  This study derives a novel family of charged black hole solutions featuring
short- and long-range modifications. These ones are achieved through a
Yukawa-like gravitational potential modification and a nonsingular electric
potential incorporation. The short-range corrections encode quantum gravity
effects, while the long-range adjustments simulate gravitational effects akin
to those attributed to dark matter. Our investigation reveals that the total
mass of the black hole undergoes corrections owing to the apparent presence of
dark matter mass and the self-adjusted electric charge mass. Two distinct
solutions are discussed: a regular black hole solution characterizing small
black holes, where quantum effects play a crucial role, and a second solution
portraying large black holes at considerable distances, where the significance
of Yukawa corrections comes into play. Notably, these long-range corrections
contribute to an increase in the total mass and hold particular interest as
they can emulate the role of dark matter. Finally, we explore the
phenomenological aspects of the black hole. Specifically, we examine the
influence of electric charge and Yukawa parameters on thermodynamic quantities,
the quasinormal modes for the charged scalar perturbations as well as for the
vector perturbations, analysis of the geodesics of light/massive particles, and
the accretion of matter onto the charged black hole solution.


  This paper presents a comparison of classification methods for linguistic
typology for the purpose of expanding an extensive, but sparse language
resource: the World Atlas of Language Structures (WALS) (Dryer and Haspelmath,
2013). We experimented with a variety of regression and nearest-neighbor
methods for use in classification over a set of 325 languages and six syntactic
rules drawn from WALS. To classify each rule, we consider the typological
features of the other five rules; linguistic features extracted from a
word-aligned Bible in each language; and genealogical features (genus and
family) of each language. In general, we find that propagating the majority
label among all languages of the same genus achieves the best accuracy in label
pre- diction. Following this, a logistic regression model that combines
typological and linguistic features offers the next best performance.
Interestingly, this model actually outperforms the majority labels among all
languages of the same family.


  We identify type IIA orientifolds that are dual to M-theory compactifications
on manifolds with G_2-holonomy. We then discuss the construction of crosscap
states in Gepner models. (Based on a talk presented by S.G. at PASCOS 2003 held
at the Tata Institute of Fundamental Research, Mumbai during Jan. 3-8, 2003.)


  In the study of femtoscopic correlations in high-energy physics, besides
Bose--Einstein correlations, one has to take final-state interactions into
account. Amongst them, Coulomb interactions play a prominent role in the case
of charged particles. Recent measurements have shown that in heavy-ion
collisions, Bose--Einstein correlations can be best described by L\'evy-type
sources instead of the more common Gaussian assumption. Furthermore,
three-dimensional measurements have indicated that, depending on the choice of
frame, a deviation from spherical symmetry observed under the assumption of
Gaussian source functions persists in the case of L\'evy-type sources. To
clarify such three-dimensional L\'evy-type correlation measurements, it is thus
important to study the effect of Coulomb interactions in the case of
non-spherical L\'evy sources. We calculated the Coulomb correction factor
numerically in the case of such a source function for assorted kinematic
domains and parameter values using the Metropolis--Hastings algorithm and
compared our results with previous methods to treat Coulomb interactions in the
presence of L\'evy sources.


  Veit Elser proposed a random graph model for percolation in which physical
dimension appears as a parameter. Studying this model combinatorially leads
naturally to the consideration of numerical graph invariants which we call
\emph{Elser numbers} $\mathsf{els}_k(G)$, where $G$ is a connected graph and
$k$ a nonnegative integer. Elser had proven that $\mathsf{els}_1(G)=0$ for all
$G$. By interpreting the Elser numbers as Euler characteristics of appropriate
simplicial complexes called \emph{nucleus complexes}, we prove that for all
graphs $G$, they are nonpositive when $k=0$ and nonnegative for $k\geq2$. The
last result confirms a conjecture of Elser. Furthermore, we give necessary and
sufficient conditions, in terms of the 2-connected structure of~$G$, for the
nonvanishing of the Elser numbers.


  Nowadays, Intelligent Tutoring Systems (ITSs) are so regarded in order to
improve education quality via new technologies in this area. One of the
problems is that the language of ITSs is different from the learner's. It
forces the learners to learn the system language. This paper tries to remove
this necessity by using an Automatic Translator Component in system structure
like Google Translate API. This system carry out a pre-test and post-test by
using Expert System and Jackson Model before and after of training a concept.
It constantly updates learner model to save all changes in learning process. So
this paper offers an E-Learning system which is web-based, intelligent,
adaptive, multilingual and remotely accessible where tutors and learners can
have non-identical language. It is also applicable Every Time and Every Where
(ETEW). Furthermore, it trains the concepts in the best method with any
language and low cost.


  It is known that, if a locally perturbed periodic self-adjoint operator on a
combinatorial or quantum graph admits an eigenvalue embedded in the continuous
spectrum, then the associated eigenfunction is compactly supported--that is, if
the Fermi surface is irreducible, which occurs generically in dimension two or
higher. This article constructs a class of operators whose Fermi surface is
reducible for all energies by coupling several periodic systems. The components
of the Fermi surface correspond to decoupled spaces of hybrid states, and in
certain frequency bands, some components contribute oscillatory hybrid states
(corresponding to spectrum) and other components contribute only exponential
ones. This separation allows a localized defect to suppress the oscillatory
(radiation) modes and retain the evanescent ones, thereby leading to embedded
eigenvalues whose associated eigenfunctions decay exponentially but are not
compactly supported.


  We focus on the study of the exact solution corresponding to charged AdS
black holes surrounded by quintessence with a cloud of strings present in
higher dimensional spacetime. We then investigate its corresponding
thermodynamic criticality in the extended phase space and show that the
spacetime dimension has no effect on the existence of small/large phase
transition for such black holes. The heat capacity is evaluated and the
geothermodynamics of Quevedo analyzed for different spacetime dimensions with
the cloud of strings and quintessence parameters. We calculate the critical
exponents describing the behavior of relevant thermodynamic quantities near the
critical point. Finally, we also discuss the uncharged case, show how it is
sensitive to the quintessence and strings cloud parameters, and when the
thermodynamic behavior of the uncharged black holes is similar to Van der Waals
fluid.


  In the world of epsilon-near-zero (ENZ) materials, the plasma is unique for
its natural ENZ properties at the plasma frequency (wp). However, for the air
plasma during femtosecond laser filamentation with wp in terahertz (THz) band,
which is also known as a broadband THz emitter, the possible ENZ effect has
long been neglected. In this work, interactions between the laser plasma in air
and the radiated THz waves were investigated, and the THz resonance absorption
at the ENZ point, which gave rise to the generation of surface plasmon waves,
has been theoretically and experimentally demonstrated for the first time, to
the best of our knowledge. Specifically, this ENZ effect was accompanied with
THz modulations along the plasma filament in multiple domains, including the
linear-to-elliptical THz polarization conversion and the multi-ring pattern of
far-field THz profiles, etc. Thanks to these novel ENZ-induced phenomena, the
understanding of this important plasma-based THz source has been enriched.
Moreover, by extending the laser plasma to the role of ENZ material, it is
promising to expedite related applications benefiting from the ENZ nature,
e.g., the strong spatial confinement of THz waves inside the ENZ region between
the bi-filaments array, which realized all-optical time-domain integration of
broadband THz pulses as we recently displayed in [arXiv].


  Unsupervised speech recognition (ASR-U) is the problem of learning automatic
speech recognition (ASR) systems from unpaired speech-only and text-only
corpora. While various algorithms exist to solve this problem, a theoretical
framework is missing from studying their properties and addressing such issues
as sensitivity to hyperparameters and training instability. In this paper, we
proposed a general theoretical framework to study the properties of ASR-U
systems based on random matrix theory and the theory of neural tangent kernels.
Such a framework allows us to prove various learnability conditions and sample
complexity bounds of ASR-U. Extensive ASR-U experiments on synthetic languages
with three classes of transition graphs provide strong empirical evidence for
our theory (code available at cactuswiththoughts/UnsupASRTheory.git).


  In this article we investigate a state-space representation of the Lee-Carter
model which is a benchmark stochastic mortality model for forecasting
age-specific death rates. Existing relevant literature focuses mainly on
mortality forecasting or pricing of longevity derivatives, while the full
implications and methods of using the state-space representation of the
Lee-Carter model in pricing retirement income products is yet to be examined.
The main contribution of this article is twofold. First, we provide a rigorous
and detailed derivation of the posterior distributions of the parameters and
the latent process of the Lee-Carter model via Gibbs sampling. Our assumption
for priors is slightly more general than the current literature in this area.
Moreover, we suggest a new form of identification constraint not yet utilised
in the actuarial literature that proves to be a more convenient approach for
estimating the model under the state-space framework. Second, by exploiting the
posterior distribution of the latent process and parameters, we examine the
pricing range of annuities, taking into account the stochastic nature of the
dynamics of the mortality rates. In this way we aim to capture the impact of
longevity risk on the pricing of annuities. The outcome of our study
demonstrates that an annuity price can be more than 4% under-valued when
different assumptions are made on determining the survival curve constructed
from the distribution of the forecasted death rates. Given that a typical
annuity portfolio consists of a large number of policies with maturities which
span decades, we conclude that the impact of longevity risk on the accurate
pricing of annuities is a significant issue to be further researched. In
addition, we find that mis-pricing is increasingly more pronounced for older
ages as well as for annuity policies having a longer maturity.


  We present results of three-dimensional (3D) simulations of the
magnetohydrodynamic Kelvin-Helmholtz instability in a stratified shear layer.
The magnetic field is taken to be uniform and parallel to the shear flow. We
describe the evolution of the fluid flow and the magnetic field for a range of
initial conditions. In particular, we investigate how the mixing rate of the
fluid depends on the Richardson number and the magnetic field strength. It was
found that the magnetic field can enhance as well as suppress mixing. Moreover,
we have performed two-dimensional (2D) simulations and discuss some interesting
differences between the 2D and 3D results.


