A function on an algebra is congruence preserving if, for any congruence, it
maps pairs of congruent elements onto pairs of congruent elements. We show that
on the algebra of complete binary trees whose leaves are labeled by letters of
an alphabet containing at least three letters a function is congruence
preserving if and only if it is polynomial. This exhibits an example of a non
commutative and non associative affine complete algebra. As far as we know, it
is the first example of such an algebra.


  Generation of steady quantumness in the presence of an environment is of
utmost importance if we are to build practical quantum devices. We propose a
scheme of generating steady coherence and magic in a qubit system attached to a
heat bath at a certain temperature through its interaction with another qubit
system attached to a spin bath. Coherence generation in the reduced qubit is
always possible in this model. The steady coherence in the reduced qubit
attached to the heat bath may be used to enhance the subsequent transient
performance of a quantum absorption refrigerator. For the case of generation of
magic, which is the quantum resource responsible for implementation of gates
which are not simulable via stabilizer computation, we show that there exists a
critical temperature of the heat bath beyond which it is not possible to create
magic in the reduced qubit attached to the heat bath. Below the critical
temperature, the strength of interaction between the qubits must lie within a
certain region for creation of magic. We further note that by increasing the
strength of coupling of the second qubit to the spin bath, typified by the
reset probability, keeping the coupling strength of the first qubit to the heat
bath fixed, it is possible to increase the critical temperature of the heat
bath for creation of magic.


  Living organisms process information without any central control unit and
without any ruling clock. We have been studying a novel computational strategy
that uses a geometrically arranged excitable field, i.e., "field computation."
As an extension of this research, in the present article we report the
construction of a "direction detector" on an excitable field. Using a numerical
simulation, we show that the direction of a input source signal can be detected
by applying the characteristic as a "coincidence detector" embedded on an
excitable field. In addition, we show that this direction detection actually
works in an experiment using an excitable chemical system. These results are
discussed in relation to the future development of "field computation."


  Most current AI systems rely on the premise that the input visual data are
sufficient to achieve competitive performance in various computer vision tasks.
However, the classic task setup rarely considers the challenging, yet common
practical situations where the complete visual data may be inaccessible due to
various reasons (e.g., restricted view range and occlusions). To this end, we
investigate a computer vision task setting with incomplete visual input data.
Specifically, we exploit the Scene Graph Generation (SGG) task with various
levels of visual data missingness as input. While insufficient visual input
intuitively leads to performance drop, we propose to supplement the missing
visions via the natural language dialog interactions to better accomplish the
task objective. We design a model-agnostic Supplementary Interactive Dialog
(SI-Dial) framework that can be jointly learned with most existing models,
endowing the current AI systems with the ability of question-answer
interactions in natural language. We demonstrate the feasibility of such a task
setting with missing visual input and the effectiveness of our proposed dialog
module as the supplementary information source through extensive experiments
and analysis, by achieving promising performance improvement over multiple
baselines.


  As robots are becoming more and more ubiquitous in human environments, it
will be necessary for robotic systems to better understand and predict human
actions. However, this is not an easy task, at times not even for us humans,
but based on a relatively structured set of possible actions, appropriate cues,
and the right model, this problem can be computationally tackled. In this
paper, we propose to use an ensemble of long-short term memory (LSTM) networks
for human action prediction. To train and evaluate models, we used the MoGaze
dataset - currently the most comprehensive dataset capturing poses of human
joints and the human gaze. We have thoroughly analyzed the MoGaze dataset and
selected a reduced set of cues for this task. Our model can predict (i) which
of the labeled objects the human is going to grasp, and (ii) which of the macro
locations the human is going to visit (such as table or shelf). We have
exhaustively evaluated the proposed method and compared it to individual cue
baselines. The results suggest that our LSTM model slightly outperforms the
gaze baseline in single object picking accuracy, but achieves better accuracy
in macro object prediction. Furthermore, we have also analyzed the prediction
accuracy when the gaze is not used, and in this case, the LSTM model
considerably outperformed the best single cue baseline


  We formulate the theory of nonlinear viscoelastic hydrodynamics of
anisotropic crystals in terms of dynamical Goldstone scalars of spontaneously
broken translational symmetries, under the assumption of homogeneous lattices
and absence of plastic deformations. We reformulate classical elasticity
effective field theory using surface calculus in which the Goldstone scalars
naturally define the position of higher-dimensional crystal cores, covering
both elastic and smectic crystal phases. We systematically incorporate all
dissipative effects in viscoelastic hydrodynamics at first order in a
long-wavelength expansion and study the resulting rheology equations. In the
process, we find the necessary conditions for equilibrium states of
viscoelastic materials. In the linear regime and for isotropic crystals, the
theory includes the description of Kelvin-Voigt materials. Furthermore, we
provide an entirely equivalent description of viscoelastic hydrodynamics as a
novel theory of higher-form superfluids in arbitrary dimensions where the
Goldstone scalars of partially broken generalised global symmetries play an
essential role. An exact map between the two formulations of viscoelastic
hydrodynamics is given. Finally, we study holographic models dual to both these
formulations and map them one-to-one via a careful analysis of boundary
conditions. We propose a new simple holographic model of viscoelastic
hydrodynamics by adopting an alternative quantisation for the scalar fields.


  We present Bhasacitra, a dialect mapping system for South Asia built on a
database of linguistic studies of languages of the region annotated for topic
and location data. We analyse language coverage and look towards applications
to typology by visualising example datasets. The application is not only meant
to be useful for feature mapping, but also serves as a new kind of interactive
bibliography for linguists of South Asian languages.


  In this work we semiclassically analyzed the high lying eigenstates of a
mixed type Hamiltonian system. For the regular states we employ the
Einstein-Brillouin-Keller quantization, while for the chaotic states, following
the principle of uniform semiclassical condensation, we obtain a prediction for
their wavefunction autocorrelation function.


