  I give a brief overview of the exploration of baryon properties in meson
photo- and electroproduction. These processes provide ample information for the
study of electromagnetic couplings of baryon resonances and to search for
states, yet to be discovered. The CLAS detector, combined with the use of
energy-tagged polarized photons and polarized electrons, as well as polarized
targets and the measurement of recoil polarization, provide the tools for a
comprehensive nucleon resonance program. I briefly present the status of this
program, prospects for the next few years, and plans for the Jefferson Lab 12
GeV upgrade.


  A major challenge in the safety assessment of automated vehicles is to ensure
that risk for all traffic participants is as low as possible. A concept that is
becoming increasingly popular for testing in automated driving is
scenario-based testing. It is founded on the assumption that most time on the
road can be seen as uncritical and in mainly critical situations contribute to
the safety case. Metrics describing the criticality are necessary to
automatically identify the critical situations and scenarios from measurement
data. However, established metrics lack universality or a concept for metric
combination. In this work, we present a multidimensional evaluation model that,
based on conventional metrics, can evaluate scenes independently of the scene
type. Furthermore, we present two new, further enhanced evaluation approaches,
which can additionally serve as universal metrics. The metrics we introduce are
then evaluated and discussed using real data from a motion dataset.


  The theory of distributed computing, lagging in its development behind
practice, has been biased in its modelling by employing mechanisms within the
model mimicking reality. Reality means, processors can fail. But theory is
about predicting consequences of reality, hence if we capture reality by
"artificial models," but those nevertheless make analysis simpler, we should
pursue the artificial models.
  Recently the idea was advocated to analyze distributed systems and view
processors as infallible. It is the message delivery substrate that causes
problems. This view not only can effectively emulate reality, but above all
seems to allow to view any past models as \emph{synchronous} models.
Synchronous models are easier to analyze than asynchronous ones. Furthermore,
it gives rise to models we haven't contemplated in the past. One such model,
presented here, is the Hybrid Message-Adversary. We motivate this model through
the need to analyze Byzantine faults. The Hybrid model exhibits a phenomenon
not seen in the past.


  This paper is concerned with a wireless system operating in MIMO fading
channels with channel state information being known at both transmitter and
receiver. By spatiotemporal subchannel selection and power control, it aims to
minimize the average transmit power (ATP) of the MIMO system while achieving an
exponential type of average bit error rate (BER) for each data stream. Under
the constraints of a given fixed individual outage probability (OP) and average
BER for each subchannel, based on a traditional upper bound and a dynamic upper
bound of Q function, two closed-form ATP expressions are derived, respectively,
and they correspond to two different power allocation schemes. Numerical
results are provided to validate the theoretical analysis, and show that the
power allocation scheme with the dynamic upper bound can achieve more power
savings than the one with the traditional upper bound.


  We consider wavepackets composed of two modulated carrier Bloch waves with
opposite group velocities in the one dimensional periodic Nonlinear
Schroedinger/Gross-Pitaevskii equation. These can be approximated by first
order coupled mode equations (CMEs) for the two slowly varying envelopes. Under
a suitably selected periodic perturbation of the periodic structure the CMEs
possess a spectral gap of the corresponding spatial operator and allow families
of exponentially localized solitary waves parametrized by velocity. This leads
to a family of approximate solitary waves in the periodic nonlinear
Schroedinger equation. Besides a formal derivation of the CMEs a rigorous
justification of the approximation and an error estimate in the supremum norm
are provided. Several numerical tests corroborate the analysis.


  We consider the question of determining how the topological structure
influences a consensus dynamical process taking place on a network. By
considering a large dataset of real-world networks we first determine that the
removal of edges according to their communicability angle -an angle between
position vectors of the nodes in an Euclidean communicability space- increases
the average time of consensus by a factor of 5.68 in real-world networks. The
edge betweenness centrality also identifies -in a smaller proportion- those
critical edges for the consensus dynamics, i.e., its removal increases the time
of consensus by a factor of 3.70. We justify theoretically these findings on
the basis of the role played by the algebraic connectivity and the
isoperimetric number of networks on the dynamical process studied, and their
connections with the properties mentioned before. Finally, we study the role
played by global topological parameters of networks on the consensus dynamics.
We determine that the network density and the average distance-sum -an
analogous of the node degree for shortest-path distances, account for more than
80% of the variance of the average time of consensus in the real-world networks
studied.


  In this paper we study the dependence of the H\"older estimates on the
geometry of a domain with holes for the Neumann problem. For this, we study the
H\"older regularity of the solutions to the Dirichlet and Neumann problems in
the disk (and in the exterior of the disk), from which we get a relation
between harmonic extensions and harmonic functions with prescribed Neumann
condition on the boundary of the disk (for both interior and exterior
problems).


  Compute-in-memory (CIM) accelerators built upon non-volatile memory (NVM)
devices excel in energy efficiency and latency when performing Deep Neural
Network (DNN) inference, thanks to their in-situ data processing capability.
However, the stochastic nature and intrinsic variations of NVM devices often
result in performance degradation in DNN inference. Introducing these non-ideal
device behaviors during DNN training enhances robustness, but drawbacks include
limited accuracy improvement, reduced prediction confidence, and convergence
issues. This arises from a mismatch between the deterministic training and
non-deterministic device variations, as such training, though considering
variations, relies solely on the model's final output. In this work, we draw
inspiration from the control theory and propose a novel training concept:
Negative Feedback Training (NFT) leveraging the multi-scale noisy information
captured from network. We develop two specific NFT instances, Oriented
Variational Forward (OVF) and Intermediate Representation Snapshot (IRS).
Extensive experiments show that our methods outperform existing
state-of-the-art methods with up to a 46.71% improvement in inference accuracy
while reducing epistemic uncertainty, boosting output confidence, and improving
convergence probability. Their effectiveness highlights the generality and
practicality of our NFT concept in enhancing DNN robustness against device
variations.


  Here we propose a new nonparametric framework for two-sample testing, named
as the OVL-$q$ ($q = 1, 2, \ldots$). This can be regarded as a natural
extension of the Smirnov test, which is equivalent to the OVL-1. We
specifically focus on the OVL-2, implement its fast algorithm, and show its
superiority over other statistical tests in some experiments.


  We study theoretically the electronic and transport properties of a diffusive
superconductor-normal metal-superconductor (SNS) junction in the presence of a
perpendicular magnetic field. We show that the field dependence of the critical
current crosses over from the well-known Fraunhofer pattern in wide junctions
to a monotonous decay when the width of the normal wire is smaller than the
magnetic length \xi_H = \sqrt{\Phi_0/H}, where H is the magnetic field and
\Phi_0 the flux quantum. We demonstrate that this behavior is a direct
consequence of the magnetic vortex structure appearing in the normal region and
predict how such structure is manifested in the local density of states.


  Local-box hydrodynamical model atmospheres provide statistical information
about the spatial dependence, as well as temporal evolution, of a star's
emergent radiation field. Here, we consider late-type stellar atmospheres for
which temporal changes of the radiative output are primarily related to
convective (granular) surface flows. We derived relations for evaluating the
granulation-induced, disk-integrated thus observable fluctuations of the
stellar brightness and location of the photocenter from radiation intensities
available from a local model. Apart from their application in the context of
hydrodynamical stellar atmospheres, these formulae provide some broader insight
into the nature of the fluctuations under consideration. Brightness
fluctuations scale inversely proportional to the square root of the number of
convective cells (the statistically independently radiating surface elements)
present on the stellar surface and increase with more pronounced
limb-darkening. Fluctuations of the stellar photocentric position do not depend
on the number of cells and are largely insensitive to the degree of
limb-darkening. They amount to a small fraction of the typical cell size, and
can become a limiting factor for high-precision astrometry in the case of
extreme giants. The temporal brightness and positional fluctuations are
statistically uncorrelated but closely related in magnitude.


  We present a class of algorithms capable of directly training deep neural
networks with respect to large families of task-specific performance measures
such as the F-measure and the Kullback-Leibler divergence that are structured
and non-decomposable. This presents a departure from standard deep learning
techniques that typically use squared or cross-entropy loss functions (that are
decomposable) to train neural networks. We demonstrate that directly training
with task-specific loss functions yields much faster and more stable
convergence across problems and datasets. Our proposed algorithms and
implementations have several novel features including (i) convergence to first
order stationary points despite optimizing complex objective functions; (ii)
use of fewer training samples to achieve a desired level of convergence, (iii)
a substantial reduction in training time, and (iv) a seamless integration of
our implementation into existing symbolic gradient frameworks. We implement our
techniques on a variety of deep architectures including multi-layer perceptrons
and recurrent neural networks and show that on a variety of benchmark and real
data sets, our algorithms outperform traditional approaches to training deep
networks, as well as some recent approaches to task-specific training of neural
networks.


  Few-shot segmentation (FSS) aims to segment novel classes in a query image by
using only a small number of supporting images from base classes. However, in
cross-domain few-shot segmentation (CD-FSS), leveraging features from
label-rich domains for resource-constrained domains poses challenges due to
domain discrepancies. This work presents a Dynamically Adaptive Refine (DARNet)
method that aims to balance generalization and specificity for CD-FSS. Our
method includes the Channel Statistics Disruption (CSD) strategy, which
perturbs feature channel statistics in the source domain, bolstering
generalization to unknown target domains. Moreover, recognizing the variability
across target domains, an Adaptive Refine Self-Matching (ARSM) method is also
proposed to adjust the matching threshold and dynamically refine the prediction
result with the self-matching method, enhancing accuracy. We also present a
Test-Time Adaptation (TTA) method to refine the model's adaptability to diverse
feature distributions. Our approach demonstrates superior performance against
state-of-the-art methods in CD-FSS tasks.


  We present a test platform for the Athena X-IFU detection chain, which will
serve as the first demonstration of the representative end-to-end detection and
readout chain for the X-IFU, using prototypes of the future flight electronics
and currently available subsystems. This test bench, housed in a commercial
two-stage ADR cryostat, includes a focal plane array placed at the 50 mK cold
stage of the ADR with a kilopixel array of transition-edge sensor
microcalorimeter spectrometers and associated cold readout electronics.
Prototype room temperature electronics for the X-IFU provide the readout, and
will evolve over time to become more representative of the X-IFU mission
baseline. The test bench yields critical feedback on subsystem designs and
interfaces, in particular the warm readout electronics, and will provide an
in-house detection system for continued testing and development of the warm
readout electronics and for the validation of X-ray calibration sources. In
this paper, we describe the test bench subsystems and design, characterization
of the cryostat, and current status of the project.