  It is an open secret that ImageNet is treated as the panacea of pretraining.
Particularly in medical machine learning, models not trained from scratch are
often finetuned based on ImageNet-pretrained models. We posit that pretraining
on data from the domain of the downstream task should almost always be
preferred instead. We leverage RadNet-12M, a dataset containing more than 12
million computed tomography (CT) image slices, to explore the efficacy of
self-supervised pretraining on medical and natural images. Our experiments
cover intra- and cross-domain transfer scenarios, varying data scales,
finetuning vs. linear evaluation, and feature space analysis. We observe that
intra-domain transfer compares favorably to cross-domain transfer, achieving
comparable or improved performance (0.44% - 2.07% performance increase using
RadNet pretraining, depending on the experiment) and demonstrate the existence
of a domain boundary-related generalization gap and domain-specific learned
features.


  Reallocation scheduling is one of the most fundamental problems in various
areas such as supply chain management, logistics, and transportation science.
In this paper, we introduce the reallocation problem that models the scheduling
in which products are with fixed cost, non-fungible, and reallocated in
parallel, and comprehensively study the complexity of the problem under various
settings of the transition time, product size, and capacities. We show that the
problem can be solved in polynomial time for a fundamental setting where the
product size and transition time are both uniform. We also show that the
feasibility of the problem is NP-complete even for little more general
settings, which implies that no polynomial-time algorithm constructs a feasible
schedule of the problem unless P$=$NP. We then consider the relaxation of the
problem, which we call the capacity augmentation, and derive a reallocation
schedule feasible with the augmentation such that the completion time is at
most the optimal of the original problem. When the warehouse capacity is
sufficiently large, we design constant-factor approximation algorithms under
all the settings. We also show the relationship between the reallocation
problem and the bin packing problem when the warehouse and carry-in capacities
are sufficiently large.


  Cooperation arises in nature at every scale, from within cells to entire
ecosystems. In the framework of evolutionary game theory, public goods games
(PGGs) are used to analyse scenarios where individuals can cooperate or defect,
and can predict when and how these behaviours emerge. However, too few examples
motivate the transferal of knowledge from one application of PGGs to another.
Here, we focus on PGGs arising in disease modelling of cancer evolution and the
spread of infectious diseases. We use these two systems as case studies for the
development of the theory and applications of PGGs, which we succinctly review
and compare. We also posit that applications of evolutionary game theory to
decision-making in cancer, such as interactions between a clinician and a
tumour, can learn from the PGGs studied in epidemiology, where cooperative
behaviours such as quarantine and vaccination compliance have been more
thoroughly investigated. Furthermore, instances of cellular-level cooperation
observed in cancers point to a corresponding area of potential interest for
modellers of other diseases, be they viral, bacterial or otherwise. We aim to
demonstrate the breadth of applicability of PGGs in disease modelling while
providing a starting point for those interested in quantifying cooperation
arising in healthcare.


  Aims. We use Hermite splines to interpolate pressure and its derivatives
simultaneously, thereby preserving mathematical relations between the
derivatives. The method therefore guarantees that thermodynamic identities are
obeyed even between mesh points. In addition, our method enables an estimation
of the precision of the interpolation by comparing the Hermite-spline results
with those of frequent cubic (B-) spline interpolation.
  Methods. We have interpolated pressure as a function of temperature and
density with quintic Hermite 2D-splines. The Hermite interpolation requires
knowledge of pressure and its first and second derivatives at every mesh point.
To obtain the partial derivatives at the mesh points, we used tabulated values
if given or else thermodynamic equalities, or, if not available, values
obtained by differentiating B-splines.
  Results. The results were obtained with the grid of the SAHA-S
equation-of-state (EOS) tables. The maximum $lg P$ difference lies in the range
from $10^{-9}$ to $10^{-4}$, and $\Gamma_1$ difference varies from $10^{-9}$ to
$10^{-3}$. Specifically, for the points of a solar model, the maximum
differences are one order of magnitude smaller than the aforementioned values.
The poorest precision is found in the dissociation and ionization regions,
occurring at $T \sim 1.5\cdot 10^3 - 10^5$ K. The best precision is achieved at
higher temperatures, $T>10^5$ K. To discuss the significance of the
interpolation errors we compare them with the corresponding difference between
two different equation-of-state formalisms, SAHA-S and OPAL 2005. We find that
the interpolation errors of the pressure are a few orders of magnitude less
than the differences from between the physical formalisms, which is
particularly true for the solar-model points.


  We present results from a chiral soliton model calculation for the spectrum
of baryons with a single heavy quark (charm or bottom) and non-zero
strangeness. We treat the strange components within a three flavor collective
coordinate quantization of the soliton that fully accounts for light flavor
symmetry breaking. Heavy baryons emerge by binding a heavy meson to the
soliton. The dynamics of this heavy meson is described by the heavy quark
effective theory with finite mass effects included.


  While implicit generative models such as GANs have shown impressive results
in high quality image reconstruction and manipulation using a combination of
various losses, we consider a simpler approach leading to surprisingly strong
results. We show that texture loss alone allows the generation of perceptually
high quality images. We provide a better understanding of texture constraining
mechanism and develop a novel semantically guided texture constraining method
for further improvement. Using a recently developed perceptual metric employing
"deep features" and termed LPIPS, the method obtains state-of-the-art results.
Moreover, we show that a texture representation of those deep features better
capture the perceptual quality of an image than the original deep features.
Using texture information, off-the-shelf deep classification networks (without
training) perform as well as the best performing (tuned and calibrated) LPIPS
metrics. The code is publicly available.


  Density functional calculations of the electronic structure of the complex
hydride NaAlH4 and the reference systems NaH and AlH3 are reported. We find a
substantially ionic electronic structure for NaAlH4, which emphasizes the
importance of solid state effects in this material. The relaxed hydrogen
positions in NaAlH4 are in good agreement with recent experiment. The
electronic structure of AlH3 is also ionic. Implications for the binding of
complex hydrides are discussed.


  Reinforcement Learning is gaining attention by the wireless networking
community due to its potential to learn good-performing configurations only
from the observed results. In this work we propose a stateless variation of
Q-learning, which we apply to exploit spatial reuse in a wireless network. In
particular, we allow networks to modify both their transmission power and the
channel used solely based on the experienced throughput. We concentrate in a
completely decentralized scenario in which no information about neighbouring
nodes is available to the learners. Our results show that although the
algorithm is able to find the best-performing actions to enhance aggregate
throughput, there is high variability in the throughput experienced by the
individual networks. We identify the cause of this variability as the
adversarial setting of our setup, in which the most played actions provide
intermittent good/poor performance depending on the neighbouring decisions. We
also evaluate the effect of the intrinsic learning parameters of the algorithm
on this variability.


  Molecular outflows are a direct consequence of accretion, and therefore they
represent one of the best tracers of accretion processes in the still poorly
understood early phases of high-mass star formation. Previous studies suggested
that the SiO abundance decreases with the evolution of a massive young stellar
object probably because of a decay of jet activity, as witnessed in low-mass
star-forming regions. We investigate the SiO excitation conditions and its
abundance in outflows from a sample of massive young stellar objects through
observations of the SiO(8-7) and CO(4-3) lines with the APEX telescope. Through
a non-LTE analysis, we find that the excitation conditions of SiO increase with
the velocity of the emitting gas. We also compute the SiO abundance through the
SiO and CO integrated intensities at high velocities. For the sources in our
sample we find no significant variation of the SiO abundance with evolution for
a bolometric luminosity-to-mass ratio of between 4 and 50 $L_\odot/M_\odot$. We
also find a weak increase of the SiO(8-7) luminosity with the bolometric
luminosity-to-mass ratio. We speculate that this might be explained with an
increase of density in the gas traced by SiO. We find that the densities
constrained by the SiO observations require the use of shock models that
include grain-grain processing. For the first time, such models are compared
and found to be compatible with SiO observations. A pre-shock density of
$10^5\, $cm$^{-3}$ is globally inferred from these comparisons. Shocks with a
velocity higher than 25 km s$^{-1}$ are invoked for the objects in our sample
where the SiO is observed with a corresponding velocity dispersion. Our
comparison of shock models with observations suggests that sputtering of
silicon-bearing material (corresponding to less than 10% of the total silicon
abundance) from the grain mantles is occurring.


  Multi-robot collaboration has become a needed component in unknown
environment exploration due to its ability to accomplish various challenging
situations. Potential-field-based methods are widely used for autonomous
exploration because of their high efficiency and low travel cost. However,
exploration speed and collaboration ability are still challenging topics.
Therefore, we propose a Distributed Multi-Robot Potential-Field-Based
Exploration (DMPF-Explore). In particular, we first present a Distributed
Submap-Based Multi-Robot Collaborative Mapping Method (DSMC-Map), which can
efficiently estimate the robot trajectories and construct the global map by
merging the local maps from each robot. Second, we introduce a
Potential-Field-Based Exploration Strategy Augmented with Modified Wave-Front
Distance and Colored Noises (MWF-CN), in which the accessible frontier
neighborhood is extended, and the colored noise provokes the enhancement of
exploration performance. The proposed exploration method is deployed for
simulation and real-world scenarios. The results show that our approach
outperforms the existing ones regarding exploration speed and collaboration
ability.


  We use a sample of 55 groups and 6 clusters of galaxies ranging in mass from
7 x 10^11 Msun to 1.5 x 10^15 Msun to examine the correlation of the Ks-band
luminosity with mass discovered by Lin et al. (2003). We use the 2MASS catalog
and published redshifts to construct complete magnitude limited redshift
surveys of the groups. From these surveys we explore the IR photometric
properties of groups members including their IR color distribution and
luminosity function. Although we find no significant difference between the
group Ks luminosity function and the general field, there is a difference
between the color distribution of luminous group members and their counterparts
(generally background) in the field. There is a significant population of
luminous galaxies with H-Ks > 0.35 which are rarely, if ever, members of the
groups in our sample. The most luminous galaxies which populate the groups have
a very narrow range of IR color. Over the entire mass range covered by our
sample, the Ks luminosity increases with mass as L ~ M^(0.64 +/- 0.06) implying
that the mass-to-light ratio in the Ks-band increases with mass. The agreement
between this result and earlier investigations of essentially non-overlapping
sets of systems shows that this window in galaxy formation and evolution is
insensitive to the selection of the systems and to the details of the mass and
luminosity computations.