  A new expression for the Green's function of a finite one-dimensional lattice
with nearest neighbor interaction is derived via discrete Fourier transform.
Solution of the Heisenberg spin chain with periodic and open boundary
conditions is considered as an example. Comparison to Bethe ansatz clarifies
the relation between the two approaches.


  We review our recent formulation of Colombeau type algebras as Hausdorff
sequence spaces with ultranorms, defined by sequences of exponential weights.
We extend previous results and give new perspectives related to echelon type
spaces, possible generalisations, asymptotic algebras, concepts of association,
and applications thereof.


  In this paper, we discuss an extension of the Split Hamiltonian Monte Carlo
(Split HMC) method for Gaussian process model (GPM). This method is based on
splitting the Hamiltonian in a way that allows much of the movement around the
state space to be done at low computational cost. To this end, we approximate
the negative log density (i.e., the energy function) of the distribution of
interest by a quadratic function U0 for which Hamiltonian dynamics can be
solved analytically. The overall energy function U is then written as U0 + U1,
where U1 is the approximation error. The Hamiltonian is then split into two
parts; one part is based on U0 is handled analytically, the other part is based
on U1 for which we approximate Hamiltonian's equations by discretizing time. We
use simulated and real data to compare the performance of our method to the
standard HMC. We find that splitting the Hamiltonian for GP models could lead
to substantial improvement (up to 10 folds) of sampling efficiency, which is
measured in terms of the amount of time required for producing an independent
sample with high acceptance probability from posterior distributions.


  We consider distributed learning scenarios where M machines interact with a
parameter server along several communication rounds in order to minimize a
joint objective function. Focusing on the heterogeneous case, where different
machines may draw samples from different data-distributions, we design the
first local update method that provably benefits over the two most prominent
distributed baselines: namely Minibatch-SGD and Local-SGD. Key to our approach
is a slow querying technique that we customize to the distributed setting,
which in turn enables a better mitigation of the bias caused by local updates.


  A data classifier is the basic structural unit of an artificial neural
network. These classifiers, known as perceptron, make an output prediction over
the linear summation of the input information. Quantum versions of artificial
neural networks are considered to provide more efficient and faster artificial
intelligence and learning algorithms. The most generic and realistic open
quantum systems are the quantum systems in thermal environments and the
information carried by the thermal reservoirs is the temperature information.
This study shows that an open quantum system that is in contact with many
information channels is a natural information classifier. More specifically, it
has been demonstrated that a two-level quantum system can classify temperature
information of distinct thermal reservoirs. The results of the manuscript are
of importance to the construction of thermal quantum neural networks and the
development of minimal quantum thermal machines. Also, a physical model,
proposed and discussed with realistic parameters, shows that faster operating
thermal quantum classifiers can be built than the classical versions.


  The novel coronavirus disease (COVID-19) is known as the causative virus of
outbreak pneumonia initially recognized in the mainland of China, late December
2019. COVID-19 reaches out to many countries in the world, and the number of
daily cases continues to increase rapidly. In order to simulate, track, and
forecast the trend of the virus spread, several mathematical and statistical
models have been developed.
Susceptible-Exposed-Infected-Quarantined-Recovered-Death-Insusceptible
(SEIQRDP) model is one of the most promising dynamic systems that has been
proposed for estimating the transmissibility of the COVID-19. In the present
study, we propose a Fractional-order SEIQRDP model to analyze the COVID-19
epidemic. The Fractional-order paradigm offers a flexible, appropriate, and
reliable framework for pandemic growth characterization. In fact,
fractional-order operator is not local and consider the memory of the
variables. Hence, it takes into account the sub-diffusion process of confirmed
and recovered cases growth. The results of the validation of the model using
real COVID-19 data are presented, and the pertinence of the proposed model to
analyze, understand, and predict the epidemic is discussed.


  Effectively and efficiently deploying graph neural networks (GNNs) at scale
remains one of the most challenging aspects of graph representation learning.
Many powerful solutions have only ever been validated on comparatively small
datasets, often with counter-intuitive outcomes -- a barrier which has been
broken by the Open Graph Benchmark Large-Scale Challenge (OGB-LSC). We entered
the OGB-LSC with two large-scale GNNs: a deep transductive node classifier
powered by bootstrapping, and a very deep (up to 50-layer) inductive graph
regressor regularised by denoising objectives. Our models achieved an
award-level (top-3) performance on both the MAG240M and PCQM4M benchmarks. In
doing so, we demonstrate evidence of scalable self-supervised graph
representation learning, and utility of very deep GNNs -- both very important
open issues. Our code is publicly available at:
https://github.com/deepmind/deepmind-research/tree/master/ogb_lsc.


  We extend a recent global analysis of nucleon parton distributions carried
out at next-to-leading order (NLO) in the MSbar scheme to provide distributions
in the so-called DIS scheme. We pay particular attention to the translation of
the heavy quark distributions in going from the MSbar to the DIS scheme. We
repeat the global analysis at leading order (LO) and comment on the major
effects produced when going from LO to NLO. Finally we include in the global
analysis a freely parameterised form of possible higher twist contributions to
make an exploratory study of the size of these effects as a function of x.


  Based on the nonlinear steepest descent method of Deift and Zhou for
oscillatory Riemann--Hilbert problems and the Dbar approach, the long-time
asymptotic behavior of solutions to the fifth-order modified Korteweg-de Vries
equation on the line is studied in the case of initial conditions that belong
to some weighted Sobolev spaces. Using techniques in Fourier analysis and the
idea of $I$-method, we give its global well-posedness in lower regularity
Sobolev spaces, and then obtain the asymptotic behavior in these spaces with
weights.


