  We present the first laboratory experimental results concerning the
scattering of a low-mode internal tide by a gaussian topography. Experiments
performed at the Coriolis Platform in Grenoble used a recently-conceived
internal wave generator as a means of producing a high-quality mode-1 wave
field. The evolution of the wave field in the absence and presence of a
supercritical Gaussian was studied by performing spatiotemporal modal
decompositions of velocity field data obtained using Particle Image Velocimetry
(PIV). The results support predictions that large-amplitude supercritical
topography produces significant reflection of the internal tide and transfer of
energy from low to high modes.


  Automatic speaker verification (ASV) vendors and corpus providers would both
benefit from tools to reliably extrapolate performance metrics for large
speaker populations without collecting new speakers. We address false alarm
rate extrapolation under a worst-case model whereby an adversary identifies the
closest impostor for a given target speaker from a large population. Our models
are generative and allow sampling new speakers. The models are formulated in
the ASV detection score space to facilitate analysis of arbitrary ASV systems.


  Within a phenomenological approach the density of induced uniaxial anisotropy
in nanostructures K(r) is treated as a physical field additional to the
magnetization M(r). The equilibrium distributions of K(r) are formed under the
influence of the surfaces and internal interactions. The functions K(r) and
M(r) are calculated for a magnetic layer between two nonmagnetic spacers. It is
shown that the transition from the phase with magnetization in plane to the
perpendicular phase occurs continuously via an intermediate phase which is
inhomogeneous across the layer-thickness. The theory explains experimentally
observed thickness dependences of the effective anisotropy and gives a method
to obtain values of the characteristic parameters for the theory from
experimental data. It is shown that the separation of the induced anisotropy
into volume and surface contribution is valid only for thick films.


  As an extension of the ideas of Hanbury-Brown and Twiss, a method is proposed
to eliminate the phase noise of white chaotic light in the regime where it is
dominant, and to measure the much smaller Poisson fluctuations from which the
incoming flux can be reconstructed (via the equality between variance and
mean). The best effect is achieved when the timing resolution is finer than the
inverse bandwidth of the spectral filter. There may be applications to radio
astronomy at the phase noise dominated frequencies of $1 - 10$GHz, in terms of
potentially increasing the sensitivity of telescopes by an order of magnitude.


  We give a broad overview of the history of microwave superconductivity and
explore the technological developments that have followed from the unique
electrodynamic properties of superconductors. Their low loss properties enable
resonators with high quality factors that can nevertheless handle extremely
high current densities. This in turn enables superconducting particle
accelerators, high-performance filters and analog electronics, including
metamaterials, with extreme performance. The macroscopic quantum properties
have enabled new generations of ultra-high-speed digital computing and
extraordinarily sensitive detectors. The microscopic quantum properties have
enabled large-scale quantum computers, which at their heart are essentially
microwave-fueled quantum engines. We celebrate the rich history of microwave
superconductivity and look to the promising future of this exciting branch of
microwave technology.


  Here we show that any n-dimensional centrally symmetric convex body K has an
n-dimensional perturbation T which is convex and centrally symmetric, such that
the isotropic constant of T is universally bounded. T is close to K in the
sense that the Banach-Mazur distance between T and K is O(log n). If K has a
non-trivial type then the distance is universally bounded. In addition, if K is
quasi-convex then there exists a quasi-convex T with a universally bounded
isotropic constant and with a universally bounded distance to K.


  Most state-of-the-art methods for medical image segmentation adopt the
encoder-decoder architecture. However, this U-shaped framework still has
limitations in capturing the non-local multi-scale information with a simple
skip connection. To solve the problem, we firstly explore the potential
weakness of skip connections in U-Net on multiple segmentation tasks, and find
that i) not all skip connections are useful, each skip connection has different
contribution; ii) the optimal combinations of skip connections are different,
relying on the specific datasets. Based on our findings, we propose a new
segmentation framework, named UDTransNet, to solve three semantic gaps in
U-Net. Specifically, we propose a Dual Attention Transformer (DAT) module for
capturing the channel- and spatial-wise relationships to better fuse the
encoder features, and a Decoder-guided Recalibration Attention (DRA) module for
effectively connecting the DAT tokens and the decoder features to eliminate the
inconsistency. Hence, both modules establish a learnable connection to solve
the semantic gaps between the encoder and the decoder, which leads to a
high-performance segmentation model for medical images. Comprehensive
experimental results indicate that our UDTransNet produces higher evaluation
scores and finer segmentation results with relatively fewer parameters over the
state-of-the-art segmentation methods on different public datasets. Code:
https://github.com/McGregorWwww/UDTransNet.


  In this contribution we describe the new model of multiple partonic
interactions (MPI) that has been implemented in Herwig++. Tuning its two free
parameters is enough to find a good description of CDF underlying event data.
We show extrapolations to the LHC and compare them to results from other
models.


  Seismic fault detection holds significant geographical and practical
application value, aiding experts in subsurface structure interpretation and
resource exploration. Despite some progress made by automated methods based on
deep learning, research in the seismic domain faces significant challenges,
particularly because it is difficult to obtain high-quality, large-scale,
open-source, and diverse datasets, which hinders the development of general
foundation models. Therefore, this paper proposes Seismic Fault SAM, which, for
the first time, applies the general pre-training foundation model-Segment
Anything Model (SAM)-to seismic fault interpretation. This method aligns the
universal knowledge learned from a vast amount of images with the seismic
domain tasks through an Adapter design. Specifically, our innovative points
include designing lightweight Adapter modules, freezing most of the
pre-training weights, and only updating a small number of parameters to allow
the model to converge quickly and effectively learn fault features; combining
2.5D input strategy to capture 3D spatial patterns with 2D models; integrating
geological constraints into the model through prior-based data augmentation
techniques to enhance the model's generalization capability. Experimental
results on the largest publicly available seismic dataset, Thebe, show that our
method surpasses existing 3D models on both OIS and ODS metrics, achieving
state-of-the-art performance and providing an effective extension scheme for
other seismic domain downstream tasks that lack labeled data.


  We consider a low temperature plasma environment in the Low Earth Orbital
(LEO) region in presence of charged space debris particles. The dynamics of
(2+1) dimensional nonlinear dust ion acoustic waves with weak transverse
perturbation, generated in the system is found to be governed by a forced
Kadomtsev-Petviashvili (KP) equation, where the forcing term depends on charged
space debris function. The bending phenomena of some exact dust ion acoustic
solitary wave solutions in x-t and x-y planes are shown; that are resulted from
consideration of different types of possible localized debris functions. A
family of exact pinned accelerated solitary wave solutions has been obtained
where the velocity changes over time but the amplitude remains constant. The
shape of debris function also changes during its propagation. Also, a special
exact solitary wave solution has been derived for the dust ion acoustic wave;
that gets curved in spatial dimensions with the curvature depending upon nature
of forcing debris function. Such intricate solitary wave solutions may be
useful in modelling real experimental data.


  Kontsevich has proven that the Lie homology of the Lie algebra of symplectic
vector fields can be computed in terms of the homology of a graph complex. We
prove that the Leibniz homology of this Lie algebra can be computed in terms of
the homology of a variant of the graph complex endowed with an action of the
symmetric groups. The resulting isomorphism is shown to be a
Zinbiel-associative bialgebra isomorphism.


  Reduction in effective spacetime dimensionality can occur in field-theory
models more general than the widely studied dimensional reductions based on
technically consistent truncations. Situations where wavefunction factors
depend nontrivially on coordinates transverse to the effective lower dimension
can give rise to unusual patterns of gauge symmetry breaking. Leading-order
gauge modes can be left massless, but naturally occurring Stueckelberg modes
can couple importantly at quartic order and higher, thus generating a "covert"
pattern of gauge symmetry breaking. Such a situation is illustrated in a
five-dimensional model of scalar electrodynamics in which one spatial dimension
is taken to be an interval with Dirichlet/Robin boundary conditions on opposing
ends. This simple model illuminates a mechanism which also has been found in
gravitational braneworld scenarios.


  We consider different phase spaces for the Toda flows and the less familiar
SVD flows. For the Toda flow, we handle symmetric and non-symmetric matrices
with real simple eigenvalues, possibly with a given profile. Profiles encode,
for example, band matrices and Hessenberg matrices. For the SVD flow, we assume
simplicity of the singular values. In all cases, an open cover is constructed,
as are corresponding charts to Euclidean space. The charts linearize the flows,
converting it into a linear differential system with constant coefficients and
diagonal matrix. A variant construction transform the flows into uniform
straight line motion. Since limit points belong to the phase space, asymptotic
behavior becomes a local issue. The constructions rely only on basic facts of
linear algebra, making no use of symplectic geometry.


  Learning discriminative spatiotemporal representation is the key problem of
video understanding. Recently, Vision Transformers (ViTs) have shown their
power in learning long-term video dependency with self-attention.
Unfortunately, they exhibit limitations in tackling local video redundancy, due
to the blind global comparison among tokens. UniFormer has successfully
alleviated this issue, by unifying convolution and self-attention as a relation
aggregator in the transformer format. However, this model has to require a
tiresome and complicated image-pretraining phrase, before being finetuned on
videos. This blocks its wide usage in practice. On the contrary, open-sourced
ViTs are readily available and well-pretrained with rich image supervision.
Based on these observations, we propose a generic paradigm to build a powerful
family of video networks, by arming the pretrained ViTs with efficient
UniFormer designs. We call this family UniFormerV2, since it inherits the
concise style of the UniFormer block. But it contains brand-new local and
global relation aggregators, which allow for preferable accuracy-computation
balance by seamlessly integrating advantages from both ViTs and UniFormer.
Without any bells and whistles, our UniFormerV2 gets the state-of-the-art
recognition performance on 8 popular video benchmarks, including scene-related
Kinetics-400/600/700 and Moments in Time, temporal-related Something-Something
V1/V2, untrimmed ActivityNet and HACS. In particular, it is the first model to
achieve 90% top-1 accuracy on Kinetics-400, to our best knowledge. Code will be
available at https://github.com/OpenGVLab/UniFormerV2.


