  Radio astronomy observational facilities are under constant upgradation and
development to achieve better capabilities including increasing the time and
frequency resolutions of the recorded data, and increasing the receiving and
recording bandwidth. As only a limited spectrum resource has been allocated to
radio astronomy by the International Telecommunication Union, this results in
the radio observational instrumentation being inevitably exposed to undesirable
radio frequency interference (RFI) signals which originate mainly from
terrestrial human activity and are becoming stronger with time. RFIs degrade
the quality of astronomical data and even lead to data loss. The impact of RFIs
on scientific outcome is becoming progressively difficult to manage. In this
article, we motivate the requirement for RFI mitigation, and review the RFI
characteristics, mitigation techniques and strategies. Mitigation strategies
adopted at some representative observatories, telescopes and arrays are also
introduced. We also discuss and present advantages and shortcomings of the four
classes of RFI mitigation strategies, applicable at the connected causal
stages: preventive, pre-detection, pre-correlation and post-correlation. The
proper identification and flagging of RFI is key to the reduction of data loss
and improvement in data quality, and is also the ultimate goal of developing
RFI mitigation techniques. This can be achieved through a strategy involving a
combination of the discussed techniques in stages. Recent advances in high
speed digital signal processing and high performance computing allow for
performing RFI excision of large data volumes generated from large telescopes
or arrays in both real time and offline modes, aiding the proposed strategy.


  It is conjectured that the base varieties of the Iitaka fibrations are
bounded when the Iitaka volumes are bounded above. We confirm this conjecture
for Iitaka $\epsilon$-lc Fano type fibrations.


  We analyze the spectral properties of a phenomenological model for a weakly
doped two-dimensional antiferromagnet, in which the carriers move within one of
the two sublattices where they were introduced. Such a constraint results in
the free carrier spectra with the maxima at ${\bf k}=(\pm \pi /2 ,\pm \pi /2)$
observed in some cuprates. We consider the spectral properties of the model by
taking into account fluctuations of the spins in the antiferromagnetic
background. We show that such fluctuations lead to a non-pole-like structure of
the single-hole Green's function and these fluctuations can be responsible for
some anomalous "strange metal" properties of underdoped cuprates in the
nonsuperconducting regime.


  Motivated by high-stakes decision-making domains like personalized medicine
where user information is inherently sensitive, we design privacy preserving
exploration policies for episodic reinforcement learning (RL). We first provide
a meaningful privacy formulation using the notion of joint differential privacy
(JDP)--a strong variant of differential privacy for settings where each user
receives their own sets of output (e.g., policy recommendations). We then
develop a private optimism-based learning algorithm that simultaneously
achieves strong PAC and regret bounds, and enjoys a JDP guarantee. Our
algorithm only pays for a moderate privacy cost on exploration: in comparison
to the non-private bounds, the privacy parameter only appears in lower-order
terms. Finally, we present lower bounds on sample complexity and regret for
reinforcement learning subject to JDP.


  The temperature and magnetic field behavior of non-uniform diamagnetic phase
of strongly correlated electron gas at the conditions of dHvA effect is
analyzed. It is shown, that in the framework of Lifschitz-Kosevich-Shoenberg
approximation the magnetic induction splitting, as well as the range of
existence of Condon domains, are characterized by strong dependencies on
temperature, magnetic field and impurities of the sample.


  We investigate how vibration affects molecular photoemission dynamics,
through simulations on two-dimension asymmetric model molecules including the
electronic and nuclear motions in a fully correlated way. We show that a slight
anisotropy in the electron-ion momentum sharing is sufficient to prevent one
from unambigously characterizing the vibrationnaly averaged photoemission
dynamics in terms of stereo Wigner delays. We further show that vibrational
resolution can be retrieved in fixed-nuclei simulations, using effective
molecular conformations that are specific to each vibrational channel. The
optimal internuclear distances found empirically in 1-photon processes can be
identified a priori using simple physical arguments. They also turn out to be
efficient to simulate vibrationnally-resolved \rabbit measurements and to
account for interchannel coherences in broadband 1-photon ionization.


  The induction motors have wide range of applications for due to its
well-known advantages like brushless structures, low costs and robust
performances. Over the past years, many kind of control methods are proposed
for the induction motors and direct torque control has gained huge importance
inside of them due to fast dynamic torque responses and simple control
structures. However, the direct torque control method has still some handicaps
against the other control methods and most of the important of these handicaps
is high torque ripple. This paper suggests a new approach, Fuzzy logic based
space vector modulation, on the direct torque controlled induction motors and
aim of the approach is to overcome high torque ripple disadvantages of
conventional direct torque control. In order to test and compare the proposed
direct torque control method with conventional direct torque control method
simulations, in Matlab/Simulink,have been carried out in different working
conditions. The simulation results showed that a significant improvement in the
dynamic torque and speed responses when compared to the conventional direct
torque control method.


  Recently, several studies proposed non-linear transformations, such as a
logarithmic or Gaussianization transformation, as efficient tools to recapture
information about the (Gaussian) initial conditions. During non-linear
evolution, part of the cosmologically relevant information leaks out from the
second moment of the distribution. This information is accessible only through
complex higher order moments or, in the worst case, becomes inaccessible to the
hierarchy. The focus of this work is to investigate these transformations in
the framework of Fisher information using cosmological perturbation theory of
the matter field with Gaussian initial conditions. We show that at each order
in perturbation theory, there is a polynomial of corresponding order exhausting
the information on a given parameter. This polynomial can be interpreted as the
Taylor expansion of the maximally efficient "sufficient" observable in the
non-linear regime. We determine explicitly this maximally efficient observable
for local transformations. Remarkably, this optimal transform is essentially
the simple power transform with an exponent related to the slope of the power
spectrum; when this is -1, it is indistinguishable from the logarithmic
transform. This transform Gaussianizes the distribution, and recovers the
linear density contrast. Thus a direct connection is revealed between undoing
of the non-linear dynamics and the efficient capture of Fisher information. Our
analytical results were compared with measurements from the Millennium
Simulation density field. We found that our transforms remain very close to
optimal even in the deeply non-linear regime with \sigma^2 \sim 10.


  We propose a method to study the thermodynamic behaviour of small systems
beyond the weak coupling and Markovian approximation, which is different in
spirit from conventional approaches. The idea is to redefine the system and
environment such that the effective, redefined system is again coupled weakly
to Markovian residual baths and thus, allows to derive a consistent
thermodynamic framework for this new system-environment partition. To achieve
this goal we make use of the reaction coordinate mapping, which is a general
method in the sense that it can be applied to an arbitrary (quantum or
classical and even time-dependent) system coupled linearly to an arbitrary
number of harmonic oscillator reservoirs. The core of the method relies on an
appropriate identification of a part of the environment (the reaction
coordinate), which is subsequently included as a part of the system. We
demonstrate the power of this concept by showing that non-Markovian effects can
significantly enhance the steady state efficiency of a three-level-maser heat
engine, even in the regime of weak system-bath coupling. Furthermore, we show
for a single electron transistor coupled to vibrations that our method allows
one to justify master equations derived in a polaron transformed reference
frame.


  Hardware and software architecture of a novel spacecraft Attitude
Determination and Control System (ADCS) based on smartphones using Variable
Speed Control Moment Gyroscope (VSCMG) as actuator is proposed here. A
spacecraft ground simulator testbed for Hardware-in-the-loop (HIL) attitude
estimation and control with VSCMG is also described. The sensor breakouts with
independent micro-controller units are used in the conventional ADCS units,
which are replaced by a single integrated off-the-shelf smartphone. On-board
sensing, data acquisition, data uplink/downlink, state estimation and real-time
feedback control objectives can be performed using this novel spacecraft ADCS.
The attitude control and attitude determination (estimation) schemes have
appeared in prior publications, but are presented in brief here. Experimental
results from running the attitude estimation (filtering) scheme with the
"onboard" sensors of the smartphone in the HIL simulator are given. These
results, obtained in the Spacecraft Guidance, Navigation and Control Laboratory
at NMSU, demonstrate the excellent performance of this estimation scheme with
the noisy raw data from the smartphone sensors.


  Generic object detection is a category-independent task that relies on
accurate modeling of objectness. We show that for accurate semantic analysis,
the network needs to learn all object-level predictions that appear at any
stage of learning, including the pre-defined ground truth (GT) objects and the
ambiguous decoy objects that the network misidentifies as foreground. Yet, most
relevant models focused mainly on improving the learning of the GT objects. A
few methods that consider decoy objects utilize loss functions that only focus
on the single-response, i.e., the loss response of a single ambiguous pixel,
and thus do not benefit from the wealth of information that an object-level
ambiguity learning design can provide. Inspired by the human visual system,
which first discerns the boundaries of ambiguous regions before delving into
the semantic meaning, we propose a novel loss function, Spatial Coherence Loss
(SCLoss), that incorporates the mutual response between adjacent pixels into
the widely-used single-response loss functions. We demonstrate that the
proposed SCLoss can gradually learn the ambiguous regions by detecting and
emphasizing their boundaries in a self-adaptive manner. Through comprehensive
experiments, we demonstrate that replacing popular loss functions with SCLoss
can improve the performance of current state-of-the-art (SOTA) salient or
camouflaged object detection (SOD or COD) models. We also demonstrate that
combining SCLoss with other loss functions can further improve performance and
result in SOTA outcomes for different applications.


  The two BL Lac objects 1ES 1215+303 and 1ES 1218+304, separated by 0.8 deg,
were observed with the MAGIC telescopes in 2010 and 2011. The 20 hours of data
registered in January 2011 resulted in the first detection at Very High Energy
(>100 GeV) of 1ES 1215+303 (also known as ON-325). This observation was
triggered by a high optical state of the source reported by the Tuorla blazar
monitoring program. Comparison with the 25 hours of data carried out from
January to May 2010 suggests that 1ES 1215+303 was flaring also in VHE
gamma-rays in 2011. In addition, the Swift ToO observations in X-rays showed
that the flux was almost doubled respect to previous observations (December
2009). Instead, 1ES 1218+304 is a well known VHE gamma-ray emitter lying in the
same field of view, which was then simultaneously observed with the MAGIC
telescopes. The overall observation time of nearly 45 hours has permitted to
measure the spectrum of this source with a much higher precision than
previously reported by MAGIC. Here, we present the results of the MAGIC and the
multi-wavelength observations of these two VHE gamma-ray emitting AGNs.