  In this paper, we focus on the design of binary constant weight codes that
admit low-complexity encoding and decoding algorithms, and that have a size
$M=2^k$. For every integer $\ell \geq 3$, we construct a $(n=2^\ell,
M=2^{k_{\ell}}, d=2)$ constant weight code ${\cal C}[\ell]$ of weight $\ell$ by
encoding information in the gaps between successive $1$'s. The code is
associated with an integer sequence of length $\ell$ with a constraint defined
as {\em anchor-decodability} that ensures low complexity for encoding and
decoding. The complexity of the encoding is linear in the input size $k$, and
that of the decoding is poly-logarithmic in the input size $n$, discounting the
linear time spent on parsing the input. Both the algorithms do not require
expensive computation of binomial coefficients, unlike the case in many
existing schemes. Among codes generated by all anchor-decodable sequences, we
show that ${\cal C}[\ell]$ has the maximum size with $k_{\ell} \geq
\ell^2-\ell\log_2\ell + \log_2\ell - 0.279\ell - 0.721$. As $k$ is upper
bounded by $\ell^2-\ell\log_2\ell +O(\ell)$ information-theoretically, the code
${\cal C}[\ell]$ is optimal in its size with respect to two higher order terms
of $\ell$. In particular, $k_\ell$ meets the upper bound for $\ell=3$ and
one-bit away for $\ell=4$. On the other hand, we show that ${\cal C}[\ell]$ is
not unique in attaining $k_{\ell}$ by constructing an alternate code ${\cal
\hat{C}}[\ell]$ again parameterized by an integer $\ell \geq 3$ with a
different low-complexity decoder, yet having the same size $2^{k_{\ell}}$ when
$3 \leq \ell \leq 7$. Finally, we also derive new codes by modifying ${\cal
C}[\ell]$ that offer a wider range on blocklength and weight while retaining
low complexity for encoding and decoding. For certain selected values of
parameters, these modified codes too have an optimal $k$.


  The use of attention models for automated image captioning has enabled many
systems to produce accurate and meaningful descriptions for images. Over the
years, many novel approaches have been proposed to enhance the attention
process using different feature representations. In this paper, we extend this
approach by creating a guided attention network mechanism, that exploits the
relationship between the visual scene and text-descriptions using spatial
features from the image, high-level information from the topics, and temporal
context from caption generation, which are embedded together in an ordered
embedding space. A pairwise ranking objective is used for training this
embedding space which allows similar images, topics and captions in the shared
semantic space to maintain a partial order in the visual-semantic hierarchy and
hence, helps the model to produce more visually accurate captions. The
experimental results based on MSCOCO dataset shows the competitiveness of our
approach, with many state-of-the-art models on various evaluation metrics.


  A review is given on the various aspects of gamma-ray burst afterglow
observations, and the inferences derived from the data. After a short history
of optical transient search and the BeppoSAX discoveries, the main topics
included are prompt multiwavelength emission, dark bursts, spectral lines line
and continuum variability, the early light curve behaviour, jet breaks, X-ray
flares, late afterglow features, polarization, and orphan afterglows.


  In this work we derive by Gamma-convergence techniques a model for brittle
fracture linearly elastic plates. Precisely, we start from a brittle linearly
elastic thin film with positive thickness $\rho$ and study the limit as $\rho$
tends to 0. The analysis is performed with no a priori restrictions on the
admissible displacements and on the geometry of the fracture set. The limit
model is characterized by a Kirchhoff-Love type of structure.


  In the present work, we generalize earlier considerations for intrinsic
localized modes consisting of a few excited sites, as developed in the
one-component discrete nonlinear Schrodinger equation model, to the case of
two-component systems. We consider all the different combinations of "up" (zero
phase) and "down" ({\pi} phase) site excitations and are able to compute not
only the corresponding existence curves, but also the eigenvalue dependences of
the small eigenvalues potentially responsible for instabilities, as a function
of the nonlinear parameters of the model representing the self/cross phase
modulation in optics and the scattering length ratios in the case of matter
waves in optical lattices. We corroborate these analytical predictions by means
of direct numerical computations. We infer that all the modes which bear two
adjacent nodes with the same phase are unstable in the two component case and
the only solutions that may be linear stable are ones where each set of
adjacent nodes, in each component is out of phase.


  We generalize the method of computing functional determinants with a single
excluded zero eigenvalue developed by McKane and Tarlie to differential
operators with multiple zero eigenvalues. We derive general formulas for such
functional determinants of $r\times r$ matrix second order differential
operators $O$ with $0 < n \leqslant 2r$ linearly independent zero modes. We
separately discuss the cases of the homogeneous Dirichlet boundary conditions,
when the number of zero modes cannot exceed $r$, and the case of twisted
boundary conditions, including the periodic and anti-periodic ones, when the
number of zero modes is bounded above by $2r$. In all cases the determinants
with excluded zero eigenvalues can be expressed only in terms of the $n$ zero
modes and other $r-n$ or $2r-n$ (depending on the boundary conditions)
solutions of the homogeneous equation $O h=0$, in the spirit of Gel'fand-Yaglom
approach. In instanton calculations, the contribution of the zero modes is
taken into account by introducing the so-called collective coordinates. We show
that there is a remarkable cancellation of a factor (involving scalar products
of zero modes) between the Jacobian of the transformation to the collective
coordinates and the functional fluctuation determinant with excluded zero
eigenvalues. This cancellation drastically simplifies instanton calculations
when one uses our formulas.


  The rolling pad instability is caused by electromagnetic interactions in
systems of horizontal layers with strongly different electric conductivities.
We analyze the instability for a simplified model of a liquid metal battery
(LMB), a promising device for large-scale stationary energy storage. Numerical
simulations of the flow and the dynamics of electromagnetically coupled
interfacial waves are performed using OpenFOAM. The work confirms the earlier
conclusions that the instability is a significant factor affecting battery's
operation. The critical role played by the ratio between the density
differences across the two interfaces is elucidated. It is found that the ratio
determines the stability characteristics and the type (symmetrically or
antisymmetrically coupled) of dominant interfacial waves.


  Differential Privacy (DP) is an important privacy-enhancing technology for
private machine learning systems. It allows to measure and bound the risk
associated with an individual participation in a computation. However, it was
recently observed that DP learning systems may exacerbate bias and unfairness
for different groups of individuals. This paper builds on these important
observations and sheds light on the causes of the disparate impacts arising in
the problem of differentially private empirical risk minimization. It focuses
on the accuracy disparity arising among groups of individuals in two
well-studied DP learning methods: output perturbation and differentially
private stochastic gradient descent. The paper analyzes which data and model
properties are responsible for the disproportionate impacts, why these aspects
are affecting different groups disproportionately and proposes guidelines to
mitigate these effects. The proposed approach is evaluated on several datasets
and settings.


  Mortgages account for the largest portion of household debt in the United
States, totaling around \$12 trillion nationwide. In times of financial
hardship, alleviating mortgage burdens is essential for supporting affected
households. The mortgage servicing industry plays a vital role in offering this
assistance, yet there has been limited research modelling the complex
relationship between households and servicers. To bridge this gap, we developed
an agent-based model that explores household behavior and the effectiveness of
relief measures during financial distress. Our model represents households as
adaptive learning agents with realistic financial attributes. These households
experience exogenous income shocks, which may influence their ability to make
mortgage payments. Mortgage servicers provide relief options to these
households, who then choose the most suitable relief based on their unique
financial circumstances and individual preferences. We analyze the impact of
various external shocks and the success of different mortgage relief strategies
on specific borrower subgroups. Through this analysis, we show that our model
can not only replicate real-world mortgage studies but also act as a tool for
conducting a broad range of what-if scenario analyses. Our approach offers
fine-grained insights that can inform the development of more effective and
inclusive mortgage relief solutions.


  Due to effective pattern mining and feature representation, neural
forecasting models based on deep learning have achieved great progress. The
premise of effective learning is to collect sufficient data. However, in time
series forecasting, it is difficult to obtain enough data, which limits the
performance of neural forecasting models. To alleviate the data scarcity
limitation, we design Sequence Decomposition Adaptation Network (SeDAN) which
is a novel transfer architecture to improve forecasting performance on the
target domain by aligning transferable knowledge from cross-domain datasets.
Rethinking the transferability of features in time series data, we propose
Implicit Contrastive Decomposition to decompose the original features into
components including seasonal and trend features, which are easier to transfer.
Then we design the corresponding adaptation methods for decomposed features in
different domains. Specifically, for seasonal features, we perform joint
distribution adaptation and for trend features, we design an Optimal Local
Adaptation. We conduct extensive experiments on five benchmark datasets for
multivariate time series forecasting. The results demonstrate the effectiveness
of our SeDAN. It can provide more efficient and stable knowledge transfer.


  We show that detecting real roots for honestly n-variate (n+2)-nomials (with
integer exponents and coefficients) can be done in time polynomial in the
sparse encoding for any fixed n. The best previous complexity bounds were
exponential in the sparse encoding, even for n fixed. We then give a
characterization of those functions k(n) such that the complexity of detecting
real roots for n-variate (n+k(n))-nomials transitions from P to NP-hardness as
n tends to infinity. Our proofs follow in large part from a new complexity
threshold for deciding the vanishing of A-discriminants of n-variate
(n+k(n))-nomials. Diophantine approximation, through linear forms in
logarithms, also arises as a key tool.


  We investigate and clarify the role of topology and the issues surrounding
the epsilon regime for staggered quarks. We study unimproved and improved
staggered quark Dirac operators on quenched lattice QCD gluon backgrounds
generated using a Symanzik-improved gluon action. For the improved Dirac
operators we find a clear separation of the spectrum into would-be zero modes
and others. The number of would-be zero modes depends on the topological charge
as predicted by the continuum Index Theorem, and the expectation values of
their chirality are large for the most improved actions (approx 0.7). The
remaining modes have low chirality and show clear signs of clustering into
quartets that become degenerate in the continuum limit. We demonstrate that the
lattice spacing and volume dependence of the eigenvalues follow expectations.
Furthermore, the non-zero modes follow the random matrix theory predictions for
all topological charge sectors. The values of the chiral condensate extracted
from fits to the theoretical distributions are consistent with each other, and
with the results obtained from the total density of eigenvalues using the
Banks-Casher relation. We conclude that staggered quarks respond correctly to
QCD topology when both fermion and gauge actions are improved.