  In this paper, we give a complete picture of Howe correspondence for the
setting ($O(E, b), Pin(E, b), \Pi$), where $O(E, b)$ is an orthogonal group
(real or complex), $Pin(E, b)$ is the two-fold Pin-covering of $O(E, b)$, and
$\Pi$ is the spinorial representation of $Pin(E, b)$. More precisely, for a
dual pair ($G, G'$) in $O(E, b)$, we determine explicitly the nature of its
preimages $(\tilde{G}, \tilde{G'})$ in $Pin(E, b)$, and prove that apart from
some exceptions, $(\tilde{G}, \tilde{G'})$ is always a dual pair in $Pin(E,
b)$; then we establish the Howe correspondence for $\Pi$ with respect to
$(\tilde{G}, \tilde{G'})$.


  Reinforcement Learning (RL) from Human Preference-based feedback is a popular
paradigm for fine-tuning generative models, which has produced impressive
models such as GPT-4 and Claude3 Opus. This framework often consists of two
steps: learning a reward model from an offline preference dataset followed by
running online RL to optimize the learned reward model. In this work,
leveraging the idea of reset, we propose a new RLHF algorithm with provable
guarantees. Motivated by the fact that offline preference dataset provides
informative states (i.e., data that is preferred by the labelers), our new
algorithm, Dataset Reset Policy Optimization (DR-PO), integrates the existing
offline preference dataset into the online policy training procedure via
dataset reset: it directly resets the policy optimizer to the states in the
offline dataset, instead of always starting from the initial state
distribution. In theory, we show that DR-PO learns to perform at least as good
as any policy that is covered by the offline dataset under general function
approximation with finite sample complexity. In experiments, we demonstrate
that on both the TL;DR summarization and the Anthropic Helpful Harmful (HH)
dataset, the generation from DR-PO is better than that from Proximal Policy
Optimization (PPO) and Direction Preference Optimization (DPO), under the
metric of GPT4 win-rate. Code for this work can be found at
https://github.com/Cornell-RL/drpo.


  In a previous paper we determined the near horizon limit of an extremal brane
world black hole, charged with respect to a Maxwell field on the brane, in the
single brane Randall-Sundrum model. This paper is largely an extension of that
work. The same black hole is considered. A metric expansion around the near
horizon limit is set up and the correction terms of the first two subleading
orders are determined. It is found that the corrected bulk metric can still be
sliced by a brane even though the $Ads_{2}$ symmetry of the near horizon metric
is broken by correction terms. The induced metric on the brane is determined to
second correction order and compared with the predictions of 4d General
Relativity. It is found that large black holes asymptote the extremal Reissner
Nordstr\"om solution and thus agreement with 4d General Realtivity is obtained
in this limit.


  A key challenge facing the design of differential privacy in the
non-interactive setting is to maintain the utility of the released data. To
overcome this challenge, we utilize the Diaconis-Freedman-Meckes (DFM) effect,
which states that most projections of high-dimensional data are nearly
Gaussian. Hence, we propose the RON-Gauss model that leverages the novel
combination of dimensionality reduction via random orthonormal (RON) projection
and the Gaussian generative model for synthesizing differentially-private data.
We analyze how RON-Gauss benefits from the DFM effect, and present multiple
algorithms for a range of machine learning applications, including both
unsupervised and supervised learning. Furthermore, we rigorously prove that (a)
our algorithms satisfy the strong $\epsilon$-differential privacy guarantee,
and (b) RON projection can lower the level of perturbation required for
differential privacy. Finally, we illustrate the effectiveness of RON-Gauss
under three common machine learning applications -- clustering, classification,
and regression -- on three large real-world datasets. Our empirical results
show that (a) RON-Gauss outperforms previous approaches by up to an order of
magnitude, and (b) loss in utility compared to the non-private real data is
small. Thus, RON-Gauss can serve as a key enabler for real-world deployment of
privacy-preserving data release.


  Boundary value problems for Sturm-Liouville operators with potentials from
the class $W_2^{-1}$ on a star-shaped graph are considered. We assume that the
potentials are known on all the edges of the graph except two, and show that
the potentials on the remaining edges can be constructed by fractional parts of
two spectra. A uniqueness theorem is proved, and an algorithm for the
constructive solution of the partial inverse problem is provided. The main
ingredient of the proofs is the Riesz-basis property of specially constructed
systems of functions.


  We present multi-point optimization: an optimization technique that allows to
train several models simultaneously without the need to keep the parameters of
each one individually. The proposed method is used for a thorough empirical
analysis of the loss landscape of neural networks. By extensive experiments on
FashionMNIST and CIFAR10 datasets we demonstrate two things: 1) loss surface is
surprisingly diverse and intricate in terms of landscape patterns it contains,
and 2) adding batch normalization makes it more smooth. Source code to
reproduce all the reported results is available on GitHub:
https://github.com/universome/loss-patterns.


  Unsupervised image-to-image translation methods have achieved tremendous
success in recent years. However, it can be easily observed that their models
contain significant entanglement which often hurts the translation performance.
In this work, we propose a principled approach for improving style-content
disentanglement in image-to-image translation. By considering the information
flow into each of the representations, we introduce an additional loss term
which serves as a content-bottleneck. We show that the results of our method
are significantly more disentangled than those produced by current methods,
while further improving the visual quality and translation diversity.


  We consider the static elastoplastic theory of dislocations in an
elastoplastic material. We use a Yang-Mills type Lagrangian (the teleparallel
equivalent of Hilbert-Einstein Lagrangian) and some Lagrangians with
anisotropic constitutive laws. The translational part of the generalized affine
connection is utilized to describe the theory of elastoplasticity in the
framework of a translation gauge theory. We obtain a system of Yang-Mills field
equations which express the balance of force and moment.


  One crucial information for a pedestrian crowd simulation is the number of
agents moving from an origin to a certain target. While this setup has a large
impact on the simulation, it is in most setups challenging to find the number
of agents that should be spawned at a source in the simulation. Often, number
are chosen based on surveys and experience of modelers and event organizers.
These approaches are important and useful but reach their limits when we want
to perform real-time predictions. In this case, a static information about the
inflow is not sufficient. Instead, we need a dynamic information that can be
retrieved each time the prediction is started. Nowadays, sensor data such as
video footage or GPS tracks of a crowd are often available. If we can estimate
the number of pedestrians who stem from a certain origin from this sensor data,
we can dynamically initialize the simulation. In this study, we use density
heatmaps that can be derived from sensor data as input for a random forest
regressor to predict the origin distributions. We study three different
datasets: A simulated dataset, experimental data, and a hybrid approach with
both experimental and simulated data. In the hybrid setup, the model is trained
with simulated data and then tested on experimental data. The results
demonstrate that the random forest model is able to predict the origin
distribution based on a single density heatmap for all three configurations.
This is especially promising for applying the approach on real data since there
is often only a limited amount of data available.


  Machine-learning of atomic-scale properties amounts to extracting
correlations between structure, composition and the quantity that one wants to
predict. Representing the input structure in a way that best reflects such
correlations makes it possible to improve the accuracy of the model for a given
amount of reference data. When using a description of the structures that is
transparent and well-principled, optimizing the representation might reveal
insights into the chemistry of the data set. Here we show how one can
generalize the SOAP kernel to introduce a distance-dependent weight that
accounts for the multi-scale nature of the interactions, and a description of
correlations between chemical species. We show that this improves substantially
the performance of ML models of molecular and materials stability, while making
it easier to work with complex, multi-component systems and to extend SOAP to
coarse-grained intermolecular potentials. The element correlations that give
the best performing model show striking similarities with the conventional
periodic table of the elements, providing an inspiring example of how machine
learning can rediscover, and generalize, intuitive concepts that constitute the
foundations of chemistry.


  Remote photoplethysmography (rPPG) based physiological measurement is an
emerging yet crucial vision task, whose challenge lies in exploring accurate
rPPG prediction from facial videos accompanied by noises of illumination
variations, facial occlusions, head movements, \etc, in a non-contact manner.
Existing mainstream CNN-based models make efforts to detect physiological
signals by capturing subtle color changes in facial regions of interest (ROI)
caused by heartbeats. However, such models are constrained by the limited local
spatial or temporal receptive fields in the neural units. Unlike them, a native
Transformer-based framework called Dual-path TokenLearner (Dual-TL) is proposed
in this paper, which utilizes the concept of learnable tokens to integrate both
spatial and temporal informative contexts from the global perspective of the
video. Specifically, the proposed Dual-TL uses a Spatial TokenLearner (S-TL) to
explore associations in different facial ROIs, which promises the rPPG
prediction far away from noisy ROI disturbances. Complementarily, a Temporal
TokenLearner (T-TL) is designed to infer the quasi-periodic pattern of
heartbeats, which eliminates temporal disturbances such as head movements. The
two TokenLearners, S-TL and T-TL, are executed in a dual-path mode. This
enables the model to reduce noise disturbances for final rPPG signal
prediction. Extensive experiments on four physiological measurement benchmark
datasets are conducted. The Dual-TL achieves state-of-the-art performances in
both intra- and cross-dataset testings, demonstrating its immense potential as
a basic backbone for rPPG measurement. The source code is available at
\href{https://github.com/VUT-HFUT/Dual-TL}{https://github.com/VUT-HFUT/Dual-TL}


  Histopathology digital scans are large-size images that contain valuable
information at the pixel level. Content-based comparison of these images is a
challenging task. This study proposes a content-based similarity measure for
high-resolution gigapixel histopathology images. The proposed similarity
measure is an expansion of cosine vector similarity to a matrix. Each image is
divided into same-size patches with a meaningful amount of information (i.e.,
contained enough tissue). The similarity is measured by the extraction of
patch-level deep embeddings of the last pooling layer of a pre-trained deep
model at four different magnification levels, namely, 1x, 2.5x, 5x, and 10x
magnifications. In addition, for faster measurement, embedding reduction is
investigated. Finally, to assess the proposed method, an image search method is
implemented. Results show that the similarity measure represents the slide
labels with a maximum accuracy of 93.18\% for top-5 search at 5x magnification.


  The existence of outflow and magnetic field in the inner region of hot
accretion flows have been confirmed by observations and numerical
magnetohydrodynamic simulations (MHD). We present self-similar solutions for
radiation inefficiently accretion flows (RIAF) around black holes in the
presence of outflow and global magnetic field. The influence of outflow is
taken into account by adopting a radius dependent of mass accretion rate $
\dot{M} = \dot{M}_{0}(r/r_{0})^{s} $ with $ s > 0 $. Also we consider
convection through a mixing length formalism to calculate convection parameter
$ \alpha_{con} $. Moreover we consider the additional magnetic field parameters
$ \beta_{r,\varphi,z}\big[= c^2_{r,\varphi,z}/(2 c^2_{s}) \big] $, where $
c^2_{r,\varphi,z} $ are the Alfv$\acute{e}$n sound speeds in three direction of
cylindrical coordinate. Our numerical results show that by increasing all
components of magnetic field, the surface density and rotational velocity
increase although the sound speed and radial infall velocity of the disc
decrease. Also we have found out that the existence of wind will lead to
reduction of surface density as well as rotational velocity. Moreover the
radial velocity, sound speed, advection parameter and the vertical thickness of
the disc will increase when outflow becomes important in the radiation
inefficiently accretion flow.


