  Personalization plays an important role in many services, just as news does.
Many studies have examined news personalization algorithms, but few have
considered practical environments. This paper provides algorithms and system
architecture for generating immediate personalized news in a practical
environment. Immediacy means changes in news trends and user interests are
reflected in recommended news lists quickly. Since news trends and user
interests rapidly change, immediacy is critical in news personalization
applications. We develop algorithms and system architecture to realize
immediacy. Our algorithms are based on collaborative filtering of user clusters
and evaluate news articles using click-through rate and decay scores based on
the time elapsed since the user's last access. Existing studies have not fully
discussed system architecture, so a major contribution of this paper is that we
demonstrate a system architecture and realize our algorithms and a
configuration example implemented on top of Amazon Web Services. We evaluate
the proposed method both offline and online. The offline experiments are
conducted through a real-world dataset from a commercial news delivery service,
and online experiments are conducted via A/B testing on production
environments. We confirm the effectiveness of our proposed method and also that
our system architecture can operate in large-scale production environments.


  Just like everything in the nature, scientific topics flourish and perish.
While existing literature well captures article's life-cycle via citation
patterns, little is known about how scientific popularity and impact evolves
for a specific topic. It would be most intuitive if we could `feel' topic's
activity just as we perceive the weather by temperature. Here, we conceive
knowledge temperature to quantify topic overall popularity and impact through
citation network dynamics. Knowledge temperature includes 2 parts. One part
depicts lasting impact by assessing knowledge accumulation with an analogy
between topic evolution and isobaric expansion. The other part gauges temporal
changes in knowledge structure, an embodiment of short-term popularity, through
the rate of entropy change with internal energy, 2 thermodynamic variables
approximated via node degree and edge number. Our analysis of representative
topics with size ranging from 1000 to over 30000 articles reveals that the key
to flourishing is topics' ability in accumulating useful information for future
knowledge generation. Topics particularly experience temperature surges when
their knowledge structure is altered by influential articles. The spike is
especially obvious when there appears a single non-trivial novel research focus
or merging in topic structure. Overall, knowledge temperature manifests topics'
distinct evolutionary cycles.


  In this paper, we deal with first and second-order alignment models with
non-universal interaction, time delay and possible lack of connection between
the agents. More precisely, we analyze the situation in which the system's
agents do not transmit information to all the other agents and also agents that
are linked to each other can suspend their interaction at certain times.
Moreover, we take into account of possible time lags in the interactions. To
deal with the considered "non-universal" connection, a graph topology over the
structure of the model has to be considered. Under a so-called Persistence
Excitation Condition, we establish the exponential convergence to consensus for
both models whenever the digraph that describes the interaction between the
agents is strongly connected.


  We illustrate the use of recursion relations in the computation of certain
one-loop helicity amplitudes containing an arbitrary number of gauge bosons.
After a brief review of the recursion relations themselves, we discuss the
resolution of the apparent conflict between the spinor helicity method used to
solve the recursion relations and the dimensional regulator used in the loop
integrals. We then outline the procedure for constructing loop amplitudes, and
present two examples of results obtained in this manner.


  Pre-trained language models (PLMs) have gained increasing popularity due to
their compelling prediction performance in diverse natural language processing
(NLP) tasks. When formulating a PLM-based prediction pipeline for NLP tasks, it
is also crucial for the pipeline to minimize the calibration error, especially
in safety-critical applications. That is, the pipeline should reliably indicate
when we can trust its predictions. In particular, there are various
considerations behind the pipeline: (1) the choice and (2) the size of PLM, (3)
the choice of uncertainty quantifier, (4) the choice of fine-tuning loss, and
many more. Although prior work has looked into some of these considerations,
they usually draw conclusions based on a limited scope of empirical studies.
There still lacks a holistic analysis on how to compose a well-calibrated
PLM-based prediction pipeline. To fill this void, we compare a wide range of
popular options for each consideration based on three prevalent NLP
classification tasks and the setting of domain shift. In response, we recommend
the following: (1) use ELECTRA for PLM encoding, (2) use larger PLMs if
possible, (3) use Temp Scaling as the uncertainty quantifier, and (4) use Focal
Loss for fine-tuning.


  In this paper, we present a novel neural network using multi scale feature
fusion at various scales for accurate and efficient semantic image
segmentation. We used ResNet based feature extractor, dilated convolutional
layers in downsampling part, atrous convolutional layers in the upsampling part
and used concat operation to merge them. A new attention module is proposed to
encode more contextual information and enhance the receptive field of the
network. We present an in depth theoretical analysis of our network with
training and optimization details. Our network was trained and tested on the
Camvid dataset and Cityscapes dataset using mean accuracy per class and
Intersection Over Union (IOU) as the evaluation metrics. Our model outperforms
previous state of the art methods on semantic segmentation achieving mean IOU
value of 74.12 while running at >100 FPS.


  Traffic light detection is essential for self-driving cars to navigate safely
in urban areas. Publicly available traffic light datasets are inadequate for
the development of algorithms for detecting distant traffic lights that provide
important navigation information. We introduce a novel benchmark traffic light
dataset captured using a synchronized pair of narrow-angle and wide-angle
cameras covering urban and semi-urban roads. We provide 1032 images for
training and 813 synchronized image pairs for testing. Additionally, we provide
synchronized video pairs for qualitative analysis. The dataset includes images
of resolution 1920$\times$1080 covering 10 different classes. Furthermore, we
propose a post-processing algorithm for combining outputs from the two cameras.
Results show that our technique can strike a balance between speed and
accuracy, compared to the conventional approach of using a single camera frame.


  We study the problem of determination of the sign of Delta m^2_{31}, or the
neutrino mass hierarchy, through observations of atmospheric neutrinos in
future detectors. We consider two proposed detector types :
  (a) Megaton sized water Cerenkov detectors, which can measure the survival
rates of nu_\mu + \bar{\nu}_\mu and nu_e + \bar{\nu}_e and (b) 100 kton sized
magnetized iron detectors, which can measure the survival rates of \nu_\mu and
\bar{\nu}_\mu. For energies and path-lengths relevant to atmospheric neutrinos,
these rates obtain significant matter contributions from P_{\mu e}, P_{\mu \mu}
and P_{ee}, leading to an appreciable sensitivity to the hierarchy. We do a
binned \chi^2 analysis of simulated data in these two types of detectors which
includes the effect of smearing in neutrino energy and direction and
incorporates detector efficiencies and relevant statistical, theoretical and
systematic errors. We also marginalize the \chi^2 over the allowed ranges of
neutrino parameters in order to accurately account for their uncertainties.
Finally, we compare the performance of both types of detectors vis a vis the
hierarchy determination.


  A family of completely integrable nonlinear deformations of systems of N
harmonic oscillators are constructed from the non-standard quantum deformation
of the sl(2,R) algebra. Explicit expressions for all the associated integrals
of motion are given, and the long-range nature of the interactions introduced
by the deformation is shown to be linked to the underlying coalgebra structure.
Separability and superintegrability properties of such systems are analysed,
and their connection with classical angular momentum chains is used to
construct a non-standard integrable deformation of the XXX hyperbolic Gaudin
system.


  A topological insulator Bi2Se3 which shows superconducting property due to Cu
doping has been studied using high resolution transmission electron microscopy
which provides for the first time, the direct evidence of intercalation sites.
A careful study exhibits the presence of two intercalation sites in this
material when projected along the [-1-1 2 0] direction. Stoichiometry of the
intercalated unit cell was also calculated from the projected potential.


  We suggest that four dimensional massive gauge vectors could be described by
coupling ordinary Yang-Mills theory to a topological gauge theory. For this the
coupling should excite a nontrivial degree of freedom from the topological
theory, corresponding to the longitudinal polarization of a massive gauge
vector. If the coupling can be selected so that further degrees of freedom are
not excited, one may entirely avoid particles such as the Higgs. Here we
discuss a simple example of this idea, obtained by coupling standard Yang-Mills
theory to the topological gauge theory of flat connections. We propose that our
example might describe a renormalizable theory of massive gauge vectors with no
additional physical degrees of freedom.


  We establish a blow-up formula for Hodge cohomology of locally free sheaves
on smooth proper varieties over an algebraically closed field of positive
characteristic. For this, we introduce a notion of relative Hodge sheaves and
study their behavior under blow-ups along smooth centers. In particular, as an
application, we study the blow-up invariance of the $E_2$-degeneracy of the
Hochschild--Kostant--Rosenberg spectral sequence for smooth proper varieties.


  This paper is devoted to the asymptotic analysis of a fractional version of
the Ginzburg-Landau equation in bounded domains, where the Laplacian is
replaced by an integro-differential operator related to the square root
Laplacian as defined in Fourier space. In the singular Ginzburg-Landau limit,
we show that solutions with uniformly bounded energy converge weakly to sphere
valued 1/2-harmonic maps, i.e., the fractional analogues of the usual harmonic
maps. In addition, the convergence holds in smooth functions spaces away from a
(n-1)-rectifiable closed set of finite (n-1)-Hausdorff measure. The proof
relies on the representation of the square root Laplacian as a
Dirichlet-to-Neumann operator in one more dimension, and on the analysis of a
boundary version of the Ginzburg-Landau equation. Besides the analysis of the
fractional Ginzburg-Landau equation, we also give a general partial regularity
result for stationary 1/2-harmonic maps in arbitrary dimension.


  We establish a connection between the scattering inverse problem and the
determination of the distribution of the position of the Levy process at the
exit time of a bounded interval in term of its Levy exponent.


  The original "magic identities" are due to J.M.Drummond, J.Henn, V.A.Smirnov
and E.Sokatchev; they assert that all n-loop box integrals for four scalar
massless particles are equal to each other [DHSS]. The authors give a proof of
the magic identities for the Euclidean metric case only and claim that the
result is also true in the Minkowski metric. However, the Minkowski case is
much more subtle and requires specification of the relative positions of cycles
of integration to make these identities correct. In this article we prove the
magic identities in the Minkowski metric case and, in particular, specify the
cycles of integration.
  Our proof of magic identities relies on previous results from [L1, L2], where
we give a mathematical interpretation of the n-loop box integrals in the
context of representations of a Lie group U(2,2) and quaternionic analysis. The
main result of [L1, L2] is a (weaker) operator version of the "magic
identities".
  No prior knowledge of physics or Feynman diagrams is assumed from the reader.
We provide a summary of all relevant results from quaternionic analysis to make
the article self-contained.