  In this paper, we study the non-asymptotic sample complexity for the pure
exploration problem in contextual bandits and tabular reinforcement learning
(RL): identifying an epsilon-optimal policy from a set of policies with high
probability. Existing work in bandits has shown that it is possible to identify
the best policy by estimating only the difference between the behaviors of
individual policies, which can be substantially cheaper than estimating the
behavior of each policy directly. However, the best-known complexities in RL
fail to take advantage of this and instead estimate the behavior of each policy
directly. Does it suffice to estimate only the differences in the behaviors of
policies in RL? We answer this question positively for contextual bandits but
in the negative for tabular RL, showing a separation between contextual bandits
and RL. However, inspired by this, we show that it almost suffices to estimate
only the differences in RL: if we can estimate the behavior of a single
reference policy, it suffices to only estimate how any other policy deviates
from this reference policy. We develop an algorithm which instantiates this
principle and obtains, to the best of our knowledge, the tightest known bound
on the sample complexity of tabular RL.


  High-precision proper motions of the globular cluster 47 Tuc have allowed us
to measure for the first time the cluster rotation in the plane of the sky and
the velocity anisotropy profile from the cluster core out to about 13'. These
profiles are coupled with prior measurements along the line of sight and the
surface-brightness profile, and fit all together with self-consistent models
specifically constructed to describe quasi-relaxed stellar systems with
realistic differential rotation, axisymmetry and pressure anisotropy. The
best-fit model provides an inclination angle i between the rotation axis and
the line-of-sight direction of 30 deg, and is able to simultaneously reproduce
the full three-dimensional kinematics and structure of the cluster, while
preserving a good agreement with the projected morphology. Literature models
based solely on line-of-sight measurements imply a significantly different
inclination angle (i=45 deg), demonstrating that proper motions play a key role
in constraining the intrinsic structure of 47 Tuc. Our best-fit global
dynamical model implies an internal rotation higher than previous studies have
shown, and suggests a peak of the intrinsic V/sigma ratio of ~0.9 at around two
half-light radii, with a non-monotonic intrinsic ellipticity profile reaching
values up to 0.45. Our study unveils a new degree of dynamical complexity in 47
Tuc, which may be leveraged to provide new insights into the formation and
evolution of globular clusters.


  We prove that a given Calabi-Yau threefold with a stable holomorphic vector
bundle can be perturbed to a solution of the Strominger system provided that
the second Chern class of the vector bundle is equal to the second Chern class
of the tangent bundle. If the Calabi-Yau threefold has strict SU(3) holonomy
then the equations of motion derived from the heterotic string effective action
are also satisfied by the solutions we obtain.


  This paper is motivated by a recent result of Wolf \cite{wolf} on the minimum
number of monochromatic 4-term arithmetic progressions(4-APs, for short) in
$\Z_p$, where $p$ is a prime number. Wolf proved that there is a 2-coloring of
$\Z_p$ with 0.000386% fewer monochromatic 4-APs than random 2-colorings; the
proof is probabilistic and non-constructive. In this paper, we present an
explicit and simple construction of a 2-coloring with 9.3% fewer monochromatic
4-APs than random 2-colorings. This problem leads us to consider the minimum
number of monochromatic 4-APs in $\Z_n$ for general $n$. We obtain both lower
bound and upper bound on the minimum number of monochromatic 4-APs in all
2-colorings of $\Z_n$. Wolf proved that any 2-coloring of $\Z_p$ has at least
$(1/16+o(1))p^2$ monochromatic 4-APs. We improve this lower bound into
$(7/96+o(1))p^2$.
  Our results on $\Z_n$ naturally apply to the similar problem on $[n]$ (i.e.,
$\{1,2,..., n\}$). In 2008, Parillo, Robertson, and Saracino \cite{prs}
constructed a 2-coloring of $[n]$ with 14.6% fewer monochromatic 3-APs than
random 2-colorings. In 2010, Butler, Costello, and Graham \cite{BCG} extended
their methods and used an extensive computer search to construct a 2-coloring
of $[n]$ with 17.35% fewer monochromatic 4-APs (and 26.8% fewer monochromatic
5-APs) than random 2-colorings. Our construction gives a 2-coloring of $[n]$
with 33.33% fewer monochromatic 4-APs (and 57.89% fewer monochromatic 5-APs)
than random 2-colorings.


  Some magnetic properties of quark matter and a microscopic origin of the
strong magnetic field in compact stars are discussed; ferromagnetic order is
discussed with the Fermi liquid theory and possible appearance of spin density
wave is suggested within the NJL model. Implications of these magnetic
properties are briefly discussed for compact stars.


  Using Pade approximations and Monte Carlo simulations, we study the phase
diagram of the Two-Neighbor Stochastic Cellular Automata, which have two
parameters $p_{1}$ and $p_{2}$ and include the mixed site-bond directed
percolation (DP) as a special case. The phase transition line $p_{1}=p_{1 {\rm
c}}(p_{2})$ has endpoints at $(p_{1}, p_{2})=(1/2,1)$ and at (0.8092, 0). The
former point (1/2,1) is a special point at which Compact DP transition occurs
and its critical exponents are known exactly. Results of time-dependent
simulation show that in the whole range of parameters, excluding this point
(1/2,1), the system belongs to the DP universality class. It is first shown
that the shape of the phase transition line near this special point has,
asymptotically, a parabolic shape, {\it i.e.}, $p_{1{\rm c}}(p_{2})-1/2 \sim
(1-p_{2})^{\theta}$ with $\theta = 1/2$ for $0 < 1-p_{2} \ll 1$. We use the
Monte Carlo data to assess the accuracy of rigorous bounds for the line
recently reported by Liggett and by Katori and Tsukahara. It is also shown that
outside the vicinity of the special point (1/2,1), the curve is well
approximated by an interpolation formula, similar to the one proposed by Yanuka
and Englman.