  Let $G$ be a locally compact group. Consider the C$^*$-algebra $C_0(G)$ of
continuous complex functions on $G$, tending to 0 at infinity. The product in
$G$ gives rise to a coproduct $\Delta_G$ on the C$^*$-algebra $C_0(G)$. A
locally compact {\it quantum} group is a pair $(A,\Delta)$ of a C$^*$-algebra
$A$ with a coproduct $\Delta$ on $A$, satisfying certain conditions. The
definition guarantees that the pair $(C_0(G),\Delta_G)$ is a locally compact
quantum group and that conversely, every locally compact quantum group
$(A,\Delta)$ is of this form when the underlying C$^*$-algebra $A$ is abelian.
  Assume now that $G$ is a locally compact group with a compact open subgroup
$K$. The algebra of complex functions on $G$ of {\it polynomial type} is a
dense multiplier Hopf $^*$-algebra with positive integrals (i.e. an algebraic
quantum group}. The characteristic function of $K$ is a group-like projection
in this algebraic quantum group.
  In this paper, we study group-like projections in an arbitrary algebraic
quantum group. We find several associated objects that generalize the classical
objects associated to a compact open subgroup of a locally compact group.


  We consider a random matrix model with both pairwise and non-pairwise
contracted indices. The partition function of the matrix model is similar to
that appearing in some replicated systems with random tensor couplings, such as
the p-spin spherical model for the spin glass. We analyze the model using
Feynman diagrammatic expansions, and provide an exhaustive characterization of
the graphs which dominate when the dimensions of the pairwise and (or)
non-pairwise contracted indices are large. We apply this to investigate the
properties of the wave function of a toy model closely related to a tensor
model in the Hamilton formalism, which is studied in a quantum gravity context,
and obtain a result in favor of the consistency of the quantum probabilistic
interpretation of this tensor model.


  We prove a central limit theorem for the components of the largest
eigenvectors of the adjacency matrix of a finite-dimensional random dot product
graph whose true latent positions are unknown. In particular, we follow the
methodology outlined in \citet{sussman2012universally} to construct consistent
estimates for the latent positions, and we show that the appropriately scaled
differences between the estimated and true latent positions converge to a
mixture of Gaussian random variables. As a corollary, we obtain a central limit
theorem for the first eigenvector of the adjacency matrix of an Erd\"os-Renyi
random graph.


  Most theoretical descriptions of the production of solar flare bremsstrahlung
radiation assume the collision of dilute accelerated particles with a cold,
dense target plasma, neglecting interactions of the fast particles with each
other. This is inadequate for situations where collisions with this background
plasma are not completely dominant, as may be the case in, for example,
low-density coronal sources. We aim to formulate a model of a self-interacting,
entirely fast electron population in the absence of a dense background plasma,
to investigate its implications for observed bremsstrahlung spectra and the
flare energy budget. We derive approximate expressions for the time-dependent
distribution function of the fast electrons using a Fokker-Planck approach. We
use these expressions to generate synthetic bremsstrahlung X-ray spectra as
would be seen from a corresponding coronal source. We find that our model
qualitatively reproduces the observed behaviour of some flares. As the flare
progresses, the model's initial power-law spectrum is joined by a lower energy,
thermal component. The power-law component diminishes, and the growing thermal
component proceeds to dominate the total emission over timescales consistent
with flare observations. The power-law exhibits progressive spectral hardening,
as is seen in some flare coronal sources. We also find that our model requires
a factor of 7 - 10 fewer accelerated electrons than the cold, thick target
model to generate an equivalent hard X-ray flux. This model forms the basis of
a treatment of self-interactions among flare fast electrons, a process which
affords a more efficient means to produce bremsstrahlung photons and so may
reduce the efficiency requirements placed on the particle acceleration
mechanism. It also provides a useful description of the thermalisation of fast
electrons in coronal sources.


  Federated learning is a very convenient approach for scenarios where (i) the
exchange of data implies privacy concerns and/or (ii) a quick reaction is
needed. In smart healthcare systems, both aspects are usually required. In this
paper, we work on the first scenario, where preserving privacy is key and,
consequently, building a unique and massive medical image data set by fusing
different data sets from different medical institutions or research centers
(computation nodes) is not an option. We propose an ensemble federated learning
(EFL) approach that is based on the following characteristics: First, each
computation node works with a different data set (but of the same type). They
work locally and apply an ensemble approach combining eight well-known CNN
models (densenet169, mobilenetv2, xception, inceptionv3, vgg16, resnet50,
densenet121, and resnet152v2) on Chest X-ray images. Second, the best two local
models are used to create a local ensemble model that is shared with a central
node. Third, the ensemble models are aggregated to obtain a global model, which
is shared with the computation nodes to continue with a new iteration. This
procedure continues until there are no changes in the best local models. We
have performed different experiments to compare our approach with centralized
ones (with or without an ensemble approach)\color{black}. The results conclude
that our proposal outperforms these ones in Chest X-ray images (achieving an
accuracy of 96.63\%) and offers very competitive results compared to other
proposals in the literature.


  We consider an insulated conductivity model with two neighboring inclusions
of $m$-convex shapes in $\mathbb{R}^{d}$ when $m\geq2$ and $d\geq3$. We
establish the pointwise gradient estimates for the insulated conductivity
problem and capture the gradient blow-up rate of order
$\varepsilon^{-1/m+\beta}$ with
$\beta=[-(d+m-3)+\sqrt{(d+m-3)^{2}+4(d-2)}]/(2m)\in(0,1/m)$, as the distance
$\varepsilon$ between these two insulators tends to zero. In particular, the
optimality of the blow-up rate is also demonstrated for a class of axisymmetric
$m$-convex inclusions.


  We use the Chapman-Enskog method to derive the Smoluchowski equation from the
Kramers equation in a high friction limit. We consider two main extensions of
this problem: we take into account a uniform rotation of the background medium
and we consider a generalized class of Kramers equations associated with
generalized free energy functionals. We mention applications of these results
to systems with long-range interactions (self-gravitating systems, 2D vortices,
bacterial populations,...). In that case, the Smoluchowski equation is
non-local. In the limit of short-range interactions, it reduces to a
generalized form of the Cahn-Hilliard equation. These equations are associated
with an effective generalized thermodynamical formalism.


  Recent advances in monochromatic aberration corrected electron microscopy
make it possible to detect the lattice vibration with both high-energy
resolution and high spatial resolution. Here, we use sub-10 meV electron energy
loss spectroscopy to investigate the local vibrational properties at surface
and interface of an amorphous SiO$_2$ (a-SiO$_2$) thin film on Si substrate. We
find that each optical mode splits into three sub-modes, i.e., surface mode,
bulk mode and interface mode, which can be measured from different locations.
The pure surface modes can be measured in the vacuum near the surface, and the
pure interface modes are expected to be obtained either at the interface
location or in the Si, while inside the SiO$_2$ the measured signal is a
mixture of bulk, surface, and interface modes. The bulk mode has the largest
vibration energy and surface mode has the lowest. The energy of surface mode is
thickness dependent, showing a blue-shift as z-thickness (parallel to fast
electron beam) of SiO$_2$ film increases, while the bulk and interface modes
have constant vibration energy. The intensity of bulk mode linearly increases
with thickness being increased, and it drops steeply to zero near the surface
and interface (within a few nanometers). The surface modes decay slowly in the
vacuum following a Bessel function. The mechanism of the observed spatially
dependent vibration behavior is discussed and quantitatively compared with
dielectric response theory analysis. Our nanometer scale measurements of
vibrations properties provide useful information about the bonding conditions
at the surface and interface and thus may help to design better silicon-based
electronic devices via surface and interface treatments


  We study the capabilities of Lyman $\beta$ and the O I 1027 and 1028 \AA\
spectral lines for understanding the properties of the chromosphere and
transition region. The oxygen transitions are located in the wing of Lyman
$\beta$ that is a candidate spectral line for the solar missions Solar
Orbiter/SPICE and Solar-C (EUVST). We examine general spectroscopic properties
of the three transitions in the quiet Sun by synthesizing them assuming
non-local thermal equilibrium taking into account partial redistribution
effects. We estimate the heights where the spectral lines are sensitive to the
physical parameters computing the response functions to temperature and
velocity using a 1D semi-empirical atmospheric model. We also synthesize the
intensity spectrum using the 3D enhanced network simulation computed with the
BIFROST code. The results indicate that Lyman $\beta$ is sensitive to the
temperature from the middle chromosphere to the transition region while it is
mainly sensitive to the line-of-sight velocity at the latter atmospheric
layers, around 2000 km above the optical surface. The O I lines form lower in
the middle chromosphere, being sensitive to the LOS velocities at lower heights
than those covered by Lyman $\beta$. The spatial distribution of intensity
signals computed with the BIFROST atmosphere, as well as the inferred
velocities from the line core Doppler shift confirm the previous results.
Therefore, these results indicate that the spectral window at 1025 \AA\
contains several spectral lines that complement each other to seamlessly trace
the thermal structure and gas dynamics from the middle chromosphere to the
lower transition region.


  We give an effective version with explicit constants of a mean value theorem
of Vaughan related to the values of \psi(y, \chi), the twisted summatory
function associated to the von Mangoldt function \Lambda and a Dirichlet
character \chi. As a consequence of this result we prove an effective variant
of the Bombieri-Vinogradov theorem with explicit constants. This effective
variant has the potential to provide explicit results in many problems. We give
examples of such results in several number theoretical problems related to
shifted primes.