  Suppose that $G$ is the group of $F$-points of a connected reductive group
over $F$, where $F/\mathbb{Q}_p$ is a finite extension. We study the
(topological) irreducibility of principal series of $G$ on $p$-adic Banach
spaces. For unitary inducing representations we obtain an optimal
irreducibility criterion, and for $G = \mathrm{GL}_n(F)$ (as well as for
arbitrary split groups under slightly stronger conditions) we obtain a variant
of Schneider's conjecture [Sch06, Conjecture 2.5]. In general we reduce the
irreducibility problem to smooth inducing representations and almost simple
simply-connected $G$. Our methods include locally analytic representation
theory, the bifunctor of Orlik--Strauch, translation functors, as well as new
results on reducibility points of smooth parabolic inductions.


  We define a translation-invariant measure and integral on $GL_2$ over a
two-dimensional local field $F$ by combining elements of the classical $GL_2$
theory and the theory developed by Fesenko for the field $F$ itself. We give
several alternate expressions for the integral, including one which agrees with
the integral defined previously by Morrow.


  Refractive optics in astronomical instruments are potentially sensitive to
temperature gradients and temperature transients. This sensitivity arises from
thermally dependent refractive indices, lens spacings, and lens dimensions. We
have therefore undertaken a detailed thermal analysis of Binospec, a wide-field
optical spectrograph under development for the converted MMT. Our goals are to
predict the temperature gradients that will be present in the Binospec optics
and structure under realistic operating conditions and to determine how design
choices affect these gradients. We begin our analysis by deriving thermal time
constants for instrument subassemblies. We then generate a low-resolution
finite difference model of the entire instrument and high-resolution models of
sensitive subassemblies. This approach to thermal analysis is applicable to a
variety of other instruments. We use measurements of the ambient temperature in
the converted MMT's dome to model Binospec's thermal environment. During
moderate conditions we find that the Binospec lens groups develop 0.14 C axial
and radial temperature gradients and that lens groups of different mass develop
0.5 C temperature differences; these numbers are doubled for the extreme
conditions. Internal heat sources do not significantly affect these results;
heat flow from the environment dominates. The instrument must be periodically
opened to insert new aperture masks, but we find that the resulting temperature
gradients and thermal stresses in the optics are small. Image shifts at the
detector caused by thermal deflections of the Binospec optical bench structure
are approx 0.1 pixel/hr. We conclude that the proposed Binospec design has
acceptable thermal properties, and briefly discuss design changes to further
reduce temperature gradients.


  We propose a new macroscopic model derived from the classical nonlinear
Boltzmann equation. A set of partial differential equations is obtained easily.
The unknowns depend on the time and space coordinates, and are related to the
distribution function, which is the unknown of the Boltzmann equation. This new
model guarantees the conservation of the mass, momentum and energy. We prove
that the set of equations coincides with the system obtained applying the
discontinuous Galerkin method to the Boltzmann equation (see A. Majorana,
Kinetic and Related models, 4 (2011), 139--151).


  Spiking neural networks (SNNs) have attracted extensive attentions in
Brain-inspired Artificial Intelligence and computational neuroscience. They can
be used to simulate biological information processing in the brain at multiple
scales. More importantly, SNNs serve as an appropriate level of abstraction to
bring inspirations from brain and cognition to Artificial Intelligence. In this
paper, we present the Brain-inspired Cognitive Intelligence Engine (BrainCog)
for creating brain-inspired AI and brain simulation models. BrainCog
incorporates different types of spiking neuron models, learning rules, brain
areas, etc., as essential modules provided by the platform. Based on these
easy-to-use modules, BrainCog supports various brain-inspired cognitive
functions, including Perception and Learning, Decision Making, Knowledge
Representation and Reasoning, Motor Control, and Social Cognition. These
brain-inspired AI models have been effectively validated on various supervised,
unsupervised, and reinforcement learning tasks, and they can be used to enable
AI models to be with multiple brain-inspired cognitive functions. For brain
simulation, BrainCog realizes the function simulation of decision-making,
working memory, the structure simulation of the Neural Circuit, and whole brain
structure simulation of Mouse brain, Macaque brain, and Human brain. An AI
engine named BORN is developed based on BrainCog, and it demonstrates how the
components of BrainCog can be integrated and used to build AI models and
applications. To enable the scientific quest to decode the nature of biological
intelligence and create AI, BrainCog aims to provide essential and easy-to-use
building blocks, and infrastructural support to develop brain-inspired spiking
neural network based AI, and to simulate the cognitive brains at multiple
scales. The online repository of BrainCog can be found at
https://github.com/braincog-x.


  we study the exponential map for A_n = R^2^n, the Cayley_Dickson algebras for
n bigher than 1,wich generalize the Complex exponential map to
Quaternions,Octonions and so forth. As application,we show that the self-map of
the unit sphere in A_n, S^(2^n)-1,given by taking k-powers has topological
degree k for k an integer number,from this we derive a suitable "Fundamental
theorem of algebra" for A_n for n bigher than 1.


  We study $J/\Psi$ production at photon-photon colliders, which can be
realised with Compton scaterring of laser photons at $e^+e^-$ colliders. We
find that the production rate through the color-octet channel is comparable to
that through the color-singlet channel. Experimentally the two mechanisms can
be studied separately because the processes have different signals.


  In this paper, we automatically create sentiment dictionaries for predicting
financial outcomes. We compare three approaches: (I) manual adaptation of the
domain-general dictionary H4N, (ii) automatic adaptation of H4N and (iii) a
combination consisting of first manual, then automatic adaptation. In our
experiments, we demonstrate that the automatically adapted sentiment dictionary
outperforms the previous state of the art in predicting the financial outcomes
excess return and volatility. In particular, automatic adaptation performs
better than manual adaptation. In our analysis, we find that annotation based
on an expert's a priori belief about a word's meaning can be incorrect --
annotation should be performed based on the word's contexts in the target
domain instead.


  A decentralized swarm approach for the fast cooperative flight of Unmanned
Aerial Vehicles (UAVs) in feature-poor environments without any external
localization and communication is introduced in this paper.
  A novel model of a UAV neighborhood is proposed to achieve robust onboard
mutual perception and flocking state feedback control, which is designed to
decrease the inter-agent oscillations common in standard reactive swarm models
employed in fast collective motion.
  The novel swarming methodology is supplemented with an enhanced Multi-Robot
State Estimation (MRSE) strategy to increase the reliability of the purely
onboard localization, which may be unreliable in real environments.
  Although MRSE and the neighborhood model may rely on information exchange
between agents, we introduce a communication-less version of the swarming
framework based on estimating communicated states to decrease dependence on the
often unreliable communication networks of large swarms.
  The proposed solution has been verified by a set of complex real-world
experiments to demonstrate its overall capability in different conditions,
including a UAV interception-motivated task with a group velocity reaching the
physical limits of the individual hardware platforms.


  In recent years, individual-based/agent-based modeling has been applied to
study a wide range of applications, ranging from engineering problems to
phenomena in sociology, economics and biology. Simulating such agent-based
models over extended spatiotemporal domains can be prohibitively expensive due
to stochasticity and the presence of multiple scales. Nevertheless, many
agent-based problems exhibit smooth behavior in space and time on a macroscopic
scale, suggesting that a useful coarse-grained continuum model could be
obtained. For such problems, the equation-free framework [16-18] can
significantly reduce the computational cost. Patch dynamics is an essential
component of this framework. This scheme is designed to perform numerical
simulations of an unavailable macroscopic equation on macroscopic time and
length scales; it uses appropriately initialized simulations of the fine-scale
agent-based model in a number of small "patches", which cover only a fraction
of the spatiotemporal domain. In this work, we construct a
finite-volume-inspired conservative patch dynamics scheme and apply it to a
financial market agent-based model based on the work of Omurtag and Sirovich
[22]. We first apply our patch dynamics scheme to a continuum approximation of
the agent-based model, to study its performance and analyze its accuracy. We
then apply the scheme to the agent-based model itself. Our computational
experiments indicate that here, typically, the patch dynamics-based simulation
requires only 20% of the full agent-based simulation in space, and need occur
over only 10% of the temporal domain.


  The analysis of the damage on plasma facing materials (PFM), due to its
direct interaction with the plasma environment, is needed to build the next
generation of nuclear machines, where tungsten has been proposed as a
candidate. In this work, we perform molecular dynamics (MD) simulations using a
machine learned inter-atomic potential, based on the Gaussian Approximation
Potential framework, to model better neutron bombardment mechanisms in pristine
W lattices. The MD potential is trained to reproduce realistic short-range
dynamics, the liquid phase, and the material recrystallization, which are
important for collision cascades. The formation of point defects is quantified
and classified by a descriptor vector (DV) based method, which is independent
of the sample temperature and its constituents, requiring only modest
computational resources. The locations of vacancies are calculated by the
k-d-tree algorithm. The analysis of the damage in the W samples is compared to
results obtained by EAM Finnis-Sinclair and Tersoff-ZBL potentials, at a sample
temperature of 300 K and a primary knock-on atom (PKA) energy range of 0.5-10
keV, where a good agreement with the reported number of Frenkel pair is
observed. Our results provide information about the advantages and limits of
the machine learned MD simulations with respect to the standard ones. The
formation of dumbbell and crowdion defects as a function of PKA is discussed.


  The warped product $M_1 \times_F M_2$ of two Riemannian manifolds $(M_1,g_1)$
and $(M_2,g_2)$ is the product manifold $M_1 \times M_2$ equipped with the
warped product metric $g=g_1 + F^2 g_2$, where $F$ is a positive function on
$M_1$. The notion of warped product manifolds is one of the most fruitful
generalizations of Riemannian products. Such a notion plays very important
roles in differential geometry as well as in physics, especially in general
relativity. In this paper we study warped product manifolds endowed with a
Tripathi connection. We establish some relationships between the Tripathi
connection of the warped product $M$ to those $M_1$ and $M_2$.