  In this paper, we investigate a new model of a linear-quadratic mean-field
stochastic Stackelberg differential game with one leader and two followers, in
which the leader is allowed to stop her strategy at a random time. Our
overarching goal is to find the Stackelberg solution of the leader and
followers for such a model. By employing the backward induction method, the
state equation is divided into two-stage equations. Moreover, by using the
maximum principle and the verification theorem, the Stackelberg solution is
obtained for such a model.


  Generation of free-space laser-like emissions of high spatiotemporal
coherence and narrow bandwidth in atmosphere opens promising opportunities for
remote spectroscopic sensing. Here, we report on generation of such laser-like
emissions, which results from the combined contributions of perturbative and
non-perturbative nonlinear optical effects in nitrogen molecules exposed to
intense mid-infrared laser fields. We systematically investigate the dependence
of the generated free-space laser spectrum on wavelength and power of the
driver laser. It is revealed that the free-space laser is produced by resonant
Raman amplification of the fifth harmonic of the driver pulses in rotational
wavepacket of the molecular nitrogen ions.


  For a T-periodic non-Hermitian Hamiltonian H(t), we construct a class of
adiabatic cyclic states of period T which are not eigenstates of the initial
Hamiltonian H(0). We show that the corresponding adiabatic geometric phase
angles are real and discuss their relationship with the conventional complex
adiabatic geometric phase angles. We present a detailed calculation of the new
adiabatic cyclic states and their geometric phases for a non-Hermitian analog
of the spin 1/2 particle in a precessing magnetic field.


  A well-known identity (Alex+) - (Alex-)=(t^{1/2}-t^{-1/2}) (Alex0) holds for
three 1-links L+, L-, and L0 which satisfy a famous local-move-relation.
  We prove a new local-move-identity for the Z[t,t^{-1}]-Alexander polynomials
of 2-links, which is a 2-dimensional analogue of the 1-dimensional one. In the
1-dimensional link case there is a well-known relation between the
Alexander-Conway polynomial and the linking number. As its 2-dimensional
analogue, we find a relation between the Z[t,t^{-1}]-Alexander polynomials of
2-links and the alinking number of 2-links. We show high dimensional analogues
of these results. Furthermore we prove that in the 2-dimensional case we cannot
normalize the Z[t,t^{-1}]-Alexander polynomials to be compatible with our
identity but that in a high-dimensional case we can do that to be compatible
with our new identity.


  The ground state hyperfine splitting of light muon-electron ions of lithium,
beryllium, boron and helium is calculated on the basis of analytical
perturbation theory in terms of small parameters of the fine structure constant
and electron-muon mass ratio. The corrections of vacuum polarization, nuclear
structure and recoil effects and electron vertex corrections are taken into
account. The dependence of the corrections on the nucleus charge Z is studied.
The obtained total values of hyperfine splitting intervals can be used for
comparison with future experimental data.


  We present the theory of a single spin meter consisting of a quantum dot in a
magnetic field under microwave irradiation combined with a charge counter. We
show that when a current is passed through the dot, a change in the average
occupation number occurs if the microwaves are resonant with the on-dot Zeeman
splitting. The width of the resonant change is given by the microwave induced
Rabi frequency, making the quantum dot a sensitive probe of the local magnetic
field and enabling the detection of the state of a nearby spin. If the dot-spin
and the nearby spin have different g-factors a non-demolition readout of the
spin state can be achieved. The conditions for a reliable spin readout are
found.


  We establish the local uniqueness of steady transonic shock solutions with
spherical symmetry for the three-dimensional full Euler equations. These
transonic shock-fronts are important for understanding transonic shock
phenomena in divergent nozzles. From mathematical point of view, we show the
uniqueness of solutions of a free boundary problem for a multidimensional
quasilinear system of mixed-composite elliptic--hyperbolic type. To this end,
we develop a decomposition of the Euler system which works in a general
Riemannian manifold, a method to study a Venttsel problem of nonclassical
nonlocal elliptic operators, and an iteration mapping which possesses locally a
unique fixed point. The approach reveals an intrinsic structure of the steady
Euler system and subtle interactions of its elliptic and hyperbolic part.


  The Fermi Large Area Telescope (LAT) reported the first definitive GeV
detections of the binaries LS I +61\degree 303 and LS 5039 in the first year
after its launch in June, 2008. These detections were unambiguous as a
consequence of the reduced positional uncertainty and the detection of
modulated gamma-ray emission on the corresponding orbital periods. An analysis
of new data from the LAT, comprising 30 months of observations, identifies a
change in the gamma-ray behavior of LS I +61\degree 303. An increase in flux is
detected in March 2009 and a steady decline in the orbital flux modulation is
observed. Significant emission up to 30GeV is detected by the LAT; prior
datasets led to upper limits only. Contemporaneous TeV observations no longer
detected the source, or found it -in one orbit- close to periastron, far from
the phases at which the source previously appeared at TeV energies. The
detailed numerical simulations and models that exist within the literature do
not predict or explain many of these features now observed at GeV and TeV
energies. New ideas and models are needed to fully explain and understand this
behavior. A detailed phase-resolved analysis of the spectral characterization
of LS I +61\degree 303 in the GeV regime ascribes a power law with an
exponential cutoff spectrum along each analyzed portion of the system's orbit.
The on-source exposure of LS 5039 is also substantially increased with respect
to our prior publication. In this case, whereas the general gamma-ray
properties remain consistent, the increased statistics of the current dataset
allows for a deeper investigation of its orbital and spectral evolution.


  The measurement of the quasi-free production of the eta prime meson has been
carried out at the COSY-11 detection setup using a proton beam and a deuteron
cluster target. The energy dependence of the cross section is extracted using a
fixed proton beam momentum of p_beam=3.35 GeV/c and exploiting the Fermi
momenta of nucleons inside deuterons. The data cover a range of centre-of-mass
excess energies from 0 to 24 MeV. Due to the low statistics at the present
stage only upper limits for the cross sections could be extracted. In this
article we focus on the functioning and efficiency determination of the neutron
detector.