  Existing meta-learners primarily focus on improving the average task accuracy
across multiple episodes. Different episodes, however, may vary in hardness and
quality leading to a wide gap in the meta-learner's performance across
episodes. Understanding this issue is particularly critical in industrial
few-shot settings, where there is limited control over test episodes as they
are typically uploaded by end-users. In this paper, we empirically analyse the
behaviour of meta-learners on episodes of varying hardness across three
standard benchmark datasets: CIFAR-FS, mini-ImageNet, and tiered-ImageNet.
Surprisingly, we observe a wide gap in accuracy of around 50% between the
hardest and easiest episodes across all the standard benchmarks and
meta-learners. We additionally investigate various properties of hard episodes
and highlight their connection to catastrophic forgetting during meta-training.
To address the issue of sub-par performance on hard episodes, we investigate
and benchmark different meta-training strategies based on adversarial training
and curriculum learning. We find that adversarial training strategies are much
more powerful than curriculum learning in improving the prediction performance
on hard episodes.


  We have used oxygen ions irradiation to generate controlled structural
disorder in thin manganite films. Conductive atomic force microscopy CAFM),
transport and magnetic measurements were performed to analyze the influence of
the implantation process in the physical properties of the films. CAFM images
show regions with different conductivity values, probably due to the random
distribution of point defect or inhomogeneous changes of the local Mn3+/4+
ratio to reduce lattice strains of the irradiated areas. The transport and
magnetic properties of these systems are interpreted in this context.
Metal-insulator transition can be described in the frame of a percolative
model. Disorder increases the distance between conducting regions, lowering the
observed TMI. Point defect disorder increases localization of the carriers due
to increased disorder and locally enhanced strain field. Remarkably, even with
the inhomogeneous nature of the samples, no sign of low field magnetoresistance
was found. Point defect disorder decreases the system magnetization but doesn t
seem to change the magnetic transition temperature. As a consequence, an
important decoupling between the magnetic and the metal-insulator transition is
found for ion irradiated films as opposed to the classical double exchange
model scenario.


  In a recent letter, Carlos Vilchez-Roman criticizes Bornmann et al. (2015)
for using data which cannot be reproduced without access to an in-house version
of the Web-of-Science (WoS) at the Max Planck Digital Libraries (MPDL, Munich).
We agree with the norm of replicability and therefore returned to our data. Is
the problem only a practical one of automation or does the in-house processing
add analytical value to the data? Is the newly emerging situation in any sense
different from a further professionalization of the field? In our opinion, a
political economy of science indicators has in the meantime emerged with a
competitive dynamic that affects the intellectual organization of the field.


  We consider gluonic contributions to the heavy flavor Wilson coefficients at
3-loop order in QCD with two heavy quark lines in the asymptotic region $Q^2
\gg m_{1(2)}^2$. Here we report on the complete result in the case of two equal
masses $m_1 = m_2$ for the massive operator matrix element $A_{gg,Q}^{(3)}$,
which contributes to the corresponding heavy flavor transition matrix element
in the variable flavor number scheme. Nested finite binomial sums and iterated
integrals over square-root valued alphabets emerge in the result for this
quantity in $N$ and $x$-space, respectively. We also present results for the
case of two unequal masses for the flavor non-singlet OMEs and on the scalar
integrals ic case of $A_{gg,Q}^{(3)}$, which were calculated without a further
approximation. The graphs can be expressed by finite nested binomial sums over
generalized harmonic sums, the alphabet of which contains rational letters in
the ratio $\eta = m_1^2/m_2^2$.


  We present VR PreM+, an innovative VR system designed to enhance web
exploration beyond traditional computer screens. Unlike static 2D displays, VR
PreM+ leverages 3D environments to create an immersive pre-learning experience.
Using keyword-based information retrieval allows users to manage and connect
various content sources in a dynamic 3D space, improving communication and data
comparison. We conducted preliminary and user studies that demonstrated
efficient information retrieval, increased user engagement, and a greater sense
of presence. These findings yielded three design guidelines for future VR
information systems: display, interaction, and user-centric design. VR PreM+
bridges the gap between traditional web browsing and immersive VR, offering an
interactive and comprehensive approach to information acquisition. It holds
promise for research, education, and beyond.


  The event by event fluctuations of the ratio of positively to negatively
charged hadrons are predicted within the UrQMD model. Corrections for finite
acceptance and finite net charge are derived. These corrections are relevant to
compare experimental data and transport model results to previous predictions.
The calculated fluctuations at RHIC and SPS energies are shown to be compatible
with a hadron gas. Thus, deviating by a factor of 3 from the predictions for a
thermalized quark-gluon plasma.


  This paper investigates the problem of best arm identification in
$\textit{contaminated}$ stochastic multi-arm bandits. In this setting, the
rewards obtained from any arm are replaced by samples from an adversarial model
with probability $\varepsilon$. A fixed confidence (infinite-horizon) setting
is considered, where the goal of the learner is to identify the arm with the
largest mean. Owing to the adversarial contamination of the rewards, each arm's
mean is only partially identifiable. This paper proposes two algorithms, a
gap-based algorithm and one based on the successive elimination, for best arm
identification in sub-Gaussian bandits. These algorithms involve mean estimates
that achieve the optimal error guarantee on the deviation of the true mean from
the estimate asymptotically. Furthermore, these algorithms asymptotically
achieve the optimal sample complexity. Specifically, for the gap-based
algorithm, the sample complexity is asymptotically optimal up to constant
factors, while for the successive elimination-based algorithm, it is optimal up
to logarithmic factors. Finally, numerical experiments are provided to
illustrate the gains of the algorithms compared to the existing baselines.


  The areal footprint of memristors is a key consideration in material-based
neuromorophic computing and large-scale architecture integration. Electronic
transport in the most widely investigated memristive devices is mediated by
filaments, posing a challenge to their scalability in architecture
implementation. Here we present a compelling alternative memristive device and
demonstrate that areal downscaling leads to enhancement in memristive memory
window, while maintaining analogue behavior, contrary to expectations. Our
device designs directly integrated on semiconducting Nb-SrTiO$_3$ allows
leveraging electric field effects at edges, increasing the dynamic range in
smaller devices. Our findings are substantiated by studying the microscopic
nature of switching using scanning transmission electron microscopy, in
different resistive states, revealing an interfacial layer whose physical
extent is influenced by applied electric fields. The ability of Nb-SrTiO$_3$
memristors to satisfy hardware and software requirements with downscaling,
while significantly enhancing memristive functionalities, makes them strong
contenders for non-von Neumann computing, beyond CMOS.


  We formulate and solve the equations governing the dynamics of a microscopic
artificial swimmer composed of a head and of a tail made of a thin film of
permanent magnetic material. This is a variant of the model swimmer proposed by
Dreyfus et al. in 2005, whose tail is a filament obtained from the assembly of
super-paramagnetic beads. The swimmer is actuated by an oscillating magnetic
field, and its geometry is inspired by that of sperm cells. Using values for
the geometric and material parameters that are realistic for a magnetic
multilayer, we show that the model swimmer can reach swimming speeds exceeding
one body length per second, under reasonable values of the driving magnetic
field. This provides a proof of principle for the viability of the concept. In
addition, we discuss the possibility to steer the system along curved paths.
Finally, we compare the propulsion mechanism (swimming `gait') of our swimmer
with that of sperm cells. The main difference between the two is that, contrary
to its biological template, our artificial system does not rely on the
propagation of bending waves along the tail, at least for the range of material
and geometric parameters explored in this article.


  We consider the problem of aggregating models learned from sequestered,
possibly heterogeneous datasets. Exploiting tools from Bayesian nonparametrics,
we develop a general meta-modeling framework that learns shared global latent
structures by identifying correspondences among local model parameterizations.
Our proposed framework is model-independent and is applicable to a wide range
of model types. After verifying our approach on simulated data, we demonstrate
its utility in aggregating Gaussian topic models, hierarchical Dirichlet
process based hidden Markov models, and sparse Gaussian processes with
applications spanning text summarization, motion capture analysis, and
temperature forecasting.


  Let $G$ be a graph, $r \geq t$ integers, and $N \subseteq E(G)$. An
$(r,t)$-threshold-coloring of $G$ with respect to $N$ is a mapping $c: V(G)
\rightarrow \{0,\ldots,r-1\}$ such that $|c(u)-c(v)| \leq t$ for every $uv \in
N$ and $|c(u)-c(v)|>t$ for every $uv \in E(G) \setminus N$. A graph is total
threshold colorable if there exist integers $r,t$ such that for every $N
\subseteq E(G)$, $G$ admits an $(r,t)$-threshold-coloring with respect to $N$.
We show that every prism is total threshold colorable, and that the Petersen
graph is total threshold colorable. In contrast to this fact we show that
Moebius ladders are not total threshold colorable, from which it follows that
there is no characterization of being total threshold colorable in terms of a
finite set of forbidden subgraphs.


  In this paper we discuss the obstacle problem for the $p$-Laplace operator.
We prove optimal growth results for the solution. Of particular interest is the
point-wise regularity of the solution at free boundary points. The most
surprising result we prove is the one for the $p$-obstacle problem: Find the
smallest $u$ such that $$ \hbox{div} (|\nabla u|^{p-2}\nabla u) \leq 0, \qquad
u\geq \phi, \qquad \hbox{in } B_1, $$ with $\phi \in C^{1,1}(B_1)$ and given
boundary datum on $\partial B_1$. We prove that the solution is uniformly
$C^{1,1}$ at free boundary points. Similar results are obtained in the case of
an inhomogeneity belonging to $L^\infty$. When applied to the corresponding
parabolic problem, these results imply that any solution which is Lipschitz in
time is $C^{1,\frac{1}{p-1}}$ in the spatial variables.


  Continuous control tasks in reinforcement learning are important because they
provide an important framework for learning in high-dimensional state spaces
with deceptive rewards, where the agent can easily become trapped into
suboptimal solutions. One way to avoid local optima is to use a population of
agents to ensure coverage of the policy space, yet learning a population with
the "best" coverage is still an open problem. In this work, we present a novel
approach to population-based RL in continuous control that leverages properties
of normalizing flows to perform attractive and repulsive operations between
current members of the population and previously observed policies. Empirical
results on the MuJoCo suite demonstrate a high performance gain for our
algorithm compared to prior work, including Soft-Actor Critic (SAC).