  Metadata, often termed "data about data," is crucial for organizing,
understanding, and managing vast omics datasets. It aids in efficient data
discovery, integration, and interpretation, enabling users to access,
comprehend, and utilize data effectively. Its significance spans the domains of
scientific research, facilitating data reproducibility, reusability, and
secondary analysis. However, numerous perceptual and technical barriers hinder
the sharing of metadata among researchers. These barriers compromise the
reliability of research results and hinder integrative meta-analyses of omics
studies . This study highlights the key barriers to metadata sharing, including
the lack of uniform standards, privacy and legal concerns, limitations in study
design, limited incentives, inadequate infrastructure, and the dearth of
well-trained personnel for metadata management and reuse. Proposed solutions
include emphasizing the promotion of standardization, educational efforts, the
role of journals and funding agencies, incentives and rewards, and the
improvement of infrastructure. More accurate, reliable, and impactful research
outcomes are achievable if the scientific community addresses these barriers,
facilitating more accurate, reliable, and impactful research outcomes.


  A preliminary analysis of zero hadronic energy dimuon production in the NuTeV
experiment is presented (nu_mu (anti-nu_mu) + N -> nu_mu(anti-nu_mu) + mu^+ +
mu^- + N). The data show a signal in excess of background which is attributed
to neutrino trident production. This excess is consistent with the Standard
Model prediction including electroweak interference. Other sources of
low-hadronic energy dimuon events are also considered.


  We analytically derive an equation describing vesicle evolution in a fluid
where some stationary flow is excited regarding that the vesicle shape is close
to a sphere. A character of the evolution is governed by two dimensionless
parameters, $S$ and $\Lambda$, depending on the vesicle excess area, viscosity
contrast, membrane viscosity, strength of the flow, bending module, and ratio
of the elongation and rotation components of the flow. We establish the ``phase
diagram'' of the system on the $S-\Lambda$ plane: we find curves corresponding
to the tank-treading to tumbling transition (described by the saddle-node
bifurcation) and to the tank-treading to trembling transition (described by the
Hopf bifurcation).


  On the example of a mean-field Fredrickson-Andersen kinetically constrained
model, we focus on the known property that equilibrium dynamics take place at a
first-order dynamical phase transition point in the space of time-realizations.
We investigate the finite-size properties of this first order transition. By
discussing and exploiting a mapping of the classical dynamical transition -an
argued glassiness signature- to a first-order quantum transition, we show that
the quantum analogy can be exploited to extract finite-size properties, which
in many respects are similar to those in genuine mean-field quantum systems
with a first-order transition. We fully characterize the finite-size properties
of the order parameter across the first order transition.


  In this paper, we propose a general framework for designing numerical schemes
that have both well-balanced (WB) and asymptotic preserving (AP) properties,
for various kinds of kinetic models. We are interested in two different
parameter regimes, 1) When the ratio between the mean free path and the
characteristic macroscopic length $\epsilon$ tends to zero, the density can be
described by (advection) diffusion type (linear or nonlinear) macroscopic
models; 2) When $\epsilon$ = O(1), the models behave like hyperbolic equations
with source terms and we are interested in their steady states. We apply the
framework to three different kinetic models: neutron transport equation and its
diffusion limit, the transport equation for chemotaxis and its Keller-Segel
limit, and grey radiative transfer equation and its nonlinear diffusion limit.
Numerical examples are given to demonstrate the properties of the schemes.


  We describe a new class of boundary conditions for AdS_{d+1} under which the
boundary metric becomes a dynamical field. The key technical point is to show
that contributions from boundary counter-terms in the bulk gravitational action
render such fluctuations normalizable. In the context of AdS/CFT, the analogue
of Neumann boundary conditions for AdS promotes the CFT metric to a dynamical
field but adds no explicit gravitational dynamics; the gravitational dynamics
is just that induced by the conformal fields. Other AdS boundary conditions
couple the CFT to a gravity theory of choice. We use this correspondence to
briefly explore the coupled CFT + gravity theories and, in particular, for d=3
we show that coupling topologically massive gravity to a large N CFT preserves
the perturbative stability of the theory with negative (3-dimensional) Newton's
constant.


  Context: Recent calculations of pulsation modes in rapidly rotating
polytropic models and models based on the Self-Consistent Field method
(MacGregor et al. 2007) have shown that the frequency spectrum of low degree
pulsation modes can be described by an empirical formula similar to Tassoul's
asymptotic formula (Tassoul 1980), provided that the underlying rotation
profile is not too differential (Lignieres & Georgeot 2008, Reese et al. 2009).
  Aims: Given the simplicity of this asymptotic formula, we investigate whether
it can provide a means by which to identify pulsation modes in rapidly rotating
stars.
  Methods: We develop a new mode identification scheme which consists in
scanning a multidimensional parameter space for the formula coefficients which
yield the best-fitting asymptotic spectra. This mode identification scheme is
then tested on artificial spectra based on the asymptotic formula, on random
frequencies and on spectra based on full numerical eigenmode calculations for
which the mode identification is known beforehand. We also investigate the
effects of adding random frequencies to mimic the effects of chaotic modes
which are also expected to show up in such stars (Lignieres & Georgeot 2008).


  Using an exactly solvable statistical model we discuss the equation of state
of large/heavy and short-living quark gluon plasma (QGP) bags. We argue that
the large width of the QGP bags explains not only the observed deficit in the
number of hadronic resonances, but also clarifies the reason why the heavy QGP
bags cannot be directly observed even as metastable states in a hadronic phase.
Also the Regge trajectories of large and heavy QGP bags are established both in
a vacuum and in a strongly interacting medium. It is shown that at high
temperatures the average mass and width of the QGP bags behave in accordance
with the upper bound of the Regge trajectory asymptotics (the linear
asymptotics), whereas for temperatures below T_H/2 (T_H is the Hagedorn
temperature) they obey the lower bound of the Regge trajectory asymptotics (the
square root one). Thus, for T < T_H/2 the spin of the QGP bags is restricted
from above, whereas for T> T_H/2 these bags demonstrate the standard Regge
behavior consistent with the string models.


  This paper investigates epidemic control behavioral synchronization for a
class of complex networks resulting from spread of epidemic diseases via
pinning feedback control strategy. Based on the quenched mean field theory,
epidemic control synchronization models with inhibition of contact behavior is
constructed, combining with the epidemic transmission system and the complex
dynamical network carrying extra controllers. By the properties of convex
functions and Gerschgorin theorem, the epidemic threshold of the model is
obtained, and the global stability of disease-free equilibrium is analyzed. For
individual's infected situation, when epidemic spreads, two types of feedback
control strategies depended on the diseases' information are designed: the one
only adds controllers to infected individuals, the other adds controllers both
to infected and susceptible ones. And by using Lyapunov stability theory, under
designed controllers, some criteria that guarantee epidemic control
synchronization system achieving behavior synchronization are also derived.
Several numerical simulations are performed to show the effectiveness of our
theoretical results. As far as we know, this is the first work to address the
controlling behavioral synchronization induced by epidemic spreading under the
pinning feedback mechanism. It is hopeful that we may have more deeper insight
into the essence between disease's spreading and collective behavior
controlling in complex dynamical networks.


  This paper investigates the construction of model space priors from an
alternative point of view to the usual indicators for inclusion of covariates
in a given model. Assumptions about indicator variables often lead to
Beta-Binomial priors on the model space, which do not appropriately penalize
for model complexity when the parameters are fixed. This can be alleviated by
changing the parameters of the prior to depend on the number of covariates,
though justification for this is lacking from a first-principles point of view.
We propose viewing the model space as a partially ordered set. When the number
of covariates increases, an isometry argument leads to the Poisson distribution
as the unique, natural limiting prior over model dimension. This limiting prior
is derived using two constructions that view an individual model as though it
is a "local" null hypothesis and compares its prior probability to the
probability of the alternatives that nest it. We show that this prior induces a
posterior that concentrates on a finite true model asymptotically.
Additionally, we provide a deterministic algorithm that takes advantage of the
nature of the prior and explores good models in polynomial time.


  Text-to-SQL models are pivotal for making Electronic Health Records (EHRs)
accessible to healthcare professionals without SQL knowledge. With the
advancements in large language models, these systems have become more adept at
translating complex questions into SQL queries. Nonetheless, the critical need
for reliability in healthcare necessitates these models to accurately identify
unanswerable questions or uncertain predictions, preventing misinformation. To
address this problem, we present a self-training strategy using pseudo-labeled
unanswerable questions to enhance the reliability of text-to-SQL models for
EHRs. This approach includes a two-stage training process followed by a
filtering method based on the token entropy and query execution. Our
methodology's effectiveness is validated by our top performance in the EHRSQL
2024 shared task, showcasing the potential to improve healthcare
decision-making through more reliable text-to-SQL systems.


  Often both Aggregate Data (AD) studies and Individual Patient Data (IPD)
studies are available for specific treatments. Combining these two sources of
data could improve the overall meta-analytic estimates of treatment effects.
Moreover, often for some studies with AD, the associated IPD maybe available,
albeit at some extra effort or cost to the analyst. We propose a method for
combining treatment effects across trials when the response is from the
exponential family of distribution and hence a generalized linear model
structure can be used. We consider the case when treatment effects are fixed
and common across studies. Using the proposed combination method, we evaluate
the wisdom of choosing AD when IPD is available by studying the relative
efficiency of analyzing all IPD studies versus combining various percentages of
AD and IPD studies. For many different models design constraints under which
the AD estimators are the IPD estimators, and hence fully efficient, are known.
For such models we advocate a selection procedure that chooses AD studies over
IPD studies in a manner that force least departure from design constraints and
hence ensures a fully efficient combined AD and IPD estimator.