  In information theory, the well-known log-sum inequality is a fundamental
tool which indicates the non-negativity for the relative entropy. In this
article, we establish a set of inequalities which are similar to the log-sum
inequality involving two functions defined on scalars. The parametric extended
log-sum inequalities are shown. We extend these inequalities for the
commutative matrices. In addition, utilizing the L\"{o}wner partial order
relation and the Hansen-Pedersen theory for non-commutative positive
semi-definite matrices we demonstrate a number of matrix-inequalities analogous
to the log-sum inequality.


  Sterile neutrinos that couple to the Standard Model via the neutrino magnetic
dipole portals have been extensively studied at various experiments. In this
work, we scrutinize these interactions for sterile neutrinos in the mass range
of $\unit[0.1]{}-\unit[50]{MeV}$ through the nuclear and electron recoils at
various neutrino scattering experiments. For the $e$-flavor specific dipole
portal, we demonstrate that Dresden-II can provide leading constraints for $m_N
\lesssim \unit[0.5]{MeV}$, setting aside currently unresolved theoretical
uncertainties. For the $\mu$-flavor case, we show that the COHERENT experiment
can probe a unique parameter region for $m_N$ in the range of
$\unit[10]{}-\unit[40]{MeV}$ with the full dataset collected by the CsI[Na]
scintillation detector, including both the energy and timing structure of the
neutrino beam. We also present limits on the parameter regions of the
$\tau$-flavor dipole portal using measurements of the solar neutrino flux from
dark matter direct detection experiments.


  We give examples of locally PI but not PI division rings of GK-dimension n
for every positive integer n.


  We study the scattering rigidity problem for standard stationary manifolds
using timelike geodesics with a fixed momentum. Taking advantage of the
symmetry of this manifolds, we use Hamiltonian reduction to show that this
problem is related to scattering rigidity for $\mathcal{MP}$-systems, a problem
studied before. This gives several new rigidity results (up to some gauge) for
this kind of Lorentzian manifolds.


  The bounded derived category of a finite dimensional algebra of finite global
dimension is equivalent the stable category of $\mathbb{Z}$-graded modules over
its trivial extension \cite{Happel}. In particular, given two derived
equivalent finite dimensional algebras $\Lambda_{1}$ and $\Lambda_{2}$ of
finite global dimension, their trivial extensions are stable equivalent. The
converse is not true in general. The goal of this paper is to study cases where
derived equivalences between $\Lambda_{1}$ and $\Lambda_{2}$ arise from an
equivalence of categories involving their trivial extension. Thanks to a graded
version of Happel's theorem, we show that one can construct a
$\mathbb{Z}$-grading on the $\Lambda_{i}$ so that such an equivalence involving
their trivial extension yield a derived equivalence between the category of
$\mathbb{Z}$-graded module over $\Lambda_{i}$. We describe explicitly the
tilting object associated to this derived equivalence (in the non-graded and in
the graded case) for triangular matrix algebras. Finally, we apply these
results to the particular case of gentle algebras. In this context, we study
how one can obtain a derived equivalence between $\Lambda_{1}$ and
$\Lambda_{2}$ (in the non-graded and the graded case) from graded generalized
Kauer moves.


  Ethereum 2.0 is a major upgrade to improve its scalability, throughput, and
security. In this version, RANDAO is the scheme to randomly select the users
who propose, confirm blocks, and get rewards. However, a vulnerability,
referred to as the `Last Revealer Attack' (LRA), compromises the randomness of
this scheme by introducing bias to the Random Number Generator (RNG) process.
This vulnerability is first clarified again in this study. After that, we
propose a Shamir's Secret Sharing (SSS)-based RANDAO scheme to mitigate the
LRA. Through our analysis, the proposed method can prevent the LRA under
favorable network conditions.


  We discuss a methodology of machine learning to deduce the neutron star
equation of state from a set of mass-radius observational data. We propose an
efficient procedure to deal with a mapping from finite data points with
observational errors onto an equation of state. We generate training data and
optimize the neural network. Using independent validation data (mock
observational data) we confirm that the equation of state is correctly
reconstructed with precision surpassing observational errors. We finally
discuss the relation between our method and Bayesian analysis with an emphasis
put on generality of our method for underdetermined problems.


  The aim of this paper is to obtain the existence of solution for the
fractional p-Laplacian Dirichlet problem with mixed derivatives
\begin{eqnarray*}
&{_{t}}D_{T}^{\alpha}\left(|_{0}D_{t}^{\alpha}u(t))|^{p-2}{_{0}}D_{t}^{\alpha}u(t)\right)
= f(t,u(t)), \;t\in [0,T],\\ &u(0) = u(T) = 0, \end{eqnarray*} where
$\frac{1}{p} < \alpha <1$, $1<p<\infty$ and $f:[0,T]\times \mathbb{R} \to
\mathbb{R}$ is a Carath\'eodory function wich satisfies some growth conditions.
We obtain the existence of nontrivial solution by using the Mountain Pass
Theorem.


  We construct a family of representations of an arbitrary variant $S_a$ of a
semigroup $S$, induced by a given representation of $S$, and investigate
properties of such representations and their kernels.


  Curating labeled training data has become the primary bottleneck in machine
learning. Recent frameworks address this bottleneck with generative models to
synthesize labels at scale from weak supervision sources. The generative
model's dependency structure directly affects the quality of the estimated
labels, but selecting a structure automatically without any labeled data is a
distinct challenge. We propose a structure estimation method that maximizes the
$\ell_1$-regularized marginal pseudolikelihood of the observed data. Our
analysis shows that the amount of unlabeled data required to identify the true
structure scales sublinearly in the number of possible dependencies for a broad
class of models. Simulations show that our method is 100$\times$ faster than a
maximum likelihood approach and selects $1/4$ as many extraneous dependencies.
We also show that our method provides an average of 1.5 F1 points of
improvement over existing, user-developed information extraction applications
on real-world data such as PubMed journal abstracts.


  We report observations of a remarkable scaling behavior with respect to
concentration in the passive microbead rheology of two highly entangled
polymeric solutions, polyethylene oxide (PEO) and hyaluronic acid (HA). This
behavior was reported previously [Hill et al., PLOS ONE (2014)] for human lung
mucus, a complex biological hydrogel, motivating the current study for
synthetic polymeric solutions PEO and HA. The strategy is to identify, and
focus within, a wide range of lag times ${\tau}$ for which passive micron
diameter beads exhibit self-similar (fractional, power law)
mean-squared-displacement (MSD) statistics. For lung mucus, PEO at three
different molecular weights (Mw), and HA at one Mw, we find ensemble-averaged
MSDs of the form ${\langle}{\Delta}r^{2}({\tau}){\rangle} =
4D_{\alpha}{\tau}^{\alpha}$, all within a common band, [1/60 sec, 3 sec], of
lag times ${\tau}$. We employ the MSD power law parameters
$(D_{\alpha},{\alpha})$ to classify each polymeric solution over a range of
highly entangled concentrations. By the generalized Stokes-Einstein relation,
power law MSD implies power law elastic $G'({\omega})$ and viscous
$G''({\omega})$ moduli for frequencies $1/{\tau}$, [0.33 sec$^{-1}$, 60
sec$^{-1}$]. A natural question surrounds the polymeric properties that dictate
$D_{\alpha}$ and ${\alpha}$, e.g. polymer concentration c, Mw, and stiffness
(persistence length). In [Hill et al., PLOS ONE (2014)], we showed the MSD
exponent ${\alpha}$ varies linearly, while the pre-factor $D_{\alpha}$ varies
exponentially, with concentration, i.e. the semi-log plot,
$(log(D_{\alpha}),{\alpha})(c)$ of the classifier data is collinear. Here we
show the same result for three distinct Mw PEO and HA at a single Mw. Future
studies are required to explore the generality of these results for polymeric
solutions, and to understand this scaling behavior with polymer concentration.


  The floating zone technique is a well-established single crystal growth
method in materials research, able to produce volumetrically large specimens
with extremely high purities. However, traditional furnace designs have relied
on heating from high-powered bulb sources in combination with parabolic
mirrors, and hence are constrained to transparent growth chambers with large
solid angles of optical access. This results in a stark limitation on
achievable processing gas pressures, and in turn renders a range of compounds
unsuitable for crystal growth by the floating zone technique, either due to
excessive volatility or due to metastability. Here, we demonstrate a novel
high-pressure laser-based floating zone system (HP-LFZ). The use of lasers for
heating allows implementation of a high-strength metal growth chamber,
permitting greatly enhanced processing pressures over conventional mirror-based
designs, with the current design allowing for pressures up to 1000 bar. We
demonstrate a series of example single crystal growths using this design in
pressures up to 675 bar, a significant increase over processing pressures
attainable in commercially available floating zone systems. The general utility
of the HP-LFZ is also illustrated via growths of a range of complex oxides.


  We demonstrate that the collinear expansion fails in the case of gluon
emission from a fast quark produced in $eA$ DIS. In this approximation the N=1
rescattering contribution to the gluon spectrum vanishes. We show that the
higher-twist approach by Guo, Wang and Zhang [4,5] based on the collinear
expansion is wrong. The nonzero gluon spectrum obtained in [4,5] is a
consequence of unjustified neglecting some important terms in the collinear
expansion.


  We study random walk with unbounded jumps in random environment. The
environment is stationary and ergodic, uniformly elliptic and decays
polynomially with speed $Dj^{-(3+\varepsilon_0)}$ for some small
$\varepsilon_0>0$ and proper $D>0.$ We prove a law of large number with
positive velocity under the condition that the annealed mean of the hitting
time of the positive half lattice is finite. Secondly, we consider birth and
death process with bounded jumps in stationary and ergodic environment. Under
the uniformly elliptic condition, we prove a law of large number and give the
explicit formula of its velocity.