  The EPDiff equation (or dispersionless Camassa-Holm equation in 1D) is a well
known example of geodesic motion on the Diff group of smooth invertible maps
(diffeomorphisms). Its recent two-component extension governs geodesic motion
on the semidirect product ${\rm Diff}\circledS{\cal F}$, where $\mathcal{F}$
denotes the space of scalar functions. This paper generalizes the second
construction to consider geodesic motion on ${\rm Diff} \circledS\mathfrak{g}$,
where $\mathfrak{g}$ denotes the space of scalar functions that take values on
a certain Lie algebra (for example,
$\mathfrak{g}=\mathcal{F}\otimes\mathfrak{so}(3)$). Measure-valued delta-like
solutions are shown to be momentum maps possessing a dual pair structure,
thereby extending previous results for the EPDiff equation. The collective
Hamiltonians are shown to fit into the Kaluza-Klein theory of particles in a
Yang-Mills field and these formulations are shown to apply also at the
continuum PDE level. In the continuum description, the Kaluza-Klein approach
produces the Kelvin circulation theorem.


  Server Availability (SA) is an important measure of overall systems security.
Important security systems rely on the availability of their hosting servers to
deliver critical security services. Many of these servers offer management
interface through web mainly using an Apache server. This paper investigates
the increase of Server Availability by the use of Artificial Neural Networks
(ANN) to predict software aging phenomenon. Several resource usage data is
collected and analyzed on a typical long-running software system (a web
server). A Multi-Layer Perceptron feed forward Artificial Neural Network was
trained on an Apache web server data-set to predict future server resource
exhaustion through uni-variate time series forecasting. The results were
benchmarked against those obtained from non-parametric statistical techniques,
parametric time series models and empirical modeling techniques reported in the
literature.


  We find the contribution of constituent quark loop mechanism to the branching
ratio B_{4\gamma} = \Gamma_{4\gamma}/\Gamma_{4\gamma} \sim 5.45 \cdot 10^{-16}
for the reasonable choice of constituent quark mass m \sim 280 MeV. This result
is in agreement with vector-dominance approach result obtained years ago. Thus
the main contribution arises from QED mechanism \pi^0 \to \gamma (\gamma^*) \to
\gamma (3\gamma) including light-light scattering block with electron loop.
This contribution was investigated in paper of one of us and gave B_{4\gamma}
\sim 2.6 \cdot 10^{-11}.


  We prove central limit theorem under diffusive scaling for the displacement
of a random walk on ${\mathbb Z}^d$ in stationary divergence-free random drift
field, under the ${\mathcal H}_{-1}$-condition imposed on the drift field. The
condition is equivalent to assuming that the stream tensor be stationary and
square integrable. This improves the best existing result of Komorowski, Landim
and Olla (2012), where it is assumed that the stream tensor be in ${\mathcal
L}^{\max\{2+\delta,d\}}$, with $\delta>0$. Our proof relies on the relaxed
sector condition of Horv\'ath, T\'oth and Vet\H{o} (2012), and is technically
rather simpler than existing earlier proofs of similar results by Oelschl\"ager
(1988) and Komorowski, Landim, Olla (2012).


  Architecture reverse engineering has become an emerging attack against deep
neural network (DNN) implementations. Several prior works have utilized
side-channel leakage to recover the model architecture while the target is
executing on a hardware acceleration platform. In this work, we target an
open-source deep-learning accelerator, Versatile Tensor Accelerator (VTA), and
utilize electromagnetic (EM) side-channel leakage to comprehensively learn the
association between DNN architecture configurations and EM emanations. We also
consider the holistic system -- including the low-level tensor program code of
the VTA accelerator on a Xilinx FPGA and explore the effect of such low-level
configurations on the EM leakage. Our study demonstrates that both the
optimization and configuration of tensor programs will affect the EM
side-channel leakage.
  Gaining knowledge of the association between the low-level tensor program and
the EM emanations, we propose NNReArch, a lightweight tensor program scheduling
framework against side-channel-based DNN model architecture reverse
engineering. Specifically, NNReArch targets reshaping the EM traces of
different DNN operators, through scheduling the tensor program execution of the
DNN model so as to confuse the adversary. NNReArch is a comprehensive
protection framework supporting two modes, a balanced mode that strikes a
balance between the DNN model confidentiality and execution performance, and a
secure mode where the most secure setting is chosen. We implement and evaluate
the proposed framework on the open-source VTA with state-of-the-art DNN
architectures. The experimental results demonstrate that NNReArch can
efficiently enhance the model architecture security with a small performance
overhead. In addition, the proposed obfuscation technique makes reverse
engineering of the DNN architecture significantly harder.


  We introduce a novel Deep Learning framework, which quantitatively estimates
image segmentation quality without the need for human inspection or labeling.
We refer to this method as a Quality Assurance Network -- QANet. Specifically,
given an image and a `proposed' corresponding segmentation, obtained by any
method including manual annotation, the QANet solves a regression problem in
order to estimate a predefined quality measure with respect to the unknown
ground truth. The QANet is by no means yet another segmentation method.
Instead, it performs a multi-level, multi-feature comparison of an
image-segmentation pair based on a unique network architecture, called the
RibCage.
  To demonstrate the strength of the QANet, we addressed the evaluation of
instance segmentation using two different datasets from different domains,
namely, high throughput live cell microscopy images from the Cell Segmentation
Benchmark and natural images of plants from the Leaf Segmentation Challenge.
While synthesized segmentations were used to train the QANet, it was tested on
segmentations obtained by publicly available methods that participated in the
different challenges. We show that the QANet accurately estimates the scores of
the evaluated segmentations with respect to the hidden ground truth, as
published by the challenges' organizers.
  The code is available at: TBD.


  Quantum dimer models on bipartite lattices exhibit Rokhsar-Kivelson (RK)
points with exactly known critical ground states and deconfined spinons. We
examine generic, weak, perturbations around these points. In d=2+1 we find a
first order transition between a ``plaquette'' valence bond crystal and a
region with a devil's staircase of commensurate and incommensurate valence bond
crystals. In the part of the phase diagram where the staircase is incomplete,
the incommensurate states exhibit a gapless photon and deconfined spinons on a
set of finite measure, almost but not quite a deconfined phase in a compact
U(1) gauge theory in d=2+1! In d=3+1 we find a continuous transition between
the U(1) resonating valence bond (RVB) phase and a deconfined staggered valence
bond crystal. In an appendix we comment on analogous phenomena in quantum
vertex models, most notably the existence of a continuous transition on the
triangular lattice in d=2+1.


  NuSTAR observatory, with its 3 - 78 keV broadband spectral coverage, enables
the detections of the high-energy cutoff in a number of active galaxies,
including several individual radio loud ones. In this work we present
systematic and uniform analyses of 55 NuSTAR spectra for a large sample of 28
radio galaxies, 20 of which are FR II galaxies. We perform spectral fitting to
measure the high energy cut-off $E_{cut}$, photon index $\Gamma$, reflection
factor R and Fe K$\alpha$ line equivalent width. Measurements of $E_{cut}$ are
given for 13 sources, and lower limits for the rest. We find those $E_{cut}$
non-detections could primarily be attributed to the obviously smaller net
photon counts in their spectra. This indicates that the NuSTAR spectra of the
majority of our sample are dominated by the thermal corona emission, and the
$E_{cut}$ distribution of the sample is indistinguishable from that of a radio
quiet one in literature. The flatter NuSTAR spectra we observed, comparing with
radio quiet sources, are thus unlikely due to jet contamination. The radio
galaxies also show weaker X-ray reflection (both in R and Fe K$\alpha$ line EW)
comparing with radio quiet ones. Combining with the radio quiet sample we see a
correlation between R and EW, but with considerably large scatter. Notably, the
radio loud and quiet sources appear to follow a common $\Gamma$ - R correlation
trend, supporting the outflowing corona model for both populations in which
higher bulk outflowing velocity yields weaker reflection and flatter X-ray
slope.


  We present a study of coherent pion production in neutrino-nucleus
interactions using the formalism based on partially conserved axial current
theorem which connects the neutrino-nucleus cross section to the pion-nucleus
elastic scattering cross section. Pion-nucleus elastic scattering cross section
is calculated using Glauber model which takes three inputs, nuclear densities,
pion-nucleon cross section and $\alpha_{\pi N}$ which is the ratio of real to
imaginary part of $\pi N$ forward scattering amplitude, for which the
parametrizations are obtained from measured data. We calculate the differential
and integrated cross sections for charge and neutral current coherent pion
production in neutrino (anti-neutrino)-nucleus scattering for a range of
nuclear targets from light to heavy materials such as lithium, carbon,
hydrocarbon, oxygen, silicon, argon, iron and lead. The results of these cross
section calculations are compared with the measured data and with the
calculations from the Berger-Sehgal model and GENIE package. There is an
excellent agreement between the calculated and measured cross sections with
Glauber model. While GENIE and Berger-Sehgal model give a good description of
the data in the lower energy range the present calculations describe the data
in all energy ranges. Predictions are also made for upcoming experiments like
INO and DUNE in the coherent region of neutrino cross section.


  The main goal of this thesis is to provide an exploration of the use of
computational intelligence techniques to study the numerical solution of the
Ornstein-Zernike equation for simple liquids. In particular, a continuous model
of the hard sphere fluid is studied. There are two main proposals in this
contribution. First, the use of neural networks as a way to parametrize closure
relation when solving the Ornstein-Zernike equation. It is explicitly shown
that in the case of the hard sphere fluid, the neural network approach seems to
reduce to the so-called Hypernetted Chain closure. For the second proposal, we
explore the fact that if more physical information is incorporated into the
theoretical formalism, a better estimate can be obtained with the use of
evolutionary optimization techniques. When choosing the modified Verlet closure
relation, and leaving a couple of free parameters to be adjusted, the results
are as good as those obtained from molecular simulations. The thesis is then
closed with a brief summary of the main findings and outlooks on different ways
to improve the proposals presented here.


  We analyse multimodal time-series data corresponding to weight, sleep and
steps measurements. We focus on predicting whether a user will successfully
achieve his/her weight objective. For this, we design several deep long
short-term memory (LSTM) architectures, including a novel cross-modal LSTM
(X-LSTM), and demonstrate their superiority over baseline approaches. The
X-LSTM improves parameter efficiency by processing each modality separately and
allowing for information flow between them by way of recurrent
cross-connections. We present a general hyperparameter optimisation technique
for X-LSTMs, which allows us to significantly improve on the LSTM and a prior
state-of-the-art cross-modal approach, using a comparable number of parameters.
Finally, we visualise the model's predictions, revealing implications about
latent variables in this task.