  We use a convolutional restricted Boltzmann machine (CRBM) neural network to
construct a variational wave function (WF) for the Hubbard model on a square
lattice and study it using the variational Monte Carlo (VMC) method. In the
wave function, the CRBM acts as a correlation factor to a mean-field BCS state.
The number of variational parameters in the WF does not grow automatically with
the lattice size and it is computationally much more efficient compared to
other neural network based WFs. We find that in the intermediate to strong
coupling regime of the model at half-filling, the wave function outperforms
even the highly accurate long range backflow-Jastrow correlated wave function.
Using the WF, we study the ground state of the half-filled model as a function
of onsite Coulomb repulsion $U$. We consider two cases for the
next-nearest-neighbor hopping parameter, e.g., $t'=0$ as well as a frustrated
model case with $t'\neq 0$. By examining several quantities, e.g., double
occupancy, charge gap, momentum distribution, and spin-spin correlations, we
find that the weekly correlated phase in both cases is paramagnetic metallic
(PM). As $U$ is increased, the system undergoes a first-order Mott transition
to an insulating state at a critical $U_c$, the value of which depends upon
$t'$. The Mott state in both cases is spin gapped with long range
antiferromagnetic (AF) order. Remarkably, the AF order emerges spontaneously
from the wave function which does not have any explicitly broken symmetry in
it. Apart from some quantitative differences in the results for the two values
of $t'$, we find some interesting qualitative differences in the way the Mott
transition takes place in the two cases.


  A sketch-and-select Arnoldi process to generate a well-conditioned basis of a
Krylov space at low cost is proposed. At each iteration the procedure utilizes
randomized sketching to select a limited number of previously computed basis
vectors to project out of the current basis vector. The computational cost
grows linearly with the dimension of the Krylov space. The subset selection
problem for the projection step is approximately solved with a number of
heuristic algorithms and greedy methods used in statistical learning and
compressive sensing.


  we explore the consequences of assuming a simple 3-parameter form, first
without T-violation, for the neutrino mass matrix M in the basis $\nu_e,
\nu_\mu, \nu_\tau$ with a new symmetry. This matrix determines the three
neutrino masses m_1, m_2, m_3, as well as the mapping matrix U that
diagonalizes M. Since U, without T-violation, yields three measurable
parameters $s_{12}, s_{23}, s_{13}$, our form expresses six measurable
quantities in terms of three parameters, with results in agreement with the
experimental data. More precise measurements can give stringent tests of the
model as well as determining the values of its three parameters. An extension
incorporating T-violation is also discussed.


  We introduce the Attentive Unsupervised Text (W)riter (AUTR), which is a word
level generative model for natural language. It uses a recurrent neural network
with a dynamic attention and canvas memory mechanism to iteratively construct
sentences. By viewing the state of the memory at intermediate stages and where
the model is placing its attention, we gain insight into how it constructs
sentences. We demonstrate that AUTR learns a meaningful latent representation
for each sentence, and achieves competitive log-likelihood lower bounds whilst
being computationally efficient. It is effective at generating and
reconstructing sentences, as well as imputing missing words.


  We show how to systematically apply the Faddeev-Jackiw symplectic method to
General Relativity (GR) and to GR extensions. This provides a new coherent
frame for Hamiltonian analyses of gravitational theories. The emphasis is on
the classical dynamics, uncovering the constraints, the gauge transformations
and the number of degrees of freedom; but the method results are also relevant
for canonical quantization approaches. We illustrate the method with three
applications: GR and to two Brans-Dicke cases (the standard case $\omega \not=
- 3/2$ and the case with one less degree of freedom, $\omega = - 3/2$). We
clarify subtleties of the symplectic approach and comment on previous
symplectic-based Hamiltonian analyses of extended theories of gravity, pointing
out that the present approach is systematic, complete and robust.


  We study the first Dirichlet eigenfunction of a class of Schr\"odinger
operators with a convex potential V on a domain $\Omega$. We find two length
scales $L_1$ and $L_2$, and an orientation of the domain $\Omega$, which
determine the shape of the level sets of the eigenfunction. As an intermediate
step, we also establish bounds on the first eigenvalue in terms of the first
eigenvalue of an associated ordinary differential operator.


  We study cosmological vector and tensor perturbations in Horava-Lifshitz
gravity, adopting the most general Sotiriou-Visser-Weinfurtner generalization
without the detailed balance but with projectability condition. After deriving
the general formulas in a flat FRW background, we find that the vector
perturbations are identical to those given in general relativity. This is true
also in the non-flat cases. For the tensor perturbations, high order
derivatives of the curvatures produce effectively an anisotropic stress, which
could have significant efforts on the high-frequency modes of gravitational
waves, while for the low-frenquency modes, the efforts are negligible. The
power spectrum is scale-invariant in the UV regime, because of the particular
dispersion relations. But, due to lower-order corrections, it will eventually
reduce to that given in GR in the IR limit. Applying the general formulas to
the de Sitter and power-law backgrounds, we calculate the power spectrum and
index, using the uniform approximations, and obtain their analytical
expressions in both cases.


  We present results on the static three- and four-quark potentials in SU(3)
and SU(4) respectively within quenched lattice QCD. We use an analytic
multi-hit procedure for the time links and a variational approach to determine
the ground state. The three- and four-quark potentials extracted are consistent
with a sum of two-body potentials, possibly with a weak many-body component.
The results give support to the $\Delta$ ansatz for the baryonic area law.


  We consider the $D4-D8-\bar{D8}$ brane system which serves as ultraviolet
completion of the Nambu-Jona-Lasinio model, where the only degrees of freedom
carrying baryon charge are fermions. By turning on chemical potential for this
charge one may expect the formation of the Fermi liquid ground state. At strong
coupling we use the dual holographic description to investigate the responses
of the system to small perturbations. In the chirally symmetric phase we find
that the density dependent part of the heat capacity vanishes linearly with
temperature. We also observe a zero sound excitation in the collisionless
regime, whose speed is equal to that of normal sound in the hydrodynamic
regime. Both the linear dependence of the heat capacity and the existence of
zero sound are properties of the Fermi liquid ground state. We also compute the
two-point function of the currents at vanishing frequency but do not find any
singularities at finite values of the momentum.


  Searches are being carried out at the Large Hadron Collider (LHC) for the
decay of the CP-odd scalar ($A^0$) in Two-Higgs-Doublet Models (2HDMs) with
Natural Flavour Conservation (NFC) in the channel $A^0\to h^0 Z$ (with
$m_{h^0}=125$ GeV and $Z$ on-shell). In the absence of any signal, limits on
the parameter space of $[\tan\beta, \cos(\beta-\alpha), m_{A^0}]$ in each 2HDM
are derived for $m_{A^0} > 225$ GeV. In this work we consider the scenario of
inverted hierarchy with $m_{h^0}<125$ GeV and $m_{H^0}=125$ GeV in which the
decay $A^0\to h^0 Z^{(*)}$ (i.e. including the case of an off-shell $Z$) can
have a large branching ratio in the 2HDM (Type I) for $m_{A^0}<225$ GeV. We
calculate the signal cross section $\sigma(gg\to A^0)\times {\rm BR}(A^0\to
h^0Z^{(*)})\times {\rm BR}(h^0\to b\overline b)$ in the 2HDM (Type I) with NFC
and compare its magnitude with the cross section for the case of normal
hierarchy ($m_{h^0}=125$ GeV) that is currently being searched for at the LHC.
For the experimentally unexplored region $m_{A^0} < 225$ GeV it is shown that
the above cross section for signal events in the scenario of inverted hierarchy
can be of the order of a few picobarns. Such sizeable cross sections are
several orders of magnitude larger than the cross sections for the case of
normal hierarchy, thus motivating an extension of the ongoing searches for
$A^0\to h^0 Z^{(*)}$ to probe the scenario of inverted hierarchy.


  We generalize the definition of topological entropy given by Adler, Konheim,
and McAndrew (AKM) for piecewise continuous self-maps defined on a compact
interval (pc-maps). For this notion of entropy, we prove that the properties of
the AKM-entropy in the compact-continuous setting get naturally extended,
including that it can be estimated using Bowen's formula disregarding the
metric used to compute it. Additionally, for piecewise strictly monotonic
pc-maps, we prove that the Misiurewicz-Szlenk formula based on the asymptotic
behavior of the number of monotony pieces for iterations of the map coincides
with our notion of topological entropy. In this way, we unify the notions of
entropy commonly used for piecewise continuous interval maps, proving that they
all coincide when the space is compact.


  The origin of AC resistivity in the high Tc superconductors is not addressed
adequately in literature. We found out, Josephson Junction (JJ) decoupling is
the main origin of the AC resistivity in high Tc superconductors. We have
measured the AC resistivity in the superconductors in the low frequency range
by measuring the complex AC impedance of superconducting YBa2Cu3O7 (YBCO)
polycrystalline samples. Our data shows that under certain conditions when the
number density of Josephson Junctions (JJ) present in the sample and the JJ
critical current crosses a threshold value, AC resistivity in the
superconducting state keeps on increasing with lowering temperature. The
underlying mechanism is an interesting interplay of JJ coupling energy,
amplitude of the supply AC voltage and the current applied to the
superconducting sample. The effect of the applied AC current of different
frequencies and the variation of temperature were studied in detail. To find
out the exact relation between the JJ coupling energy, JJ number density,
applied AC frequency, the amplitude of AC current and the AC resistivity in the
superconductors, we have studied samples of different grain sizes, pressurized
with different pressure and sintered at different physical situations. These
results have important implications for the understanding of the origin of AC
resistivity and characterization of superconducting samples. In this paper we
also extend the capability of the AC impedance studies in superconductors for
the characterization of materials for device applications.


  We propose a new variational family for Bayesian neural networks. We
decompose the variational posterior into two components, where the radial
component captures the strength of each neuron in terms of its magnitude; while
the directional component captures the statistical dependencies among the
weight parameters. The dependencies learned via the directional density provide
better modeling performance compared to the widely-used Gaussian
mean-field-type variational family. In addition, the strength of input and
output neurons learned via the radial density provides a structured way to
compress neural networks. Indeed, experiments show that our variational family
improves predictive performance and yields compressed networks simultaneously.


  Dense crowd counting is a challenging task that demands millions of head
annotations for training models. Though existing self-supervised approaches
could learn good representations, they require some labeled data to map these
features to the end task of density estimation. We mitigate this issue with the
proposed paradigm of complete self-supervision, which does not need even a
single labeled image. The only input required to train, apart from a large set
of unlabeled crowd images, is the approximate upper limit of the crowd count
for the given dataset. Our method dwells on the idea that natural crowds follow
a power law distribution, which could be leveraged to yield error signals for
backpropagation. A density regressor is first pretrained with self-supervision
and then the distribution of predictions is matched to the prior by optimizing
Sinkhorn distance between the two. Experiments show that this results in
effective learning of crowd features and delivers significant counting
performance. Furthermore, we establish the superiority of our method in less
data setting as well. The code and models for our approach is available at
https://github.com/val-iisc/css-ccnn.