  In this paper, a dynamic hybrid active-passive reconfigurable intelligent
surface (HRIS) is proposed to further enhance the massive
multiple-input-multiple-output (MIMO) system, since it supports the dynamic
placement of active and passive elements. Specifically, considering the impact
of the hardware impairments (HWIs), we investigate the channel-aware
configuration of the receive antennas at the base station (BS) and the
active/passive elements at the HRIS to improve the reliability of system. To
this end, we investigate the average mean-square-error (MSE) minimization
problem for the HRIS-aided massive MIMO system by jointly optimizing the BS
receive antenna selection matrix, the reflection phase coefficients, the
reflection amplitude matrix, and the mode selection matrix of the HRIS under
the power budget of the HRIS. To tackle the non-convexity and intractability of
this problem, we first transform the binary and discrete variables into
continuous ones, and then propose a penalty-based exact block coordinate
descent (BCD) algorithm to solve these subproblems alternately. Numerical
simulations demonstrate the great superiority of the proposed scheme over the
conventional benchmark schemes.


  Causal discovery from time-series data has been a central task in machine
learning. Recently, Granger causality inference is gaining momentum due to its
good explainability and high compatibility with emerging deep neural networks.
However, most existing methods assume structured input data and degenerate
greatly when encountering data with randomly missing entries or non-uniform
sampling frequencies, which hampers their applications in real scenarios. To
address this issue, here we present CUTS, a neural Granger causal discovery
algorithm to jointly impute unobserved data points and build causal graphs, via
plugging in two mutually boosting modules in an iterative framework: (i) Latent
data prediction stage: designs a Delayed Supervision Graph Neural Network
(DSGNN) to hallucinate and register unstructured data which might be of high
dimension and with complex distribution; (ii) Causal graph fitting stage:
builds a causal adjacency matrix with imputed data under sparse penalty.
Experiments show that CUTS effectively infers causal graphs from unstructured
time-series data, with significantly superior performance to existing methods.
Our approach constitutes a promising step towards applying causal discovery to
real applications with non-ideal observations.


  The activation function deployed in a deep neural network has great influence
on the performance of the network at initialisation, which in turn has
implications for training. In this paper we study how to avoid two problems at
initialisation identified in prior works: rapid convergence of pairwise input
correlations, and vanishing and exploding gradients. We prove that both these
problems can be avoided by choosing an activation function possessing a
sufficiently large linear region around the origin, relative to the bias
variance $\sigma_b^2$ of the network's random initialisation. We demonstrate
empirically that using such activation functions leads to tangible benefits in
practice, both in terms test and training accuracy as well as training time.
Furthermore, we observe that the shape of the nonlinear activation outside the
linear region appears to have a relatively limited impact on training. Finally,
our results also allow us to train networks in a new hyperparameter regime,
with a much larger bias variance than has previously been possible.


  We show a Birthday Paradox for self-intersections of Markov chains with
uniform stationary distribution. As an application, we analyze Pollard's Rho
algorithm for finding the discrete logarithm in a cyclic group $G$ and find
that if the partition in the algorithm is given by a random oracle, then with
high probability a collision occurs in $\Theta(\sqrt{|G|})$ steps. Moreover,
for the parallelized distinguished points algorithm on $J$ processors we find
that $\Theta(\sqrt{|G|}/J)$ steps suffices. These are the first proofs of the
correct order bounds which do not assume that every step of the algorithm
produces an i.i.d. sample from $G$.


  In this paper we present some factorization properties for unbounded local
positive maps. We show that an unbounded local positive map $\phi $ on the
minimal tensor product of the locally $C^{\ast }$-algebras $\mathcal{A}$ and
$C^{\ast }(\mathcal{D}_{\mathcal{E}}),$ where $\mathcal{D}_{\mathcal{E}}$ is a
Fr\'{e}chet quantized domain, that is dominated by $\varphi \otimes $id is of
the forma $\psi \otimes $id, where $\psi $ is an unbounded local positive map
dominated by $\varphi $. As an application of this result, we show that given a
local positive map $\varphi :$ $\mathcal{A}\rightarrow $ $\mathcal{B},$ the
local positive map $\varphi \otimes $id$_{M_{n}\left( \mathbb{C}\right) }$ is
local decomposable for some $n\geq 2$ if and only if $\varphi $ is a local
$CP$-map. Also, we show that an unbounded local $CCP$-map $\phi $ on the
minimal tensor product of the unital locally $C^{\ast }$-algebras $\mathcal{A}$
and $\mathcal{B},$ that is dominated by $\varphi \otimes \psi $ is of the forma
$\varphi \otimes \widetilde{\psi }$, where $\widetilde{\psi }$ is an unbounded
local $CCP$- map dominated by $\psi $, whenever $\varphi $ is pure.


  State-of-the-art deep learning methods have shown a remarkable capacity to
model complex data domains, but struggle with geospatial data. In this paper,
we introduce SpaceGAN, a novel generative model for geospatial domains that
learns neighbourhood structures through spatial conditioning. We propose to
enhance spatial representation beyond mere spatial coordinates, by conditioning
each data point on feature vectors of its spatial neighbours, thus allowing for
a more flexible representation of the spatial structure. To overcome issues of
training convergence, we employ a metric capturing the loss in local spatial
autocorrelation between real and generated data as stopping criterion for
SpaceGAN parametrization. This way, we ensure that the generator produces
synthetic samples faithful to the spatial patterns observed in the input.
SpaceGAN is successfully applied for data augmentation and outperforms compared
to other methods of synthetic spatial data generation. Finally, we propose an
ensemble learning framework for the geospatial domain, taking augmented
SpaceGAN samples as training data for a set of ensemble learners. We
empirically show the superiority of this approach over conventional ensemble
learning approaches and rivaling spatial data augmentation methods, using
synthetic and real-world prediction tasks. Our findings suggest that SpaceGAN
can be used as a tool for (1) artificially inflating sparse geospatial data and
(2) improving generalization of geospatial models.


  In this paper, the classification in [Lin,Wei,Ye] of solutions to Toda
systems of type $A$ with singular sources is generalized to Toda systems of
types $C$ and $B$. Like in the $A$ case, the solution space is shown to be
parametrized by the abelian subgroup and a subgroup of the unipotent subgroup
in the Iwasawa decomposition of the corresponding complex simple Lie group. The
method is by studying the Toda systems of types $C$ and $B$ as reductions of
Toda systems of type $A$ with symmetries. The theories of Toda systems as
integrable systems as developed in [Leznov, Saveliev, Nie], in particular the
$W$-symmetries and the iterated integral solutions, play essential roles in
this work, together with certain characterizing properties of minors of
symplectic and orthogonal matrices.


  The Kapitza-Dirac effect, which refers to electron scattering at standing
light waves, is studied in the Bragg regime with counterpropagating
elliptically polarized electromagnetic waves with the same intensity,
wavelength, and degree of polarization for two different setups. In the first
setup, where the electric-field components of the counterpropagating waves have
the same sense of rotation, we find distinct spin effects. The spins of the
scattered electrons and of the nonscattered electrons, respectively, precess
with a frequency that is of the order of the Bragg-reflection Rabi frequency.
When the electric-field components of the counterpropagating waves have an
opposite sense of rotation, which is the second considered setup, the standing
wave has linear polarization, and no spin effects can be observed. Our results
are based on numerical solutions of the time-dependent Dirac equation and the
analytical solution of a relativistic Pauli equation, which accounts for the
leading relativistic effects.


  The axial vector meson $K_1(1270)$ was studied within the chiral unitary
approach, where it was shown that it has a two-pole structure. We reanalyze the
high-statistics WA3 experiment $K^- p\to K^-\pi^+\pi^- p$ at 63 GeV, which
established the existence of both $K_1(1270)$ and $K_1(1400)$, and we show that
it clearly favors our two-pole interpretation. We also reanalyze the
traditional K-matrix interpretation of the WA3 data and find that the good fit
of the data obtained there comes from large cancellations of terms of unclear
physical interpretation.


  It is shown by Connes, Douglas and Schwarz that gauge theory on
noncommutative torus describes compactifications of M-theory to tori with
constant background three-form field. This indicates that noncommutative gauge
theories on more general manifolds also can be useful in string theory. We
discuss a framework to noncommutative quantum gauge theory on Poisson manifolds
by using the deformation quantization. The Kontsevich formula for the star
product was given originally in terms of the perturbation expansion and it
leads to a non-renormalizable quantum field theory. We discuss the
nonperturbative path integral formulation of Cattaneo and Felder as a possible
approach to construction of noncommutative quantum gauge theory on Poisson
manifolds. Some other aspects of classical and quantum noncommutative field
theory are also discussed.


  In this paper, we present a generic approach of a dynamical data-driven model
order reduction technique for three-dimensional fluid-structure interaction
problems. A low-order continuous linear differential system is identified from
snapshot solutions of a high-fidelity solver. The reduced order model (ROM)
uses different ingredients like proper orthogonal decomposition (POD), dynamic
mode decomposition (DMD) and Tikhonov-based robust identification techniques.
An interpolation method is used to predict the capsule dynamics for any value
of the governing non-dimensional parameters that are not in the training
database. Then a dynamical system is built from the predicted solution.
Numerical evidence shows the ability of the reduced model to predict the
time-evolution of the capsule deformation from its initial state, whatever the
parameter values. Accuracy and stability properties of the resulting low-order
dynamical system are analyzed numerically. The numerical experiments show a
very good agreement, measured in terms of modified Hausdorff distance between
capsule solutions of the full-order and low-order models both in the case of
confined and unconfined flows. This work is a first milestone to move towards
real time simulation of fluid-structure problems, which can be extended to
non-linear low-order systems to account for strong material and flow
non-linearities. It is a valuable innovation tool for rapid design and for the
development of innovative devices.


  In $1980$ White conjectured that every element of the toric ideal of a
matroid is generated by quadratic binomials corresponding to symmetric
exchanges. We prove White's conjecture for high degrees with respect to the
rank. This extends our result arXiv:1302.5236 confirming White's conjecture `up
to saturation'. Furthermore, we study degrees of Gr\"{o}bner bases and Betti
tables of the toric ideals of matroids of a fixed rank.