  We construct an example of a group $G = \mathbb{Z}^2 \times G_0$ for a finite
abelian group $G_0$, a subset $E$ of $G_0$, and two finite subsets $F_1,F_2$ of
$G$, such that it is undecidable in ZFC whether $\mathbb{Z}^2\times E$ can be
tiled by translations of $F_1,F_2$. In particular, this implies that this
tiling problem is aperiodic, in the sense that (in the standard universe of
ZFC) there exist translational tilings of $E$ by the tiles $F_1,F_2$, but no
periodic tilings. Previously, such aperiodic or undecidable translational
tilings were only constructed for sets of eleven or more tiles (mostly in
$\mathbb{Z}^2$). A similar construction also applies for $G = \mathbb{Z}^d$ for
sufficiently large $d$. If one allows the group $G_0$ to be non-abelian, a
variant of the construction produces an undecidable translational tiling with
only one tile $F$.
  The argument proceeds by first observing that a single tiling equation is
able to encode an arbitrary system of tiling equations, which in turn can
encode an arbitrary system of certain functional equations once one has two or
more tiles. In particular, one can use two tiles to encode tiling problems for
an arbitrary number of tiles.


  The Windows Scheduling Problem, also known as the Pinwheel Problem, is to
schedule periodic jobs subject to their processing frequency demands. Instances
are given as a set of jobs that have to be processed infinitely often such that
the time interval between two consecutive executions of the same job j is no
longer than the job's given period $p_j$.
  The key contribution of this work is a new interpretation of the problem
variant with exact periods, where the time interval between consecutive
executions must be strictly $p_j$. We show that this version is equivalent to a
natural combinatorial problem we call Partial Coding. Reductions in both
directions can be realized in polynomial time, so that both hardness proofs and
algorithms for Partial Coding transfer to Windows Scheduling.
  Applying this new perspective, we obtain a number of new results regarding
the computational complexity of various Windows Scheduling Problem variants. We
prove that even the case of one processor and unit-length jobs does not admit a
pseudo-polynomial time algorithm unless SAT can be solved by a randomized
method in expected quasi-polynomial time. This result also extends to the case
of inexact periods, which answers a question that has remained open for more
than two decades. Furthermore, we report an error found in a hardness proof
previously given for the multi-machine case without machine migration, and we
show that this variant reduces to the single-machine case. Finally, we prove
that even with unit-length jobs the problem is co-NP-hard when jobs are allowed
to migrate between machines.


  Recently, reinforcement learning models have achieved great success,
mastering complex tasks such as Go and other games with higher scores than
human players. Many of these models store considerable data on the tasks and
achieve high performance by extracting visual and time-series features using
convolutional neural networks (CNNs) and recurrent neural networks,
respectively. However, these networks have very high computational costs
because they need to be trained by repeatedly using the stored data. In this
study, we propose a novel practical approach called reinforcement learning with
convolutional reservoir computing (RCRC) model. The RCRC model uses a fixed
random-weight CNN and a reservoir computing model to extract visual and
time-series features. Using these extracted features, it decides actions with
an evolution strategy method. Thereby, the RCRC model has several desirable
features: (1) there is no need to train the feature extractor, (2) there is no
need to store training data, (3) it can take a wide range of actions, and (4)
there is only a single task-dependent weight parameter to be trained.
Furthermore, we show the RCRC model can solve multiple reinforcement learning
tasks with a completely identical feature extractor.


  Stochastic gradient methods are scalable for solving large-scale optimization
problems that involve empirical expectations of loss functions. Existing
results mainly apply to optimization problems where the objectives are one- or
two-level expectations. In this paper, we consider the multi-level
compositional optimization problem that involves compositions of multi-level
component functions and nested expectations over a random path. It finds
applications in risk-averse optimization and sequential planning. We propose a
class of multi-level stochastic gradient methods that are motivated from the
method of multi-timescale stochastic approximation. First we propose a basic
$T$-level stochastic compositional gradient algorithm, establish its almost
sure convergence and obtain an $n$-iteration error bound $O (n^{-1/2^T})$. Then
we develop accelerated multi-level stochastic gradient methods by using an
extrapolation-interpolation scheme to take advantage of the smoothness of
individual component functions. When all component functions are smooth, we
show that the convergence rate improves to $O(n^{-4/(7+T)})$ for general
objectives and $O (n^{-4/(3+T)})$ for strongly convex objectives. We also
provide almost sure convergence and rate of convergence results for nonconvex
problems. The proposed methods and theoretical results are validated using
numerical experiments.


  We have constructed a heavy baryon effective field theory with photon as an
external field in accordance with the symmetry requirements similar to the
heavy quark effective field theory. By treating the heavy baryon and
anti-baryon equally on the same footing in the effective field theory, we have
calculated the spin polarisabilities $\gamma_i, i=1...4$ of the nucleon at
third order and at fourth-order of the spin-dependent Compton scattering. At
leading order (LO), our results agree with the corresponding results of the
heavy baryon chiral perturbation theory, at the next-to-leading order(NLO) the
results show a large correction to the ones in the heavy baryon chiral
perturbation theory due to baryon-antibaryon coupling terms. The low energy
theorem is satisfied both at LO and at NLO. The contributions arising from the
heavy baryon-antibaryon vertex were found to be significant and the results of
the polarisabilities obtained from our theory is much closer to the
experimental data.


  Conventional Synthetic Aperture Radar (SAR) systems are limited in their
ability to satisfy the increasing requirement for improved spatial resolution
and wider coverage. The demand for high resolution requires high sampling
rates, while coverage is limited by the pulse repetition frequency.
Consequently, sampling rate reduction is of high practical value in SAR
imaging. In this paper, we introduce a new algorithm, equivalent to the
well-known Range-Doppler method, to process SAR data using the Fourier series
coefficients of the raw signals. We then demonstrate how to exploit the
algorithm features to reduce sampling rate in both range and azimuth axes and
process the signals at sub-Nyquist rates, by using compressed sensing (CS)
tools. In particular, we demonstrate recovery of an image using only a portion
of the received signal's bandwidth and also while dropping a large percentage
of the transmitted pulses. The complementary pulses may be used to capture
other scenes within the same coherent processing interval. In addition, we
propose exploiting the ability to reconstruct the image from narrow bands in
order to dynamically adapt the transmitted waveform energy to vacant spectral
bands, paving the way to cognitive SAR. The proposed recovery algorithms form a
new CS-SAR imaging method that can be applied to high-resolution SAR data
acquired at sub-Nyquist rates in range and azimuth. The performance of our
method is assessed using simulated and real data sets. Finally, our approach is
implemented in hardware using a previously suggested Xampling radar prototype.


  We develop a local cohomology theory for FI$^m$-modules, and show that it in
many ways mimics the classical theory for multi-graded modules over a
polynomial ring. In particular, we define an invariant of FI$^m$-modules using
this local cohomology theory which closely resembles an invariant of
multi-graded modules over Cox rings defined by Maclagan and Smith. It is then
shown that this invariant behaves almost identically to the invariant of
Maclagan and Smith.


  This revision includes clarified exposition and simplified analysis.
Solutions of the Einstein equations which are periodic and have standing
gravitational waves are valuable approximations to more physically realistic
solutions with outgoing waves. A variational principle is found which has the
power to provide an accurate estimate of the relationship between the mass and
angular momentum of the system, the masses and angular momenta of the
components, the rotational frequency of the frame of reference in which the
system is periodic, the frequency of the periodicity of the system, and the
amplitude and phase of each multipole component of gravitational radiation.
Examination of the boundary terms of the variational principle leads to
definitions of the effective mass and effective angular momentum of a periodic
geometry which capture the concepts of mass and angular momentum of the source
alone with no contribution from the gravitational radiation. These effective
quantities are surface integrals in the weak-field zone which are independent
of the surface over which they are evaluated, through second order in the
deviations of the metric from flat space.


  Can the behavior of a random matrix be improved by modifying a small fraction
of its entries? Consider a random matrix $A$ with i.i.d. entries. We show that
the operator norm of $A$ can be reduced to the optimal order $O(\sqrt{n})$ by
zeroing out a small submatrix of $A$ if and only if the entries have zero mean
and finite variance. Moreover, we obtain an almost optimal dependence between
the size of the removed submatrix and the resulting operator norm. Our approach
utilizes the cut norm and Grothendieck-Pietsch factorization for matrices, and
it combines the methods developed recently by C. Le and R. Vershynin and by E.
Rebrova and K. Tikhomirov.


  Having completed my search for faint PNe in the LMC, including the outer 64
deg2 area not covered in the original UKST survey, I now have the most complete
number of PNe within any galaxy with which to assess stellar parameters. I
present preliminary estimates for planetary nebula central star temperatures
for 688 LMC PNe using the excitation class parameter derived from emission
lines in the nebula. These are then compared to a photoionisation model in
order to evaluate the contribution of metallicity when determining stellar
temperatures using only emission lines. I include measurements from my latest
confirmatory spectroscopic observations which have yielded a further 110 new
LMC PNe while confirming the 102 previously known PNe in the outer LMC. These
observations, providing low and medium resolution spectra from 3650Ang to
6900Ang, have been added to my comparable data for PNe in the central 25deg2 of
the LMC. The combined data were used to measure fluxes in prepa- ration for a
number of projects related to luminosity functions, chemical abundances,
central star properties and LMC kinematics. Here I provide a preliminary look
at the range of derived central star effective temperature estimates. I also
show a correlation between the central star temperatures and the expansion
velocity of the nebula.