  We develop a theory of probabilistic coherence spaces equipped with an
additional extensional structure and apply it to approximating probability of
convergence of ground type programs of probabilistic PCF whose free variables
are of ground types. To this end we define an adapted version of Krivine
Machine which computes polynomial approximations of the semantics of these
programs in the model. These polynomials provide approximations from below and
from above of probabilities of convergence; this is made possible by extending
the language with an error symbol which is extensionally maximal in the model.


  We use gauge/gravity duality to study the thermodynamics of a generic almost
conformal theory, specified by its beta function. Three different phases are
identified, a high temperature phase of massless partons, an intermediate
quasi-conformal phase and a low temperature confining phase. The limit of a
theory with infrared fixed point, in which the coupling does not run to
infinity, is also studied. The transitions between the phases are of first
order or continuous, depending on the parameters of the beta function. The
results presented follow from gauge/gravity duality; no specific boundary
theory is assumed, only its beta function.


  For linear differential equations of the form $u'(t)=[A + B(t)] u(t)$,
$t\geq0$, with a possibly unbounded operator $A$, we construct and deduce error
bounds for two families of second-order exponential splittings. The role of
quadratures when integrating the twice-iterated Duhamel's formula is
reformulated: we show that their choice defines the structure of the splitting.
Furthermore, the reformulation allows us to consider quadratures based on the
Birkhoff interpolation to obtain not only pure-stages splittings but also those
containing derivatives of $B(t)$ and commutators of $A$ and $B(t)$. In this
approach, the construction and error analysis of the splittings are carried out
simultaneously. We discuss the accuracy of the members of the families.
Numerical experiments are presented to complement the theoretical
consideration.


  Altermagnets are a new class of magnetic materials, which exhibit large spin
splitting, but due to the combined spin and real space group symmetry
protection maintain zero net macroscopic magnetization. Such a characteristic
may prove them to be superior in applications in superconducting
heterostructures and thus here we investigate the Andreev reflection at the
altermagnet/superconductor interface. We compare and contrast altermagnets to
other magnetic materials, revealing qualitative differences in the behavior of
altermagnetic junction depending on the Fermi surface orientation. We study the
resonant states arising in setups with strong tunneling barriers and show that
sensitivity to non-magnetic disorder is also dependent on the orientation. Our
results provide a building block for altermagnetic superconducting
heterostructures such as Josephson $\pi$ junctions with superior properties.


  Cosmological particle production by a time-dependent scalar field is common
in cosmology. We focus on the mechanism of asymmetry production when
interaction explicitly violates symmetry and its motion is rapid enough to
create particles by itself. Combining the exact WKB analysis and the
Landau-Zener transition, we point out that perturbation before the
non-perturbative analysis may drastically change the structure of the Stokes
lines of the theory. The Exact WKB can play an important role in avoiding such
discrepancies.


  Let $A$ be an $(m \times n)$ integral matrix, and let $P=\{ x : A x \leq b\}$
be an $n$-dimensional polytope. The width of $P$ is defined as $ w(P)=min\{
x\in \mathbb{Z}^n\setminus\{0\} :\: max_{x \in P} x^\top u - min_{x \in P}
x^\top v \}$. Let $\Delta(A)$ and $\delta(A)$ denote the greatest and the
smallest absolute values of a determinant among all $r(A) \times r(A)$
sub-matrices of $A$, where $r(A)$ is the rank of a matrix $A$. We prove that if
every $r(A) \times r(A)$ sub-matrix of $A$ has a determinant equal to $\pm
\Delta(A)$ or $0$ and $w(P)\ge (\Delta(A)-1)(n+1)$, then $P$ contains $n$
affine independent integer points. Also we have similar results for the case of
\emph{$k$-modular} matrices. The matrix $A$ is called \emph{totally
$k$-modular} if every square sub-matrix of $A$ has a determinant in the set
$\{0,\, \pm k^r :\: r \in \mathbb{N} \}$. When $P$ is a simplex and $w(P)\ge
\delta(A)-1$, we describe a polynomial time algorithm for finding an integer
point in $P$. Finally we show that if $A$ is \emph{almost unimodular}, then
integer program $\max \{c^\top x :\: x \in P \cap \mathbb{Z}^n \}$ can be
solved in polynomial time. The matrix $A$ is called \emph{almost unimodular} if
$\Delta(A) \leq 2$ and any $(r(A)-1)\times(r(A)-1)$ sub-matrix has a
determinant from the set $\{0,\pm 1\}$.


  Scaling properties of nuclei- and photon-initiated air showers are examined
in wide primary energy range ($10^{14}\div 10^{22}$~eV) taking into account
Landau-Pomeranchuk-Migdal and geomagnetic field effects. It is shown that the
invariance in lateral distribution and also the universal dependence between
shower age and root mean square radius of electron component exist up to the
highest energies. The implications of shower universality for reanalysis,
cross-checking and validation of the results of different experiments together
with decreasing of the influence of hadronic model uncertainties are discussed
in detail.


  Social participatory sensing is a newly proposed paradigm that tries to
address the limitations of participatory sensing by leveraging online social
networks as an infrastructure. A critical issue in the success of this paradigm
is to assure the trustworthiness of contributions provided by participants. In
this paper, we propose an application-agnostic reputation framework for social
participatory sensing systems. Our framework considers both the quality of
contribution and the trustworthiness level of participant within the social
network. These two aspects are then combined via a fuzzy inference system to
arrive at a final trust rating for a contribution. A reputation score is also
calculated for each participant as a resultant of the trust ratings assigned to
him. We adopt the utilization of PageRank algorithm as the building block for
our reputation module. Extensive simulations demonstrate the efficacy of our
framework in achieving high overall trust and assigning accurate reputation
scores.


  In recent years, a large number of XAI (eXplainable Artificial Intelligence)
solutions have been proposed to explain existing ML (Machine Learning) models
or to create interpretable ML models. Evaluation measures have recently been
proposed and it is now possible to compare these XAI solutions. However,
selecting the most relevant XAI solution among all this diversity is still a
tedious task, especially when meeting specific needs and constraints. In this
paper, we propose AutoXAI, a framework that recommends the best XAI solution
and its hyperparameters according to specific XAI evaluation metrics while
considering the user's context (dataset, ML model, XAI needs and constraints).
It adapts approaches from context-aware recommender systems and strategies of
optimization and evaluation from AutoML (Automated Machine Learning). We apply
AutoXAI to two use cases, and show that it recommends XAI solutions adapted to
the user's needs with the best hyperparameters matching the user's constraints.


  The T-reversal-violating optical gyrotropy is discussed. A new optical
phenomenon of photon polarization plane rotation in a medium exposed to an
electric field caused by the Stark mixing of atom (molecule) levels and T-odd
phenomenon of photon polarization plane rotation appearing at photon passing
through non-center-symmetrical diffraction grating are considered. The
experimental methods to observe these phenomena are proposed.


  We compute the algebra of differential invariants of unparametrized curves in
the homogeneous G(2) flag varieties, namely in G(2)/P. This gives a solution to
the equivalence problem for such curves. We consider the cases of integral and
generic curves and relate the equivalence problems for all three choices of the
parabolic subgroup P.


  We calculate the baryon asymmetry generated at the electroweak phase
transition in the minimal supersymmetric standard model, treating the particles
in a WKB approximation in the bubble wall background. A set of diffusion
equations for the particle species relevant to baryon generation, including
source terms arising from the CP violation associated with the complex phase
$\delta$ of the $\mu$ parameter, are derived from Boltzmann equations, and
solved. The conclusion is that $\delta$ must be $\gsim 0.1$ to generate a
baryon asymmetry consistent with nucleosynthesis. We compare our results to
several other recent computations of the effect, arguing that some are
overestimates.


  In this paper we discuss the optimal control of a quasilinear parabolic state
equation. Its form is leaned on the kind of problems arising for example when
controlling the anisotropic Allen-Cahn equation as a model for crystal growth.
Motivated by this application we consider the state equation as a result of a
gradient flow of an energy functional. The quasilinear term is strongly
monotone and obeys a certain growth condition and the lower order term is
non-monotone. The state equation is discretized implicitly in time with
piecewise constant functions. The existence of the control-to-state operator
and its Lipschitz-continuity is shown for the time discretized as well as for
the time continuous problem. Latter is based on the convergence proof of the
discretized solutions. Finally we present for both the existence of global
minimizers. Also convergence of a subsequence of time discrete optimal controls
to a global minimizer of the time continuous problem can be shown. Our results
hold in arbitrary space dimensions.


  In the high-dimensional regression model a response variable is linearly
related to $p$ covariates, but the sample size $n$ is smaller than $p$. We
assume that only a small subset of covariates is `active' (i.e., the
corresponding coefficients are non-zero), and consider the model-selection
problem of identifying the active covariates. A popular approach is to estimate
the regression coefficients through the Lasso ($\ell_1$-regularized least
squares). This is known to correctly identify the active set only if the
irrelevant covariates are roughly orthogonal to the relevant ones, as
quantified through the so called `irrepresentability' condition. In this paper
we study the `Gauss-Lasso' selector, a simple two-stage method that first
solves the Lasso, and then performs ordinary least squares restricted to the
Lasso active set. We formulate `generalized irrepresentability condition'
(GIC), an assumption that is substantially weaker than irrepresentability. We
prove that, under GIC, the Gauss-Lasso correctly recovers the active set.


  For a general class of unitary quantum maps, whose underlying classical phase
space is divided into several invariant domains of positive measure, we
establish analogues of Weyl's law for the distribution of eigenphases. If the
map has one ergodic component, and is periodic on the remaining domains, we
prove the Schnirelman-Zelditch-Colin de Verdiere Theorem on the
equidistribution of eigenfunctions with respect to the ergodic component of the
classical map (quantum ergodicity). We apply our main theorems to quantised
linked twist maps on the torus. In the Appendix, S. Zelditch connects these
studies to some earlier results on `pimpled spheres' in the setting of
Riemannian manifolds. The common feature is a divided phase space with a
periodic component.