  The edit distance is a way of quantifying how similar two strings are to one
another by counting the minimum number of character insertions, deletions, and
substitutions required to transform one string into the other. A simple dynamic
programming computes the edit distance between two strings of length $n$ in
$O(n^2)$ time, and a more sophisticated algorithm runs in time $O(n+t^2)$ when
the edit distance is $t$ [Landau, Myers and Schmidt, SICOMP 1998]. In pursuit
of obtaining faster running time, the last couple of decades have seen a flurry
of research on approximating edit distance, including polylogarithmic
approximation in near-linear time [Andoni, Krauthgamer and Onak, FOCS 2010],
and a constant-factor approximation in subquadratic time [Chakrabarty, Das,
Goldenberg, Kouck\'y and Saks, FOCS 2018].
  We study sublinear-time algorithms for small edit distance, which was
investigated extensively because of its numerous applications. Our main result
is an algorithm for distinguishing whether the edit distance is at most $t$ or
at least $t^2$ (the quadratic gap problem) in time
$\tilde{O}(\frac{n}{t}+t^3)$. This time bound is sublinear roughly for all $t$
in $[\omega(1), o(n^{1/3})]$, which was not known before. The best previous
algorithms solve this problem in sublinear time only for $t=\omega(n^{1/3})$
[Andoni and Onak, STOC 2009].
  Our algorithm is based on a new approach that adaptively switches between
uniform sampling and reading contiguous blocks of the input strings. In
contrast, all previous algorithms choose which coordinates to query
non-adaptively. Moreover, it can be extended to solve the $t$ vs
$t^{2-\epsilon}$ gap problem in time $\tilde{O}(\frac{n}{t^{1-\epsilon}}+t^3)$.


  Thanks to the latest advancements in wavefront shaping, optical methods have
proven crucial to achieve imaging and control light in multiply scattering
media, like biological tissues. However, the stability times of living
biological specimens often prevent such methods from gaining insights into
relevant functioning mechanisms in cellular and organ systems. Here we present
a recursive and online optimization routine, borrowed from time series
analysis, to optimally track the transfer matrix of dynamic scattering media
over arbitrarily long timescales. While preserving the advantages of both
optimization-based routines and transfer-matrix measurements, it operates in a
memory-efficient manner. Because it can be readily implemented in existing
wavefront shaping setups, featuring amplitude and/or phase modulation and
phase-resolved or intensity-only acquisition, it paves the way for efficient
optical investigations of living biological specimens.


  Understanding the growth dynamics of the microbubbles produced by plasmonic
heating can benefit a wide range of applications like microfluidics, catalysis,
micro-patterning and photo-thermal energy conversion. Usually, surface
plasmonic bubbles are generated on plasmonic structures pre-deposited on the
surface subject to laser heating. In this work, we investigate the growth
dynamics of surface microbubbles generated in plasmonic nanoparticle (NP)
suspension. We observe much faster bubble growth rates compared to those in
pure water with surface plasmonic structures. Our analyses show that the
volumetric heating effect around the surface bubble due to the existence of NPs
in the suspension is the key to explain this difference. Such volumetric
heating increases the temperature around the surface bubble more efficiently
compared to surface heating which enhances the expelling of dissolved gas. We
also find that the bubble growth rates can be tuned in a very wide range by
changing the concentration of NPs, besides laser power and dissolved gas
concentration.


  As an important tool characterizing the long time behavior of Markov
processes, the Donsker-Varadhan LDP (large deviation principle) does not
directly apply to distribution dependent SDEs/SPDEs since the solutions are
non-Markovian. We establish this type LDP for several different models of
distribution dependent SDEs/SPDEs which may also with memories, by comparing
the original equations with the corresponding distribution independent ones. As
preparations, the existence, uniqueness and exponential convergence are also
investigated for path-distribution dependent SPDEs which should be interesting
by themselves.


  The global attraction is established for all finite energy solutions to a
model $\mathbf{U}(1)$-invariant nonlinear Klein-Gordon equation in one
dimension coupled to a finite number of nonlinear oscillators: We prove that
{\it each finite energy solution} converges as $t\to\pm\infty$ to the set of
all ``nonlinear eigenfunctions'' of the form $\phi(x)e\sp{-i\omega t}$ if all
oscillators are strictly nonlinear, and the distances between all neighboring
oscillators are sufficiently small. The {\it global attraction} is caused by
the nonlinear energy transfer from lower harmonics to the continuous spectrum
and subsequent dispersive radiation. This result for one oscillator was
obtained in [KK07].
  We construct counterexamples showing that the convergence to the solitary
waves may break down if the distance between some of the neighboring
oscillators is sufficiently large or if some of the oscillators are harmonic.
In these cases, the global attractor can contain ``multifrequency solitary
waves'' or linear combinations of distinct solitary waves.


  An Auslander-Reiten formula for complexes of modules is presented. This
formula contains as a special case the classical Auslander Reiten formula. The
Auslander-Reiten translate of a complex is described explicitly, and various
applications are discussed.


  Mirror symmetry for higher genus curves is usually formulated and studied in
terms of Landau-Ginzburg models; however the critical locus of the
superpotential is arguably of greater intrinsic relevance to mirror symmetry
than the whole Landau-Ginzburg model. Accordingly, we propose a new approach to
the A-model of the mirror, viewed as a trivalent configuration of rational
curves together with some extra data at the nodal points. In this context, we
introduce a version of Lagrangian Floer theory and the Fukaya category for
trivalent graphs, and show that homological mirror symmetry holds, namely, that
the Fukaya category of a trivalent configuration of rational curves is
equivalent to the derived category of a non-Archimedean generalized Tate curve.
To illustrate the concrete nature of this equivalence, we show how explicit
formulas for theta functions and for the canonical map of the curve arise
naturally under mirror symmetry.


  Radiotherapy, due to its technology-intensive nature and reliance on digital
data and human-machine interactions, is particularly suited to benefit from
artificial intelligence (AI) to improve the accuracy and efficiency of its
clinical workflow. Recently, various artificial intelligence (AI) methods have
been successfully developed to exploit the benefit of the inherent physical
properties of particle therapy. Many reviews about AI applications in
radiotherapy have already been published, but none were specifically dedicated
to particle therapy. In this article, we present a comprehensive review of the
recent published works on AI applications in particle therapy, which can be
classified into particle therapy treatment planning, adaptive particle therapy,
range and dose verification and other applications in particle therapy.
Although promising results reported in these works demonstrate how AI-based
methods can help exploit the intrinsic physic advantages of particle therapy,
challenges remained to be address before AI applications in particle therapy
enjoy widespread implementation in clinical practice.


  Strangeness measurement at RHIC energies constitutes one of the favorite
theme of the STAR Collaboration. Besides the fact that strangeness enhancement
has been proposed as a quark gluon plasma signature, its production provides
various and relevant information about the collision evolution and especially
on the hadronization process. The investigation of short-lived strange
particles as well as their anti-particles and resonances allows an attempt of
characterization of the matter created in RHIC heavy ion collisions.


  We give a full geometrical description of local totally geodesic unit vector
field on Riemannian 2-manifold, considering the field as a local imbedding of
the manifold into its unit tangent bundle with the Sasaki metric.


  We propose a method for extending a given asset pricing formula to account
for two additional sources of risk: the risk associated with future changes in
market--calibrated parameters and the remaining risk associated with
idiosyncratic variations in the individual assets described by the formula. The
paper makes simple and natural assumptions for how these risks behave. These
extra risks should always be included when using the formula as a basis for
portfolio management. We investigate an idealized typical portfolio problem,
and argue that a rational and workable trading strategy can be based on
minimizing the quadratic risk over the time intervals between trades. The
example of the variance gamma pricing formula for equity derivatives is
explored, and the method is seen to yield tractable decision strategies in this
case.


  Safe deployment of autonomous robots in diverse scenarios requires agents
that are capable of efficiently adapting to new environments while satisfying
constraints. In this work, we propose a practical and theoretically-justified
approach to maintaining safety in the presence of dynamics uncertainty. Our
approach leverages Bayesian meta-learning with last-layer adaptation. The
expressiveness of neural-network features trained offline, paired with
efficient last-layer online adaptation, enables the derivation of tight
confidence sets which contract around the true dynamics as the model adapts
online. We exploit these confidence sets to plan trajectories that guarantee
the safety of the system. Our approach handles problems with high dynamics
uncertainty, where reaching the goal safely is potentially initially
infeasible, by first \textit{exploring} to gather data and reduce uncertainty,
before autonomously \textit{exploiting} the acquired information to safely
perform the task. Under reasonable assumptions, we prove that our framework
guarantees the high-probability satisfaction of all constraints at all times
jointly, i.e. over the total task duration. This theoretical analysis also
motivates two regularizers of last-layer meta-learning models that improve
online adaptation capabilities as well as performance by reducing the size of
the confidence sets. We extensively demonstrate our approach in simulation and
on hardware.


  The Blood-Oxygen-Level-Dependent (BOLD) signal of resting-state fMRI
(rs-fMRI) records the temporal dynamics of intrinsic functional networks in the
brain. However, existing deep learning methods applied to rs-fMRI either
neglect the functional dependency between different brain regions in a network
or discard the information in the temporal dynamics of brain activity. To
overcome those shortcomings, we propose to formulate functional connectivity
networks within the context of spatio-temporal graphs. We train a
spatio-temporal graph convolutional network (ST-GCN) on short sub-sequences of
the BOLD time series to model the non-stationary nature of functional
connectivity. Simultaneously, the model learns the importance of graph edges
within ST-GCN to gain insight into the functional connectivities contributing
to the prediction. In analyzing the rs-fMRI of the Human Connectome Project
(HCP, N=1,091) and the National Consortium on Alcohol and Neurodevelopment in
Adolescence (NCANDA, N=773), ST-GCN is significantly more accurate than common
approaches in predicting gender and age based on BOLD signals. Furthermore, the
brain regions and functional connections significantly contributing to the
predictions of our model are important markers according to the neuroscience
literature.


  We give a survey of our recent work describing a method which combines the
Sasaki join construction with the admissible K\"ahler construction of to obtain
new extremal and new constant scalar curvature Sasaki metrics, including
Sasaki-Einstein metrics. The constant scalar curvature Sasaki metrics also
provide explicit solutions to the CR Yamabe problem. In this regard we give
examples of the lack of uniqueness when the Yamabe invariant is positive.