  Recent advancements in learning Discrete Representations as opposed to
continuous ones have led to state of art results in tasks that involve
Language, Audio and Vision. Some latent factors such as words, phonemes and
shapes are better represented by discrete latent variables as opposed to
continuous. Vector Quantized Variational Autoencoders (VQVAE) have produced
remarkable results in multiple domains. VQVAE learns a prior distribution $z_e$
along with its mapping to a discrete number of $K$ vectors (Vector
Quantization). We propose applying VQ along the feature axis. We hypothesize
that by doing so, we are learning a mapping between the codebook vectors and
the marginal distribution of the prior feature space. Our approach leads to
33\% improvement as compared to prevous discrete models and has similar
performance to state of the art auto-regressive models (e.g. PixelSNAIL). We
evaluate our approach on a static prior using an artificial toy dataset
(blobs). We further evaluate our approach on benchmarks for CIFAR-10 and
ImageNet.


  Chemical design of SiO2-based glasses with high elastic moduli and low weight
is of great interest. However, it is difficult to find a universal expression
to predict the elastic moduli according to the glass composition before
synthesis since the elastic moduli are a complex function of interatomic bonds
and their ordering at different length scales. Here we show that the densities
and elastic moduli of SiO2-based glasses can be efficiently predicted by
machine learning (ML) techniques across a complex compositional space with
multiple (>10) types of additive oxides besides SiO2. Our machine learning
approach relies on a training set generated by high-throughput molecular
dynamic (MD) simulations, a set of elaborately constructed descriptors that
bridges the empirical statistical modeling with the fundamental physics of
interatomic bonding, and a statistical learning/predicting model developed by
implementing least absolute shrinkage and selection operator with a gradient
boost machine (GBM-LASSO). The predictions of the ML model are comprehensively
compared and validated with a large amount of both simulation and experimental
data. By just training with a dataset only composed of binary and ternary glass
samples, our model shows very promising capabilities to predict the density and
elastic moduli for k-nary SiO2-based glasses beyond the training set. As an
example of its potential applications, our GBM-LASSO model was used to perform
a rapid and low-cost screening of many (~105) compositions of a multicomponent
glass system to construct a compositional-property database that allows for a
fruitful overview on the glass density and elastic properties.


  We discuss the signatures of a representative Higgsless model with ideal
fermion delocalization in vector-boson fusion processes, focusing on the gold-
and silver-plated decay modes of the gauge bosons at the CERN-Large Hadron
Collider. For this purpose, we have developed a fully-flexible parton-level
Monte-Carlo program, which allows for the calculation of cross sections and
kinematic distributions within experimentally feasible selection cuts at
NLO-QCD accuracy. We find that Kaluza-Klein resonances give rise to very
distinctive distributions of the decay leptons. Similar to the Standard Model
case, within the Higgsless scenario the perturbative treatment of the
vector-boson scattering processes is under excellent control.


  We show that a behaviour analogous to degenerate hyperbolicity can occur in
nearest-neighbour cellular automata (CA) with three states. We construct a
3-state rule by "lifting" elementary CA rule 140. Such "lifted" rule is
equivalent to rule 140 when arguments are restricted to two symbols, otherwise
it behaves as identity. We analyze the structure of multi-step preimages of 0,
1 and 2 under this rule by using minimal finite state machines (FSM), and
exploit regularities found in these FSM. This allows to construct explicit
expressions for densities of 0s and 1s after $n$ iterations of the rule
starting from Bernoulli distribution. When the initial Bernoulli distribution
is symmetric, the densities of all three symbols converge to their stationary
values in linearly-exponential fashion, similarly as in finite-dimensional
dynamical systems with hyperbolic fixed point with degenerate eigenvalues.


  It is shown that the recently obtained quantum wave functionals in terms of
the CJZ variables for generic 2d dilaton gravity are equivalent to the
previously reported exact quantum wave functionals in geometrical variables. A
third representation of these exact quantum states is also presented.


  The Alesker-Bernig-Schuster theorem asserts that each irreducible
representation of the special orthogonal group appears with multiplicity at
most one as a subrepresentation of the space of continuous
translation-invariant valuations with fixed degree of homogeneity. Moreover,
the theorem describes in terms of highest weights which irreducible
representations appear with multiplicity one. In this paper, we present a
refinement of this result, namely the explicit construction of a highest weight
vector in each irreducible subrepresentation. We then describe how important
natural operations on valuations (pullback, pushforward, Fourier transform,
Lefschetz operator, Alesker-Poincar\'e pairing) act on these highest weight
vectors. We use this information to prove the Hodge-Riemann relations for
valuations in the case of Euclidean balls as reference bodies. Since special
cases of the Hodge-Riemann relations have recently been used to prove new
geometric inequalities for convex bodies, our work immediately extends the
scope of these inequalities.


  This paper aims to improve a theorem of Janos Kollar. For a given Complex
projective threefold X of general type, suppose the plurigenus p_k(X)\ge 2,
Kollar proved that the (11k+5)-canonical map is birational. Here we show that
either the (7k+3)-canonical map or the (7k+5)-canonical map is birational and
that the m-canonical map is stably birational for m\ge 13k+6. If P_k(X)\ge 3,
then the m-canonical map is stably birational for m\ge 10k+8. In particular,
the 12-canonical map is birational when p_g(X)\ge 2 and the 11-canonical map is
birational when p_g(X)\ge 3.


  Pose estimation is a vital step in many robotics and perception tasks such as
robotic manipulation, autonomous vehicle navigation, etc. Current
state-of-the-art pose estimation methods rely on deep neural networks with
complicated structures and long inference times. While highly robust, they
require computing power often unavailable on mobile robots. We propose a
CNN-based pose refinement system which takes a coarsely estimated 3D pose from
a computationally cheaper algorithm along with a bounding box image of the
object, and returns a highly refined pose. Our experiments on the YCB-Video
dataset show that our system can refine 3D poses to an extremely high precision
with minimal training data.


  Multiclass classification problems such as image annotation can involve a
large number of classes. In this context, confusion between classes can occur,
and single label classification may be misleading. We provide in the present
paper a general device that, given an unlabeled dataset and a score function
defined as the minimizer of some empirical and convex risk, outputs a set of
class labels, instead of a single one. Interestingly, this procedure does not
require that the unlabeled dataset explores the whole classes. Even more, the
method is calibrated to control the expected size of the output set while
minimizing the classification risk. We show the statistical optimality of the
procedure and establish rates of convergence under the Tsybakov margin
condition. It turns out that these rates are linear on the number of labels. We
apply our methodology to convex aggregation of confidence sets based on the
V-fold cross validation principle also known as the superlearning principle. We
illustrate the numerical performance of the procedure on real data and
demonstrate in particular that with moderate expected size, w.r.t. the number
of labels, the procedure provides significant improvement of the classification
risk.


  We probe the room temperature photoluminescence of $N$-layer molybdenum
ditelluride (MoTe$_2$) in the continuous wave (cw) regime. The
photoluminescence quantum yield of monolayer MoTe$_2$ is three times larger
than in bilayer MoTe$_2$ and forty times greater than in the bulk limit. Mono-
and bilayer MoTe$_2$ display almost symmetric emission lines at $1.10~\rm eV$
and $1.07~\rm eV$, respectively, which predominantly arise from direct
radiative recombination of the A exciton. In contrast, $N\geq3-$layer MoTe$_2$
exhibits a much reduced photoluminescence quantum yield and a broader,
redshifted and seemingly bimodal photoluminescence spectrum. The low- and
high-energy contributions are attributed to emission from the indirect and
direct optical band gaps, respectively. Bulk MoTe$_2$ displays a broad emission
line with a dominant contribution at 0.94~eV that is assigned to emission from
the indirect optical band gap. As compared to related systems (such as MoS$_2$,
MoSe$_2$, WS$_2$ and WSe$_2$), the smaller energy difference between the
monolayer direct optical band gap and the bulk indirect optical band gap leads
to a smoother increase of the photoluminescence quantum yield as $N$ decreases.
In addition, we study the evolution of the photoluminescence intensity in
monolayer MoTe$_2$ as a function of the exciton formation rate $W_\mathrm{abs}$
up to $3.6\times 10^{22}~\rm{cm}^{-2} s^{-1}$. The lineshape of the
photoluminescence spectrum remains largely independent of $W_\mathrm{abs}$,
whereas the photoluminescence intensity grows sub-linearly above
$W_\mathrm{abs}\sim 10^{21}~\rm cm^{-2} s^{-1}$. This behavior is assigned to
exciton-exciton annihilation and is well-captured by an elementary rate
equation model.


  The following is an improved version of Chapter 12 of my book [Sm17]. Among
others, we present a new unified approach to the Archimedean
Positivstellens\"atze for quadratic modules and semirings in Section 12.4 and
we add a number of new results on Positivstellens\"atze for semirings and the
corresponding moment problems.All references to formulas and to the
bibliography of the book are retained. This version is essentially based on
results from the recent paper [SmS23]. We will also use a result form book
[Sm20].


  Using coherent states as initial states, we investigate the quantum dynamics
of the Lipkin-Meshkov-Glick (LMG) and Dicke models in the semi-classical limit.
They are representative models of bounded systems with one- and two-degrees of
freedom, respectively. The first model is integrable, while the second one has
both regular and chaotic regimes. Our analysis is based on the survival
probability. Within the regular regime, the energy distribution of the initial
coherent states consists of quasi-harmonic sub-sequences of energies with
Gaussian weights. This allows for the derivation of analytical expressions that
accurately describe the entire evolution of the survival probability, from
$t=0$ to the saturation of the dynamics. The evolution shows decaying
oscillations with a rate that depends on the anharmonicity of the spectrum and,
in the case of the Dicke model, on interference terms coming from the
simultaneous excitation of its two-degrees of freedom. As we move away from the
regular regime, the complexity of the survival probability is shown to be
closely connected with the properties of the corresponding classical phase
space. Our approach has broad applicability, since its central assumptions are
not particular of the studied models.


  We show a calculable example of stable supersymmetry (SUSY) breaking models
with O(10) eV gravitino mass based on the combination of D-term gauge mediation
and U(1)' mediation. A potential problem of the negative mass squared for the
SUSY standard model (SSM) sfermions in the D-term gauge mediation is solved by
the contribution from the U(1)' mediation. On the other hand, the splitting
between the SSM gauginos and sfermions in the U(1)' mediation is circumvented
by the contributions from the D-term gauge mediation. Since the U(1)' mediation
does not introduce any new SUSY vacua, we achieve a completely stable model
under thermal effects. Our model, therefore, has no cosmological difficulty.