  The elastic scattering differential cross section is calculated for proton
scattering from $^6$He at 717 MeV, using single scattering terms of the
multiple scattering expansion of the total transition amplitude (MST). We
analyse the effects of different scattering frameworks, specifically the
Factorized Impulse Approximation (FIA) and the Fixed Scatterer (adiabatic)
Approximation (FSA) and the uncertainties associated with the use different
structure models.


  We investigate the sensitivity of the composite cellular automaton of H.
Fuk\'{s} [Phys. Rev. E 55, R2081 (1997)] to noise and assess the density
classification performance of the resulting probabilistic cellular automaton
(PCA) numerically. We conclude that the composite PCA performs the density
classification task reliably only up to very small levels of noise. In
particular, it cannot outperform the noisy Gacs-Kurdyumov-Levin automaton, an
imperfect classifier, for any level of noise. While the original composite CA
is nonergodic, analyses of relaxation times indicate that its noisy version is
an ergodic automaton, with the relaxation times decaying algebraically over an
extended range of parameters with an exponent very close (possibly equal) to
the mean-field value.


  We prove that the generalized Swift--Hohenberg equation with nonlinear
right-hand side, a natural generalization of the Swift--Hohenberg equation
arising in physics and describing inter alia pattern formation, has no
nontrivial local conservation laws.


  In this paper, we apply ideas of Dijkgraaf and Witten on 2+1 dimensional
topological quantum field theory to arithmetic curves, that is, the spectra of
rings of integers in algebraic number fields. In the first three sections, we
define classical Chern-Simons functionals on spaces of Galois representations.
In the highly speculative section 5, we consider the far-fetched possibility of
using Chern-Simons theory to construct L-functions.


  Due to the exponential growth of interconnected devices and reduced cell
coverage, beyond fifth-generation networks will be dense. Thus, instead of
wired backhaul, wireless backhaul will be cost-effective and flexible. For
security in multi-transmitter systems, sub-optimal and optimal transmitter
selection schemes exist. However, including backhaul activity knowledge
available (BKA) and backhaul activity knowledge unavailable (BKU) cases and
transmitter selection schemes, there is no generalized secrecy analysis method.
Moreover, evaluation of the ergodic secrecy rate (ESR) of the optimal selection
schemes is impossible using existing solution approaches. To address these, we
propose two sub-optimal and optimal transmitter selection schemes for a
small-cell multi-transmitter system in BKU or BKA cases in the presence of
multiple colluding eavesdroppers. We derive the distribution of the ratio of
the destination channel SNR and eavesdropping channel SNR, thereby providing
different secrecy performance metrics uniformly irrespective of selection
schemes and BKU or BKA cases. Simplified asymptotic expressions are provided to
elucidate the influence of the system parameters and of the backhaul
reliability. We observe that the secrecy performance improves when the
knowledge of backhaul link activity is utilized, and the improvement is most
noticeable when the backhaul is highly unreliable. We also observe that while
the secrecy performance degrades with an increasing number of eavesdroppers,
neither the asymptotic saturation value of the secrecy outage probability nor
the rate of improvement of the ESR with signal-to-noise-ratio depends on the
number of eavesdroppers.


  In this letter we present a measurement of the phase-space density
distribution (PSDD) of ultra-cold \Rb atoms performing 1D anomalous diffusion.
The PSDD is imaged using a direct tomographic method based on Raman velocity
selection. It reveals that the position-velocity correlation function
$C_{xv}(t)$ builds up on a timescale related to the initial conditions of the
ensemble and then decays asymptotically as a power-law. We show that the decay
follows a simple scaling theory involving the power-law asymptotic dynamics of
position and velocity. The generality of this scaling theory is confirmed using
Monte-Carlo simulations of two distinct models of anomalous diffusion.


  In this paper, we propose Emo2Vec which encodes emotional semantics into
vectors. We train Emo2Vec by multi-task learning six different emotion-related
tasks, including emotion/sentiment analysis, sarcasm classification, stress
detection, abusive language classification, insult detection, and personality
recognition. Our evaluation of Emo2Vec shows that it outperforms existing
affect-related representations, such as Sentiment-Specific Word Embedding and
DeepMoji embeddings with much smaller training corpora. When concatenated with
GloVe, Emo2Vec achieves competitive performances to state-of-the-art results on
several tasks using a simple logistic regression classifier.


  Using the recent formula presented by He and Cachazo-Mason-Skinner for the
link representation of tree-level N=8 supergravity amplitudes, we derived a
CSW-like expansion for the Next-to-MHV 6- and 7-graviton amplitudes by using
the global residue theorem; a technique introduced originally for Yang-Mills
amplitudes. We analytically checked the equivalence of one of the CSW terms and
its corresponding Risager's diagram. For the remaining 6-graviton and all
7-graviton terms, we numerically checked the agreement with Risager's
expansion. We showed that the conditions for the absence of contributions at
infinity of the global residue theorem are satisfied for any number of
particles. This means that our technique and Risager's should disagree starting
at twelve particles where Risager's method is known to fail.


  We present the OC16-CE80 Chinese-English mixlingual speech database which was
released as a main resource for training, development and test for the
Chinese-English mixlingual speech recognition (MixASR-CHEN) challenge on
O-COCOSDA 2016. This database consists of 80 hours of speech signals recorded
from more than 1,400 speakers, where the utterances are in Chinese but each
involves one or several English words. Based on the database and another two
free data resources (THCHS30 and the CMU dictionary), a speech recognition
(ASR) baseline was constructed with the deep neural network-hidden Markov model
(DNN-HMM) hybrid system. We then report the baseline results following the
MixASR-CHEN evaluation rules and demonstrate that OC16-CE80 is a reasonable
data resource for mixlingual research.