  We prove that a generalized Schwarzschild-like ansatz can be consistently
employed to construct $d$-dimensional static vacuum black hole solutions in any
metric theory of gravity for which the Lagrangian is a scalar invariant
constructed from the Riemann tensor and its covariant derivatives of arbitrary
order. Namely, we show that, apart from containing two arbitrary functions
$a(r)$ and $f(r)$ (essentially, the $g_{tt}$ and $g_{rr}$ components), in any
such theory the line-element may admit as a base space {\em any}
isotropy-irreducible homogeneous space. Technically, this ensures that the
field equations generically reduce to two ODEs for $a(r)$ and $f(r)$, and
dramatically enlarges the space of black hole solutions and permitted horizon
geometries for the considered theories. We then exemplify our results in
concrete contexts by constructing solutions in particular theories such as
Gauss-Bonnet, quadratic, $F(R)$ and $F$(Lovelock) gravity, and certain
conformal gravities.


  To extract information about polarized gluons in the proton, production of
charmed hadrons, in particular, \Lambda_c^+ baryon in pp collisions was
studied. We calculated the transverse momentum distribution and the
pseudo-rapidity distribution of the spin correlation asymmetry A_{LL} between
the initial proton and the produced \Lambda_c^+. Those statistical
sensitivities were also calculated under the condition of RHIC experiment. We
found that the pseudo-rapidity distribution of A_{LL} is promising for testing
the model of polarized gluons in the proton and also the spin-dependent
fragmentation model of a charm quark decaying into \Lambda_c^+ baryon.


  Fractional tempered stable motion (fTSm)} is defined and studied. FTSm has
the same covariance structure as fractional Brownian motion, while having tails
heavier than Gaussian but lighter than stable. Moreover, in short time it is
close to fractional stable L\'evy motion, while it is approximately fractional
Brownian motion in long time. A series representation of fTSm is derived and
used for simulation and to study some of its sample path properties.


  Recent likelihood theory produces $p$-values that have remarkable accuracy
and wide applicability. The calculations use familiar tools such as maximum
likelihood values (MLEs), observed information and parameter rescaling. The
usual evaluation of such $p$-values is by simulations, and such simulations do
verify that the global distribution of the $p$-values is uniform(0, 1), to high
accuracy in repeated sampling. The derivation of the $p$-values, however,
asserts a stronger statement, that they have a uniform(0, 1) distribution
conditionally, given identified precision information provided by the data. We
take a simple regression example that involves exact precision information and
use large sample techniques to extract highly accurate information as to the
statistical position of the data point with respect to the parameter:
specifically, we examine various $p$-values and Bayesian posterior survivor
$s$-values for validity. With observed data we numerically evaluate the various
$p$-values and $s$-values, and we also record the related general formulas. We
then assess the numerical values for accuracy using Markov chain Monte Carlo
(McMC) methods. We also propose some third-order likelihood-based procedures
for obtaining means and variances of Bayesian posterior distributions, again
followed by McMC assessment. Finally we propose some adaptive McMC methods to
improve the simulation acceptance rates. All these methods are based on
asymptotic analysis that derives from the effect of additional data. And the
methods use simple calculations based on familiar maximizing values and related
informations. The example illustrates the general formulas and the ease of
calculations, while the McMC assessments demonstrate the numerical validity of
the $p$-values as percentage position of a data point. The example, however, is
very simple and transparent, and thus gives little indication that in a wide
generality of models the formulas do accurately separate information for almost
any parameter of interest, and then do give accurate $p$-value determinations
from that information. As illustration an enigmatic problem in the literature
is discussed and simulations are recorded; various examples in the literature
are cited.


  This work reviews current theoretical approaches of biophysics and
bioinformatics for the description of nucleosome arrangements in chromatin and
transcription factor binding to nucleosomal organized DNA. The role of
nucleosomes in gene regulation is discussed from molecular-mechanistic and
biological point of view. In addition to classical problems of this field,
actual questions of epigenetic regulation are discussed. The authors selected
for discussion what seem to be the most interesting concepts and hypotheses.
Mathematical approaches are described in a simplified language to attract
attention to the most important directions of this field.


  Recently, clustering moving object trajectories kept gaining interest from
both the data mining and machine learning communities. This problem, however,
was studied mainly and extensively in the setting where moving objects can move
freely on the euclidean space. In this paper, we study the problem of
clustering trajectories of vehicles whose movement is restricted by the
underlying road network. We model relations between these trajectories and road
segments as a bipartite graph and we try to cluster its vertices. We
demonstrate our approaches on synthetic data and show how it could be useful in
inferring knowledge about the flow dynamics and the behavior of the drivers
using the road network.


  Electron gases at the surfaces of (001), (110), and (111) oriented SrTiO3
(STO) have been created using Ar+-irradiation with fully metallic behavior and
low-temperature-mobility as large as 5500 cm2V-1s-1, 1300 cm2V-1s-1 and 8600
cm2V-1s-1 for (001)-, (110)-, and (111)-surfaces, respectively. The in-plane
anisotropic magnetoresistance (AMR) have been studied for the samples with the
current along different crystal axis directions to subtract the Lorentz Force
effect. The AMR shows features which coincide with the fixed orientations to
the crystalline axes, with 4-fold, 2-fold and nearly-6-fold symmetries for
(001)-, (110) and (111)-surfaces, respectively, independent of the current
directions. These features are possibly caused by the polarization of spin
orbit texture of the 2D Fermi surfaces. In addition, a 6-fold to 2-fold
symmetry breaking for (111)-surfaces is observed. Our results demonstrate the
effect of symmetry of two-dimensional electronic structure on the transport
behaviors for the electron gases at STO surfaces.


  We introduce a Littlewood-Paley characterization of modulation spaces and use
it to give an alternative proof of the algebra property, somehow implicitly
contained in Sugimoto (2011), of the intersection $M^s_{p,q}(\mathbb{R}^d) \cap
M_{\infty, 1}(\mathbb{R}^d)$ for $d \in \mathbb{N}$, $p, q \in [1, \infty]$ and
$s \geq 0$. We employ this algebra property to show the local well-posedness of
the Cauchy problem for the cubic nonlinear Schr\"odinger equation in the above
intersection. This improves Theorem 1.1 by B\'enyi and Okoudjou (2009), where
only the case $q = 1$ is considered, and closes a gap in the literature. If $q
> 1$ and $s > d \left(1 - \frac{1}{q}\right)$ or if $q = 1$ and $s \geq 0$ then
$M^s_{p,q}(\mathbb{R}^d) \hookrightarrow M_{\infty, 1}(\mathbb{R}^d)$ and the
above intersection is superfluous. For this case we also reobtain a
H\"older-type inequality for modulation spaces.


  We use numerical simulation of cosmological reionization to calculate the
secondary CMB anisotropies in a representative flat low density cosmological
model. We show that the kinetic Sunyaev-Zel'dovich effect (scattering off of
moving electrons in the ionized intergalactic medium) is dominated by the
nonlinear hydrodynamic and gravitational evolution of the density and velocity
fields, rather than the detailed distribution of the ionization fraction
(``patchy reionization'') on all angular scales. Combining our results with the
recent calculation of secondary CMB anisotropies by Springel et al., we are
able to accurately predict the power spectrum of the kinetic SZ effect on
almost all angular scales.


  We consider the motion of a particle governed by a weakly random Hamiltonian
flow. We identify temporal and spatial scales on which the particle trajectory
converges to a spatial Brownian motion. The main technical issue in the proof
is to obtain error estimates for the convergence of the solution of the
stochastic acceleration problem to a momentum diffusion. We also apply our
results to the system of random geometric acoustics equations and show that the
energy density of the acoustic waves undergoes a spatial diffusion.


  A method is described to solve the Poisson problem for a three dimensional
source distribution that is periodic into one direction. Perpendicular to the
direction of periodicity a free space (or open) boundary is realized. In beam
physics, this approach allows to calculate the space charge field of a
continualized charged particle distribution with periodic pattern.
  The method is based on a particle mesh approach with equidistant grid and
fast convolution with a Green's function. The periodic approach uses only one
period of the source distribution, but a periodic extension of the Green's
function.
  The approach is numerically efficient and allows the investigation of
periodic- and pseudo-periodic structures with period lengths that are small
compared to the source dimensions, for instance of laser modulated beams or of
the evolution of micro bunch structures. Applications for laser modulated beams
are given.


  We report the synthesis, electronic properties, and electronic structure of
ullmannite-type PtSbS, which has a cubic crystal structure without space
inversion symmetry. Electrical resistivity and magnetization measured at low
temperatures suggested that this compound is a bulk superconductor with a
superconducting transition temperature of Tc = 0.15 K. First principles
calculations indicated that Fermi surfaces of PtSbS include strongly nested
hole pockets, which can make this compound interesting if they contribute to
the emergence of superconductivity.


  Non-resonant X-ray magnetic scattering has been used to study the magnetic
structure of multiferroic TbMnO3 in its ferroelectric phase. Circularly
polarized X-rays were combined with a full polarization analysis of the
scattered beam to reveal important new information on the magnetic structure of
this canonical multiferroic. An applied electric field is shown to create a
magnetic nearly mono-domain state in which the cylcoidal order on the Mn
sublattice rotates either clockwise or counter-clockwise depending on the sign
of the field. It is demonstrated how this technique provides sensitivity to the
absolute sense of rotation of the Mn moments, and to components of the ordering
on the Tb sublattice and phase shifts that earlier neutron diffraction
experiments could not resolve.


  In this paper, we consider the well-posedness and stability of a
one-dimensional system of degenerate wave equations coupled via zero order
terms with one boundary fractional damping acting on one end only. We prove
optimal polynomial energy decay rate of order $1/t^{(3-\tau)}$. The method is
based on the frequency domain approach combined with multiplier technique.


  Good frames were suggested in [Sh:h] as the (bare-bones) parallel, in the
context of AECs, to superstable (among elementary classes). Here we consider
$(\mu,\lambda,\kappa)$-frames as candidates for being (in the context of AECs)
the correct parallel to the class of $|T|^+$-saturated models of a strictly
stable theory (among elementary classes). One thing we lose compared to the
superstable case is that going up by induction on cardinals is problematic (for
stages of small cofinality). But this arises only when we try to lift such
classes to higher cardinals. Also, we may use, as a replacement, the existence
of prime models over unions of increasing chains. For this context we
investigate the dimension.