  As the temperature of a cooling object decreases as it relaxes to thermal
equilibrium, it is intuitively assumed that a hot object should take longer to
cool than a warm one. Yet, some 2,300 years ago, Aristotle observed that "to
cool hot water quickly, begin by putting it in the sun". In the 1960s, this
counterintuitive phenomenon was rediscovered as the statement that "hot water
can freeze faster than cold water" and has become known as the Mpemba effect;
it has since been the subject of much experimental investigation and some
controversy. Although many specific mechanisms have been proposed, no general
consensus exists as to the underlying cause. Here we demonstrate the Mpemba
effect in a controlled setting - the thermal quench of a colloidal system
immersed in water, which serves as a heat bath. Our results are reproducible
and agree quantitatively with calculations based on a recently proposed
theoretical framework. By carefully choosing parameters, we observe cooling
that is exponentially faster than that observed using typical parameters, in
accord with the recently predicted strong Mpemba effect. Our experiments
outline the generic conditions needed to accelerate heat removal and relaxation
to thermal equilibrium and support the idea that the Mpemba effect is not
simply a scientific curiosity concerning how water freezes into ice - one of
the many anomalous features of water - but rather the prototype for a wide
range of anomalous relaxation phenomena of broad technological importance.


  We set up a general framework to describe $\pi\pi$ scattering below 1 GeV
based on chiral low-energy expansion with possible spin-0 and 1 resonances.
Partial wave amplitudes are obtained with the $N/D$ method, which satisfy
unitarity, analyticity and approximate crossing symmetry. Comparison with the
phase shift data in the J=0 channel favors a scalar resonance near the $\rho$
mass.


  Existing approaches to coalition formation often assume that requirements
associated with tasks are precisely specified by the human operator. However,
prior work has demonstrated that humans, while extremely adept at solving
complex problems, struggle to explicitly state their solution strategy.
Further, existing approaches often ignore the fact that experts may utilize
different, but equally-valid, solutions (i.e., heterogeneous strategies) to the
same problem. In this work, we propose a two-part framework to address these
challenges. First, we tackle the challenge of inferring implicit strategies
directly from expert demonstrations of coalition formation. To this end, we
model and infer such heterogeneous strategies as capability-based requirements
associated with each task. Next, we propose a method capable of adaptively
selecting one of the inferred strategies that best suits the target team
without requiring additional training. Specifically, we formulate and solve a
constrained optimization problem that simultaneously selects the most
appropriate strategy given the target team's capabilities, and allocates its
constituents into appropriate coalitions. We evaluate our approach against
several baselines, including some that resemble existing approaches, using
detailed numerical simulations, StarCraft II battles, and a multi-robot
emergency-response scenario. Our results indicate that our framework
consistently outperforms all baselines in terms of requirement satisfaction,
resource utilization, and task success rates.


  Direct electronic structure measurements of a variety of the colossal
magnetoresistive oxides show the presence of a pseudogap at the Fermi energy
E_F which drastically suppresses the electron spectral function at E_F. The
pseudogap is a strong function of the layer number of the samples (sample
dimensionality) and is strongly temperature dependent, with the changes
beginning at the ferromagnetic transition temperature T_c. These trends are
consistent with the major transport trends of the CMR oxides, implying a direct
relationship between the pseudogap and transport, including the "colossal"
conductivity changes which occur across T_c. The k-dependence of the
temperature-dependent effects indicate that the pseudogap observed in these
compounds is not due to the extrinsic effects proposed by Joynt.


  Thanks to Hrushovski-Loeser's work on motivic Milnor fibers, we give a
model-theoretic proof for the motivic Thom-Sebastiani theorem in the case of
regular functions. Moreover, slightly extending of Hrushovski-Loeser's
construction adjusted to Sebag, Loeser and Nicaise's motivic integration for
formal schemes and rigid varieties, we formulate and prove an analogous result
for formal functions. The latter is meaningful as it has been a crucial element
of constructing Kontsevich-Soibelman's theory of motivic Donaldson-Thomas
invariants.


  We introduce the notion of weight system for finite type invariants of
integral homology 3-spheres, and we show that invariants of type m are
determined, modulo invariants of type m-1, by their associated weight system.


  We study the boundaries of relatively hyperbolic HHGs. Using the simplicial
structure on the hierarchically hyperbolic boundary, we characterize both
relative hyperbolicity and being thick of order 1 among HHGs. In the case of
relatively hyperbolic HHGs, we show that the Bowditch boundary of the group is
the quotient of the HHS boundary obtained by collapsing the limit sets of the
peripheral subgroups to a point. In establishing this, we give a construction
that allows one to modify an HHG structure by including a collection of
hyperbolically embedded subgroups into the HHG structure.


  We calculate the chi**2 of various NN potential models with respect to the pp
scattering data. We find that only the potential models which were explicitly
fitted to the pp data give a reasonable description of these data. Most models
give a pretty large chi**2 on the very low-energy pp data, due to incorrect 1S0
phase shifts.


  A notion of unique ergodicity relative to the fixed-point subalgebra is
defined for automorphisms of unital C*-algebras. It is proved that the free
shift on any reduced amalgamated free product C*-algebra is uniquely ergodic
relative to its fixed-point subalgebra, as are autormorphisms of reduced group
C*-algebras arising from certain automorphisms of groups. A generalization of
Haagerup's inequality, yielding bounds on the norms of certain elements in
reduced amalgamated free product C*-algebras, is proved.


  Coherent control is a method used to manipulate the state of matter using
oscillatory electromagnetic radiation which relies on the non-adiabatic
interaction. It is commonly applied in quantum processing applications. This
technique is interesting in the context of ferromagnetic materials because of
the ability to combine it with spintronics for the purpose of fundamental spin
transport research, low-power information processing, and potentially future
quantum bit (Qubit) applications. In this work we address the theoretical
grounds of coherent manipulation in practical ferromagnetic systems. We study
electromagnetic radiation driven interaction that is enhanced in the presence
of spin polarized currents and map the conditions that allow coherent
manipulation for which Rabi oscillations take place. The role of the magnetic
anisotropy field is shown to act as an additional oscillatory driving field. We
discuss the Gilbert losses in the context of effective coherence decay rates
and show that it is possible to control these rates by application of a static
spin current. The case of coherent manipulation using oscillatory spin currents
that is free of radiation is discussed as well. Our work paves the way towards
spin current amplification as well as radiation-free coherent control schemes
that may potentially lead to novel Qubits that are robust and scalable.


  In this article we study bounded operators $T$ on Banach space $X$ which
satisfy the discrete Gomilko Shi-Feng condition $$\int_{0}^{2\pi}|\langle
R(re^{it},T)^{2}x,x^*\rangle |dt \leq
\frac{C}{(r^2-1)}\norme{x}\norme{x^*},\quad r>1, x\in X, x^* \in X^*. $$ We
show that it is equivalent to a certain derivative bounded functional calculus
and also to a bounded functional calculus relative to Besov space. Also on
Hilbert space discrete Gomilko Shi-Feng condition is equivalent to
power-boundedness. Finally we discuss the last equivalence on general Banach
space involving the concept of $\gamma$-boundedness.


  When instantons are put into the Higgs phase, vortices are attached to
instantons. We construct such composite solitons as 1/4 BPS states in
five-dimensional supersymmetric U(Nc) gauge theory with Nf(>=Nc) fundamental
hypermultiplets. We solve the hypermultiplet BPS equation and show that all 1/4
BPS solutions are generated by an Nc x Nf matrix which is holomorphic in two
complex variables, assuming the vector multiplet BPS equation does not give
additional moduli. We determine the total moduli space formed by topological
sectors patched together and work out the multi-instanton solution inside a
single vortex with complete moduli. Small instanton singularities are
interpreted as small sigma-model lump singularities inside the vortex. The
relation between monopoles and instantons in the Higgs phase is also clarified
as limits of calorons in the Higgs phase. Another type of instantons stuck at
an intersection of two vortices and dyonic instantons in the Higgs phase are
also discussed.


  Modeling nonlinear spatiotemporal dynamical systems has primarily relied on
partial differential equations (PDEs). However, the explicit formulation of
PDEs for many underexplored processes, such as climate systems, biochemical
reaction and epidemiology, remains uncertain or partially unknown, where very
limited measurement data is yet available. To tackle this challenge, we propose
a novel deep learning architecture that forcibly encodes known physics
knowledge to facilitate learning in a data-driven manner. The coercive encoding
mechanism of physics, which is fundamentally different from the penalty-based
physics-informed learning, ensures the network to rigorously obey given
physics. Instead of using nonlinear activation functions, we propose a novel
elementwise product operation to achieve the nonlinearity of the model.
Numerical experiment demonstrates that the resulting physics-encoded learning
paradigm possesses remarkable robustness against data noise/scarcity and
generalizability compared with some state-of-the-art models for data-driven
modeling.


  Neural Networks have been applied for time series prediction with good
experimental results that indicate the high capacity to approximate functions
with good precision. Most neural models used in these applications use
activation functions with fixed parameters. However, it is known that the
choice of activation function strongly influences the complexity and
performance of the neural network and that a limited number of activation
functions have been used. In this work, we propose the use of a family of free
parameter asymmetric activation functions for neural networks and show that
this family of defined activation functions satisfies the requirements of the
universal approximation theorem. A methodology for the global optimization of
this family of activation functions with free parameter and the weights of the
connections between the processing units of the neural network is used. The
central idea of the proposed methodology is to simultaneously optimize the
weights and the activation function used in a multilayer perceptron network
(MLP), through an approach that combines the advantages of simulated annealing,
tabu search and a local learning algorithm, with the purpose of improving
performance in the adjustment and forecasting of time series. We chose two
learning algorithms: backpropagation with the term momentum (BPM) and
LevenbergMarquardt (LM).


  The stress tensor for the quantized electromagnetic field is calculated in
the region between a pair of dispersive, dielectric half-spaces. This
generalizes the stress tensor for the Casimir energy to the case where the
boundaries have finite reflectivity. We also include the effects of finite
temperature. This allows us to discuss the circumstances under which the weak
energy condition and the null energy condition can be violated in the presence
of finite reflectivity and finite temperature.


  In this note, we aim to extend the previous work on an N-barrier maximum
principle (\cite{hung2015n,hung2015maximum}) to a more general class of systems
of two equations. Moreover, an N-barrier maximum principle for systems of three
equations is established.


  A unified description of NN and pi-N elastic scattering is presented in the
framework of the one solitary boson exchange potential (OSBEP). This model
already successfully applied to analyze NN scattering is now extended to
describe pi-N scattering while also improving its accuracy in the NN domain. We
demonstrate the importance of regularization of pi-N scattering amplitudes
involving Delta isobars and derivative meson-nucleon couplings, as this model
always yields finite amplitudes without recourse to phenomenological form
factors. We find an empirical scaling relation of the meson self interaction
coupling constants consistent with that previously found in the study of NN
scattering. Finally, we demonstrate that the OSBEP model does not contradict
the soft-pion theorems of pi-N scattering.