  Electron Compton scattering of target photons into the gamma-ray energy band
(inverse Compton scattering --IC--) is commonly expected to dominate the very
high energy spectra in gamma-ray bursts especially during the afterglow phase.
For sufficiently large center-of-mass energies in these collisions, the effect
of the electron recoil starts reducing the scattering cross section (the
Klein-Nishina regime). The IC spectra generated in the Klein-Nishina regime is
softer and has a smaller flux level compared to the synchrotron spectra
produced by the same electrons. The detection of afterglow emission from nearby
GRB 190819A in the very high energy (VHE) domain with H.E.S.S. has revealed an
unexpected feature: the slope of the VHE spectrum matches well the slope of the
X-ray spectra, despite expectations that for the IC production process, the
impact of the Klein-Nishina effect should be strong. The multi-wavelength
spectral energy distribution appears to be inconsistent with predictions of
one-zone synchrotron-self-Compton models. We study the possible impact of
two-zone configuration on the properties of IC emission when the magnetic field
strength differs considerably between the two zones. Synchrotron photons from
the strong magnetic field zone provide the dominant target for cooling of the
electrons in the weak magnetic field zone, which results in a formation of hard
electron distribution and consequently of a hard IC emission. We show that the
two-zone model can provide a good description of the X-ray XRT and VHE H.E.S.S.
data.


  It has been generally assumed in the automatic speech recognition (ASR)
literature that it is better for models to have access to wider context
windows. Yet, many of the potential reasons this might be true in the
supervised setting do not necessarily transfer over to the case of unsupervised
learning. We investigate how much context is necessary to achieve high-quality
pre-trained acoustic models using self-supervised learning. We principally
investigate contrastive predictive coding (CPC), which we adapt to be able to
precisely control the amount of context visible to the model during training
and inference. We find that phone discriminability in the resulting model
representations peaks at around 40~ms of preceding context, and that having too
much context (beyond around 320 ms) substantially degrades the quality of the
representations. Surprisingly, we find that this pattern also transfers to
supervised ASR when the pre-trained representations are used as frozen input
features. Our results point to potential changes in the design of current
upstream architectures to better facilitate a variety of downstream tasks.


  The magnetorotational instability (MRI) can destabilize hydrodynamically
stable rotational flows, thereby allowing angular momentum transport in
accretion disks. A notorious problem for MRI is its questionable applicability
in regions with low magnetic Prandtl number, as they are typical for
protoplanetary disks and the outer parts of accretion disks around black holes.
Using the WKB method, we extend the range of applicability of MRI by showing
that the inductionless versions of MRI, such as the helical MRI and the
azimuthal MRI, can easily destabilize Keplerian profiles ~ 1/r^(3/2) if the
radial profile of the azimuthal magnetic field is only slightly modified from
the current-free profile ~ 1/r. This way we further show how the formerly known
lower Liu limit of the critical Rossby number, Ro=-0.828, connects naturally
with the upper Liu limit, Ro=+4.828.


  We report on the nano-electron beam assisted fabrication of atomically sharp
iron-based tips and on the creation of a nano-soldering iron for
nano-interconnects using Fe-filled multiwalled carbon nanotubes (MWCNTs). High
energy electron beam machining has been proven a powerful tool to modify
desired nanostructures for technological applications and to form molecular
junctions and interconnections between carbon nanotubes. Recent studies showed
the high degree of complexity in the creation of direct interconnections
between multiwalled and CNTs having dissimilar diameters. Our technique allows
for carving a MWCNT into a nanosoldering iron that was demonstrated capable of
joining two separated halves of a tube. This approach could easily be extended
to the interconnection of two largely dissimilar CNTs, between a CNT and a
nanowire or between two nanowires.


  Recently developed methods for PT-symmetric models are applied to
quantum-mechanical matrix models. We consider in detail the case of potentials
of the form $V=-(g/N^{p/2-1})Tr(iM)^{p}$ and show how the calculation of all
singlet wave functions can be reduced to solving a one-dimensional PT-symmetric
model. The large-N limit of this class of models exists, and properties of the
lowest-lying singlet state can be computed using WKB. For $p=3,4$, the energy
of this state for small values of $N$ appears to show rapid convergence to the
large-N limit. For the special case of $p=4$, we extend recent work on the
$-gx^{4}$ potential to the matrix model: we show that the PT-symmetric matrix
model is equivalent to a hermitian matrix model with a potential proportional
to $+(4g/N)Tr\Pi^{4}$. However, this hermitian equivalent model includes an
anomaly term $\hbar\sqrt{2g/N}Tr\Pi$. In the large-N limit, the anomaly term
does not contribute at leading order to the properties of singlet states.


  Reliability in terms of functional properties from the safety-liveness
spectrum is an indispensable requirement of low-level operating-system (OS)
code. However, with evermore complex and thus less predictable hardware,
quantitative and probabilistic guarantees become more and more important.
Probabilistic model checking is one technique to automatically obtain these
guarantees. First experiences with the automated quantitative analysis of
low-level operating-system code confirm the expectation that the naive
probabilistic model checking approach rapidly reaches its limits when
increasing the numbers of processes. This paper reports on our work-in-progress
to tackle the state explosion problem for low-level OS-code caused by the
exponential blow-up of the model size when the number of processes grows. We
studied the symmetry reduction approach and carried out our experiments with a
simple test-and-test-and-set lock case study as a representative example for a
wide range of protocols with natural inter-process dependencies and long-run
properties. We quickly see a state-space explosion for scenarios where
inter-process dependencies are insignificant. However, once inter-process
dependencies dominate the picture models with hundred and more processes can be
constructed and analysed.


  The coefficients of the volume, surface, coulomb, asymmetry and pairing
energy terms of the semiempirical liquid drop model mass formula have been
determined by furnishing best fit to the observed mass excesses. Slightly
different sets of the weighting parameters for liquid drop model mass formula
have been obtained from minimizations of chisquare and mean square deviation.
The most recent experimental and estimated mass excesses from
Audi-Wapstra-Thibault atomic mass table have been used for the least square
fitting procedure. Equation of state, nuclear incompressibility, nuclear mean
free path and the most stable nuclei for corresponding atomic numbers, all are
in good agreement with the experimental results.


  While topological quantum computation is intrinsically fault-tolerant at zero
temperature, it loses its topological protection at any finite temperature. We
present a scheme to protect the information stored in a system supporting
non-cyclic anyons against thermal and measurement errors. The correction
procedure builds on the work of G\'acs [Gacs 1986] and Harrington [Harrington
2004] and operates as a local cellular automaton. In contrast to previously
studied schemes, our scheme is valid for both abelian and non-abelian anyons
and accounts for measurement errors. We analytically prove the existence of a
fault-tolerant threshold for a certain class of non-Abelian anyon models, and
numerically simulate the procedure for the specific example of Ising anyons.
The result of our simulations are consistent with a threshold between $10^{-4}$
and $10^{-3}$.


  The conditions whereby epitaxy is achieved are commonly believed to be mostly
governed by misfit strain. We report on a systematic investigation of growth
and interface structure of single crystalline tungsten thin films on two
different metal oxide substrates, Al$_{2}$O$_{3}$ ($11\bar{2}0$) and MgO
($001$). We demonstrate that despite a significant mismatch, enhanced crystal
quality is observed for tungsten grown on the sapphire substrates. This is
promoted by stronger adhesion and chemical bonding with sapphire compared to
magnesium oxide, along with the restructuring of the tungsten layers close to
the interface. The latter is supported by ab initio calculations using density
functional theory. Finally, we demonstrate the growth of magnetic
heterostructures consisting of high-quality tungsten layers in combination with
ferromagnetic CoFe layers, which are relevant for spintronic applications.


  In homogenous space Sol we study compact surfaces with constant mean
curvature and with non-empty boundary. We ask how the geometry of the boundary
curve imposes restrictions over all possible configurations that the surface
can adopt. We obtain a flux formula and we establish results that assert that,
under some restrictions, the symmetry of the boundary is inherited into the
surface.


  We provide a prescription for constructing Hamiltonians representing the low
energy physics of correlated electron materials with dynamically screened
Coulomb interactions. The key feature is a renormalization of the hopping and
hybridization parameters by the processes that lead to the dynamical screening.
The renormalization is shown to be non-negligible for various classes of
correlated electron materials. The bandwidth reduction effect is necessary for
connecting models to materials behavior and for making quantitative predictions
for low-energy properties of solids.


  In this paper we introduce a new probability distribution on (0,1),
associated with the I-function, namely, the I-function distribution. This
distribution generalizes several known distributions with positive support. It
is also shown that the distribution of products, quotients and powers of
independent I-function variates are I-function variates. Another distribution
called the I-function inverse Gaussian distribution is also introduced and
studied.


  We prove existence of weak solutions (in the probabilistic sense) for a
general class of stochastic semilinear wave equations on bounded domains of
$R^d$ driven by a possibly discontinuous square integrable martingale.


  We demonstrate a scalable readout scheme for an infrared single-photon
pulse-position camera consisting of WSi superconducting nanowire single-photon
detectors (SNSPDs). For an N x N array, only 2 x N wires are required to obtain
the position of a detection event. As a proof-of-principle, we show results
from a 2 x 2 array.


  We study the bifurcation diagram of the Onsager free-energy func- tional for
liquid crystals with orientation parameter on the sphere in three dimensions.
In particular, we concentrate on a very general class of two- body interaction
potentials including the Onsager kernel. The problem is reformulated as a
non-linear eigenvalue problem for the kernel operator, and a general method to
find the corresponding eigenvalues and eigenfunctions is presented. Our main
tools for this analysis are spherical harmonics and a special algorithm for
computing expansions of products of spherical harmon- ics in terms of spherical
harmonics. We find an explicit expression for the set of all bifurcation
points. Using a Lyapunov-Schmidt reduction, we derive a bifurcation equation
depending on five state variables. The dimension of this state space is further
reduced to two dimensions by using the rotational symmetry of the problem and
the theory of invariant polynomials. On the basis of these result, we show that
the first bifurcation occurring in case of the Onsager interaction potential is
a transcritical bifurcation and that the corresponding solution is uniaxial. In
addition, we prove some global proper- ties of the bifurcation diagram such as
the fact that the trivial solution is the unique local minimiser for high
temperatures, that it is not a local minimiser if the temperature is low, the
boundedness of all equilibria of the functional and that the bifurcation
branches are either unbounded or that they meet another bifurcation branch.