  In this paper, we discuss an initial-boundary value problem (IBVP) for the
multi-term time-fractional diffusion equation with x-dependent coefficients. By
means of the Mittag-Leffler functions and the eigenfunction expansion, we
reduce the IBVP to an equivalent integral equation to show the unique existence
and the analyticity of the solution for the equation. Especially, in the case
where all the coefficients of the time-fractional derivatives are non-negative,
by the Laplace and inversion Laplace transforms, it turns out that the decay
rate of the solution for long time is dominated by the lowest order of the
time-fractional derivatives. Finally, as an application of the analyticity of
the solution, the uniqueness of an inverse problem in determining the
fractional orders in the multi-term time-fractional diffusion equations from
one interior point observation is established.


  We have derived relations between full-width-half-maxima and equivalent
widths of metallic absorption lines in the spectra of RR~Lyrae stars to
estimate new upper limits on the axial equatorial rotational velocities of
RR~Lyrae and metal-poor red horizontal branch stars (RHB). We also have derived
the variations of RR~Lyrae macroturbulent velocities during the pulsation
cycles. In RRab cycles the line widths are dominated by phase-dependent
convolutions of axial rotation and macroturbulence, which we designate as
V_macrot. The behavior of V_macrot is remarkably uniform among the RRab stars,
but the behavior of V_macrot among RRc stars varies strongly from star to star.
The RRab stars exhibit an upper limit on V_macrot of 5 +/- 1 km/s with weak
evidence of an anti-correlation with T_eff. The RRc minima range from 2 to 12
km/s. The abrupt decline in large rotations with decreasing T_eff at the blue
boundary of the instability strip and the apparently smooth continuous
variation among the RRab and RHB stars suggests that HB stars gain/lose surface
angular momentum on time scales short compared to HB lifetimes. V_macrot values
for our metal-poor RHB stars agree well with those derived by Fourier analysis
of an independent but less metal-poor sample of Carney et al. (2008); they
conform qualitatively to the expectations of Tanner et al. (2013). A general
conclusion of our investigation is that surface angular momentum as measured by
V_rot*sini is not a reliable indicator of total stellar angular momentum
anywhere along the HB.


  We have measured the ac-Stark shift of the 4s2 1S0 - 4s4p 3P1 line in 40Ca
for perturbing laser wavelengths between 780 nm and 1064 nm with a time domain
Ramsey-Borde atom interferometer. We found a zero crossing of the shift for the
mS = 0 - mP = 0 transition and \sigma polarized perturbation at 800.8(22) nm.
The data was analyzed by a model deriving the energy shift from known
transition wavelengths and strengths. To fit our data, we adjusted the Einstein
A coefficients of the 4s3d 3D - 4s4p 3P and 4s5s 3S - 4s4p 3P fine structure
multiplets. With these we can predict vanishing ac-Stark shifts for the 1S0 m =
0 - 3P1 m = 1 transition and \sigma- light at 983(12) nm and at 735.5(20) nm
for the transition to the 3P0 level.


  Applying time-dependent local density approximation (TDLDA), we study the
high-order harmonic generation (HHG) of ethylene subjected to the one-color
($\omega=2.72$ eV) and the two-color ($\omega_1=2.72$ eV and $\omega_2=5.44$
eV) ultrashort intense laser pulses. The HHG spectrum of ethylene in the
one-color laser field shows the obvious plateaus and odd order harmonics are
produced while the two-color laser field can result in the breaking of the
symmetry and generation of the even order harmonic. The ionization
probabilities are obtained showing the increase of the ionization probability
of higher charge state by the two-color laser field. The temporal structures of
HHG spectrum of ethylene is explored by means of the time-frequency analysis
showing new insights of the HHG mechanisms in the one-color and the two-color
laser fields.


  In today's urban landscape, traffic congestion poses a critical challenge,
especially during outlier scenarios. These outliers can indicate abrupt traffic
peaks, drops, or irregular trends, often arising from factors such as
accidents, events, or roadwork. Moreover, Given the dynamic nature of traffic,
the need for real-time traffic modeling also becomes crucial to ensure accurate
and up-to-date traffic predictions. To address these challenges, we introduce
the Outlier Weighted Autoencoder Modeling (OWAM) framework. OWAM employs
autoencoders for local outlier detection and generates correlation scores to
assess neighboring traffic's influence. These scores serve as a weighted factor
for neighboring sensors, before fusing them into the model. This information
enhances the traffic model's performance and supports effective real-time
updates, a crucial aspect for capturing dynamic traffic patterns. OWAM
demonstrates a favorable trade-off between accuracy and efficiency, rendering
it highly suitable for real-world applications. The research findings
contribute significantly to the development of more efficient and adaptive
traffic prediction models, advancing the field of transportation management for
the future. The code and datasets of our framework is publicly available under
https://github.com/himanshudce/OWAM.


  We investigate with a transport approach the cold and hot nuclear matter
effects on the charmonium transverse momentum distributions in relativistic
heavy ion collisions. The newly defined nuclear modification factor
$r_{AA}=\langle p_{T}^2\rangle_{AA}/\langle p_{T}^2\rangle_{pp}$ and elliptic
flow $v_2$ for $J/\psi$ are sensitive to the nature of the hot medium and the
thermalization of heavy quarks. From SPS through RHIC to LHC colliding
energies, we observe dramatic changes in the centrality dependence of $r_{AA}$.
We find that at LHC energy, the finally observed charmonia are dominated by the
regeneration from thermalized heavy quarks.