  We introduce a new regularization model for incompressible fluid flow, which
is a regularization of the EMAC formulation of the Navier-Stokes equations
(NSE) that we call EMAC-Reg. The EMAC (energy, momentum, and angular momentum
conserving) formulation has proved to be a useful formulation because it
conserves energy, momentum and angular momentum even when the divergence
constraint is only weakly enforced. However it is still a NSE formulation and
so cannot resolve higher Reynolds number flows without very fine meshes. By
carefully introducing regularization into the EMAC formulation, we create a
model more suitable for coarser mesh computations but that still conserves the
same quantities as EMAC, i.e., energy, momentum, and angular momentum. We show
that EMAC-Reg, when semi-discretized with a finite element spatial
discretization is well-posed and optimally accurate. Numerical results are
provided that show EMAC-Reg is a robust coarse mesh model.


  The productivity and efficiency of port operations strongly depend on how
fast a ship can be unloaded and loaded again. With this in mind, ship-to-shore
cranes perform the critical task of transporting containers into and onto a
ship and must do so as fast as possible. Though the problem of minimizing the
time spent in moving the payload has been addressed in previous studies, the
different heights of the container stacks have not been the focus. In this
paper, we perform a change of variable and reformulate the optimization problem
to deal with the constraints on the stack heights. As consequence, these
constraints become trivial and easy to represent since they turn into bound
constraints when the problem is discretized for the numerical solver. To
validate the idea, we simulate a small-scale scenario where different stack
heights are used. The results confirm our idea and the representation of the
stack constraints become indeed trivial. This approach is promising to be
applied in real crane operations and has the potential to enhance their
automation.


  We define and construct integral canonical models for automorphic vector
bundles over Shimura varieties of abelian type.
  More precisely, we first build on Kisin's work to construct integral
canonical models over rings of integers of number fields with finitely many
primes inverted for Shimura varieties of abelian type with hyperspecial level
at all primes we do not invert, compatible with Kisin's construction. We then
define a notion of an integral canonical model for the standard principal
bundles lying over Shimura varieties and proceed to construct them in the
abelian type case. With these in hand, one immediately also gets integral
models for automorphic vector bundles.


  Magnetic Resonance Imaging (MRI) has become an important technique in the
clinic for the visualization, detection, and diagnosis of various diseases.
However, one bottleneck limitation of MRI is the relatively slow data
acquisition process. Fast MRI based on k-space undersampling and high-quality
image reconstruction has been widely utilized, and many deep learning-based
methods have been developed in recent years. Although promising results have
been achieved, most existing methods require fully-sampled reference data for
training the deep learning models. Unfortunately, fully-sampled MRI data are
difficult if not impossible to obtain in real-world applications. To address
this issue, we propose a data refinement framework for self-supervised MR image
reconstruction. Specifically, we first analyze the reason of the performance
gap between self-supervised and supervised methods and identify that the bias
in the training datasets between the two is one major factor. Then, we design
an effective self-supervised training data refinement method to reduce this
data bias. With the data refinement, an enhanced self-supervised MR image
reconstruction framework is developed to prompt accurate MR imaging. We
evaluate our method on an in-vivo MRI dataset. Experimental results show that
without utilizing any fully sampled MRI data, our self-supervised framework
possesses strong capabilities in capturing image details and structures at high
acceleration factors.


  Hartree-Fock approximation suffers from two inabilities including i) the
divergence of electron Fermi velocity , and ii) existence of bandwidth not
confirmed experimentally. Here, we study the effects of minimal length on the
ground state energy of the electron gas in the Hartree-Fock approximation. Our
results indicate that considering some mathematical terms, similar to those of
used for the minimal length correction to the Hamiltonian of system, can
eliminate the weaknesses of Hartree-Fock approximation. These corrections, on
the other hand, can be considered as relativistic corrections of electron in
solids. Physically, it is obtained that electrons in metals can be employed to
test the quantum gravity scenario, if the value of its parameter ($\beta$) lies
within the range of 2 to 10, depending on the used metal. Indeed, the latter
addresses an upper bound on $\beta$ which is comparable with previous works
meaning that these types of systems may be employed in testing quantum gravity
scenarios. To overcome the infinite Fermi velocity in Hartree-Fock method, the
screening potential is used based on the Lindhard theory. We also found that
considering the generalized Heisenberg uncertainly leads to some additional
oscillating terms in the Friedel oscillations.


  Human explanation (e.g., in terms of feature importance) has been recently
used to extend the communication channel between human and agent in interactive
machine learning. Under this setting, human trainers provide not only the
ground truth but also some form of explanation. However, this kind of human
guidance was only investigated in supervised learning tasks, and it remains
unclear how to best incorporate this type of human knowledge into deep
reinforcement learning. In this paper, we present the first study of using
human visual explanations in human-in-the-loop reinforcement learning (HRL). We
focus on the task of learning from feedback, in which the human trainer not
only gives binary evaluative "good" or "bad" feedback for queried state-action
pairs, but also provides a visual explanation by annotating relevant features
in images. We propose EXPAND (EXPlanation AugmeNted feeDback) to encourage the
model to encode task-relevant features through a context-aware data
augmentation that only perturbs irrelevant features in human salient
information. We choose five tasks, namely Pixel-Taxi and four Atari games, to
evaluate the performance and sample efficiency of this approach. We show that
our method significantly outperforms methods leveraging human explanation that
are adapted from supervised learning, and Human-in-the-loop RL baselines that
only utilize evaluative feedback.


  The lower excitation spectrum of the nucleon and $\Delta$ is calculated in a
relativistic chiral quark model. Contributions of the second order self-energy
and exchange diagrams due-to pion fields to the mass spectrum of the SU(2)
baryons are estimated. A splitting between N(939) and positive parity nucleon
resonance (Roper resonance) $N^*(1440)$ is reproduced with a reasonable
accuracy. The obtained structure of one-meson exchange interaction confirms a
prediction of the large $N_c$ limit approach stating that the mass splitting
between various baryon states receive contributions from operators which
simultaneously couple spin, isospin and orbital momentum. It is shown that
one-meson exchange interaction generates a splitting between negative parity
$N^*(1/2^-)$ and $N^*(3/2^-)$ states, and also between $\Delta^*(3/2^-)$ and
$\Delta^*(1/2^-)$ states in contrast to the non-relativistic Goldstone-Boson
Exchange based quark models. This splitting is due-to a relativistic operator
which couples the lower and upper orbital momentum of two interacting valence
quarks.


  To achieve magnetic resonance (MR)-only radiotherapy, a method needs to be
employed to estimate a synthetic CT (sCT) for generating electron density maps
and patient positioning reference images. We investigated 2D and 3D
convolutional neural network (CNN) methods to generate a male pelvic sCT using
a T1-weighted MR image. A retrospective study was performed using CTs and
T1-weighted MR images of 20 prostate cancer patients. The proposed 2D CNN
model, which contained 27 convolutional layers, was modified from the SegNet
for better performance. 3D version of the CNN model was also developed. Both
CNN models were trained from scratch to map intensities of T1-weighted MR
images to CT Hounsfield Unit (HU) values. Each sCT was generated in a
five-fold-cross-validation framework and compared with the corresponding CT
using voxel-wise mean absolute error (MAE), and dice similarity coefficient
(DSC), recall, and precision for bony structures. Wilcoxon signed-rank tests
were performed to evaluate the differences between the both models. The MAE
averaged across all patients were 40.5 $\pm$ 5.4 HU and 37.6 $\pm$ 5.1 HU for
the 2D and 3D CNN models, respectively. The DSC, recall, and precision of the
bony structures were 0.81 $\pm$ 0.04, 0.85 $\pm$ 0.04, and 0.77 $\pm$ 0.09 for
the 2D CNN model, and 0.82 $\pm$ 0.04, 0.84 $\pm$ 0.04, and 0.80 $\pm$ 0.08 for
the 3D CNN model, respectively. P values of the Wilcoxon signed-rank tests were
less than 0.05 except for recall, which was 0.6. The 2D and 3D CNN models
generated accurate pelvic sCTs for the 20 patients using T1-weighted MR images.
The evaluation metrics and statistical tests indicated that the 3D model was
able to generate sCTs with better MAE, bone DSC, and bone precision. The
accuracy of the dose calculation and patient positioning using generated sCTs
will be tested and compared for the two models in the future.


  We prove the existence of generalized solution for incompressible and viscous
non-Newtonian two-phase fluid flow for spatial dimension 2 and 3. The phase
boundary moves along with the fluid flow plus its mean curvature while exerting
surface tension force to the fluid. An approximation scheme combining the
Galerkin method and the phase field method is adopted.


  We derive stability conditions of Asymmetric Nuclear Matter ($ANM$) and
discuss the relation to mechanical and chemical instabilities of general
two-component systems. We show that the chemical instability may appear as an
instability of the system against isoscalar-like rather than isovector-like
fluctuations if the interaction between the two constituent species has an
attractive character as in the case of $ANM$. This leads to a new kind of
liquid-gas phase transition, of interest for fragmentation experiments with
radioactive beams.


  Understanding the importance of links in transmitting information in a
network can provide ways to hinder or postpone ongoing dynamical phenomena like
the spreading of epidemic or the diffusion of information. In this work, we
propose a new measure based on stochastic diffusion processes, the
\textit{transmission centrality}, that captures the importance of links by
estimating the average number of nodes to whom they transfer information during
a global spreading diffusion process. We propose a simple algorithmic solution
to compute transmission centrality and to approximate it in very large networks
at low computational cost. Finally we apply transmission centrality in the
identification of weak ties in three large empirical social networks, showing
that this metric outperforms other centrality measures in identifying links
that drive spreading processes in a social network.


  We point out that by comparing the total mass (in gravitational units) of the
earth-moon system, as determined by lunar laser ranging, with the sum of the
lunar mass as independently determined by its gravitational action on
satellites or asteroids, and the earth mass, as determined by the LAGEOS
geodetic survey satellite, one can get a direct measure of the mass of
earth-bound dark matter lying between the radius of the moon's orbit and the
geodetic satellite orbit. Current data show that the mass of such earth-bound
dark matter must be less than $4 \times 10^{-9}$ of the earth's mass.