  The interaction between a vortex and an impacting body which is oriented
normally to it is complex due to the interaction of inviscid and viscous
three-dimensional mechanisms. To model this process, direct numerical
simulations of a thin cylinder intersecting a columnar vortex oriented normally
to it are conducted. By varying the impact parameter and the Reynolds number,
the two regimes of interaction mentioned in the literature are distinguished:
the weak and strong vortex regimes. Low impact parameters, representing strong
vortices, led to ejection and interaction of secondary vorticity from the
cylinder's boundary layer, while high impact parameters, representing weak
vortices, led to approximately inviscid interaction of the cylinder with the
primary vortex through deformations. No significant effect of the Reynolds
number in the overall phenomenology is found, even if larger Reynolds numbers
lead to the formation of increasingly smaller and more intense vortex
structures in the parameter range studied. Finally, the hydrodynamic force
curves on the cylinder are analyzed, showing that intense forces could be
locally generated for some parameter regimes, but that the average force on the
cylinder does not substantially deviate from baseline cases where no vortex was
present. Our results shed light on the underlying mechanisms of vortex-body
interactions and their dependence on various parameters.


  We propose a Healthcare Graph Convolutional Network (HealGCN) to offer
disease self-diagnosis service for online users based on Electronic Healthcare
Records (EHRs). Two main challenges are focused in this paper for online
disease diagnosis: (1) serving cold-start users via graph convolutional
networks and (2) handling scarce clinical description via a symptom retrieval
system. To this end, we first organize the EHR data into a heterogeneous graph
that is capable of modeling complex interactions among users, symptoms and
diseases, and tailor the graph representation learning towards disease
diagnosis with an inductive learning paradigm. Then, we build a disease
self-diagnosis system with a corresponding EHR Graph-based Symptom Retrieval
System (GraphRet) that can search and provide a list of relevant alternative
symptoms by tracing the predefined meta-paths. GraphRet helps enrich the seed
symptom set through the EHR graph when confronting users with scarce
descriptions, hence yield better diagnosis accuracy. At last, we validate the
superiority of our model on a large-scale EHR dataset.


  Correlations in the hadron distributions produced in relativistic Au+Au
collisions are studied in the discrete wavelet expansion method. The analysis
is performed in the space of pseudorapidity (|eta| < 1) and azimuth (full 2 pi)
in bins of transverse momentum (p_t) from 0.14 < p_t < 2.1 GeV/c. In peripheral
Au+Au collisions a correlation structure ascribed to mini-jet fragmentation is
observed. It evolves with collision centrality and p_t in a way not seen before
which suggests strong dissipation of minijet fragmentation in the
longitudinally-expanding medium.


  The synchronous gauge in gravity ($g_{0 \lambda} = - \delta_{0 \lambda}$) is
ill-defined due to the singularity at $p_0 = 0$ in the graviton propagator.
Previously we studied "softening" this gauge by considering instead the gauge
$n^\lambda g_{\lambda \mu} = 0$, $n^\lambda = (1, - \varepsilon (\partial^j
\partial_j )^{- 1} \partial^k ) $ in the limit $\varepsilon \to 0$. We now
explore the possibility of using a principal value prescription (not in the
standard Cauchy sense), which amounts, roughly speaking, to replacing
singularities $p_0^{-j} \Rightarrow [ (p_0 + i \varepsilon )^{-j} + (p_0 - i
\varepsilon )^{-j} ] / 2$, which then behave like distributions. We show that
such a propagator follows upon adding to the action a gauge-violating term of a
general form, which reduces to $ \sim \int f_\lambda \Lambda^{\lambda \mu}
f_\mu \d^4 x $ with a constant operator $\Lambda^{\lambda \mu}$ depending on
$\partial$ and a metric functional $f_\lambda$. The contribution of
Faddeev-Popov's ghosts to the effective action is analysed. For the required
intermediate regularization, the discrete structure of the theory at small
distances is implied. It is shown that the ghost contribution can be
disregarded in the limit $ \varepsilon \to 0$.


  Power utilities are adopting Automated Demand Response (ADR) to replace the
costly fuel-fired generators and to preempt congestion during peak electricity
demand. Similarly, third-party Demand Response (DR) aggregators are leveraging
controllable small-scale electrical loads to provide on-demand grid support
services to the utilities. Some aggregators and utilities have started
employing Artificial Intelligence (AI) to learn the energy usage patterns of
electricity consumers and use this knowledge to design optimal DR incentives.
Such AI frameworks use open communication channels between the
utility/aggregator and the DR customers, which are vulnerable to
\textit{causative} data integrity cyberattacks. This paper explores
vulnerabilities of AI-based DR learning and designs a data-driven attack
strategy informed by DR data collected from the New York University (NYU)
campus buildings. The case study demonstrates the feasibility and effects of
maliciously tampering with (i) real-time DR incentives, (ii) DR event data sent
to DR customers, and (iii) responses of DR customers to the DR incentives.


  We study the fractal pointwise convergence for the equation
$i\hbar\partial_tu + P(D)u = 0$, where the symbol $P$ is real, homogeneous and
non-singular. We prove that for initial data $f\in H^s(\mathbb{R}^n)$ with
$s>(n-\alpha+1)/2$ the solution $u$ converges to $f$ $\mathcal{H}^\alpha$-a.e,
where $\mathcal{H}^\alpha$ is the $\alpha$-dimensional Hausdorff measure. We
improve upon this result depending on the dispersive strength of $P$. On the
other hand, for a family of polynomials $P$ and given $\alpha$, we exploit a
Talbot-like effect to construct initial data whose solutions $u$ diverge in
sets of Hausdorff dimension $\alpha$. To compute the dimension of the sets of
divergence, we adopt the Mass Transference Principle from Diophantine
approximation. We also construct counterexamples for quadratic symbols like the
saddle to show that our positive results are sometimes best possible.


  Random walk on changing graphs is considered. For sequences of finite graphs
increasing monotonically towards a limiting infinite graph, we establish
transition probability upper bounds. It yields sufficient transience criteria
for simple random walk on slowly growing graphs, upon knowing the volume and
Cheeger constant of each graph. For much more specialized cases, we establish
matching lower bounds, and deduce sufficient (weak) recurrence criteria. We
also address recurrence directly in relation to a universality conjecture of
[DHS]. We answer a related question of [SZ, Problem 1.8] about "inhomogeneous
merging" in the negative.


  We investigate the computational complexity of separating two distinct
vertices s and z by vertex deletion in a temporal graph. In a temporal graph,
the vertex set is fixed but the edges have (discrete) time labels. Since the
corresponding Temporal (s, z)-Separation problem is NP-hard, it is natural to
investigate whether relevant special cases exist that are computationally
tractable. To this end, we study restrictions of the underlying (static)
graph---there we observe polynomial-time solvability in the case of bounded
treewidth---as well as restrictions concerning the "temporal evolution" along
the time steps. Systematically studying partially novel concepts in this
direction, we identify sharp borders between tractable and intractable cases.


  This study introduces a novel approach, combining substruct counting,
$k$-mers, and Daylight-like fingerprints, to expand the representation of
chemical structures in SMILES strings. The integrated method generates
comprehensive molecular embeddings that enhance discriminative power and
information content. Experimental evaluations demonstrate its superiority over
traditional Morgan fingerprinting, MACCS, and Daylight fingerprint alone,
improving chemoinformatics tasks such as drug classification. The proposed
method offers a more informative representation of chemical structures,
advancing molecular similarity analysis and facilitating applications in
molecular design and drug discovery. It presents a promising avenue for
molecular structure analysis and design, with significant potential for
practical implementation.


  We show that the mean dynamical entropy of a quantum map on the sphere is
positive and tends logarithmically to infinity in the semiclassical limit. A
link between chaotic dynamics of classical systems and the random matrix-like
properties of spectra of the corresponding quantum analogues is demonstrated.


  We present a new active learning algorithm based on nonparametric estimators
of the regression function. Our investigation provides probabilistic bounds for
the rates of convergence of the generalization error achievable by proposed
method over a broad class of underlying distributions. We also prove minimax
lower bounds which show that the obtained rates are almost tight.


  The empirical studies of Graph Neural Networks (GNNs) broadly take the
original node feature and adjacency relationship as singleview input, ignoring
the rich information of multiple graph views. To circumvent this issue, the
multiview graph analysis framework has been developed to fuse graph information
across views. How to model and integrate shared (i.e. consistency) and
view-specific (i.e. complementarity) information is a key issue in multiview
graph analysis. In this paper, we propose a novel Multiview Variational Graph
Information Bottleneck (MVGIB) principle to maximize the agreement for common
representations and the disagreement for view-specific representations. Under
this principle, we formulate the common and view-specific information
bottleneck objectives across multiviews by using constraints from mutual
information. However, these objectives are hard to directly optimize since the
mutual information is computationally intractable. To tackle this challenge, we
derive variational lower and upper bounds of mutual information terms, and then
instead optimize variational bounds to find the approximate solutions for the
information objectives. Extensive experiments on graph benchmark datasets
demonstrate the superior effectiveness of the proposed method.


  Multiplex and multi-directional control of metabolic pathways is crucial for
metabolic engineering to improve product yield of fuels, chemicals, and
pharmaceuticals. To achieve this goal, artificial transcriptional regulators
such as CRISPR-based transcription regulators have been developed to
specifically activate or repress genes of interest. Here, we found that by
deploying guide RNAs to target on DNA sites at different locations of genetic
cassettes, we could use just one synthetic CRISPR-based transcriptional
regulator to simultaneously activate and repress gene expressions. By using the
pairwise datasets of guide RNAs and gene expressions, we developed a
data-driven predictive model to rationally design this system for fine-tuning
expression of target genes. We demonstrated that this system could achieve
programmable control of metabolic fluxes when using yeast to produce versatile
chemicals. We anticipate that this master CRISPR-based transcription regulator
will be a valuable addition to the synthetic biology toolkit for metabolic
engineering, speeding up the design-build-test cycle in industrial
biomanufacturing as well as generating new biological insights on the fates of
eukaryotic cells.