  We introduce a feasible method of constructing the entanglement witness that
detects the genuine entanglement of a given pure multiqubit state. We
illustrate our method in the scenario of constructing the witnesses for the
multiqubit states that are broadly theoretically and experimentally
investigated. It is shown that our method can construct the effective witnesses
for experiments. We also investigate the entanglement detection of symmetric
states and mixed states.


  Given a vector bundle $A\to M$ we study the geometry of the graded manifolds
$T^*[k]A[1]$, including their canonical symplectic structures, compatible
Q-structures and Lagrangian Q-submanifolds. We relate these graded objects to
classical structures, such as higher Courant algebroids on
$A\oplus\bigwedge^{k-1}A^*$ and higher Dirac structures therein, semi-direct
products of Lie algebroid structures on $A$ with their coadjoint
representations up to homotopy, and branes on certain AKSZ $\sigma$-models.


  The aim of this paper is twofold: (1) On the one hand, the paper revisits the
spectral analysis of semigroups in a general Banach space setting. It presents
some new and more general versions, and provides comprehensible proofs, of
classical results such as the spectral mapping theorem, some (quantified)
Weyl's Theorems and the Krein-Rutman Theorem. Motivated by evolution PDE
applications, the results apply to a wide and natural class of generators which
split as a dissipative part plus a more regular part, without assuming any
symmetric structure on the operators nor Hilbert structure on the space, and
give some growth estimates and spectral gap estimates for the associated
semigroup. The approach relies on some factorization and summation arguments
reminiscent of the Dyson-Phillips series in the spirit of those used in
[87,82,48,81]. (2) On the other hand, we present the semigroup spectral
analysis for three important classes of "growth-fragmentation" equations,
namely the cell division equation, the self-similar fragmentation equation and
the McKendrick-Von Foerster age structured population equation. By showing that
these models lie in the class of equations for which our general semigroup
analysis theory applies, we prove the exponential rate of convergence of the
solutions to the associated remarkable profile for a very large and natural
class of fragmentation rates. Our results generalize similar estimates obtained
in \cite{MR2114128,MR2536450} for the cell division model with (almost)
constant total fragmentation rate and in \cite{MR2832638,MR2821681} for the
self-similar fragmentation equation and the cell division equation restricted
to smooth and positive fragmentation rate and total fragmentation rate which
does not increase more rapidly than quadratically. It also improves the
convergence results without rate obtained in \cite{MR2162224,MR2114413} which
have been established under similar assumptions to those made in the present
work.


  In a very recent work, G. E. Andrews defined the combinatorial objects which
he called {\it singular overpartitions} with the goal of presenting a general
theorem for overpartitions which is analogous to theorems of Rogers--Ramanujan
type for ordinary partitions with restricted successive ranks. As a small part
of his work, Andrews noted two congruences modulo 3 which followed from
elementary generating function manipulations. In this work, we prove that
Andrews' results modulo 3 are two examples of an infinite family of congruences
modulo 3 which hold for that particular function. We also expand the
consideration of such arithmetic properties to other functions which are part
of Andrews' framework for singular overpartitions.


  In online reinforcement learning (RL), efficient exploration remains
particularly challenging in high-dimensional environments with sparse rewards.
In low-dimensional environments, where tabular parameterization is possible,
count-based upper confidence bound (UCB) exploration methods achieve minimax
near-optimal rates. However, it remains unclear how to efficiently implement
UCB in realistic RL tasks that involve non-linear function approximation. To
address this, we propose a new exploration approach via \textit{maximizing} the
deviation of the occupancy of the next policy from the explored regions. We add
this term as an adaptive regularizer to the standard RL objective to balance
exploration vs. exploitation. We pair the new objective with a provably
convergent algorithm, giving rise to a new intrinsic reward that adjusts
existing bonuses. The proposed intrinsic reward is easy to implement and
combine with other existing RL algorithms to conduct exploration. As a proof of
concept, we evaluate the new intrinsic reward on tabular examples across a
variety of model-based and model-free algorithms, showing improvements over
count-only exploration strategies. When tested on navigation and locomotion
tasks from MiniGrid and DeepMind Control Suite benchmarks, our approach
significantly improves sample efficiency over state-of-the-art methods. Our
code is available at https://github.com/tianjunz/MADE.


  This work is a supplement on the previous research about primordial
electromagnetic fields. In this work, three important problems are discussed:
the evolution of primordial electric fields, the electric and particle
densities' solitons in plasma before recombination and their influences on the
power spectra of cosmic microwave background. Detailed computations show that
the primordial electric fields dissipate by Landau damping effect on both large
scale and small scale and there is no impact on the spectrum. While, before
recombination, there exist solitary waves stably propagating in plasma whose
speed is significantly slower than that of baryonic acoustic oscillations,
working only at extremely small scale. On the other hand, the amplitude of
solitons is so weak that only a significantly small contribution on the phase
of baryon acoustic oscillations, so there merely exist the messages about such
electric solitary waves on the spectrum. In a word, as relevant monographs on
cosmology, neglecting the electromagnetic fields (electric fields at least) is
a reasonable treatment on the calculations of cosmic microwave background.
However, the protonic density fluctuations show a form of KdV equation while
its propagation as a stable solitary wave, leading a probability to the origin
of fluctuation promoting the generation and evolution of galaxies.


  It is a longstanding conjecture that for a finite group $G$, the exponent of
the second homology group $H_2(G, \mathbb{Z})$ divides the exponent of $G$. In
this paper, we prove this conjecture for $p$-groups of class at most $p$,
finite nilpotent groups of odd exponent and of nilpotency class 5, $p$-central
metabelian $p$-groups, and groups considered by L. E . Wilson in \cite{LEW}.
Moreover, we improve several bounds given by various authors. We achieve most
of our results using an induction argument.


  We revisit the standard axioms of domain theory with emphasis on their
relation to the concept of partiality, explain how this idea arises naturally
in probability theory and quantum mechanics, and then search for a mathematical
setting capable of providing a satisfactory unification of the two.


  We study the rich behavior of ergodicity and conservativity of Cartesian
products of infinite measure preserving transformations. A class of
transformations is constructed such that for any subset $R\subset \mathbb Q\cap
(0,1)$ there exists $T$ in this class such that $T^p\times T^q$ is ergodic if
and only if $\frac{p}{q} \in R$. This contrasts with the finite measure
preserving case where $T^p\times T^q$ is ergodic for all nonzero $p$ and $q$ if
and only if $T\times T$ is ergodic. We also show that our class is rich in the
behavior of conservative products.
  For each positive integer $k$, a family of rank-one infinite measure
preserving transformations is constructed which have ergodic index $k$, but
infinite conservative index.


  The cost of information processing in physical systems calls for a trade-off
between performance and energetic expenditure. Here we formulate and study a
computation-dissipation bottleneck in mesoscopic systems used as input-output
devices. Using both real datasets and synthetic tasks, we show how
non-equilibrium leads to enhanced performance. Our framework sheds light on a
crucial compromise between information compression, input-output computation
and dynamic irreversibility induced by non-reciprocal interactions.


  We study the orbital and spin configurations of up to six electrons or holes
charged into self-assembled InAs/GaAs quantum dots via single-particle
pseudopotential and many-particle configuration interaction method. We find
that while the charging of {\it electrons} follows both Hund's rule and the
Aufbau principle, the charging of {\it holes} follows a non-trivial charging
pattern which violates both the Aufbau principle and Hund's rule, and is robust
against the details of the quantum dot size. The predicted hole charging
sequence offers a new interpretation of recent charging experiments.


  This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for
multi-target tracking: one to estimate the set of alive trajectories at each
time step and another to estimate the set of all trajectories, which includes
alive and dead trajectories, at each time step. The filters are based on
propagating a Poisson multi-Bernoulli (PMB) density on the corresponding set of
trajectories through the filtering recursion. After the update step, the
posterior is a PMB mixture (PMBM) so, in order to obtain a PMB density, a
Kullback-Leibler divergence minimisation on an augmented space is performed.
The developed filters are computationally lighter alternatives to the
trajectory PMBM filters, which provide the closed-form recursion for sets of
trajectories with Poisson birth model, and are shown to outperform previous
multi-target tracking algorithms.


  In eikonal and quenched approximations at least, it is argued that the strong
coupling fermionic QCD amplitudes obtained with the help of the newly
discovered effective locality property, depart from a dependence on the sole
$SU_c(3)$ quadratic Casimir operator, evaluated over the fundamental gauge
group representation. This result, in contradistinction with Perturbation
Theory, but also with a number of non-perturbative approaches such as the MIT
Bag, the Stochastic Vacuum Models, and Lattice simulations, accounts, instead,
for the full algebraic content of the rank-2 $SU_c(3)$-Lie algebra


  Context: Lyman $\alpha$ blobs (LABs) are large-scale radio-quiet Lyman
$\alpha$ (Ly$\alpha$) nebula at high-$z$ that occur predominantly in overdense
proto-cluster regions. Especially the prototypical SSA22a-LAB1 at $z=3.1$ has
become an observational reference for LABs across the electromagnetic spectrum.
  Aims: We want to understand the powering mechanisms that drive the LAB to
gain empirical insights into galaxy formation processes within a rare dense
environment at high-$z$.
  Methods: LAB 1 was observed for 17.5h with the VLT/MUSE integral-field
spectrograph. We produced optimally extracted narrow band images in Ly$\alpha$
$\lambda1216$ and HeII $\lambda1640$. By using a moment based analysis we
mapped the kinematics of the blob.
  Results: We detect Ly$\alpha$ emission to surface-brightness limits of
$10^{-19}$erg s$^{-1}$cm$^{-2}$arcsec$^{-2}$. At this depth we reveal a bridge
between LAB 1 and its northern neighbour LAB 8, as well as a shell-like
filament towards the south of LAB 1. We find a coherent large scale east-west
$\sim$1000 km s$^{-1}$ velocity gradient that is aligned perpendicular to the
major axis of the blob. We detect HeII emission in three distinct regions, but
we can only provide upper limits for CIV.
  Conclusions: Various gas excitation mechanisms are at play in LAB 1: Ionising
radiation and feedback effects dominate near the embedded galaxies, while
Ly$\alpha$ scattering is contributing at larger distances. However,
HeII/Ly$\alpha$ ratios combined with upper limits on CIV/Ly$\alpha$ can not
discriminate between AGN ionisation and feedback driven shocks. The alignment
of the angular momentum vector parallel to the morphological principal axis
appears odds with the predicted norm for high-mass halos, but likely reflects
that LAB\,1 resides at a node of multiple intersecting filaments of the cosmic
web.
  (Abridged)