  We reply to the Comment made in arXiv:1107.4435v1 [quant-ph] (Phys. Lett. A
\textbf{374} (2010) 1097) by noting some erroneous considerations therein
resulting in a misleading view of the quantum key distribution protocol in
question. We then correct the rates provided for the Intercept-and-Resend
attack and we complete the analysis of Eve's attack based on a double CNOT
gate.


  A signed labeled forest is defined as a (plane) forest labeled by {1,2,...,
n} along with minus signs associated to some vertices. Signed labeled forests
can be viewed as an extension of signed permutations. We define the inversion
number, the flag major index and the R-major index on signed labeled forests.
They can be considered as type B analogues of the indices for labeled forests
introduced by Bjorner and Wachs. The flag major index for signed labeled
forests is based on the flag major index on signed permutations introduced by
Adin and Roichman, whereas the R-major index for signed labeled forests is
based on the R-major index that we introduce for signed permutations, which is
closely related to the major defined by Reiner. We obtain q-hook length
formulas by q-counting signed labelings of a given forest with respect to the
above indices, from which we see that these three indices are equidistributed
for signed labeled forests. Our formulas for the major indices and the
inversion number are type B analogues of the formula due to Bjorner and Wachs.
We also give a type D analogue with respect to the inversion number of
even-signed labeled forests.


  While most robotics simulation libraries are built for low-dimensional and
intrinsically serial tasks, soft-body and multi-agent robotics have created a
demand for simulation environments that can model many interacting bodies in
parallel. Despite the increasing interest in these fields, no existing
simulation library addresses the challenge of providing a unified,
highly-parallelized, GPU-accelerated interface for simulating large robotic
systems. Titan is a versatile CUDA-based C++ robotics simulation library that
employs a novel asynchronous computing model for GPU-accelerated simulations of
robotics primitives. The innovative GPU architecture design permits
simultaneous optimization and control on the CPU while the GPU runs
asynchronously, enabling rapid topology optimization and reinforcement learning
iterations. Kinematics are solved with a massively parallel integration scheme
that incorporates constraints and environmental forces. We report dramatically
improved performance over CPU-based baselines, simulating as many as 300
million primitive updates per second, while allowing flexibility for a wide
range of research applications. We present several applications of Titan to
high-performance simulations of soft-body and multi-agent robots.


  As a tool for studying the structure of nuclei far off stability the
technique of gamma-ray spectroscopy after low-energy single-nucleon transfer
reactions with radioactive nuclear beams in inverse kinematics was
investigated. Modules of the MINIBALL germanium array and a thin
position-sensitive parallel plate avalanche counter (PPAC) to be employed in
future experiments at REX-ISOLDE were used in a test experiment performed with
a stable 36S beam on deuteron and 9Be targets. It is demonstrated that the
Doppler broadening of gamma lines detected by the MINIBALL modules is
considerably reduced by exploiting their segmentation, and that for beam
intensities up to 10^6 particles/s the PPAC positioned around zero degrees with
respect to the beam axis allows not only to significantly reduce the gamma
background by requiring coincidences with the transfer products but also to
control the beam and its intensity by single particle counting. The predicted
large neutron pickup cross sections of neutron-rich light nuclei on 2H and 9Be
targets at REX-ISOLDE energies of 2.2 MeV A are confirmed.


  Most platforms, including Reddit, face a dilemma when applying interventions
such as subreddit bans to toxic communities -- do they risk angering their user
base by proactively enforcing stricter controls on discourse or do they defer
interventions at the risk of eventually triggering negative media reactions
which might impact their advertising revenue? In this paper, we analyze
Reddit's previous administrative interventions to understand one aspect of this
dilemma: the relationship between the media and administrative interventions.
More specifically, we make two primary contributions. First, using a mediation
analysis framework, we find evidence that Reddit's interventions for violating
their content policy for toxic content occur because of media pressure. Second,
using interrupted time series analysis, we show that media attention on
communities with toxic content only increases the problematic behavior
associated with that community (both within the community itself and across the
platform). However, we find no significant difference in the impact of
administrative interventions on subreddits with and without media pressure.
Taken all together, this study provides evidence of a media-driven moderation
strategy at Reddit and also suggests that such a strategy may not have a
significantly different impact than a more proactive strategy.


  Historically, papers have been physically bound to the journal in which they
were published but in the electronic age papers are available individually, no
longer tied to their respective journals. Hence, papers now can be read and
cited based on their own merits, independently of the journal's physical
availability, reputation, or Impact Factor. We compare the strength of the
relationship between journals' Impact Factors and the actual citations received
by their respective papers from 1902 to 2009. Throughout most of the 20th
century, papers' citation rates were increasingly linked to their respective
journals' Impact Factors. However, since 1990, the advent of the digital age,
the strength of the relation between Impact Factors and paper citations has
been decreasing. This decrease began sooner in physics, a field that was
quicker to make the transition into the electronic domain. Furthermore, since
1990, the proportion of highly cited papers coming from highly cited journals
has been decreasing, and accordingly, the proportion of highly cited papers not
coming from highly cited journals has also been increasing. Should this pattern
continue, it might bring an end to the use of the Impact Factor as a way to
evaluate the quality of journals, papers and researchers.


  The chemical potiential for the ground states of the atomic elements have
been calculated within the semiclassical approximation The present work closely
follows Schwinger and Englert's semiclassical treatment of atomic structure.


  Millimeter wave (mmWave) based gesture recognition technology provides a good
human computer interaction (HCI) experience. Prior works focus on the
close-range gesture recognition, but fall short in range extension, i.e., they
are unable to recognize gestures more than one meter away from considerable
noise motions. In this paper, we design a long-range gesture recognition model
which utilizes a novel data processing method and a customized artificial
Convolutional Neural Network (CNN). Firstly, we break down gestures into
multiple reflection points and extract their spatial-temporal features which
depict gesture details. Secondly, we design a CNN to learn changing patterns of
extracted features respectively and output the recognition result. We
thoroughly evaluate our proposed system by implementing on a commodity mmWave
radar. Besides, we also provide more extensive assessments to demonstrate that
the proposed system is practical in several real-world scenarios.


  Multiple approaches to use deep learning for image restoration have recently
been proposed. Training such approaches requires well registered pairs of high
and low quality images. While this is easily achievable for many imaging
modalities, e.g. fluorescence light microscopy, for others it is not.
Cryo-transmission electron microscopy (cryo-TEM) could profoundly benefit from
improved denoising methods, unfortunately it is one of the latter. Here we show
how recent advances in network training for image restoration tasks, i.e.
denoising, can be applied to cryo-TEM data. We describe our proposed method and
show how it can be applied to single cryo-TEM projections and whole
cryo-tomographic image volumes. Our proposed restoration method dramatically
increases contrast in cryo-TEM images, which improves the interpretability of
the acquired data. Furthermore we show that automated downstream processing on
restored image data, demonstrated on a dense segmentation task, leads to
improved results.


  It is shown that density-of-states fluctuations, which can be interpreted as
the order-parameter susceptibility \chi_OP in a Fermi liquid, are anomalously
strong as a result of the existence of Goldstone modes and associated strong
fluctuations. In a 2-d system with a long-range Coulomb interaction, a suitably
defined \chi_OP diverges as 1/T^2 as a function of temperature in the limit of
small wavenumber and frequency. In contrast, standard statistics suggest
\chi_OP = O(T), a discrepancy of three powers of T. The reasons behind this
surprising prediction, as well as ways to observe it, are discussed.


  GW number count can be used as a novel tracer of the large scale structure
(LSS) in the luminosity distance space (LDS), just like galaxies in the
redshift space. It is possible to obtain the $D_L-D_A$ duality relation with
clustering effect. However, several LSS induced errors will contaminate the GW
luminosity distance measurement, such as the peculiar velocity dispersion error
of the host galaxy as well as the foreground lensing magnification. The
distance uncertainties induced from these effects will degrade the GW
clustering from a spectroscopic-like data down to a photometric-like data. In
this paper, we investigate how these LSS induced distance errors modify our
cosmological parameter precision inferred from the LDS clustering. We consider
two of the next generation GW observatories, namely the Big Bang Observatory
(BBO) and the Einstein Telescope (ET). We forecast the parameter estimation
errors on the angular diameter distance $D_A$, luminosity distance space Hubble
parameter $H_L$ and structure growth rate $f_L\sigma_8$ with a Fisher matrix
method. Generally speaking, the GW source clustering data can be used for
cosmological studies below $D_L<5$ Gpc, while above this scale the lensing
errors will increase significantly. We find that for BBO, it is possible to
constrain the cosmological parameters with a relative error of $10^{-3}$ to
$10^{-2}$ below $D_L<5$ Gpc. The velocity dispersion error is dominant in the
low luminosity distance range, while the lensing magnification error is the
bottleneck in the large luminosity distance range. To reduce the lensing error,
we assumed a $50\%$ delensing efficiency. Even with this optimal assumption,
the fractional error increased to $O(1)$ at luminosity distance $D_L=25$ Gpc.
The results for ET are similar as those from BBO. Due to the GW source number
in ET is less than that from BBO, the corresponding results also get a bit
worse.


  A complete astrophysical and dynamical study of the close visual binary
system (CVBS) (A7V + F0V), Finsen 350, is presented. Beginning with the entire
observational spectral energy distribution (SED) and the magnitude difference
between the subcomponents, Al-Wardat's complex method for analyzing close
visual binary stars (CVBS) was applied as a reverse method of building the
individual and entire synthetic SEDs of the system. This was combined with
Docobo's analytic method to calculate the new orbits. Although possible short
($\approx$ 9 years) and long period ($\approx$ 18 years) orbits could be
considered taking into account the similar results of the stellar masses
obtained for each of them (3.07 and 3.41 $M_{\odot}$, respectively), we
confirmed that the short solution is correct. In addition, other physical,
geometrical and dynamical parameters of this system such as the effective
temperatures, surface gravity accelerations, absolute magnitudes, radii, the
dynamical parallax, etc., are reported. The main sequence phase of both
components with age around 0.79 Gy is approved.