  Data-driven control that circumvents the process of system identification by
providing optimal control inputs directly from system data has attracted
renewed attention in recent years. In this paper, we focus on understanding the
effects of the regularization on the data-enabled predictive control (DeePC)
algorithm. We provide theoretical motivation and interpretation for including a
quadratic regularization term. Our analysis shows that the quadratic
regularization term leads to robust and optimal solutions with regards to
disturbances affecting the data. Moreover, when the input/output constraints
are inactive, the quadratic regularization leads to a closed-form solution of
the DeePC algorithm and thus enables fast calculations. On this basis, we
propose a framework for data-driven synchronization and power regulations of
power converters, which is tested by high-fidelity simulations and experiments.


  Measurements are presented of the single-diffractive dijet cross section and
the diffractive cross section as a function of the proton fractional momentum
loss $\xi$ and the four-momentum transfer squared $t$. Both processes pp$\to$pX
and pp$\to$Xp, ie with the proton scattering to either side of the interaction
point, are measured, where X includes at least two jets; the results of the two
processes are averaged. The analyses are based on data collected simultaneously
with the CMS and TOTEM detectors at the LHC in proton-proton collisions at
$\sqrt{s}$= 8 TeV during a dedicated run with $\beta^{\ast} =$ 90 m at low
instantaneous luminosity and correspond to an integrated luminosity of 37.5
nb$^{-1}$. The single-diffractive dijet cross section
$\sigma^\mathrm{pX}_{\mathrm{jj}}$, in the kinematic region $\xi \lt$ 0.1, 0.03
$\lt |$t$| \lt 1$ GeV$^2$, with at least two jets with transverse momentum
$p_\mathrm{T} >$ 40 GeV, and pseudorapidity $|\eta| \lt$ 4.4, is 21.7$\pm$0.9
(stat)$^{+3.0}_{-3.3}$ (syst) $\pm$ 0.9 (lum) nb. The ratio of the
single-diffractive to inclusive dijet yields, normalised per unit of $\xi$, is
presented as a function of $x$, the longitudinal momentum fraction of the
proton carried by the struck parton. The ratio in the kinematic region defined
above, for $x$ values in the range $-$2.9 $\leq \log_{10} x \leq$ $-$1.6, is $R
= (\sigma^\mathrm{pX}_{\mathrm{jj}}/\Delta\xi)/\sigma_{\mathrm{jj}} =$
0.025$\pm$0.001 (stat) $\pm$ 0.003 (syst), where
$\sigma^\mathrm{pX}_{\mathrm{jj}}$ and $\sigma_{\mathrm{jj}}$ are the
single-diffractive and inclusive dijet cross sections, respectively. The
results are compared with predictions from models of diffractive and
nondiffractive interactions. Monte Carlo predictions based on the HERA
diffractive parton distribution functions agree well with the data when
corrected for the effect of soft rescattering between the spectator partons.


  This paper investigates the optimal choices of financial derivatives to
complete a financial market in the framework of stochastic volatility (SV)
models. We introduce an efficient and accurate simulation-based method,
applicable to generalized diffusion models, to approximate the optimal
derivatives-based portfolio strategy. We build upon the double optimization
approach (i.e. expected utility maximization and risk exposure minimization)
proposed in Escobar-Anel et al. (2022); demonstrating that strangle options are
the best choices for market completion within equity options. Furthermore, we
explore the benefit of using volatility index derivatives and conclude that
they could be more convenient substitutes when only long-term maturity equity
options are available.


  We analyze the scalar potentials of maximal gauged three-dimensional
supergravities which reveal a surprisingly rich structure. In contrast to
maximal supergravities in dimensions D>3, all these theories admit a maximally
supersymmetric (N=16) ground state with negative cosmological constant
Lambda<0, except for the gauge group SO(4,4)^2, for which Lambda=0. We compute
the mass spectra of bosonic and fermionic fluctuations around these vacua and
identify the unitary irreducible representations of the relevant background
(super)isometry groups to which they belong. In addition, we find several
stationary points which are not maximally supersymmetric, and determine their
complete mass spectra as well. In particular, we show that there are analogs of
all stationary points found in higher dimensions, among them de Sitter vacua in
the theories with noncompact gauge groups SO(5,3)^2 and SO(4,4)^2, as well as
anti-de Sitter vacua in the compact gauged theory preserving 1/4 and 1/8 of the
supersymmetries. All the dS vacua have tachyonic instabilities, whereas there
do exist non-supersymmetric AdS vacua which are stable, again in contrast to
the D>3 theories.


  Nonequilibrium Green's functions represent a promising tool for describing
central nuclear reactions. Even at the single-particle level, though, the
Green's functions contain more information that computers may handle in the
foreseeable future. In this study, we explore slab collisions in one dimension,
first in the mean field approximation and demonstrate that only function
elements close to the diagonal in arguments are relevant, in practice, for the
reaction calculations. This bodes well for the application of the Green's
functions to the reactions. Moreover we demonstrate that an initial state for a
reaction calculation may be generated through adiabatic transformation of
interactions. Finally, we report on our progress in incorporating correlations
into the dynamic calculations.


  The problem of completeness of the forward rate based bond market model
driven by a L\'evy process under the physical measure is examined. The
incompleteness of market in the case when the L\'evy measure has a density
function is shown. The required elements of the theory of stochastic
integration over the compensated jump measure under a martingale measure is
presented and the corresponding integral representation of local martingales is
proven.


  The surface molecular doping of organic semiconductors can play an important
role in the development of organic electronic or optoelectronic devices.
Single-crystal rubrene remains a leading molecular candidate for applications
in electronics due to its high hole mobility. In parallel, intensive research
into the fabrication of flexible organic electronics requires the careful
design of functional interfaces to enable optimal device characteristics. To
this end, the present work seeks to understand the effect of surface molecular
doping on the electronic band structure of rubrene single crystals. Our
angle-resolved photoemission measurements reveal that the Fermi level moves in
the band gap of rubrene depending on the direction of surface electron-transfer
reactions with the molecular dopants, yet the valence band dispersion remains
essentially unperturbed. This indicates that surface electron-transfer doping
of a molecular single crystal can effectively modify the near-surface charge
density, while retaining good charge-carrier mobility.


  We introduce a new approach to universal quantum knot invariants that
emphasizes generating functions instead of generators and relations. All the
relevant generating functions are shown to be perturbed Gaussians of the form
$Pe^G$, where $G$ is quadratic and $P$ is a suitably restricted "perturbation".
After developing a calculus for such Gaussians in general we focus on the rank
one invariant $\mathbf{Z}_\mathbb{D}$ in detail. We discuss how it dominates
the $\mathfrak{sl}_2$-colored Jones polynomials and relates to knot genus and
Whitehead doubling. In addition to being a strong knot invariant that behaves
well under natural operations on tangles $\mathbf{Z}_\mathbb{D}$ is also
computable in polynomial time in the crossing number of the knot. We provide a
full implementation of the invariant and provide a table in an appendix.


  The governing equation is $u_t = (a(x)u_x)_x$, $0\le x\le 1$, $t>0$,
$u(x,0)=0$, $u(0,t)=0$, $a(1)u'(1,t)=f(t)$. The extra data are $u(1,t)=g(t)$.
It is assumed that $a(x)$ is a piecewise-constant function, and $f\not\equiv
0$. It is proved that the function $a(x)$ is uniquely defined by the above
data. No restrictions on the number of discontinuity points of $a(x)$ and on
their locations are made. The number of discontinuity points is finite, but
this number can be arbitrarily large.
  If $a(x)\in C^2[0,1]$, then a uniqueness theorem has been established earlier
for multidimensional problem, $x\in \mathbb{R}^n, n>1$ (see MR1211417
(94e:35004)) for the stationary problem with infinitely many boundary data. The
novel point in this work is the treatment of the discontinuous
piecewise-constant function $a(x)$ and the proof of Property C for a pair of
the operators $\{\ell_1, \ell_2 \}$, where $\ell_j:= -\frac{d^2}{dx^2} + k^2
q_j^2(x)$, $j=1,2$, and $q_j^2(x)>0$ are piecewise-constant functions, and for
the pair $\{L_1, L_2 \}$, where $L_ju:=-[a_j(x)u'(x)]'+\lambda u$, $j=1,2$, and
$a_j(x)>0$ are piecewise-constant functions. Property C stands for completeness
of the set of products of solutions of homogeneous differential equations (see
MR1759536 (2001f:34048))


  Artificial Intelligence (AI) or Machine Learning (ML) systems have been
widely adopted as value propositions by companies in all industries in order to
create or extend the services and products they offer. However, developing
AI/ML systems has presented several engineering problems that are different
from those that arise in, non-AI/ML software development. This study aims to
investigate how software engineering (SE) has been applied in the development
of AI/ML systems and identify challenges and practices that are applicable and
determine whether they meet the needs of professionals. Also, we assessed
whether these SE practices apply to different contexts, and in which areas they
may be applicable. We conducted a systematic review of literature from 1990 to
2019 to (i) understand and summarize the current state of the art in this field
and (ii) analyze its limitations and open challenges that will drive future
research. Our results show these systems are developed on a lab context or a
large company and followed a research-driven development process. The main
challenges faced by professionals are in areas of testing, AI software quality,
and data management. The contribution types of most of the proposed SE
practices are guidelines, lessons learned, and tools.


  Greenhouse gas, produced from various industries such as Power,
Manufacturing, Transport, Chemical, or Agriculture, is the major source of
global warming. While the transport industry is among the top three major
contributors, accounting for 16.2% of global emissions. To counter this, many
countries are responding actively to achieve net or absolute zero-emission
goals by replacing fossil fuel with renewable energy sources. In response to
this initiative, this chapter provides a systematic review of the use of
zero-emission vehicles for a specific use case of package delivery. It first
compares different green delivery systems that use unmanned aerial vehicles,
electric vehicles, and fuel-cell trucks for certain weight categories.
Specifically, a coordination of unmanned aerial vehicle and ground-based
electric truck envisions a new paradigm of ground-based zero-emission vehicles
where unmanned aerial vehicles can fly in the air beyond the visual line of
sight empowered by future-generation wireless technologies. The integration of
zero-emission vehicles for package delivery will encounter many challenges in
analyzing, modelling, planning, and designing a green logistics system. This
chapter investigates these challenges in the adoption of zero-emission vehicles
with the existing research issues from a technical, environmental, economic,
and political point of view. In addition, this study also sheds a new research
perspective on artificial intelligence and integrated solutions for
zero-emission deliveries.