  This paper examines the impact of coronavirus (COVID-19) on stock market
volatility (SMV) in Pakistan by controlling the effect of exchange rate,
interest rate and government/central bank interventions to combat the pandemic.
We used the vector autoregressive (VAR) model over a sample period ranging from
February 25, 2020 to December 7, 2020. We find that a shock to total daily
coronavirus cases in Pakistan lead to a significant increase in SMV. This
result is aligned with a vast literature on pandemics and investors uncertainty
and remains robust to several robustness checks applied in our analysis.


  We employ an effective Lagrangian approach and isobar model to investigate
the role of the $N^*_{c\bar{c}}(4261)$ resonance with hidden charm in the
$\pi^- p \to J/\psi n$ reaction. The total and differential cross sections of
this reaction are predicted by including contributions from both
$N^*_{c\bar{c}}(4261)$ and nucleon pole. It is found that the maximal value of
the total cross section can exceed 11 $\mu b$ and a clear
$N^*_{c\bar{c}}(4261)$ peak is visible there, well distinguished from
background. As center-of-mass energy increases to about 4.8 GeV, the background
makes the differential cross section for backward angles rather different. Our
theoretical results would provide valuable information for looking for the
$N^*_{c\bar{c}}(4261)$ resonance in future experiments.


  We revisit the 2d Navier--Stokes equations on the periodic $\beta$-plane,
with the Coriolis parameter varying as $\beta y$, and obtain bounds on the
number of determining modes and nodes of the flow. The number of modes {and
nodes} scale as $cG_0^{1/2} + c'(M/\beta)^{1/2}$ and $cG_0^{2/3} +
c'(M/\beta)^{1/2}$ respectively, where the Grashof number
$G_0=|f_v|_{L^2}^{}/(\mu^2\kappa_0^2)$ and $M$ involves higher derivatives of
the forcing $f_v$. For large $\beta$ (strong rotation), this results in fewer
degrees of freedom than the classical (non-rotating) bound that scales as
$cG_0$.


  With reference to a particle tunneling through two successive barriers, it
seems to have been generally accepted that the tunneling time does not depend
on the separation distance between the barriers. This phenomenon has been
called the {\it generalized Hartman effect}. In this letter, we point out a
lack of mathematical rigour in the reasoning by which this effect was deduced
about ten years ago. A mathematically rigorous treatment shows us that the
tunneling time does indeed depend on the length of the free space between the
barriers.


  Quarkonium production is an excellent probe to investigate the properties of
the hot and dense medium, which can be created in heavy-ion collisions. The
production and hadronisation of heavy quarks are well separated in the
space-time evolution of a collision and provide a wealth of information of the
underlying QCD dynamics from the dense to the eventually dilute system. We
report on the latest results from ALICE at the LHC on quarkonium production in
heavy-ion collisions at mid- and forward rapidity. The nuclear modification
factor as a function of centrality, transverse momentum, and rapidity is
presented for charmonium and bottomonium states in Pb--Pb collisions at
$\sqrt{s_{\rm NN}} = 5.02$~TeV. In addition, results on the nuclear
modification factor of J/$\psi$ in Xe--Xe collisions at $\sqrt{s_{\rm NN}}=5.44
$~TeV are presented. Experimental results are compared with available
phenomenological calculations.


  We propose a generalisation of Gibbs' statistical mechanics into the domain
of non-negligible phase space correlations. Derived are the probability
distribution and entropy as a generalised ensemble average, replacing
Gibbs-Boltzmann-Shannon's entropy definition enabling construction of new forms
of statistical mechanics. The general entropy may also be of importance in
information theory and data analysis. Application to generalised Lorentzian
phase space elements yields the Gibbs-Lorentzian power law probability
distribution and statistical mechanics. Details can be found in arXiv:1406.6639


  We examine the novel task of domain-independent scientific concept extraction
from abstracts of scholarly articles and present two contributions. First, we
suggest a set of generic scientific concepts that have been identified in a
systematic annotation process. This set of concepts is utilised to annotate a
corpus of scientific abstracts from 10 domains of Science, Technology and
Medicine at the phrasal level in a joint effort with domain experts. The
resulting dataset is used in a set of benchmark experiments to (a) provide
baseline performance for this task, (b) examine the transferability of concepts
between domains. Second, we present two deep learning systems as baselines. In
particular, we propose active learning to deal with different domains in our
task. The experimental results show that (1) a substantial agreement is
achievable by non-experts after consultation with domain experts, (2) the
baseline system achieves a fairly high F1 score, (3) active learning enables us
to nearly halve the amount of required training data.


  We introduce a novel enhanced sampling approach named OPES flooding for
calculating the kinetics of rare events from atomistic molecular dynamics
simulation. This method is derived from the
On-the-fly-Probability-Enhanced-Sampling (OPES) approach [Invernizzi and
Parrinello, JPC Lett. 2020], which has been recently developed for calculating
converged free energy surfaces for complex systems. In this paper, we describe
the theoretical details of the OPES flooding technique and demonstrate the
application on three systems of increasing complexity: barrier crossing in a
two-dimensional double well potential, conformational transition in the alanine
dipeptide in gas phase, and the folding and unfolding of the chignolin
polypeptide in aqueous environment. From extensive tests, we show that the
calculation of accurate kinetics not only requires the transition state to be
bias-free, but the amount of bias deposited should also not exceed the
effective barrier height measured along the chosen collective variables. In
this vein, the possibility of computing rates from biasing suboptimal order
parameters has also been explored. Furthermore, we describe the choice of
optimum parameter combinations for obtaining accurate results from limited
computational effort.