  We use a first-principles based kinetic Monte Carlo simulation to study the
movement of a solid iron nanocrystal inside a carbon nanotube driven by the
electrical current. The origin of the iron nanocrystal movement is the
electromigration force. Even though the iron nanocrystal appears to be moving
as a whole, we find that the core atoms of the nanocrystal is completely
stationary, and only the surface atoms are moving. Movement in the contact
region with the carbon nanotube is driven by electromigration forces, and the
movement on the remaining surfaces is driven by diffusion. Results of our
calculations also provide a simple model which can predict the center of mass
speed of the iron nanocrystal over a wide range of parameters. We find both
qualitative and quantitative agreement of the iron nanocrystal center of mass
speed with experimental data.


  We discuss the recent proposal of implementing Doubly Special Relativity in
configuration space by means of Finsler geometry. Although this formalism leads
to a consistent description of the dynamics of a particle, it does not seem to
give a complete description of the physics. In particular, the Finsler line
element is not invariant under the deformed Lorentz transformations of Doubly
Special Relativity. We study in detail some simple applications of the
formalism.


  We study the depletion-induced self-assembly of indented colloids. Using
state-of-the-art Monte Carlo simulation techniques that treat the depletant
particles explicitly, we demonstrate that colloids assemble by a lock-and-key
mechanism, leading to colloidal polymerization. The morphology of the chains
that are formed depends sensitively on the size of the colloidal indentation,
with smaller values additionally permitting chain branching. In contrast to the
case of spheres with attractive patches, Wertheim's thermodynamic perturbation
theory fails to provide a fully quantitative description of the polymerization
transition. We trace this failure to a neglect of packing effects and we
introduce a modified theory that accounts better for the shape of the colloids,
yielding improved agreement with simulation.


  It has recently been shown that relaxed spherically symmetric dark matter
halos develop from the inside out, by permanently adapting their inner
structure to the boundary conditions imposed by the current accretion rate.
Such a growth allows one to infer the typical density profiles of halos. Here
we follow the same approach to infer the typical spherically averaged profiles
of the main structural and kinematic properties of triaxial, anisotropic,
rotating halos. Specifically, we derive their density, spatial velocity
dispersion, phase-space density, anisotropy and specific angular momentum
profiles. The results obtained are in agreement with available data on these
profiles from \nbody simulations.


  Graph embedding is a powerful method to represent graph neurological data
(e.g., brain connectomes) in a low dimensional space for brain connectivity
mapping, prediction and classification. However, existing embedding algorithms
have two major limitations. First, they primarily focus on preserving
one-to-one topological relationships between nodes (i.e., regions of interest
(ROIs) in a connectome), but they have mostly ignored many-to-many
relationships (i.e., set to set), which can be captured using a hyperconnectome
structure. Second, existing graph embedding techniques cannot be easily adapted
to multi-view graph data with heterogeneous distributions. In this paper, while
cross-pollinating adversarial deep learning with hypergraph theory, we aim to
jointly learn deep latent embeddings of subject0specific multi-view brain
graphs to eventually disentangle different brain states. First, we propose a
new simple strategy to build a hyperconnectome for each brain view based on
nearest neighbour algorithm to preserve the connectivities across pairs of
ROIs. Second, we design a hyperconnectome autoencoder (HCAE) framework which
operates directly on the multi-view hyperconnectomes based on hypergraph
convolutional layers to better capture the many-to-many relationships between
brain regions (i.e., nodes). For each subject, we further regularize the
hypergraph autoencoding by adversarial regularization to align the distribution
of the learned hyperconnectome embeddings with that of the input
hyperconnectomes. We formalize our hyperconnectome embedding within a geometric
deep learning framework to optimize for a given subject, thereby designing an
individual-based learning framework. Our experiments showed that the learned
embeddings by HCAE yield to better results for brain state classification
compared with other deep graph embedding methods methods.


  Given a graph $G$, one may ask: "What sets of eigenvalues are possible over
all weighted adjacency matrices of $G$?" (The weight of an edge is positive or
negative, while the diagonal entries can be any real numbers.) This is known as
the Inverse Eigenvalue Problem for graphs (IEP-$G$). A mild relaxation of this
question considers the multiplicity list instead of the exact eigenvalues
themselves. That is, given a graph $G$ on $n$ vertices and an ordered partition
$\mathbf{m}= (m_1, \ldots, m_\ell)$ of $n$, is there a weighted adjacency
matrix where the $i$-th distinct eigenvalue has multiplicity $m_i$? This is
known as the ordered multiplicity IEP-$G$. Recent work solved the ordered
multiplicity IEP-$G$ for all graphs on 6 vertices.
  In this work, we develop zero forcing methods for the ordered multiplicity
IEP-$G$ in a multitude of different contexts. Namely, we utilize zero forcing
parameters on powers of graphs to achieve bounds on consecutive multiplicities.
We are able to provide general bounds on sums of multiplicities of eigenvalues
for graphs. This includes new bounds on the the sums of multiplicities of
consecutive eigenvalues as well as more specific bounds for trees. Using these
results, we verify the previous results above regarding the IEP-$G$ on six
vertices. In addition, applying our techniques to skew-symmetric matrices, we
are able to determine all possible ordered multiplicity lists for
skew-symmetric matrices for connected graphs on five vertices.


  In this paper we investigate the reaction--diffusion system corresponding to
the Newton--Leipnik chaotic system originally developed to model the rigid body
motion through linear feedback (LFRBM). We develop a nonlinear synchronization
scheme for the proposed reaction--diffusion system and prove its global
stability in the local sense by means of the eigenvalues of the Jacobian and
global sense through an appropriate Lyapunov functional. A numerical example is
presented to illustrate the results of this study.


  Formula for calculating the atomic scattering factor for spherical x-ray
waves is derived and used to solve the near field effects problem in X-ray
Fluorescence Holography theory. A rigorous formalism to calculate the X-ray
fluorescence hologram for given atomic distributions is then given so that
quantitative structural information can be derived from XFH measurements.


  Turbulent dynamics generate random fluctuations in density and velocity in
the Interstellar Medium (ISM) of spiral and dwarf galaxies. Observationally,
H~{\sc i} 21-cm radiation provides a good probe of these stochastic processes
and helps us to know more about their nature and generating mechanism.
Structure function, auto-correlation function, power spectrum are some of
statistical estimators of these fluctuations which have been used in
literature, whose studies helps in understanding the generating mechanisms of
these structures. In this thesis we are interested in estimating the column
density and velocity power spectrum at scales as large as 10 Kpc, to understand
the energy associated with these structures and the type of forcing of the
turbulence. Estimating power spectrum from observations can be done either by
using the directly measured quantity visibility or by reconstructing the sky
brightness distribution (or image) from the visibilities. By using simulated
observations of a model galaxy with known power spectrum we find that the
visibility based estimator reproduce the true power spectrum. The image based
estimator has a scale dependent bias that is highly correlated to the
incompleteness in the baseline coverage of the interferometer. Interestingly,
we found that the noise in the reconstructed image has zero mean and hence the
locally averaged quantities estimated from the reconstructed image are
unbiased. We implement the visibility moment estimator as discussed in Dutta et
al. (2015) and estimate the line of sight velocity fluctuation of the spiral
galaxy NGC6946. Our result strongly indicate presence of large scale turbulence
in the spiral galaxies. We conclude that we would need to have higher signal to
noise H~{\sc i}observations to definitively comment on the generating
mechanisms of the large scale turbulence.


  Threshold based models have been widely used in characterizing collective
behavior on social networks. An individual's threshold indicates the minimum
level of influence that must be exerted, by other members of the population
engaged in some activity, before the individual will join the activity. In this
work, we begin with a homogeneous version of the Linear Threshold model
proposed by Kempe et al. in the context of viral marketing, and generalize this
model to arbitrary threshold distributions. We show that the evolution can be
modeled as a discrete time Markov chain, and, by using a certain scaling, we
obtain a fluid limit that provides an ordinary differential equation model
(o.d.e.). We find that the threshold distribution appears in the o.d.e. via its
hazard rate function. We demonstrate the accuracy of the o.d.e. approximation
and derive explicit expressions for the trajectory of influence under the
uniform threshold distribution. Also, for an exponentially distributed
threshold, we show that the fluid dynamics are equivalent to the well-known SIR
model in epidemiology. We also numerically study how other hazard functions
(obtained from the Weibull and loglogistic distributions) provide qualitative
different characteristics of the influence evolution, compared to traditional
epidemic models, even in a homogeneous setting. We finally show how the model
can be extended to a setting with multiple communities and conclude with
possible future directions.


  Hand segmentation and fingertip detection play an indispensable role in hand
gesture-based human-machine interaction systems. In this study, we propose a
method to discriminate hand components and to locate fingertips in RGB-D
images. The system consists of three main steps: hand detection using RGB
images providing regions which are considered as promising areas for further
processing, hand segmentation, and fingertip detection using depth image and
our modified SegNet, a single lightweight architecture that can process two
independent tasks at the same time. The experimental results show that our
system is a promising method for hand segmentation and fingertip detection
which achieves a comparable performance while model complexity is suitable for
real-time applications.


  In this paper, we propose a local squared Wasserstein-2 (W_2) method to solve
the inverse problem of reconstructing models with uncertain latent variables or
parameters. A key advantage of our approach is that it does not require prior
information on the distribution of the latent variables or parameters in the
underlying models. Instead, our method can efficiently reconstruct the
distributions of the output associated with different inputs based on empirical
distributions of observation data. We demonstrate the effectiveness of our
proposed method across several uncertainty quantification (UQ) tasks, including
linear regression with coefficient uncertainty, training neural networks with
weight uncertainty, and reconstructing ordinary differential equations (ODEs)
with a latent random variable.


  This paper introduces a new class of variational inequalities where the
obstacle is placed in the exterior domain that is disjoint from the observation
domain. This is carried out with the help of nonlocal fractional operators. The
need for such novel variational inequalities stems from the fact that the
classical approach only allows placing the obstacle either inside the
observation domain or on the boundary. A complete analysis of the continuous
problem is provided. Additionally, perturbation arguments to approximate the
problem are discussed.