  We present a system architecture implementation to perform dynamic
application allocation in shared sensor networks, where highly integrated
wireless sensor systems are used to support multiple applications. The
architecture is based on a central controller that collects the received data
from the sensor nodes, dynamically decides which applications must be
simultaneously deployed in each node and, accordingly, over-the-air reprograms
the sensor nodes. Waspmote devices are used as sensor nodes that communicate
with the controller using ZigBee protocol. Experimental results show the
viability of the proposal.


  We consider a class of nonautonomous parabolic competition-diffusion systems
on bounded radial domains under Neumann boundary conditions. We show that, if
the initial profiles satisfy a reflection inequality with respect to a
hyperplane, then global positive solutions are asymptotically (in time)
foliated Schwarz symmetric with respect to antipodal points. Additionally, a
related result for (positive and sign changing solutions) of a scalar equation
with Neumann boundary conditions is given. The asymptotic shape of solutions to
cooperative systems is also discussed.


  This letter deals with the joint information and energy processing at a
receiver of a point-to-point communication channel. In particular, the
trade-off between the achievable information rate and harvested energy for a
multiple-antenna power splitting (PS) receiver is investigated. Here, the rate-
energy region characterization is of particular interest, which is
intrinsically a non-convex problem. In this letter, an efficient algorithm is
proposed for obtaining an approximate solution to the problem in polynomial
time. This algorithm is mainly based on the Taylor approximation in conjunction
with semidefinite relaxation (SDR) which is solved by interior-point methods.
Moreover, we utilize the Gaussian randomization procedure to obtain a feasible
solution for the original problem. It is shown that by proper receiver design
the rate-energy region can be significantly enlarged compared to the state of
the art, while at the same time the receiver hardware costs is reduced by
utilizing less number of energy harvesting circuitry.


  We propose a new approach to the topological recursion of Eynard-Orantin
based on the notion of Airy structure, which we introduce in the paper. We
explain why Airy structure is a more fundamental object than the one of the
spectral curve. We explain how the concept of quantization of Airy structure
leads naturally to the formulas of topological recursion as well as their
generalizations. The notion of spectral curve is also considered in a more
general framework of Poisson surfaces endowed with foliation. We explain how
the deformation theory of spectral curves is related to Airy structures. Few
other topics (e.g. the Holomorphic Anomaly Equation) are also discussed from
the general point of view of Airy structures.


  We study the problem of stochastic stability for evolutionary dynamics under
the logit choice rule. We consider general classes of coordination games,
symmetric or asymmetric, with an arbitrary number of strategies, which
satisfies the marginal bandwagon property (i.e., there is positive feedback to
coordinate). Our main result is that the most likely evolutionary escape paths
from a status quo convention consist of a series of identical mistakes. As an
application of our result, we show that the Nash bargaining solution arises as
the long run convention for the evolutionary Nash demand game under the usual
logit choice rule. We also obtain a new bargaining solution if the logit choice
rule is combined with intentional idiosyncratic plays. The new bargaining
solution is more egalitarian than the Nash bargaining solution, demonstrating
that intentionality implies equality under the logit choice model.


  While power systems are reliable infrastructures, their complex
interconnectivities allow for propagation of disturbances through cascading
failures which causes blackouts. Meanwhile the ever increasing penetration
level of renewable generation into power grids introduces a massive amount of
uncertainty to the grid that might have a severe impact on grid vulnerability
to overload cascading failures. There are numerous studies in the literature
that focus on modeling cascading failures with different approaches. However,
there is a need for studies that simulate cascading failure considering the
uncertainty coming from high penetration of renewable generation. In this
study, the impacts of wind generation in terms of its penetration and
uncertainty levels on grid vulnerability to cascading overload failures are
studied. The simulation results on IEEE 300 bus system show that uncertainty
coming from wind energy have severe impact on grid vulnerability to cascading
overload failures. Results also suggest that higher penetration levels of wind
energy if not managed appropriately will add to this severity due to injection
of higher uncertainties into the grid.


  Practical quantum computing is rapidly becoming a reality. To harness quantum
computers' real potential in software applications, one needs to have an
in-depth understanding of all such characteristics of quantum computing
platforms (QCPs), relevant from the Software Engineering (SE) perspective.
Restrictions on copying, deletion, the transmission of qubit states, a hard
dependency on quantum algorithms are few, out of many, examples of QCP
characteristics that have significant implications for building quantum
software.
  Thus, developing quantum software requires a paradigm shift in thinking by
software engineers. This paper presents the key findings from the SE
perspective, resulting from an in-depth examination of state-of-the-art QCPs
available today. The main contributions that we present include i) Proposing a
general architecture of the QCPs, ii) Proposing a programming model for
developing quantum software, iii) Determining architecturally significant
characteristics of QCPs, and \textbf{iv)} Determining the impact of these
characteristics on various Quality Attributes (QAs) and Software Development
Life Cycle (SDLC) activities.
  We show that the nature of QCPs makes them useful mainly in specialized
application areas such as scientific computing. Except for performance and
scalability, most of the other QAs (e.g., maintainability, testability, and
reliability) are adversely affected by different characteristics of a QCP.


  Significant efforts have gone into the development of statistical models for
analyzing data in the form of networks, such as social networks. Most existing
work has focused on modeling static networks, which represent either a single
time snapshot or an aggregate view over time. There has been recent interest in
statistical modeling of dynamic networks, which are observed at multiple points
in time and offer a richer representation of many complex phenomena. In this
paper, we present a state-space model for dynamic networks that extends the
well-known stochastic blockmodel for static networks to the dynamic setting. We
fit the model in a near-optimal manner using an extended Kalman filter (EKF)
augmented with a local search. We demonstrate that the EKF-based algorithm
performs competitively with a state-of-the-art algorithm based on Markov chain
Monte Carlo sampling but is significantly less computationally demanding.


  The state-of-the-art of chemical evolution models for Irregular and Blue
Compact galaxies is reviewed. The resulting scenarios for the initial mass
function, the regime of star formation and the efficiency of gas outflows are
described: The IMF appears to be roughly the same everywhere; the SF has
possibly followed a bursting regime in BCGs and a more continuous regime in
Irregulars, but with significant exceptions; galactic winds must occur in some
of these galaxies, with efficiencies inversely proportional to the galaxy
potential well.


  Surface-enhanced Raman spectroscopy (SERS) is enabled by local surface
plasmon resonances (LSPRs) in metallic nanogaps. When SERS is excited by direct
illumination of the nanogap, the background heating of lattice and electrons
can prevent further manipulation of the molecules. To overcome this issue, we
report SERS in electromigrated gold molecular junctions excited remotely:
surface plasmon polaritons (SPPs) are excited at nearby gratings, propagate to
the junction, and couple to the local nanogap plasmon modes. Like direct
excitation, remote excitation of the nanogap can generate both SERS emission
and an open-circuit photovoltage (OCPV). We compare SERS intensity and OCPV in
both direct and remote illumination configurations. SERS spectra obtained by
remote excitation are much more stable than those obtained through direct
excitation when photon count rates are comparable. By statistical analysis of
33 devices, coupling efficiency of remote excitation is calculated to be around
10%, consistent with the simulated energy flow.


  We present theoretical results for superradiance, i.e. the collective
coherent decay of a radiating system, in semiconductor structures. An optically
active region can become superradiant if a strong magnetic field is applied.
Pumping of electrons and holes at a rate T through coupling to external
`reservoirs' leads to a novel kind of oscillations with frequency $\sim
\sqrt{T}$.}


  The large majority of commercially available multiple-input multiple-output
(MIMO) radio channel measurement devices (sounders) is based on time-division
multiplexed switching (TDMS) of a single transmit/receive radio-frequency chain
into the elements of a transmit/receive antenna array. While being
cost-effective, such a solution can cause significant measurement errors due to
phase noise and frequency offset in the local oscillators. In this paper, we
systematically analyze the resulting errors and show that, in practice,
overestimation of channel capacity by several hundred percent can occur.
Overestimation is caused by phase noise (and to a lesser extent frequency
offset) leading to an increase of the MIMO channel rank. Our analysis
furthermore reveals that the impact of phase errors is, in general, most
pronounced if the physical channel has low rank (typical for line-of-sight or
poor scattering scenarios). The extreme case of a rank-1 physical channel is
analyzed in detail. Finally, we present measurement results obtained from a
commercially employed TDMS-based MIMO channel sounder. In the light of the
findings of this paper, the results obtained through MIMO channel measurement
campaigns using TDMS-based channel sounders should be interpreted with great
care.


  Gene expression-based heterogeneity analysis has been extensively conducted.
In recent studies, it has been shown that network-based analysis, which takes a
system perspective and accommodates the interconnections among genes, can be
more informative than that based on simpler statistics. Gene expressions are
highly regulated. Incorporating regulations in analysis can better delineate
the "sources" of gene expression effects. Although conditional network analysis
can somewhat serve this purpose, it does render enough attention to the
regulation relationships. In this article, significantly advancing from the
existing heterogeneity analyses based only on gene expression networks,
conditional gene expression network analyses, and regression-based
heterogeneity analyses, we propose heterogeneity analysis based on gene
expression networks (after accounting for or "removing" regulation effects) as
well as regulations of gene expressions. A high-dimensional penalized fusion
approach is proposed, which can determine the number of sample groups and
parameter values in a single step. An effective computational algorithm is
proposed. It is rigorously proved that the proposed approach enjoys the
estimation, selection, and grouping consistency properties. Extensive
simulations demonstrate its practical superiority over closely related
alternatives. In the analysis of two breast cancer datasets, the proposed
approach identifies heterogeneity and gene network structures different from
the alternatives and with sound biological implications.


  We introduce and test an expression for calculating the variance of a
physical field in three dimensions using only information contained in the
two-dimensional projection of the field. The method is general but assumes
statistical isotropy. To test the method we apply it to numerical simulations
of hydrodynamic and magnetohydrodynamic turbulence in molecular clouds, and
demonstrate that it can recover the 3D normalised density variance with ~10%
accuracy if the assumption of isotropy is valid. We show that the assumption of
isotropy breaks down at low sonic Mach number if the turbulence is
sub-Alfvenic. Theoretical predictions suggest that the 3D density variance
should increase proportionally to the square of the Mach number of the
turbulence. Application of our method will allow this prediction to be tested
observationally and therefore constrain a large body of analytic models of star
formation that rely on it.