  In this work, we revisit the question of the linear stability of superfluid
phases of matter. Famously, Landau predicted superfluid Helium would become
unstable for large enough superfluid velocities. We demonstrate that this
instability simply follows from a thermodynamic argument, by showing that its
onset corresponds to a change of sign of one of the eigenvalues of the matrix
of second derivatives of the free energy. Turning on dissipation and without
any particular assumption on invariance under boosts, we show that a linear
dynamical instability also develops, leading to exponential growth in time of
perturbations around equilibrium. Specializing to Galilean superfluids and
assuming the existence of quasiparticles, our criterion matches Landau's
critical velocity. We also verify that it correctly reproduces the onset of the
instability in relativistic superfluids constructed using gauge/gravity
duality. Our work provides a simple, comprehensive and unified description of
the Landau instability for superfluids independently of the microscopic details
of the system.


  In this article, for a time-fractional diffusion-wave equation $\pppa u(x,t)
= -Au(x,t)$, $0<t<T$ with fractional order $\alpha \in (1,2)$, we consider the
backward problem in time: determine $u(\cdot,t)$, $0<t<T$ by $u(\cdot,T)$ and
$\ppp_tu(\cdot,T)$. We proved that there exists a countably infinite set
$\Lambda \in (0,\infty)$ with a unique accumulation point $0$ such that the
backward problem is well-posed for $T \not\in \Lambda$.


  Gallium radioactive source experiments have reported a neutrino-induced event
rate about 20\% lower than expected with a high statistical significance. We
present an explanation of this observation assuming quantum decoherence of the
neutrinos in the gallium detectors at a scale of 2~m. This explanation is
consistent with global data on neutrino oscillations, including solar
neutrinos, if decoherence effects decrease quickly with energy, for instance
with a power law $E_\nu^{-r}$ with $r\simeq 12$. Our proposal does not require
the presence of sterile neutrinos but implies a modification of the standard
quantum mechanical evolution equations for active neutrinos.


  The most intriguing properties of non-Hermitian systems are found near the
exceptional points (EPs) at which the Hamiltonian matrix becomes defective. Due
to the complex topological structure of the energy Riemann surfaces close to an
EP and the breakdown of the adiabatic theorem due to non-Hermiticity, the state
evolution in non-Hermitian systems is much more complex than that in Hermitian
systems. For example, recent experimental work [Doppler et al. Nature 537, 76
(2016)] demonstrated that dynamically encircling an EP can lead to chiral
behaviors, i.e., encircling an EP in different directions results in different
output states. Here, we propose a coupled ferromagnetic waveguide system that
carries two EPs and design an experimental setup in which the trajectory of
state evolution can be controlled in situ using a tunable external field,
allowing us to dynamically encircle zero, one or even two EPs experimentally.
The tunability allows us to control the trajectory of encircling in the
parameter space, including the size of the encircling loop and the starting/end
point. We discovered that whether or not the dynamics is chiral actually
depends on the starting point of the loop. In particular, dynamically
encircling an EP with a starting point in the parity-time-broken phase results
in non-chiral behaviors such that the output state is the same no matter which
direction the encircling takes. The proposed system is a useful platform to
explore the topology of energy surfaces and the dynamics of state evolution in
non-Hermitian systems and will likely find applications in mode switching
controlled with external parameters.


  We have proposed optical tunable CNOT (XOR) and XNOR logic gates using
two-dimensional photonic crystal (2DPhC) cavities. Where, air rods with square
lattice array have been embedded in Ag-Polymer substrate with refractive index
of 1.59. In this work, we have enhanced speed of logic gates by applying two
input signals with a phase difference at the same wavelength for 2DPhC
cavities. Where, we have adjusted the phases of input and control signals equal
with {\pi}/3 and zero, respectively. The response time of the structure and
quality factor of the cavities are in the range of femtosecond and 2000,
respectively. Then, we have used electro-optic property of the substrate
material to change the cavities resonance wavelengths. By this means, we could
design the logic gates and demonstrate a tunable range of 23nm for their
operation wavelength. The quality factor and the response times of cavities
remain constant in the tunable range of wavelength, approximately. The
evaluated least ON to OFF logic-level contrast ratios for the XOR and XNOR
logic gates are 25.45dB and 22.61dB, respectively. The bit rates of the
proposed logic gates can reach up to higher than 0.166 P(Peta)bps values.
According to the high rapid response time with acceptable quality factor of the
PhC cavities, the proposed optical logic gates can be considered as appropriate
candidates to be building blocks for applications such as optical integrated
circuits and optical processors with an ultrahigh speed of data flow.


  In this paper, based on the known rigidity theorems of Newton maps ([DS],
[RYZ]) and real polynomials ([KSS1], [CvS]), we prove the density of
hyperbolicity in the family of real Newton maps of degree d>2 with all free
critical points real.


  Nonconvex-nonconcave saddle-point optimization in machine learning has
triggered lots of research for studying non-monotone variational inequalities
(VI). In this work, we introduce two mirror frameworks, called mirror
extragradient method and mirror extrapolation method, for approximating
solutions to relatively Lipschitz and monotone-like VIs. The former covers the
well-known Nemirovski's mirror prox method and Nesterov's dual extrapolation
method, and the recently proposed Bregman extragradient method; all of them can
be reformulated into a scheme that is very similar to the original form of
extragradient method. The latter includes the operator extrapolation method and
the Bregman extrapolation method as its special cases. The proposed mirror
frameworks allow us to present a unified and improved convergence analysis for
all these existing methods under relative Lipschitzness and monotone-like
conditions that may be the currently weakest assumptions.


  This article deals with the design of saturated controls in the context of
partial differential equations. It is focused on a Korteweg-de Vries equation,
which is a nonlinear mathematical model of waves on shallow water surfaces. The
aim of this article is to study the influence of a saturating in L 2-norm
distributed control on the well-posedness and the stability of this equation.
The well-posedness is proven applying a Banach fixed point theorem. The proof
of the asymptotic stability of the closed-loop system is tackled with a
Lyapunov function together with a sector condition describing the saturating
input. Some numerical simulations illustrate the stability of the closed-loop
nonlinear partial differential equation.