  We study integrable models solvable by the nested algebraic Bethe ansatz and
possessing $GL(3)$-invariant $R$-matrix. Assuming that the monodromy matrix of
the model can be expanded into series with respect to the inverse spectral
parameter, we define zero modes of the monodromy matrix entries as the first
nontrivial coefficients of this series. Using these zero modes we establish new
relations between form factors of the elements of the monodromy matrix. We
prove that all of them can be obtained from the form factor of a diagonal
matrix element in special limits of Bethe parameters. As a result we obtain
determinant representations for form factors of all the entries of the
monodromy matrix.


  An algebraic modeling system (AMS) is a type of mathematical software for
optimization problems, which allows users to define symbolic mathematical
models in a specific language, instantiate them with given source of data, and
solve them with the aid of external solver engines. With the bursting scale of
business models and increasing need for timeliness, traditional AMSs are not
sufficient to meet the following industry needs: 1) million-variable models
need to be instantiated from raw data very efficiently; 2) Strictly feasible
solution of million-variable models need to be delivered in a rapid manner to
make up-to-date decisions against highly dynamic environments. Grassland is a
rapid AMS that provides an end-to-end solution to tackle these emerged new
challenges. It integrates a parallelized instantiation scheme for large-scale
linear constraints, and a sequential decomposition method that accelerates
model solving exponentially with an acceptable loss of optimality. Extensive
benchmarks on both classical models and real enterprise scenario demonstrate 6
~ 10x speedup of Grassland over state-of-the-art solutions on model
instantiation. Our proposed system has been deployed in the large-scale real
production planning scenario of Huawei. With the aid of our decomposition
method, Grassland successfully accelerated Huawei's million-variable production
planning simulation pipeline from hours to 3 ~ 5 minutes, supporting
near-real-time production plan decision making against highly dynamic
supply-demand environment.


  In this study OpenFOAM 4.4.2 was used to investigate flow inside the coronary
artery of the heart. This step is the first step of our future project, which
is to include conjugate heat transfer of the heart with three main coronary
arteries. Three different velocities were used as inlet boundary conditions to
see the effect of velocity increase on velocity, pressure, and wall shear of
coronary artery. Also, three different fluids namely the University of
Wisconsin solution, Gelatin, and blood was used to investigate the effect of
different fluids on flow inside coronary artery. A code based on Reynolds
Stress Navier Stokes (RANS) equations was written and was implemented with the
real boundary condition that was calculated based on MRI images. In order to
improve the accuracy of the current numerical scheme, hex dominant mesh is
utilized. When the inlet velocity increases to 0.5 m/s, velocity, wall shear
stress, and pressure increase at the narrower parts.


  We have shown that the observed drastic violation of the Curie-Weiss law in
Na2V3O7, reported in Phys. Rev. Lett. 90 (2003) 167202, is caused by
conventional crystal-field interactions and the intra-atomic spin-orbit
coupling of the V4+ ion. The fine discrete electronic structure of the 3d1
configuration, with a substantial orbital moment, is the reason for anomalous
low-temperature properties of Na2V3O7. According to the Quantum Atomistic
Solid-State Theory (QUASST) Na2V3O7 is expected to exhibit pronounced
heavy-fermion phenomena at low temperatures. This study confirm our earlier
claim that the orbital moment has to be unquenched in description of 3d-atom
compounds.
  PACS: 71.70.E, 75.10.D
  Keywords: crystal-field interactions, spin-orbit coupling, orbital moment,
Na2V3O7


  S5 0716+714 is a well known BL Lac object, one of the brightest and most
active blazars. The discovery in the Very High Energy band (VHE, E > 100 GeV)
by MAGIC happened in 2008. In January 2015 the source went through the
brightest optical state ever observed, triggering MAGIC follow-up and a VHE
detection with 13{\sigma} significance (ATel 6999). Rich multiwavelength
coverage of the flare allowed us to construct the broad-band spectral energy
distribution of S5 0716+714 during its brightest outburst. In this work we will
present the preliminary analysis of MAGIC and Fermi-LAT data of the flaring
activity in January and February 2015 for the HE (0.1 < HE < 300 GeV) and VHE
band, together with radio (Mets\"ahovi, OVRO, VLBA, Effelsberg), sub-millimeter
(SMA), optical (Tuorla, Perkins, Steward, AZT-8+ST7, LX-200, Kanata), X-ray and
UV (Swift-XRT and UVOT), in the same time-window and discuss the time
variability of the multiwavelength light curves during this impressive
outburst.


  After fixing a marking (V, W) of a quasifuchsian punctured torus group G, the
complex length l_V and the complex twist tau_V,W parameters define a
holomorphic embedding of the quasifuchsian space QF of punctured tori into C^2.
It is called the complex Fenchel-Nielsen coordinates of QF. For a complex
number c, let Q_gamma,c be the affine subspace of C^2 defined by the linear
equation l_V=c. Then we can consider the linear slice L of QF by QF \cap
Q_gamma,c which is a holomorphic slice of QF. For any positive real value c, L
always contains the so called Bers-Maskit slice BM_gamma,c. In this paper we
show that if c is sufficiently small, then L coincides with BM_gamma,c whereas
L has other components besides BM_gamma,c when c is sufficiently large. We also
observe the scaling property of L.


  Estimating depth from RGB images is a long-standing ill-posed problem, which
has been explored for decades by the computer vision, graphics, and machine
learning communities. In this article, we provide a comprehensive survey of the
recent developments in this field. We will focus on the works which use deep
learning techniques to estimate depth from one or multiple images. Deep
learning, coupled with the availability of large training datasets, have
revolutionized the way the depth reconstruction problem is being approached by
the research community. In this article, we survey more than 100 key
contributions that appeared in the past five years, summarize the most commonly
used pipelines, and discuss their benefits and limitations. In retrospect of
what has been achieved so far, we also conjecture what the future may hold for
learning-based depth reconstruction research.


  We present ISOCAM images (1.5"/px) in the LW2 (6.75 microns) and LW3 (15
microns) broad band filters of the circumnuclear starburst of NGC 4321 (M 100).
A detailed comparison with the emission at other wavelengths (U, B, Halpha, and
CO) is also presented. The nature and intensity of the mid-infrared emission is
shown to be remarkably different from one star forming region to the next. For
instance, star forming regions at the ends of the nuclear bar may contain
enshrouded Wolf-Rayet stars.


  We describe and characterize an equal-fidelity surface in Bloch space
targeted for a qubit state by means of equal-distance concept. The distance is
generalized and defined as the Euclidean distance between extended Bloch
vectors for arbitrary dimensional states. The distance is a genuine distance
according to the definition and is related to other distances between quantum
states and super-fidelity.


  Effect of guiding magnetic field on evolution of stimulated emission is
considered. It is shown that the transverse dynamics of electrons contributes
to generation process and that contribution decreases with the magnetic field
grows. The equation of generation for stimulated radiation of electron beam
passing over periodic medium in magnetic field of arbitrary value is derived.
The critical value of guiding field for which the transverse dynamics of
electron don't contribute to emission is determined. It is shown, that
transverse dynamics of electron modifies the boundary conditions. It follows
from the derived generation equation that transverse dynamics yields to ~25%
increase of the increment magnitude in high gain regime. In the limit of small
signal the generation gain twice increases when transverse dynamics evolves.
Obtained results are valid for every FEL system, which use the mechanism of
waves slowing in the slow-wave structures for generation.


  We constrain the dark energy equation of state parameter, $w$, using the
power spectrum of the thermal Sunyaev-Zeldovich (tSZ) effect. We improve upon
previous analyses by taking into account the trispectrum in the covariance
matrix and marginalising over the foreground parameters, the correlated noise,
the mass bias $B$ in the Planck universal pressure profile, and all the
relevant cosmological parameters (i.e., not just $\Omega_{\mathrm{m}}$ and
$\sigma_8$). We find that the amplitude of the tSZ power spectrum at
$\ell\lesssim 10^3$ depends primarily on $F\equiv
\sigma_{8}(\Omega_{{\mathrm{m}}}/B)^{0.40}h^{-0.21}$, where $B$ is related to
more commonly used variable $b$ by $B=(1-b)^{-1}$. We measure this parameter
with 2.6\% precision, $F=0.460\pm 0.012$ (68% CL). By fixing the bias to
$B=1.25$ and adding the local determination of the Hubble constant $H_0$ and
the amplitude of the primordial power spectrum constrained by the Planck Cosmic
Microwave Background (CMB) data, we find $w=-1.10\pm0.12$,
$\sigma_{\mathrm{8}}=0.802\pm0.037$, and $\Omega_{{\mathrm{m}}}=0.265\pm0.022$
(68% CL). Our limit on $w$ is consistent with and is as tight as that from the
distance-alone constraint from the CMB and $H_0$. Finally, by combining the tSZ
power spectrum and the CMB data we find, in the $\Lambda$ Cold Dark Matter
(CDM) model, the mass bias of $B=1.71\pm 0.17$, i.e., $1-b=0.58\pm 0.06$ (68%
CL).


  The open critical XXZ spin chain with a general right boundary and a trivial
diagonal left boundary is considered. Within this framework we propose a simple
computation of the exact generic boundary S-matrix (with diagonal and
non-diagonal entries), starting from the `bare' Bethe ansatz equations. Our
results coincide with the ones obtained by Ghoshal and Zamolodchikov, after
assuming suitable identifications of the bulk and boundary parameters.


  We found general solutions of matter stress-energy (non-)conservation in
scalar-tensor FLRW-type cosmological models by extending the logotropic
formalism to the case of non-minimal coupling between the scalar field and new
dark fluid candidates. The energy conditions expressed by the generating
function are introduced. Next, we investigate the possibility of separating
baryonic from dark matter and explain their ratio as a chameleon effect in the
presence of non-minimal coupling. To answer the question affirmatively we
analyze simple extensions of the $\Lambda$-CDM model by adding a non-minimally
coupled scalar field in the Einstein frame. Two scenarios involving either a
scalaron (quintessence) or a phantom (ghost) are numerically solved and
compared. As a result, it is shown that in both cases LCDM model can be
reproduced with a high accuracy in the region covered by observations. As
expected, in the case of the phantom (ghost) field the Big-Bang scenario is
replaced by the (matter) Bounce.