  Multimedia conferencing is the conversational exchange of multimedia content
between multiple parties. It has a wide range of applications (e.g. Massively
Multiplayer Online Games (MMOGs) and distance learning). Many multimedia
conferencing applications use video extensively, thus video mixing in
conferencing settings is of critical importance. Cloud computing is a
technology that can solve the scalability issue in multimedia conferencing,
while bringing other benefits, such as, elasticity, efficient use of resources,
rapid development, and introduction of new applications. However, proposed
cloud-based multimedia conferencing approaches so far have several deficiencies
when it comes to efficient resource usage while meeting Quality of Service
(QoS) requirements. We propose a solution to optimize resource allocation for
cloud-based video mixing service in multimedia conferencing applications, which
can support scalability in terms of number of users, while guaranteeing QoS. We
formulate the resource allocation problem mathematically as an Integer Linear
Programming (ILP) problem and design a heuristic for it. Simulation results
show that our resource allocation model can support more participants compared
to the state-of-the-art, while honoring QoS, with respect to end-to-end delay.


  We prove an asymptotic formula for the second moment of a product of two
Dirichlet L-functions on the critical line, which has a power saving in the
error term and which is uniform with respect to the involved Dirichlet
characters. As special cases we give uniform asymptotic formulae for the fourth
moment of individual Dirichlet L-functions and for the second moment of
Dedekind zeta functions of quadratic number fields on the critical line.


  Let $\mathcal{A}$ be a Weyl arrangement in an $\ell$-dimensional Euclidean
space. The freeness of restrictions of $\mathcal{A}$ was first settled by a
case-by-case method by Orlik and the second author (1993), and later by a
uniform argument by Douglass (1999). Prior to this, Orlik and Solomon (1983)
had completely determined the exponents of these arrangements by exhaustion. A
classical result due to Orlik, Solomon and the second author (1986), asserts
that the exponents of any $A_1$ restriction, i.e., the restriction of
$\mathcal{A}$ to a hyperplane, are given by $\{m_1,\ldots, m_{\ell-1}\}$, where
$\exp(\mathcal{A})=\{m_1,\ldots, m_{\ell}\}$ with $m_1 \le \cdots\le m_{\ell}$.
As a next step towards conceptual understanding of the restriction exponents we
will investigate the $A_1^2$ restrictions, i.e., the restrictions of
$\mathcal{A}$ to the subspaces of type $A_1^2$. In this paper, we give a
combinatorial description of the exponents and describe bases for the modules
of derivations of the $A_1^2$ restrictions in terms of the classical notion of
related roots by Kostant (1955).


  This paper examines the process of emergence of religious fundamentalism
through development parameters. Therefore this research work reflects an
analytical discussion on how the level of religious fundamentalism can be
explained by the economic, political administrative and legal parameters such
as GDP, Employment to Population ratio, Government Effectiveness, Voice &
Accountability, Rule of Law (World Justice Project Report) and Rule of law
(Governance Indicators).


  We present a tight-binding model study of a two-terminal graphene nanopore
device for sequential determination of DNA bases. Using Green's function
technique we investigate the changes in electronic transport properties of the
device due to insertion of different nucleotides into the nanopore created
within a zigzag graphene nanoribbon. First we try to characterise the device in
static condition and then go for sequencing application by setting the bias
across it to a specific voltage and then recording the characteristic current
signals corresponding to each nucleotides of a translocating DNA. Our
investigations show that graphene nanopores can certainly become very efficient
and reliable for sequencing applications in future.


  Using the perturbative method for the simulation of charmed particles, the
dynamical origin of charm quark elliptic flow is studied in the framework of a
multi-phase transport (AMPT) model. Besides the expected ordering relative to
that of light quarks according to quark masses, charm quark elliptic flow is
seen to be sensitive to the parton scattering cross section. To describe the
observed large elliptic flow of electrons from the decay of charmed mesons, a
charm quark elastic scattering cross section much larger than that estimated
from the perturbative QCD is required.


  Although its ubiquitous emergence in nature and variety of systems,
turbulence possesses spatio-temporal chaotic, intermittent fluctuations, and
makes it impossible to be precisely predicted. Persistent attempts for almost a
century have been devoted to capture the invariant laws and hidden deeply
universality out of the vast disorder and chaotic nature of turbulence. The
celebrated Kolmogorov -5/3 law is robust, but not comprehensive to describe the
diverse turbulences, especially in the turbulence driven by external volume
forces, e.g. thermal convection, electrokinetic turbulence and etc. Here, we
reveal that the fluxes of kinetic energy and scalar variance must be highly
coupled to establish a universal conservation law and consequently we
successfully unify a much diversity of scaling laws. As an example, in a
microfluidic electrokinetic turbulence, additional scaling of -5/3, -9/5 and
-7/3 are experimentally found in the power spectra of concentration. With this
proposed model, a full quad-cascade picture is eventually complete to unify the
various scaling laws for the most complicated physical problem of turbulence.


  The gauge equivalent formulation of the Faddeev-Skyrme model is used for the
study of the quantum theory. The rotational quantum excitations around the
soliton solution of Hopf number unity are investigated by the method of
collective coordinates. The quantum Hamiltonian of the system is found to
coincide with the Hamiltonian of a symmetrical top rotating in SU(2). Thus, the
irreducible representations of physical observables can be constructed.


  This report describes our submission to the ActivityNet Challenge at CVPR
2019. We use a 3D convolutional neural network (CNN) based front-end and an
ensemble of temporal convolution and LSTM classifiers to predict whether a
visible person is speaking or not. Our results show significant improvements
over the baseline on the AVA-ActiveSpeaker dataset.


  Anatomical studies demonstrate that brain reformats input information to
generate reliable responses for performing computations. However, it remains
unclear how neural circuits encode complex spatio-temporal patterns. We show
that neural dynamics are strongly influenced by the phase alignment between the
input and the spontaneous chaotic activity. Input structuring along the
dominant chaotic projections causes the chaotic trajectories to become stable
channels (or attractors), hence, improving the computational capability of a
recurrent network. Using mean field analysis, we derive the impact of input
structuring on the overall stability of attractors formed. Our results indicate
that input alignment determines the extent of intrinsic noise suppression and
hence, alters the attractor state stability, thereby controlling the network's
inference ability.


  The new field of quantum error correction has developed spectacularly since
its origin less than two years ago. Encoded quantum information can be
protected from errors that arise due to uncontrolled interactions with the
environment. Recovery from errors can work effectively even if occasional
mistakes occur during the recovery procedure. Furthermore, encoded quantum
information can be processed without serious propagation of errors. Hence, an
arbitrarily long quantum computation can be performed reliably, provided that
the average probability of error per quantum gate is less than a certain
critical value, the accuracy threshold. A quantum computer storing about 10^6
qubits, with a probability of error per quantum gate of order 10^{-6}, would be
a formidable factoring engine. Even a smaller, less accurate quantum computer
would be able to perform many useful tasks. (This paper is based on a talk
presented at the ITP Conference on Quantum Coherence and Decoherence, 15-18
December 1996.)


  Software has a longstanding association with a state of crisis considering
its success rate. The explosion of Internet-connected devices,
Internet-of-Things, adds to the complexity of software systems. The particular
characteristics of these systems, such as being large-scale and its
heterogeneity, pose increasingly new challenges. In this paper, we first
briefly introduce the IoT paradigm and the current state of art of software
development. Then, we delve into the particularities of developing software for
IoT systems and systems of systems, given an overview of what are the current
methodologies and tools for design, develop and test such systems. The findings
are discussed, revealing open issues and research directions, and reveal that
the nowadays IoT software development practices are still lagging behind of
what are the current best practices.


  In a Bayesian setting, inverse problems and uncertainty quantification (UQ) -
the propagation of uncertainty through a computational (forward) model - are
strongly connected. In the form of conditional expectation the Bayesian update
becomes computationally attractive. This is especially the case as together
with a functional or spectral approach for the forward UQ there is no need for
time-consuming and slowly convergent Monte Carlo sampling. The developed
sampling-free non-linear Bayesian update is derived from the variational
problem associated with conditional expectation. This formulation in general
calls for further discretisation to make the computation possible, and we
choose a polynomial approximation. After giving details on the actual
computation in the framework of functional or spectral approximations, we
demonstrate the workings of the algorithm on a number of examples of increasing
complexity. At last, we compare the linear and quadratic Bayesian update on the
small but taxing example of the chaotic Lorenz 84 model, where we experiment
with the influence of different observation or measurement operators on the
update.


  We obtain results concerning the so-called factorization for the convergence
of random variables almost everywhere (almost surely or with probability one),
belonging to the classical Lebesgue-Riesz spaces and we extend these results to
the Grand Lebesgue Spaces. We also give exact estimates for the parameters
involved and provide several examples. We also show that the obtained estimates
are, in the general case, essentially non-improvable, of course up to a
multiplicative constant.


  Standing shocks are believed to be responsible for stationary Type II solar
radio bursts, whereas drifting Type II bursts are excited by moving shocks
often related to coronal mass ejections (CMEs). Observations of either
stationary or drifting Type II bursts are common, but a transition between the
two states has not yet been reported. Here, we present a Type II burst which
shows a clear, continuous transition from a stationary to a drifting state, the
first observation of its kind. Moreover, band splitting is observed in the
stationary parts of the burst, as well as intriguing negative and positive
frequency-drift fine structures within the stationary emissions. The relation
of the radio emissions to an observed jet and a narrow CME was investigated
across multiple wavelengths, and the mechanisms leading to the transitioning
Type II burst were determined. We find that a jet eruption generates a
streamer-puff CME and that the interplay between the CME-driven shock and the
streamer is likely to be responsible for the observed radio emissions.