  Resistance fluctuation phenomenon in antidot lattices in the quantum Hall
regime are studied. Magnetoresistance of finite antidot array systems in the
quantum Hall plateau transition regime exhibits two types of oscillatory
effect. One is the aperiodic resistance fluctuations (RFs) and the other is the
Aharonov-Bohm (AB)-type oscillations. Their dependences on the magnetic field
and the gate voltage are quite distinct. While the aperiodic RFs are attributed
to the complex evolution of the conducting network of compressible channels,
the AB-type oscillations are interpreted in terms of edge states formed around
individual antidots. The self-consistent screening effect is important for the
both phenomenon, whereas, the single electron charging effect plays a minor
role in the present case.


  We introduce and characterise the performance of the Markov chain Monte Carlo
(MCMC) inference method Prune Sampling for discrete and deterministic Bayesian
networks (BNs). We developed a procedure to obtain the performance of a MCMC
sampling method in the limit of infinite simulation time, extrapolated from
relatively short simulations. This approach was used to conduct a study to
compare the accuracy, rate of convergence and the time consumption of Prune
Sampling with two conventional MCMC sampling methods: Gibbs- and Metropolis
sampling. We show that Markov chains created by Prune Sampling always converge
to the desired posterior distribution, also for networks where conventional
Gibbs sampling fails. Beside this, we demonstrate that pruning outperforms
Gibbs sampling, at least for a certain class of BNs. Though, this tempting
feature comes at a price. In the first version of Prune Sampling, for large BNs
the procedure to choose the next iteration step uniformly is rather time
intensive. Our conclusion is that Prune Sampling is a competitive method for
all types of small and medium sized BNs, but (for now) standard methods still
perform better for all types of large BNs.


  The free energy cost of confining a star polymer where $f$ flexible polymer
chains containing $N$ monomeric units are tethered to a central unit in a slit
with two parallel repulsive walls a distance $D$ apart is considered, for good
solvent conditions. Also the parallel and perpendicular components of the
gyration radius of the star polymer, and the monomer density profile across the
slit are obtained. Theoretical descriptions via Flory theory and scaling
treatments are outlined, and compared to numerical self-consistent field
calculations (applying the Scheutjens-Fleer lattice theory) and to Molecular
Dynamics results for a bead-spring model. It is shown that Flory theory and
self-consistent field (SCF) theory yield the correct scaling of the parallel
linear dimension of the star with $N$, $f$ and $D$, but cannot be used for
estimating the free energy cost reliably. We demonstrate that the same problem
occurs already for the confinement of chains in cylindrical tubes. We also
briefly discuss the problem of a free or grafted star polymer interacting with
a single wall, and show that the dependence of confining force on the
functionality of the star is different for a star confined in a nanoslit and a
star interacting with a single wall, which is due to the absence of a symmetry
plane in the latter case.


  In this short note we point out that not every star product is induced by a
Drinfel'd twist by showing that not every Poisson structure is induced by a
classical $r$-matrix. Examples include the higher genus symplectic Pretzel
surfaces and the symplectic sphere $\mathbb{S}^2$.


  The increase of computational resources has recently allowed high resolution,
three dimensional calculations of planets embedded in gaseous protoplanetary
disks. They provide estimates of the planet migration timescale that can be
compared to analytical predictions. While these predictions can result in
extremely short migration timescales for cores of a few Earth masses, recent
numerical calculations have given an unexpected outcome: the torque acting on
planets with masses between 5 M_Earth and 20 M_Earth is considerably smaller
than the analytic, linear estimate. These findings motivated the present work,
which investigates existence and origin of this discrepancy or ``offset'', as
we shall call it, by means of two and three dimensional numerical calculations.
We show that the offset is indeed physical and arises from the coorbital
corotation torque, since (i) it scales with the disk vortensity gradient, (ii)
its asymptotic value depends on the disk viscosity, (iii) it is associated to
an excess of the horseshoe zone width. We show that the offset corresponds to
the onset of non-linearities of the flow around the planet, which alter the
streamline topology as the planet mass increases: at low mass the flow
non-linearities are confined to the planet's Bondi sphere whereas at larger
mass the streamlines display a classical picture reminiscent of the restricted
three body problem, with a prograde circumplanetary disk inside a ``Roche
lobe''. This behavior is of particular importance for the sub-critical solid
cores (M <~ 15 M_Earth) in thin (H/r <~0.06) protoplanetary disks. Their
migration could be significantly slowed down, or reversed, in disks with
shallow surface density profiles.


  The paper suggests a left-right mirror symmetric model to account for the
baryogenesis and asymmetric dark matter. The model can simultaneously
accommodate the standard model, neutrino physics, matter-antimatter asymmetry
and dark matter. In particular, it naturally and elegantly explains the origin
of the baryon and dark matter asymmetries, and clearly gives the close
interrelations of them. In addition, the model predicts a number of interesting
results, e.g. the cold dark matter neutrino mass is $3.1$ times the proton
mass. It is also feasible and promising to test the model in future
experiments.


  This review on dark energy is intended for a wider audience, beginners as
well as experts. It contains important notes on various aspects of dark energy
and its alternatives. The section on Newtonian cosmology followed by heuristic
arguments to capture the pressure effects allows us to discuss the basic
features of physics of cosmic acceleration without actually resorting to the
framework of general theory of relativity. The brief discussion on
observational aspects of dark energy is followed by a detailed exposition of
underlying features of scalar field dynamic relevant to cosmology. The review
includes pedagogical presentation of generic features of models of dark energy
and its possible alternatives.


  Extensive experiments in Human Movement Science suggest that solo motions are
characterized by unique features that define the individuality or motor
signature of people. While interacting with others, humans tend to
spontaneously coordinate their movement and unconsciously give rise to joint
improvisation. However, it has yet to be shed light on the relationship between
individuality and joint improvisation. By means of an ad-hoc virtual agent, in
this work we uncover the internal mechanisms of the transition from solo to
joint improvised motion in the mirror game, a simple yet effective paradigm for
studying interpersonal human coordination. According to the analysis of
experimental data, normalized segments of velocity in solo motion are regarded
as individual motor signature, and the existence of velocity segments
possessing a prescribed signature is theoretically guaranteed. In this work, we
first develop a systematic approach based on velocity segments to generate
\emph{in-silico} trajectories of a given human participant playing solo. Then
we present an online algorithm for the virtual player to produce joint
improvised motion with another agent while exhibiting some desired kinematic
characteristics, and to account for movement coordination and mutual adaptation
during joint action tasks. Finally, we demonstrate that the proposed approach
succeeds in revealing the kinematic features transition from solo to joint
improvised motions, thus revealing the existence of a tight relationship
between individuality and joint improvisation.


  We analyze a set of 89 Type Ia supernovae (SN Ia) that have both optical and
near-infrared (NIR) photometry to derive distances and construct low redshift
($z < 0.04$) Hubble diagrams. We construct mean light curve (LC) templates
using a hierarchical Bayesian model. We explore both Gaussian process (GP) and
template methods for fitting the LCs and estimating distances, while including
peculiar velocity and photometric uncertainties. For the 56 SN Ia with both
optical and NIR observations near maximum light, the GP method yields a
NIR-only Hubble-diagram with a RMS of $0.117 \pm 0.014$ mag when referenced to
the NIR maxima. For each NIR band, a comparable GP method RMS is obtained when
referencing to NIR-max or B-max. Using NIR LC templates referenced to B-max
yields a larger RMS value of $0.138 \pm 0.014$ mag. Fitting the corresponding
optical data using standard LC fitters that use LC shape and color corrections
yields larger RMS values of $0.179 \pm 0.018$ mag with SALT2 and $0.174 \pm
0.021$ mag with SNooPy. Applying our GP method to subsets of SN Ia NIR LCs at
NIR maximum light, even without corrections for LC shape, color, or host-galaxy
dust reddening, provides smaller RMS in the inferred distances, at the $\sim
2.3 - 4.1\sigma$ level, than standard optical methods that do correct for those
effects. Our ongoing RAISIN program on the Hubble Space Telescope will exploit
this promising infrared approach to limit systematic errors when measuring the
expansion history of the universe to constrain dark energy.


  We announce a detailed numerical investigation for some class of difference
schemes, which arises from Euler implicit scheme. Such schemes demonstrate
unusual behavior and leads to origin of solitons. Applications to some
nonlinear problems are discussed.


  In this paper we derive higher derivative corrections to the eleven
dimensional supergravity by applying the Noether method with respect to the N=1
local supersymmetry. An ansatz for the higher derivative effective action,
which includes quartic terms of the Riemann tensor, is parametrized by 132
parameters. Then we show that by the requirement of the local supersymmetry,
the higher derivative effective action is essentially described by two
parameters. The bosonic parts of these two superinvariants completely match
with the known results obtained by the perturbative calculations in the type
IIA superstring theory. Since the calculations are long and systematic, we
build the computer programming to check the cancellation of the variations
under the local supersymmetry. This is an extended version of our previous
paper hep-th/0508204.


  We present a piezo-driven translatable mirror with excellent pointing
stability, capable of driving at frequencies up to tens of kilohertz. Our
system uses a tripod of piezo actuators with independently controllable drive
voltages, where the ratios of the individual drive voltages are tuned to
minimize residual tilting. Attached to a standard {\varnothing}= 12.7mm mirror,
the system has a resonance-free mechanical bandwidth up to 51kHz, with
displacements up to 2{\mu}m at 8kHz. The maximum static steering error is
5.5rad per micrometer displaced and the dynamic steering error is lower than
0.6rad/{\mu}m. This simple design should be useful for a large set of optical
applications where tilt-free displacements are required, and we demonstrate its
application in an ensemble of cold atoms trapped in periodically driven optical
lattices.


  We describe spaces of essential finite height (measured) laminations in a
surface $S$ using a parameter space we call $\mathbb S$, an ordered semi-ring.
We show that for every finite height essential lamination $L$ in $S$, there is
an action of $\pi_1(S)$ on an $\mathbb S$-tree dual to the lift of $L$ to the
universal cover of $S$.