  We present models, in which phantom energy interacts with two different types
of dark energies including variable modified Chaplygin gas (VMCG) and new
modified Chaplygin gas (NMCG). We then construct potentials for these cases. It
has been shown that the potential of the phantom field decreases from a higher
value with the evolution of the Universe.


  Under suitable conditions, we show that the Euler characteristic of a
foliated Riemannian manifold can be computed only from curvature invariants
which are transverse to the leaves. Our proof uses the hypoelliptic
sub-Laplacian on forms recently introduced by two of the authors.


  Linear wave equations on flat band networks host compact localized
eigenstates (CLS). Nonlinear wave equations on translationally invariant flat
band networks can host compact discrete breathers - time periodic and spatially
compact localized solutions. Such solutions can appear as one-parameter
families of continued linear compact eigenstates, or as discrete sets on
families of non-compact discrete breathers, or even on purely dispersive
networks with fine-tuned nonlinear dispersion. In all cases, their existence
relies on destructive interference. We use CLS amplitude distribution
properties and orthogonality conditions to derive existence criteria and
stability properties for compact discrete breathers as continued CLS.


  We consider one-dimensional Brownian motion conditioned (in a suitable sense)
to have a local time at every point and at every moment bounded by some fixed
constant. Our main result shows that a phenomenon of entropic repulsion occurs:
that is, this process is ballistic and has an asymptotic velocity approximately
4.58... as high as required by the conditioning (the exact value of this
constant involves the first zero of a Bessel function). We also study the
random walk case and show that the process is asymptotically ballistic but with
an unknown speed.


  We present a systematic treatment of the initial conditions and evolution of
cosmological perturbations in a universe containing photons, baryons,
neutrinos, cold dark matter, and a scalar quintessence field. By formulating
the evolution in terms of a differential equation involving a matrix acting on
a vector comprised of the perturbation variables, we can use the familiar
language of eigenvalues and eigenvectors. As the largest eigenvalue of the
evolution matrix is fourfold degenerate, it follows that there are four
dominant modes with non-diverging gravitational potential at early times,
corresponding to adiabatic, cold dark matter isocurvature, baryon isocurvature
and neutrino isocurvature perturbations. We conclude that quintessence does not
lead to an additional independent mode.


  We propose fractional spin hall effect (FSHE) by coupling pseudospin states
of cold bosonic atoms to optical fields. The present scheme is an extension to
interacting bosonic system of the recent work \cite{liu,zhu} on optically
induced spin hall effect in non-interacting atomic system. The system has two
different types of ground states. The 1st type of ground state is a 1/3-factor
Laughlin function, and has the property of chiral - anti-chiral interchange
antisymmetry, while the 2nd type is shown to be a 1/4-factor wave function with
chiral - anti-chiral symmetry. The fractional statistics corresponding to the
fractional spin Hall states are studied in detail, and are discovered to be
different from that corresponding to the fractional quantum Hall (FQH) states.
Therefore the present FSHE can be distinguished from FQH regime in the
measurement.


  The theoretical formulation of x-ray resonant magnetic scattering from rough
surfaces and interfaces is given for the diffuse (off-specular) scattering, and
general expressions are derived in both the Born approximation (BA) and the
distorted-wave Born approximation (DWBA) for both single and multiple
interfaces. We also give in the BA the expression for off-specular magnetic
scattering from magnetic domains. For this purpose, structural and magnetic
interfaces are defined in terms of roughness parameters related to their
height-height correlation functions and the correlations between them. The
results are generalized to the case of multiple interfaces, as in the case of
thin films or multilayers. Theoretical calculations for each of the cases are
illustrated as numerical examples and compared with experimental data of
mangetic diffuse scattering from a Gd/Fe multilayer.


  We use the concept of a Kirchhoff resistor network (alternatively random walk
on a network) to probe connected graphs and produce symmetry revealing
canonical labelings of the graph(s) nodes and edges.


  From the analysis of Dopplergrams in the K I 7699 A and Na I 5890 A spectral
lines observed with the Magneto-Optical filter at Two Heights (MOTH) experiment
during the austral summer in 2002-03 we find upward traveling waves in magnetic
regions. Our analysis shows that the dispersion relation of these waves
strongly depends on whether the wave is detected in the low-beta or high-beta
regime. Moreover, the observed dispersion relation does not show the expected
decrease of the acoustic cut-off frequency for the field guided slow magnetic
wave. Instead, we detected an increase of the travel times below the acoustic
cut-off frequency and at the same time a decrease of the travel time above it.
To study the formation height of the spectral lines employed by MOTH in greater
detail we are currently in the process of employing 3D MHD simulations carried
out with CO5BOLD to perform NLTE spectral synthesis.


  Bound orbits have traditionally been assigned to the Large and Small
Magellanic Clouds (LMC and SMC, respectively) in order to provide a formation
scenario for the Magellanic Stream (MS) and its Leading Arm (LA), two prominent
neutral hydrogen (HI) features connected to the LMC and SMC. However, Hubble
Space Telescope (HST) measurements of the proper motions of the LMC and SMC
have challenged the plausibility of bound orbits, causing the origin of the MS
to re-emerge as a contested issue. We present a new tidal model in which
structures resembling the bifurcated MS and elongated LA are able to form in a
bound orbit consistent with the HST proper motions. The LMC and SMC have
remained bound to each other only recently in our model despite being
separately bound to the Milky Way for more than 5 Gyr. We find that the MS and
LA are able to form as a consequence of LMC-dominated tidal stripping during
the recent dynamical coupling of the LMC and SMC. Our orbital model depends on
our assumption that the Milky Way has a constant circular velocity of V_cir=250
km/s up to 160 kpc, which implies a massive isothermal halo that is not
completely rejected by observations.


  Previous RGB-D salient object detection (SOD) methods have widely adopted
deep learning tools to automatically strike a trade-off between RGB and D
(depth), whose key rationale is to take full advantage of their complementary
nature, aiming for a much-improved SOD performance than that of using either of
them solely. However, such fully automatic fusions may not always be helpful
for the SOD task because the D quality itself usually varies from scene to
scene. It may easily lead to a suboptimal fusion result if the D quality is not
considered beforehand. Moreover, as an objective factor, the D quality has long
been overlooked by previous work. As a result, it is becoming a clear
performance bottleneck. Thus, we propose a simple yet effective scheme to
measure D quality in advance, the key idea of which is to devise a series of
features in accordance with the common attributes of high-quality D regions. To
be more concrete, we conduct D quality assessments for each image region,
following a multi-scale methodology that includes low-level edge consistency,
mid-level regional uncertainty and high-level model variance. All these
components will be computed independently and then be assembled with RGB and D
features, applied as implicit indicators, to guide the selective fusion.
Compared with the state-of-the-art fusion schemes, our method can achieve a
more reasonable fusion status between RGB and D. Specifically, the proposed D
quality measurement method achieves steady performance improvements for almost
2.0\% in general.


  We develop deterministic particle schemes to solve non-local scalar
conservation laws with congestion. We show that the discrete approximations
converge to the unique entropy solution with an explicit rate of convergence
under more general assumptions that the existing literature: the velocity
fields are less regular (in particular the interaction force can have a
discontinuity at the origin) with no prescribed attractive/repulsive regime and
the mobility can have unbounded support. We complement our results with some
numerical simulations, among which we show the applicability of the schemes to
the multi-species setting.


  The interpretation of electromodulated reflectance (ER) spectra of polar
quantum wells (QWs) is difficult even for homogeneous structures because of the
built-in electric field. In this work we compare the room-temperature
contactless ER and photoluminescence (PL) spectra of polar GaN/AlGaN QWs with
the effective-mass band structure calculations. We show that the emission from
the ground state transition is observed in PL but the ER is dominated by
transitions between excited states. This effect results from the
polarization-induced built-in electric field in QW that breaks the selection
rules that apply to square-like QWs, allowing many optical transitions which
cannot be separately distinguished in the ER spectrum. We develop the
guidelines for the identification of optical transitions observed in PL and ER
spectra. We conclude that an intrinsic Stokes shift, i.e., a shift between
emission and absorption, is present even for homogeneous GaN/AlGaN QWs with
large width, where the electron-hole wavefunction overlap for the fundamental
transition is weak.


  Development, regeneration and cancer involve drastic transitions in tissue
morphology. In analogy with the behavior of inert fluids, some of these
transitions have been interpreted as wetting transitions. The validity and
scope of this analogy are unclear, however, because the active cellular forces
that drive tissue wetting have been neither measured nor theoretically
accounted for. Here we show that the transition between 2D epithelial
monolayers and 3D spheroidal aggregates can be understood as an active wetting
transition whose physics differs fundamentally from that of passive wetting
phenomena. By combining an active polar fluid model with measurements of
physical forces as a function of tissue size, contractility, cell-cell and
cell-substrate adhesion, and substrate stiffness, we show that the wetting
transition results from the competition between traction forces and contractile
intercellular stresses. This competition defines a new intrinsic lengthscale
that gives rise to a critical size for the wetting transition in tissues, a
striking feature that has no counterpart in classical wetting. Finally, we show
that active shape fluctuations are dynamically amplified during tissue
dewetting. Overall, we conclude that tissue spreading constitutes a prominent
example of active wetting --- a novel physical scenario that may explain
morphological transitions during tissue morphogenesis and tumor progression.


  Internet-based services have seen remarkable success, generating vast amounts
of monitored key performance indicators (KPIs) as univariate or multivariate
time series. Monitoring and analyzing these time series are crucial for
researchers, service operators, and on-call engineers to detect outliers or
anomalies indicating service failures or significant events. Numerous advanced
anomaly detection methods have emerged to address availability and performance
issues. This review offers a comprehensive overview of time series anomaly
detection in Artificial Intelligence for IT operations (AIOps), which uses AI
capabilities to automate and optimize operational workflows. Additionally, it
explores future directions for real-world and next-generation time-series
anomaly detection based on recent advancements.


  The influence of mirror motion on the spatial self organization of a
Bose-Einstein condensate (BEC) in an optical cavity is studied. We show that
the mirror dynamics tends to destroy the process of self organization. An
additional external phonon pump is shown to modify the critical photon pump
needed to observe the onset of self organization.