  For a global breeding organization, identifying the next generation of
superior crops is vital for its success. Recognizing new genetic varieties
requires years of in-field testing to gather data about the crop's yield, pest
resistance, heat resistance, etc. At the conclusion of the growing season,
organizations need to determine which varieties will be advanced to the next
growing season (or sold to farmers) and which ones will be discarded from the
candidate pool. Specifically for soybeans, identifying their relative maturity
is a vital piece of information used for advancement decisions. However, this
trait needs to be physically observed, and there are resource limitations
(time, money, etc.) that bottleneck the data collection process. To combat
this, breeding organizations are moving toward advanced image capturing
devices. In this paper, we develop a robust and automatic approach for
estimating the relative maturity of soybeans using a time series of UAV images.
An end-to-end hybrid model combining Convolutional Neural Networks (CNN) and
Long Short-Term Memory (LSTM) is proposed to extract features and capture the
sequential behavior of time series data. The proposed deep learning model was
tested on six different environments across the United States. Results suggest
the effectiveness of our proposed CNN-LSTM model compared to the local
regression method. Furthermore, we demonstrate how this newfound information
can be used to aid in plant breeding advancement decisions.


  Using photometric galaxies from the HSC survey, we measure the stellar mass
density profiles for satellite galaxies as a function of the projected
distance, $r_p$, to isolated central galaxies (ICGs) selected from SDSS/DR7
spectroscopic galaxies at $z\sim0.1$. By stacking HSC images, we also measure
the projected stellar mass density profiles for ICGs and their stellar halos.
The total mass distributions are further measured from HSC weak lensing
signals. ICGs dominate within $\sim$0.15 times the halo virial radius
($0.15R_{200}$). The stellar mass versus total mass fractions drop with the
increase in $r_p$ up to $\sim0.15R_{200}$, beyond which they are less than 1\%
while stay almost constant, indicating the radial distribution of satellites
trace dark matter. The total stellar mass in satellites is proportional to the
virial mass of the host halo, $M_{200}$, for ICGs more massive than
$10^{10.5}M_\odot$, i.e., $M_{\ast,\mathrm{sat}} \propto M_{200}$, whereas the
relation between the stellar mass of ICGs $+$ stellar halos and $M_{200}$ is
close to $M_{\ast,\mathrm{ICG+diffuse}}\propto M_{200}^{1/2}$. Below
$10^{10.5}M_\odot$, the change in $M_{200}$ is much slower with the decrease in
$M_{\ast,\mathrm{ICG+diffuse}}$. At fixed stellar mass, red ICGs are hosted by
more massive dark matter halos and have more satellites. At
$M_{200}\sim10^{12.7}M_\odot$, both $M_{\ast,\mathrm{sat}}$ and the fraction of
stellar mass in satellites versus total stellar mass, $f_\mathrm{sat}$, tend to
be slightly higher around blue ICGs, perhaps implying the late formation of
blue galaxies. $f_\mathrm{sat}$ increases with the increase in both
$M_{\ast,\mathrm{ICG+diffuse}}$ and $M_{200}$, and scales more linearly with
$M_{200}$. We provide best-fitting formulas for these scaling relations and for
red and blue ICGs separately.


  Consider the maximal operator $$\mathscr{C} f(x) =
\sup_{\lambda\in\mathbb{R}}\Big|\sum_{\substack{y\in\mathbb{Z}^n\setminus\{0\}}}
f(x-y) e(\lambda |y|^{2d}) K(y)\Big|,\quad (x\in\mathbb{Z}^n),$$ where $d$ is a
positive integer, $K$ a Calder\'on-Zygmund kernel and $n\ge 1$. This is a
discrete analogue of a real-variable operator studied by Stein and Wainger. The
nonlinearity of the phase introduces a variety of new difficulties that are not
present in the real-variable setting. We prove $\ell^2(\mathbb{Z}^n)$-bounds
for $\mathscr{C}$, answering a question posed by Lillian Pierce.


  We work out the map between null polygonal hexagonal Wilson loops and
spinning three point functions in large $N$ conformal gauge theories by mapping
the variables describing the two different physical quantities and by working
out the precise normalization factors entering this duality. By fixing all the
kinematics we open the ground for a precise study of the dynamics underlying
these dualities -- most notably through integrability in the case of planar
maximally supersymmetric Yang-Mills theory.


  We present radio spectra spanning $0.1 - 10$ GHz for the sample of heavily
obscured luminous quasars with extremely red mid-infrared-optical colors and
compact radio emission. The spectra are constructed from targeted 10 GHz
observations and archival radio survey data, which together yield $6-11$ flux
density measurements for each object. Our suite of Python tools for modeling
the radio spectra is publicly available on Github. Our primary result is that
most (61%) of the sample have peaked or curved radio spectra and many (36%)
could be classified as Gigahertz Peaked Spectrum (GPS) sources. This indicates
compact emission regions likely arising from recently triggered radio jets.
Assuming synchrotron self-absorption (SSA) generates the peaks, we infer
compact source sizes ($3 - 100$ pc) with strong magnetic fields ($6 - 100$ mG)
and young ages ($30 - 10^4$ years). Conversely, free-free absorption (FFA)
could also create peaks due to the high column densities associated with the
deeply embedded nature of the sample. However, we find no correlations between
the existence or frequency of the peaks and any parameters of the MIR emission.
The high-frequency spectral indices are steep ($\alpha \approx -1$) and
correlate, weakly, with the ratio of MIR photon energy density to magnetic
energy density, suggesting that the spectral steepening could arise from
inverse Compton scattering off the intense MIR photon field. This study
provides a foundation for combining multi-frequency and mixed-resolution radio
survey data for understanding the impact of young radio jets on the ISM and
star formation rates of their host galaxies.


  The physics scope of polarized nucleon-nucleon collisions originating from an
internal polarized target in the HERA proton beam is summarized. Based on 240
pb^{-1} integrated luminosity at 40 GeV c.m. energy, statistical sensitivities
are given over a wide (x_F, p_T)-range for a variety of inclusive and exclusive
final states. By measuring single spin asymmetries unique information can be
obtained on higher twist contributions and their p_T-dependence. From double
spin asymmetries in both photon and J/psi production it appears possible to
measure the polarized gluon distribution in the range 0.1 < x_{gluon} < 0.4
with a good statistical accuracy. Drell-Yan pair production asymmetries in
doubly longitudinal mode, measurable in the mass range 3-10 GeV, allow
discrimination between different parametrizations of the polarized light sea
quark distributions. In doubly transverse mode access to the quark transversity
distribution might become possible. Statistically significant results can be
expected in the elastic channel.


  A new splitting is proposed for solving the Vlasov-Maxwell system. This
splitting is based on a decomposition of the Hamiltonian of the Vlasov-Maxwell
system and allows for the construction of arbitrary high order methods by
composition (independent of the specific deterministic method used for the
discretization of the phase space). Moreover, we show that for a spectral
method in space this scheme satisfies Poisson's equation without explicitly
solving it. Finally, we present some examples in the context of the time
evolution of an electromagnetic plasma instability which emphasizes the
excellent behavior of the new splitting compared to methods from the
literature.


  We present a systematic weight pruning framework of deep neural networks
(DNNs) using the alternating direction method of multipliers (ADMM). We first
formulate the weight pruning problem of DNNs as a constrained nonconvex
optimization problem, and then adopt the ADMM framework for systematic weight
pruning. We show that ADMM is highly suitable for weight pruning due to the
computational efficiency it offers. We achieve a much higher compression ratio
compared with prior work while maintaining the same test accuracy, together
with a faster convergence rate. Our models are released at
https://github.com/KaiqiZhang/admm-pruning


  We define what is a generic separating set of invariant functions (a.k.a. a
weak functional basis) for tensors. We produce then two generic separating sets
of polynomial invariants for 3D elasticity tensors, one made of 19 polynomials
and one made of 21 polynomials (but easier to compute) and a generic separating
set of 18 rational invariants. As a byproduct, a new integrity basis for the
fourth-order harmonic tensor is provided.


  In 1973 two Salam prot\'{e}g\'{e}s (Derek Capper and the author) discovered
that the conformal invariance under Weyl rescalings of the metric tensor
$g_{\mu\nu}(x)\rightarrow\Omega^2(x)g_{\mu\nu}(x)$ displayed by classical
massless field systems in interaction with gravity no longer survives in the
quantum theory. Since then these Weyl anomalies have found a variety of
applications in black hole physics, cosmology, string theory and statistical
mechanics. We give a nostalgic review. (Talk given at the {\it Salamfest},
ICTP, Trieste, March 1993.)


  In this lecture we explain the intimate relationship between modular
invariants in conformal field theory and braided subfactors in operator
algebras. Our analysis is based on an approach to modular invariants using
braided sector induction ("$\alpha$-induction") arising from the treatment of
conformal field theory in the Doplicher-Haag-Roberts framework. Many properties
of modular invariants which have so far been noticed empirically and considered
mysterious can be rigorously derived in a very general setting in the subfactor
context. For example, the connection between modular invariants and graphs (cf.
the A-D-E classification for $SU(2)_k$) finds a natural explanation and
interpretation. We try to give an overview on the current state of affairs
concerning the expected equivalence between the classifications of braided
subfactors and modular invariant two-dimensional conformal field theories.


  Merging haloes with similar masses (i.e., major mergers) pose significant
challenges for halo finders. We compare five halo finding algorithms' (AHF,
HBT, Rockstar, SubFind, and VELOCIraptor) recovery of halo properties for both
isolated and cosmological major mergers. We find that halo positions and
velocities are often robust, but mass biases exist for every technique. The
algorithms also show strong disagreement in the prevalence and duration of
major mergers, especially at high redshifts (z>1). This raises significant
uncertainties for theoretical models that require major mergers for, e.g.,
galaxy morphology changes, size changes, or black hole growth, as well as for
finding Bullet Cluster analogues. All finders not using temporal information
also show host halo and subhalo relationship swaps over successive timesteps,
requiring careful merger tree construction to avoid problematic mass accretion
histories. We suggest that future algorithms should combine phase-space and
temporal information to avoid the issues presented.