  The intelligent reflecting surface (IRS) is emphasized as a controlled
scattering cluster. To this end, scatterers and traveling paths of multipath
components are classified to build a new channel model. Unlike the conventional
modeling, where the channels between system units are modeled independently,
the new model considers the channel as a whole and decomposes it based on the
traveling paths. The model shows clearly how IRS, in the beam-space context,
converts the channel from a problem into a design element. After investigating
IRS as a scattering cluster, based on a proposed segmentation scheme, the
beamforming problem is considered with a focus on first-order reflections.
Passive beamforming at IRS is shown to have two tiers; at the scatterer and
antenna levels. A segment-activation scheme is proposed to maximize the
received signal power, where the number of transmitting antenna elements to be
used is given as a function of IRS positioning and beamforming at the receiver.
The results show that while using more transmitting antenna elements to get
narrower beams is possible, using fewer elements can give better performance,
especially for larger IRS at close distances. The developed model also proves
useful in addressing emerging issues in massive MIMO communication, namely,
stationarity and spherical wavefronts.


  Based on our high resolution, two-dimensional hydrodynamical simulations, we
propose that large cavities may be formed by the nonlinear development of the
combined thermal and gravitational instabilities, without need for stellar
energy injection in a galaxy modeling the Large Magellanic Clouds (LMC). Our
numerical model of the star formation allows us to follow the evolution of the
blastwaves due to supernovae in the inhomogeous, multi-phase, and
turbulent-like media self-consistently. Formation of kpc-scale inhomogeneity,
such as cavities, observed HI map of the LMC, is suppressed by frequent
supernovae (average supernova rate for the whole disk is ~0.001/yr). However
the supernova explosions are necessary for the hot component (T_g > 10^{6-7}
K). Position-velocity maps show that kpc-scale shells/arcs formed through the
nonlinear evolution in a model without stellar energy feedback has similar
kinematics to explosional phenomena, such as supernovae. We also find that
dense clumps and filamentary structure are formed due to a natural consequence
of the non-linear evolution of the multi-phage ISM. Although the ISM in a small
scale looks turbulent-like and transient, the global structure of the ISM is
quasi-stable. We compare the observations of HI and molecular gas of the LMC
with the numerically obtained HI and CO map. The morphology and statistical
properties of the numerical HI and CO maps are discussed.


  The product $s_\mu s_\nu$ of two Schur functions is one of the most famous
examples of a Schur-positive function, i.e. a symmetric function which, when
written as a linear combination of Schur functions, has all positive
coefficients.
  We ask when expressions of the form $s_\lambda s_\rho - s_\mu s_\nu$ are
Schur-positive. This general question seems to be a difficult one, but a
conjecture of Fomin, Fulton, Li and Poon says that it is the case at least when
$\lambda$ and $\rho$ are obtained from $\mu$ and $\nu$ by redistributing the
parts of $\mu$ and $\nu$ in a specific, yet natural, way. We show that their
conjecture is true in several significant cases. We also formulate a skew-shape
extension of their conjecture, and prove several results which serve as
evidence in favor of this extension. Finally, we take a more global view by
studying two classes of partially ordered sets suggested by these questions.


  Two mathematical models of macroevolution are studied. These models have
population dynamics at the species level, and mutations and extinction of
species are also included. The population dynamics are updated by difference
equations with stochastic noise terms that characterize population
fluctuations. The effects of the stochastic population fluctuations on
diversity and total population sizes on evolutionary time scales are studied.
In one model, species can make either predator-prey, mutualistic, or
competitive interactions, while the other model allows only predator-prey
interactions. When the noise in the population dynamics is strong enough, both
models show intermittent behavior and their power spectral densities show
approximate $1/f$ fluctuations. In the noiseless limit, the two models have
different power spectral densities. For the predator-prey model, $1/f^2$
fluctuations appears, indicating random-walk like behavior, while the other
model still shows $1/f$ noise. These results indicate that stochastic
population fluctuations may significantly affect long-time evolutionary
dynamics.


  We consider MultiCriteria Decision Analysis models which are defined over
discrete attributes, taking a finite number of values. We do not assume that
the model is monotonically increasing with respect to the attributes values.
Our aim is to define an importance index for such general models, considering
that they are equivalent to $k$-ary games (multichoice games). We show that
classical solutions like the Shapley value are not suitable for such models,
essentially because of the efficiency axiom which does not make sense in this
context. We propose an importance index which is a kind of average variation of
the model along the attributes. We give an axiomatic characterization of it.


  Any generic closed curve in the plane can be transformed into a simple closed
curve by a finite sequence of local transformations called homotopy moves. We
prove that simplifying a planar closed curve with $n$ self-crossings requires
$\Theta(n^{3/2})$ homotopy moves in the worst case. Our algorithm improves the
best previous upper bound $O(n^2)$, which is already implicit in the classical
work of Steinitz; the matching lower bound follows from the construction of
closed curves with large defect, a topological invariant of generic closed
curves introduced by Aicardi and Arnold. Our lower bound also implies that
$\Omega(n^{3/2})$ facial electrical transformations are required to reduce any
plane graph with treewidth $\Omega(\sqrt{n})$ to a single vertex, matching
known upper bounds for rectangular and cylindrical grid graphs. More generally,
we prove that transforming one immersion of $k$ circles with at most $n$
self-crossings into another requires $\Theta(n^{3/2} + nk + k^2)$ homotopy
moves in the worst case. Finally, we prove that transforming one
noncontractible closed curve to another on any orientable surface requires
$\Omega(n^2)$ homotopy moves in the worst case; this lower bound is tight if
the curve is homotopic to a simple closed curve.


  Artin-Tits groups act on a certain delta-hyperbolic complex, called the
"additional length complex". For an element of the group, acting loxodromically
on this complex is a property analogous to the property of being pseudo-Anosov
for elements of mapping class groups. By analogy with a well-known conjecture
about mapping class groups, we conjecture that "most" elements of Artin-Tits
groups act loxodromically. More precisely, in the Cayley graph of a subgroup
$G$ of an Artin-Tits group, the proportion of loxodromically acting elements in
a ball of large radius should tend to one as the radius tends to infinity. In
this paper, we give a condition guaranteeing that this proportion stays away
from zero. This condition is satisfied e.g. for Artin-Tits groups of spherical
type, their pure subgroups and some of their commutator subgroups.


  We demonstrate a series of InGaN/GaN double quantum well nanostructure
elements. We grow a layer of 2 {\mu}m undoped GaN template on top of a
(0001)-direction sapphire substrate. A 100 nm SiO2 thin film is deposited on
top as a masking pattern layer. This layer is then covered with a 300 nm
aluminum layer as the anodic aluminum oxide (AAO) hole pattern layer. After
oxalic acid etching, we transfer the hole pattern from the AAO layer to the
SiO2 layer by reactive ion etching. Lastly, we utilize metal-organic chemical
vapor deposition to grow GaN nanorods approximately 1.5 {\mu}m in size. We then
grow two layers of InGaN/GaN double quantum wells on the semi-polar face of the
GaN nanorod substrate under different temperatures. We then study the
characteristics of the InGaN/GaN quantum wells formed on the semi-polar faces
of GaN nanorods. We report the following findings from our study: first, using
SiO2 with repeating hole pattern, we are able to grow high-quality GaN nanorods
with diameters of approximately 80-120 nm; second, photoluminescence (PL)
measurements enable us to identify Fabry-Perot effect from InGaN/GaN quantum
wells on the semi-polar face. We calculate the quantum wells' cavity thickness
with obtained PL measurements. Lastly, high resolution TEM images allow us to
study the lattice structure characteristics of InGaN/GaN quantum wells on GaN
nanorod and identify the existence of threading dislocations in the lattice
structure that affects the GaN nanorod's growth mechanism.


  We consider optimization of linear stability of synchronized states between a
pair of weakly coupled limit-cycle oscillators with cross coupling, where
different components of state variables of the oscillators are allowed to
interact. On the basis of the phase reduction theory, the coupling matrix
between different components of the oscillator states that maximizes the linear
stability of the synchronized state under given constraints on overall coupling
intensity and on stationary phase difference is derived. The improvement in the
linear stability is illustrated by using several types of limit-cycle
oscillators as examples.


  Proceedings of the AAAI Fall Symposium on Artificial Intelligence in
Government and Public Sector, Arlington, Virginia, USA, October 18-20, 2018


  Exploration is crucial for training the optimal reinforcement learning (RL)
policy, where the key is to discriminate whether a state visiting is novel.
Most previous work focuses on designing heuristic rules or distance metrics to
check whether a state is novel without considering such a discrimination
process that can be learned. In this paper, we propose a novel method called
generative adversarial exploration (GAEX) to encourage exploration in RL via
introducing an intrinsic reward output from a generative adversarial network,
where the generator provides fake samples of states that help discriminator
identify those less frequently visited states. Thus the agent is encouraged to
visit those states which the discriminator is less confident to judge as
visited. GAEX is easy to implement and of high training efficiency. In our
experiments, we apply GAEX into DQN and the DQN-GAEX algorithm achieves
convincing performance on challenging exploration problems, including the game
Venture, Montezuma's Revenge and Super Mario Bros, without further fine-tuning
on complicate learning algorithms. To our knowledge, this is the first work to
employ GAN in RL exploration problems.


  We present our analysis on high energy hadron-hadron scattering in the
framework of the holographic QCD. Combining the Brower-Polchinski-Strassler-Tan
Pomeron exchange kernel and gravitational form factors of the involved hadrons
which are obtained by using the bottom-up AdS/QCD models in the
five-dimensional AdS space, we calculate the total cross sections at high
energies. We show that our calculations for the nucleon-nucleon scattering
agree with the experimental data including the recent ones taken by the TOTEM
collaboration at the LHC. The present framework is applicable to any high
energy process, in which the strong interaction can be approximated by the
Pomeron exchange. We present the results for the pion-nucleon and pion-pion
scattering as examples, which can be obtained without any additional parameter
because all the adjustable parameters are fixed via the analysis on the
nucleon-nucleon scattering. The resulting total cross section ratios are
$\sigma_{tot}^{\pi N} / \sigma_{tot}^{N N} = 0.63$ and $\sigma_{tot}^{\pi \pi}
/ \sigma_{tot}^{N N} = 0.45$, respectively.