  In the conventional person Re-ID setting, it is widely assumed that cropped
person images are for each individual. However, in a crowded scene,
off-shelf-detectors may generate bounding boxes involving multiple people,
where the large proportion of background pedestrians or human occlusion exists.
The representation extracted from such cropped images, which contain both the
target and the interference pedestrians, might include distractive information.
This will lead to wrong retrieval results. To address this problem, this paper
presents a novel deep network termed Pedestrian-Interference Suppression
Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to
enhance the feature of the target in the gallery, under the guidance of the
query. Furthermore, the involving Guidance Reversed Attention Module and the
Multi-Person Separation Loss promote QGAB to suppress the interference of other
pedestrians. Our method is evaluated on two new pedestrian-interference
datasets and the results show that the proposed method performs favorably
against existing Re-ID methods.


  We review recent results on chiral $SU(2)_L\times SU(2)_R\approx O(4)$ and
$U(1)_A$ symmetry restoration in QCD. In particular, we discuss how Ward
Identities allow one to derive general results on partner degeneration, which
shed light on the distinction between the $O(4)$ and $O(4)\times U(1)_A$
patterns of the chiral transition. For that purpose, susceptibilities
associated with the $O(4)$ and $U(1)_A$ symmetries are studied. From this
analysis we conclude that in the ideal regime of exact $O(4)$ restoration
(formally achieved in the limit of two massless flavours), $U(1)_A$ partners
degenerate as well. We also discuss the role of the thermal $f_0(500)$ state to
describe thermodynamic observables sensitive to chiral restoration, such as the
scalar susceptibility. We pay special attention to the consistency of our
results with recent lattice analysis.


  In this paper, we study the privately-own IRIDIUM satellite constellation, to
provide a location service that is independent of the GNSS. In particular, we
apply our findings to propose a new GNSS spoofing detection solution,
exploiting unencrypted IRIDIUM Ring Alert (IRA) messages that are broadcast by
IRIDIUM satellites. We firstly reverse-engineer many parameters of the IRIDIUM
satellite constellation, such as the satellites speed, packet interarrival
times, maximum satellite coverage, satellite pass duration, and the satellite
beam constellation, to name a few. Later, we adopt the aforementioned
statistics to create a detailed model of the satellite network. Subsequently,
we propose a solution to detect unintended deviations of a target user from his
path, due to GNSS spoofing attacks. We show that our solution can be used
efficiently and effectively to verify the position estimated from standard GNSS
satellite constellation, and we provide constraints and parameters to fit
several application scenarios. All the results reported in this paper, while
showing the quality and viability of our proposal, are supported by real data.
In particular, we have collected and analyzed hundreds of thousands of IRA
messages, thanks to a measurement campaign lasting several days. All the
collected data ($1000+$ hours) have been made available to the research
community. Our solution is particularly suitable for unattended scenarios such
as deserts, rural areas, or open seas, where standard spoofing detection
techniques resorting to crowd-sourcing cannot be used due to deployment
limitations. Moreover, contrary to competing solutions, our approach does not
resort to physical-layer information, dedicated hardware, or multiple receiving
stations, while exploiting only a single receiving antenna and
publicly-available IRIDIUM transmissions. Finally, novel research directions
are also highlighted.


  We exhibit 3-generator Artin groups which have finite 2-dimensional
Eilenberg-Mac Lane spaces, but which do not act properly discontinuously by
semi-simple isometries on a 2-dimensional CAT(0) complex. We prove that
infinitely many of these groups are the fundamental groups of compact,
non-positively curved 3-complexes. These examples show that the geometric
dimension of a CAT(0) group may be strictly less than its CAT(0) dimension.


  We discuss the gauge dependence of fermion mass definition and physical
result under the conventional on-shell mass renormalization scheme and the
recently proposed pole mass renormalization scheme in standard model. By the
two-loop calculations of top quark's mass counterterm and the cross section of
physical process $W^{-}W^{+}\to b\bar{b}$ we strictly prove that the on-shell
mass renormalization scheme makes fermion mass definition and the physical
result gauge dependent at the first time, while we don't find such gauge
dependence in the pole mass renormalization scheme. Besides, our calculation
also shows the difference of the physical result between the two mass
renormalization schemes cannot be neglected at two-loop level.


  We investigate semi-classical generalizations of the Charlier and Meixner
polynomials, which are discrete orthogonal polynomials that satisfy three-term
recurrence relations. It is shown that the coefficients in these recurrence
relations can be expressed in terms of Wronskians of modified Bessel functions
and confluent hypergeometric functions, respectively for the generalized
Charlier and generalized Meixner polynomials. These Wronskians arise in the
description of special function solutions of the third and fifth Painlev\'e
equations.


  A new class of functions, called the `Information sensitivity functions'
(ISFs), which quantify the information gain about the parameters through the
measurements/observables of a dynamical system are presented. These functions
can be easily computed through classical sensitivity functions alone and are
based on Bayesian and information-theoretic approaches. While marginal
information gain is quantified by decrease in differential entropy,
correlations between arbitrary sets of parameters are assessed through mutual
information. For individual parameters these information gains are also
presented as marginal posterior variances, and, to assess the effect of
correlations, as conditional variances when other parameters are given. The
easy to interpret ISFs can be used to a) identify time-intervals or regions in
dynamical system behaviour where information about the parameters is
concentrated; b) assess the effect of measurement noise on the information gain
for the parameters; c) assess whether sufficient information in an experimental
protocol (input, measurements, and their frequency) is available to identify
the parameters; d) assess correlation in the posterior distribution of the
parameters to identify the sets of parameters that are likely to be
indistinguishable; and e) assess identifiability problems for particular sets
of parameters.


  The stellar dynamic-based black hole mass measurements of M87 are twice that
determined via ionized gas kinematics; the former is closer to the estimation
from the diameter of the gravitationally-lensed ring around the black hole.
Using deeper and more comprehensive ionized gas kinematic data, we aim to
better constrain the morphology and kinematics of the nuclear ionized gas, thus
gaining insights into the reasons behind the disagreement of the measurements.
We use both Narrow and Wide Field Mode integral field spectroscopic data from
the Multi Unit Spectroscopic Explorer instrument, to model the morphology and
kinematics of multiple ionized gas emission lines in the nucleus of M87. The
new deep dataset reveals complexities in the nuclear ionized gas kinematics.
Several ionized gas filaments can be traced down into the projected sphere of
influence. We also found evidence of a partially-filled biconical outflow. The
velocity isophotes of the ionized gas disk are twisted and the position angle
of the innermost gas disk tends toward a value perpendicular to the radio jet
axis. The complexity of the nuclear morphology and kinematics precludes the
measurement of an accurate black hole mass. The results support a 6.0 $\times
10^{9}\rm M_{\odot}$ black hole in a 25\deg disk, rather than a 3.5 $\times
10^{9}\rm M_{\odot}$ black hole in a 42\deg disk. The specific RIAF model
earlier proposed to reconcile the mass measurement discrepancy was also tested.
In general, Keplerian disk models perform better than the RIAF model when
fitting the sub-arcsec ionized gas disk. A disk inclination close to 25\deg for
the nuclear gas disk, and the warp in the sub-arcsec ionized gas disk, help to
reconcile the contradictory nature of the mass discrepancy between stellar and
ionized gas black hole masses, and the mis-orientation between the axes of the
ionized gas disk and the jet.


  The $k^{th}$-power of a given graph $G=(V,E)$ is obtained from $G$ by adding
an edge between every two distinct vertices at a distance at most $k$ in $G$.
We call $G$ a $k$-Steiner power if it is an induced subgraph of the
$k^{th}$-power of some tree. Our main contribution is a polynomial-time
recognition algorithm of $4$-Steiner powers, thereby extending the
decade-year-old results of (Lin, Kearney and Jiang, ISAAC'00) for $k=1,2$ and
(Chang and Ko, WG'07) for $k=3$.
  A graph $G$ is termed $k$-leaf power if there is some tree $T$ such that: all
vertices in $V(G)$ are leaf-nodes of $T$, and $G$ is an induced subgraph of the
$k^{th}$-power of $T$. As a byproduct of our main result, we give the first
known polynomial-time recognition algorithm for $6$-leaf powers.


  The chances to win a football match can be significantly increased if the
right tactic is chosen and the behavior of the opposite team is well
anticipated. For this reason, every professional football club employs a team
of game analysts. However, at present game performance analysis is done
manually and therefore highly time-consuming. Consequently, automated tools to
support the analysis process are required. In this context, one of the main
tasks is to summarize team formations by patterns such as 4-4-2. In this paper,
we introduce an analytics approach that automatically classifies and visualizes
the team formation based on the players' position data. We focus on single
match situations instead of complete halftimes or matches to provide a more
detailed analysis. A detailed analysis of individual match situations depending
on ball possession and match segment length is provided. For this purpose, a
visual summary is utilized that summarizes the team formation in a match
segment. An expert annotation study is conducted that demonstrates 1) the
complexity of the task and 2) the usefulness of the visualization of single
situations to understand team formations. The suggested classification approach
outperforms existing methods for formation classification. In particular, our
approach gives insights about the shortcomings of using patterns like 4-4-2 to
describe team formations.


  Previously [arXiv:2103.15582v3], an expression was proposed for the
evaporation flux density of a small liquid droplet having the shape of an
axisymmetric spherical segment deposited on a horizontal substrate. The
dependence of the flux density on the polar angle was established for arbitrary
contact angles. This formula has the form of an integral and is rather
complicated for use in modeling algorithms. An approximate expression was
obtained for the evaporation flux density at small contact angles. However, the
question of which simplified formulas should be appropriate to apply in other
ranges of contact angles, for example, in the case of obtuse angles remains
open. In this paper, we propose new exact solutions for the set of discrete
"hydrophobic" contact angles. As an example, very simple exact expressions are
obtained explicitly for the evaporation flux density for droplets with contact
angles 135 and 150 degrees that do not contain integral dependencies. They can
also be used as approximate solutions for a narrow range of contact angles
around the specified values.