  Despite huge investment and efforts in the last decades, fuel cells are still
known as a fledgling industry after 170 years of the first fuel cell. It
becomes clear that these investment and efforts did not address the critical
questions. Why upscaling of fuel cells failed often when many researchers
stated their successes in small scale? Why the fuel cells with simpler
structure still lag far from the internal combustion (IC) engines and gas
turbines? Could the current investment of the hydrogen infrastructure reduce
substantially the fuel cell cost and make a breakthrough to the key issues of
durability, reliability and robustness? In this paper, we study these
fundamental questions and point out a must-way possible to reduce cost of fuel
cells and to substantially improve durability and reliability.


  We present a data-driven framework for extracting complex spatiotemporal
patterns generated by ergodic dynamical systems. Our approach, called
Vector-valued Spectral Analysis (VSA), is based on an eigendecomposition of a
kernel integral operator acting on a Hilbert space of vector-valued observables
of the system, taking values in a space of functions (scalar fields) on a
spatial domain. This operator is constructed by combining aspects of the theory
of operator-valued kernels for machine learning with delay-coordinate maps of
dynamical systems. In contrast to conventional eigendecomposition techniques,
which decompose the input data into pairs of temporal and spatial modes with a
separable, tensor product structure, the patterns recovered by VSA can be
manifestly non-separable, requiring only a modest number of modes to represent
signals with intermittency in both space and time. Moreover, the kernel
construction naturally quotients out dynamical symmetries in the data, and
exhibits an asymptotic commutativity property with the Koopman evolution
operator of the system, enabling decomposition of multiscale signals into
dynamically intrinsic patterns. Application of VSA to the Kuramoto-Sivashinsky
model demonstrates significant performance gains in efficient and meaningful
decomposition over eigendecomposition techniques utilizing scalar-valued
kernels.


  Lead recommendations for financial products such as funds or ETF is
potentially challenging in investment space due to changing market scenarios,
and difficulty in capturing financial holder's mindset and their philosophy.
Current methods surface leads based on certain product categorization and
attributes like returns, fees, category etc. to suggest similar product to
investors which may not capture the holder's investment behavior holistically.
Other reported works does subjective analysis of institutional holder's
ideology. This paper proposes a comprehensive data driven framework for
developing a lead recommendations system in holder's space for financial
products like funds by using transactional history, asset flows and product
specific attributes. The system assumes holder's interest implicitly by
considering all investment transactions made and collects possible meta
information to detect holder's investment profile/persona like investment
anticipation and investment behavior. This paper focusses on holder
recommendation component of framework which employs a bi-partite graph
representation of financial holders and funds using variety of attributes and
further employs GraphSage model for learning representations followed by link
prediction model for ranking recommendation for future period. The performance
of the proposed approach is compared with baseline model i.e., content-based
filtering approach on metric hits at Top-k (50, 100, 200) recommendations. We
found that the proposed graph ML solution outperform baseline by absolute 42%,
22% and 14% with a look ahead bias and by absolute 18%, 19% and 18% on
completely unseen holders in terms of hit rate for top-k recommendations: 50,
100 and 200 respectively.


  We present a general derivation of semi-fermionic representation for spin
operators in terms of a bilinear combination of fermions in real and imaginary
time formalisms. The constraint on fermionic occupation numbers is fulfilled by
means of imaginary Lagrange multipliers resulting in special shape of
quasiparticle distribution functions. We show how Schwinger-Keldysh technique
for spin operators is constructed with the help of semi-fermions. We
demonstrate how the idea of semi-fermionic representation might be extended to
the groups possessing dynamic symmetries (e.g. singlet/triplet transitions in
quantum dots). We illustrate the application of semi-fermionic representations
for various problems of strongly correlated and mesoscopic physics.


  The paper deals with a class of parametrized equilibrium problems, where the
objectives of the players do possess nonsmooth terms. The respective Nash
equilibria can be characterized via a parameter-dependent variational
inequality of the second kind, whose Lipschitzian stability is thoroughly
investigated. This theory is then applied to evolution of a oligopolistic
market in which the firms adopt their production strategies to changing input
costs, while each change of the production is associated with some "costs of
change". We examine both the Cournot-Nash equilibria as well as the two-level
case, when one firm decides to take over the role of the Leader (Stackelberg
equilibrium). The impact of costs of change is illustrated by academic
examples.


  Let G be a noncompact real semisimple Lie group. The regular coadjoint orbits
of G can be partitioned into a finite set of types. We show that on each
regular orbit, the Iwasawa decomposition induces a left-invariant foliation
which is isotropic with respect to the Kirillov symplectic form. Moreover, the
leaves are affine subspaces of the dual of the Lie algebra, and the dimension
of the leaves depends only on the type of the orbit. When G is a split real
form, the foliations induced from the Iwasawa decomposition are actually
Lagrangian fibrations with a global transverse Lagrangian section.


  Employing five commuting sets of five-qubit observables, we propose specific
160-661 and 160-21 state proofs of the Bell-Kochen-Specker theorem that are
also proofs of Bell's theorem. A histogram of the 'Hilbert-Schmidt' distances
between the corresponding maximal bases shows in both cases a noise-like
behaviour. The five commuting sets are also ascribed a finite-geometrical
meaning in terms of the structure of symplectic polar space W(9,2).


  The first-order logical environment FOLE [5] provides a rigorous and
principled approach to distributed interoperable first-order information
systems. FOLE has been developed in two forms: a classification form and an
interpretation form. Two papers represent FOLE in a classification form
corresponding to ideas of the Information Flow Framework [11],[12],[13]: the
first paper [6] provides a foundation that connects elements of the ERA data
model [2] with components of the first-order logical environment FOLE; the
second paper [7] provides a superstructure that extends FOLE to the formalisms
of first-order logic. The formalisms in the classification form of FOLE provide
an appropriate framework for developing the relational calculus. Two other
papers represent FOLE in an interpretation form: the first paper [8] develops
the notion of the FOLE table following the relational model [3]; the second
paper [9] discusses the notion of a FOLE relational database. All the
operations of the relational algebra have been rigorously developed [10] using
the interpretation form of FOLE. The present study demonstrates that the
classification form of FOLE is informationally equivalent to the interpretation
form of FOLE. In general, the FOLE representation uses a conceptual structures
approach, that is completely compatible with formal concept analysis [4] and
information flow [1].


  Chemical reactions in multidimensional driven systems are typically described
by a time-dependent rank-1 saddle associated with one reaction and several
orthogonal coordinates (including the solvent bath). To investigate reactions
in such systems, we develop a fast and robust method ---viz., local manifold
analysis (LMA)--- for computing the instantaneous decay rate of reactants.
Specifically, it computes the instantaneous decay rates along saddle-bound
trajectories near the activated complex by exploiting local properties of the
stable and unstable manifold associated with the normally hyperbolic invariant
manifold (NHIM). The LMA method offers substantial reduction of numerical
effort and increased reliability in comparison to direct ensemble integration.
It provides an instantaneous flux that can be assigned to every point on the
NHIM and which is associated with a trajectory ---regardless of whether it is
periodic, quasi-periodic, or chaotic--- that is bound on the NHIM. The time
average of these fluxes in the driven system corresponds to the average rate
through a given local section containing the corresponding point on the NHIM.
We find good agreement between the results of the LMA and direct ensemble
integration obtained using numerically constructed, recrossing-free dividing
surfaces.


  Thermodynamic and magnetocaloric properties of geometrically frustrated Ising
spin clusters of selected shapes and sizes are studied by exact enumeration. In
the ground state the magnetization and the entropy show step-wise variations
with an applied magnetic field. The number of steps, their widths and heights
depend on the cluster shape and size. While the character of the magnetization
plateau heights is always increasing, the entropy is not necessarily decreasing
function of the field, as one would expect. For selected clusters showing some
interesting ground-state properties, the calculations are extended to finite
temperatures by exact enumeration of densities of states in the
energy-magnetization space. In zero field the focus is laid on a peculiar
behavior of some thermodynamic quantities, such as the entropy, the specific
heat and the magnetic susceptibility. In finite fields various thermodynamic
functions are studied in the temperature-field parameter plane and particular
attention is paid to the cases showing an enhanced magnetocaloric effect. The
exact results on the finite clusters are compared with the thermodynamic limit
behavior obtained from Monte Carlo simulations.


  Researchers and scientists use aggregations of data from a diverse
combination of sources, including partners, open data providers and commercial
data suppliers. As the complexity of such data ecosystems increases, and in
turn leads to the generation of new reusable assets, it becomes ever more
difficult to track data usage, and to maintain a clear view on where data in a
system has originated and makes onward contributions. Reliable traceability on
data usage is needed for accountability, both in demonstrating the right to use
data, and having assurance that the data is as it is claimed to be. Society is
demanding more accountability in data-driven and artificial intelligence
systems deployed and used commercially and in the public sector. This paper
introduces the conceptual design of a model for data traceability based on a
Bill of Materials scheme, widely used for supply chain traceability in
manufacturing industries, and presents details of the architecture and
implementation of a gateway built upon the model. Use of the gateway is
illustrated through a case study, which demonstrates how data and artifacts
used in an experiment would be defined and instantiated to achieve the desired
traceability goals, and how blockchain technology can facilitate accurate
recordings of transactions between contributors.


  The Genocchi numbers are the coefficients of the generating function
2t/(e^t+1). In this note we will give an equation for simplicial balls which
involves this numbers. It relates the number of faces in the interior of the
ball with the number of faces in the boundary of the ball. This is a variation
of similar equations given in [McMullen 2004] and [Herrmann and Joswig 2006].


  We report the presence of spin dimerization in the ground state of the one
dimensional Kondo lattice model at quarter filling. The emergence of this new
phase of the Kondo lattice can be traced to the form of the RKKY interaction
between the localized moments and provides the first example of dimerization
induced indirectly by itinerant electrons. We propose this dimer ordering as
the driving mechanism of the spin-Peierls phase observed in the
quasi-one-dimensional organic compounds (Per)$_{2}$M(mnt)$_{2}$ (M=Pt, Pd).
Moreover, this suggests that a richer phase diagram than the Doniach paradigm
may be needed to accommodate the physics of heavy fermion materials.