  Let $(u_n)_{n \geq 0}$ be a non-degenerate Lucas sequence, given by the
relation $u_n=a_1 u_{n-1}+a_2 u_{n-2}$. Let $\ell_u(m)=lcm(m, z_u(m))$, for
$(m,a_2)=1$, where $z_u(m)$ is the rank of appearance of $m$ in $u_n$. We prove
that $$\sum_{\substack{m>x\\ (m,a_2)=1}}\frac{1}{\ell_u(m)}\leq
\exp(-(1/\sqrt{6}-\varepsilon+o(1))\sqrt{(\log x)(\log \log x)}),$$ when $x$ is
sufficiently large in terms of $\varepsilon$, and where the $o(1)$ depends on
$u$. Moreover, if $g_u(n)=\gcd(n,u_n)$, we will show that for every $k\geq 1$,
$$\sum_{n\leq x}g_u(n)^{k}\leq x^{k+1}\exp(-(1+o(1))\sqrt{(\log x)(\log \log
x)}),$$ when $x$ is sufficiently large and where the $o(1)$ depends on $u$ and
$k$. This gives a partial answer to a question posed by C. Sanna. As a
by-product, we derive bounds on $#\{n\leq x: (n, u_n)>y\}$, at least in certain
ranges of $y$, which strengthens what already obtained by Sanna. Finally, we
start the study of the multiplicative analogous of $\ell_u(m)$, finding
interesting results.


  Surface reconstruction plays a vital role in determining the surface
electronic structure and chemistry of semiconductors and metal oxides. However,
it has been commonly believed that surface reconstruction does not occur in van
der Waals layered materials, as they do not undergo significant bond breaking
during surface formation. In this study, we present evidence that charge
density wave (CDW) order in these materials can, in fact, cause CDW surface
reconstruction through interlayer coupling. Using density functional theory
calculations on the 1T-TaS2 surface, we reveal that CDW reconstruction,
involving concerted small atomic displacements in the subsurface layer, results
in a significant modification of the surface electronic structure, transforming
it from a Mott insulator to a band insulator. This new form of surface
reconstruction explains several previously unexplained observations on the
1T-TaS2 surface and has important implications for interpreting surface
phenomena in CDW-ordered layered materials.


  We study the persistence of localization for a strongly disordered
tight-binding Anderson model on the lattice $\mathbb{Z}^d$, periodically driven
on each site. Under two different sets of conditions, we show that Anderson
localization survives if the driving frequency is higher than some threshold
value that we determine. We discuss the implication of our results for recent
development in condensed matter physics, we compare them with the predictions
issuing from adiabatic theory, and we comment on the connexion with Mott's law,
derived within the linear response formalism.


  We present the results of numerical studies of superconductivity and
antiferromagnetism in a strongly correlated electron system. To do this we
construct a Hubbard model on a lattice of self-consistently embedded multi-site
clusters by means of a dynamical mean-field theory in which intra-cluster
dynamics is treated essentially exactly. We show that a class of characteristic
features which have been seen in the excitation spectra of high-$T_{c}$
cuprates (e.g., pseudogap and the spin-flip resonance), as well as their
interplay with the onset of a pairing correlations, can be captured within a
dynamical mean-field theory in which short-wavelength dynamics are rigorously
treated. Thus we infer that the observation of the neutron scattering resonance
in the superconducting state of the cuprate superconductors does not appear to
be directly tied to their quasi-2D character. Although our approach is defined
strictly in terms of fermion degrees of freedom, we show that we can readily
identify the emergence of effective low energy bosonic degrees of freedom in
the presence of a well-defined broken symmetry phase as long as their dynamics
are dominated by short-range, short-wavelength fluctuations. Our results reveal
that the dynamics of staggered spin degrees of freedom builds up coherence and
a resonance-like sharp feature emerges as pairing correlations set in. Under
conditions of superconducting broken symmetry our approach thus extends static
BCS mean field theory to provide an exact treatment of quantum fluctuations of
the BCS order parameter.


  The theory of bigravity offers one of the simplest possibilities to describe
a massive graviton while having self-accelerating cosmological solutions
without a cosmological constant. However, it has been shown recently that
bigravity is affected by early-time fast growing modes on the tensor sector.
Here we argue that we can only trust the linear analysis up to when
perturbations are in the linear regime and use a cut-off to stop the growing of
the metric perturbations. This analysis, although more consistent, still leads
to growing tensor modes that are unacceptably large for the theory to be
compatible with measurements of the cosmic microwave background (CMB), both in
temperature and polarization spectra. In order to suppress the growing modes
and make the model compatible with CMB spectra, we find it necessary to either
fine-tune the initial conditions, modify the theory or set the cut-off for the
tensor perturbations of the second metric much lower than unity. Initial
conditions such that the growing mode is sufficiently suppresed can be achieved
in scenarios in which inflation ends at the GeV scale.


  The neural combinatorial optimization (NCO) approach has shown great
potential for solving routing problems without the requirement of expert
knowledge. However, existing constructive NCO methods cannot directly solve
large-scale instances, which significantly limits their application prospects.
To address these crucial shortcomings, this work proposes a novel
Instance-Conditioned Adaptation Model (ICAM) for better large-scale
generalization of neural combinatorial optimization. In particular, we design a
powerful yet lightweight instance-conditioned adaptation module for the NCO
model to generate better solutions for instances across different scales. In
addition, we develop an efficient three-stage reinforcement learning-based
training scheme that enables the model to learn cross-scale features without
any labeled optimal solution. Experimental results show that our proposed
method is capable of obtaining excellent results with a very fast inference
time in solving Traveling Salesman Problems (TSPs) and Capacitated Vehicle
Routing Problems (CVRPs) across different scales. To the best of our knowledge,
our model achieves state-of-the-art performance among all RL-based constructive
methods for TSP and CVRP with up to 1,000 nodes.


  The strange magnetic moment of the nucleon ($\mu_s$) is examined as part of
the nucleon's isoscalar anomalous moment. The dominant up and down quark
effects in the anomalous moment may actually tend to favor $\mu_s >0$, which is
contrary to the negative values that generally result from model calculations.
The possible origins of this apparent discrepancy are considered.


  With the advance of large language models (LLMs), the research field of LLM
applications becomes more and more popular and the idea of constructing
pipelines to accomplish complex tasks by stacking LLM API calls come true.
However, this kind of methods face two limitations: narrow information coverage
and low fault tolerance. In this work, we propose a novel method called ALLIES.
Given an input query, ALLIES leverages LLMs to iteratively generate new queries
related to the original query, enabling an iterative reasoning process. By
iteratively refining and expanding the scope of the original query, ALLIES
captures and utilizes hidden knowledge that may not be directly obtainable
through retrieval. We take zero-shot open-domain question answering (ODQA) as
an application scene and evaluate ALLIES on the widely-used benchmarks, such as
NQ, WebQ and TriviaQA. The experimental results demonstrate that ALLIES
significantly outperforms other zero-shot baselines, indicating its
effectiveness in tackling those challenges. Our code is available in
https://github.com/microsoft/SimXNS/tree/main/ALLIES.


  We studied the evolution of superhumps in the peculiar SU UMa-type dwarf
nova, ER UMa. Contrary to the canonical picture of the SU UMa-type superhump
phenomena, the superhumps of ER UMa show an unexpected phase reversal during
the very early stage (~5 d after the superoutburst maximum). We interpret that
a sudden switch to so-called late superhumps occurs during the very early stage
of a superoutburst. What had been believed to be (ordinary) superhumps during
the superoutburst plateau of ER UMa were actually late superhumps. The
implication of this discovery is briefly discussed.


  The Swampland program, which looks for low energy theories consistent with
quantum gravity, has led to the introduction of a dark dimension stemming from
the cosmological constant. We show that the same argument leads to the
emergence of the electroweak scale, once the dark dimension is realised in a
warped background. A warped extra dimension at the TeV scale is, therefore,
postulated, where the long-standing problem of the hierarchy between the
electroweak scale and Planck scale can be addressed. In the emergent holistic
picture of gravity and gauge interactions, both Planck and the electroweak
scales are emergent from a theory with two fundamental scales: $10^{-2}$ eV and
$10^{10}$ GeV. Hence, a bridge is established between the two standard models
of particle physics and cosmology.


  Mapping white matter tracts is an essential step towards understanding brain
function. Diffusion Magnetic Resonance Imaging (dMRI) is the only noninvasive
technique which can detect in vivo anisotropies in the 3-dimensional diffusion
of water molecules, which correspond to nervous fibers in the living brain. In
this process, spectral data from the displacement distribution of water
molecules is collected by a magnetic resonance scanner. From the statistical
point of view, inverting the Fourier transform from such sparse and noisy
spectral measurements leads to a non-linear regression problem. Diffusion
tensor imaging (DTI) is the simplest modeling approach postulating a Gaussian
displacement distribution at each volume element (voxel). Typically the
inference is based on a linearized log-normal regression model that can fit the
spectral data at low frequencies. However such approximation fails to fit the
high frequency measurements which contain information about the details of the
displacement distribution but have a low signal to noise ratio. In this paper,
we directly work with the Rice noise model and cover the full range of
$b$-values. Using data augmentation to represent the likelihood, we reduce the
non-linear regression problem to the framework of generalized linear models.
Then we construct a Bayesian hierarchical model in order to perform
simultaneously estimation and regularization of the tensor field. Finally the
Bayesian paradigm is implemented by using Markov chain Monte Carlo.


  Based on the strong-field approximation, we obtain analytical expressions for
the initial momentum at the tunnel exit and instantaneous ionization rate of
tunneling ionization in elliptically polarized laser fields with arbitrary
ellipticity. The tunneling electron reveals a nonzero offset of the initial
momentum at the tunnel exit in the elliptically polarized laser field. We find
that the transverse and longitudinal components of this momentum offset with
respect to the instantaneous field direction are directly related to the time
derivatives of the instantaneous laser electric field along the angular and
radial directions, respectively. We further show that the nonzero initial
momentum at the tunnel exit has a significant influence on the laser phase
dependence of the instantaneous ionization rate in the nonadiabatic tunneling
regime.