  Virtual colonoscopy (VC) allows a physician to virtually navigate within a
reconstructed 3D colon model searching for colorectal polyps. Though VC is
widely recognized as a highly sensitive and specific test for identifying
polyps, one limitation is the reading time, which can take over 30 minutes per
patient. Large amounts of the colon are often devoid of polyps, and a way of
identifying these polyp-free segments could be of valuable use in reducing the
required reading time for the interrogating radiologist. To this end, we have
tested the ability of the collective crowd intelligence of non-expert workers
to identify polyp candidates and polyp-free regions. We presented twenty short
videos flying through a segment of a virtual colon to each worker, and the
crowd was asked to determine whether or not a possible polyp was observed
within that video segment. We evaluated our framework on Amazon Mechanical Turk
and found that the crowd was able to achieve a sensitivity of 80.0% and
specificity of 86.5% in identifying video segments which contained a clinically
proven polyp. Since each polyp appeared in multiple consecutive segments, all
polyps were in fact identified. Using the crowd results as a first pass, 80% of
the video segments could in theory be skipped by the radiologist, equating to a
significant time savings and enabling more VC examinations to be performed.


  The round-robin differential phase shift (RRDPS) quantum key distribution
(QKD) protocol is a unique quantum key distribution protocol whose security has
not been understood through an information-disturbance trade-off relation, and
a sufficient amount of privacy amplification was given independently of signal
disturbance. Here, we discuss the security of the RRDPS protocol in the
asymptotic regime when a good estimate of the bit error rate is available as a
measure of signal disturbance. The uniqueness of the RRDPS protocol shows up as
a peculiar form of information-disturbance trade-off curve. When the length of
a block of pulses used for encoding and the signal disturbance are both small,
it provides a significantly better key rate than that from the original
security proof. On the other hand, when the block length is large, the use of
the signal disturbance makes little improvement in the key rate. Our analysis
will bridge a gap between the RRDPS protocol and the conventional QKD
protocols.


  The data collected with a radioactively pure ZnWO$_4$ crystal scintillator
(699 g) in low background measurements during 2130 h at the underground (3600 m
w.e.) Laboratori Nazionali del Gran Sasso (INFN, Italy) were used to set a
limit on possible concentration of superheavy eka-W (seaborgium Sg, Z = 106) in
the crystal. Assuming that one of the daughters in a chain of decays of the
initial Sg nucleus decays with emission of high energy $\alpha$ particle
($Q_\alpha > 8$ MeV) and analyzing the high energy part of the measured
$\alpha$ spectrum, the limit N(Sg)/N(W) < 5.5 $\times$ 10$^{-14}$ atoms/atom at
90% C.L. was obtained (for Sg half-life of 10$^9$ yr). In addition, a limit on
the concentration of eka-Bi was set by analysing the data collected with a
large BGO scintillation bolometer in an experiment performed by another group
[L. Cardani et al., JINST 7 (2012) P10022]: N(eka-Bi)/N(Bi) < 1.1 $\times$
10$^{-13}$ atoms/atom with 90% C.L. Both the limits are comparable with those
obtained in recent experiments which instead look for spontaneous fission of
superheavy elements or use the accelerator mass spectrometry.


  We present a new chemodynamical code based on the adaptive mesh refinement
code RAMSES. The new code uses Eulerian hydrodynamics and N-body dynamics in a
cosmological framework to trace the production and advection of several
chemical species. It is the first such code to follow the self-consistent
evolution of chemical elements in cosmological volumes while maintaining
sub-kiloparsec resolution. The code will be used to simulate disk galaxies and
explore the influence of chemical evolution models and star formation on
galactic abundance ratios.


  We present a method of centroiding undersampled point spread functions (PSFs)
that may be useful, especially when dithering is not an option. If the profile
of the expected PSF is known fairly well through characterization of the
telescope and detector used for observing, one can simulate the undersampled
PSF at many positions on a simulated pixel grid. The true centroid positions
are known since the PSFs are simulated, and so one can match up each
undersampled PSF images to its true centroid location, thus forming a lookup
table. One then assigns the centroid position of an observed PSF to the
position associated with the PSF in the lookup table that has the smallest
squared residual with respect to the observed PSF. We examine a few PSF sizes
and demonstrate that the lookup table provides better centroid positions
compared to a fitting algorithm when the PSFs are undersampled, even in the
presence of noise.


  For a nonsingular matrix $A$, we propose the form $Tr(^t\!A A^{-1})$, the
trace of the product of its transpose and inverse, as a new invariant under
congruence of nonsingular matrices.


  Black holes harbor a spacetime singularity of infinite curvature, where
classical spacetime physics breaks down, and current theory cannot predict what
will happen. However, the singularity is invisible from the outside because
strong gravity traps all signals, even light, behind an event horizon. In this
essay we discuss whether it might be possible to destroy the horizon, if a body
is tossed into the black hole so as to make it spin faster and/or have more
charge than a certain limit. It turns out that one could expose a "naked"
singularity if effects of the body's own gravity can be neglected. We suspect
however that such neglect is unjustified.


  A seesaw mechanism in an extra-dimension, known as the split seesaw
mechanism, provides a natural way to realize a splitting mass spectrum of
right-handed neutrinos. It leads to one keV sterile neutrino as a dark matter
candidate and two heavy right-handed neutrinos being responsible for
leptogenesis to explain the observed baryon asymmetry of the Universe. We study
models based on $A_4$ flavor symmetry in the context of the split seesaw
mechanism. It is pointed out that most of known $A_4$ flavor models with three
right-handed neutrinos being $A_4$ triplet suffer from a degeneracy problem for
the bulk mass terms, which disturbs the split mechanism for right-handed
neutrino mass spectrum. Then we construct a new $A_4$ flavor model to work in
the split seesaw mechanism. In the model, the experimentally observed neutrino
masses and mixing angles can be realized from both type I+II seesaw
contributions. The model predicts the $\mu-\tau$ symmetry in the neutrino mass
matrix at the leading order, resulting in the vanishing $\theta_{13}$ and
maximal $\theta_{23}$. The flavor symmetry $A_4$ is broken via the flavon
vacuum alignment which can be obtained from the orbifold compactification. The
model can be consistent with all data of neutrino oscillation experiments,
cosmological discussions of dark matter abundance, leptogenesis, and recent
astrophysical data.


  With the rapid development of classical and quantum machine learning, a large
number of machine learning frameworks have been proposed. However, existing
machine learning frameworks usually only focus on classical or quantum, rather
than both. Therefore, based on VQNet 1.0, we further propose VQNet 2.0, a new
generation of unified classical and quantum machine learning framework that
supports hybrid optimization. The core library of the framework is implemented
in C++, and the user level is implemented in Python, and it supports deployment
on quantum and classical hardware. In this article, we analyze the development
trend of the new generation machine learning framework and introduce the design
principles of VQNet 2.0 in detail: unity, practicality, efficiency, and
compatibility, as well as full particulars of implementation. We illustrate the
functions of VQNet 2.0 through several basic applications, including classical
convolutional neural networks, quantum autoencoders, hybrid classical-quantum
networks, etc. After that, through extensive experiments, we demonstrate that
the operation speed of VQNet 2.0 is higher than the comparison method. Finally,
through extensive experiments, we demonstrate that VQNet 2.0 can deploy on
different hardware platforms, the overall calculation speed is faster than the
comparison method. It also can be mixed and optimized with quantum circuits
composed of multiple quantum computing libraries.


  We discuss the leading order of anisotropic hydrodynamics expansion. It has
already been shown that in the (0+1) and (1+1)-dimensional cases it is
consistent with the second order viscous hydrodynamics, and it provides a
striking agreement with the exact solutions of the Boltzmann equation. Quite
recently, a new set of equations has been proposed for the leading order of
anisotropic hydrodynamics, which is consistent with the second order viscous
hydrodynamics in the most general (3+1)-dimensional case, and does not require
a next-to-leading treatment for describing pressure anisotropies in the
transverse plane.


  Within a four dimensional manifestly N = 1 supersymmetric action, we show
that Wess-Zumino-Novikov-Witten (WZNW) terms can be embedded in an
extraordinarily simple manner into a purely chiral superaction. In order to
achieve this result it is necessary to assign spin-0 and spin-1/2 degrees of
freedom both to chiral superfields and as well to non-minimal scalar
multiplets. We propose a new formulation for the effective low-energy action of
4D, N = 1 supersymmetric QCD that is consistent with holomorphy through fourth
order in the pion superfield. After reduction to a 2D, N = 2 theory we find a
new class of manifestly supersymmetric non-linear sigma models with torsion.


  We propose a kernelized classification layer for deep networks. Although
conventional deep networks introduce an abundance of nonlinearity for
representation (feature) learning, they almost universally use a linear
classifier on the learned feature vectors. We advocate a nonlinear
classification layer by using the kernel trick on the softmax cross-entropy
loss function during training and the scorer function during testing. However,
the choice of the kernel remains a challenge. To tackle this, we theoretically
show the possibility of optimizing over all possible positive definite kernels
applicable to our problem setting. This theory is then used to device a new
kernelized classification layer that learns the optimal kernel function for a
given problem automatically within the deep network itself. We show the
usefulness of the proposed nonlinear classification layer on several datasets
and tasks.


  We report the discovery and characterisation of the transiting mini-Neptune
HD~207496~b (TOI-1099) as part of a large programme that aims to characterise
naked core planets. We obtained HARPS spectroscopic observations, one
ground-based transit, and high-resolution imaging which we combined with the
TESS photometry to confirm and characterise the TESS candidate and its host
star. The host star is an active early K dwarf with a mass of $0.80 \pm
0.04\,$M$_\odot$, a radius of $0.769 \pm 0.026\,$R$_\odot$, and a G magnitude
of 8. We found that the host star is young, $\sim 0.52\,$ Myr, allowing us to
gain insight into planetary evolution. We derived a planetary mass of $6.1 \pm
1.6\,\mathrm{M}_E$,\, a planetary radius of $2.25 \pm 0.12\,\mathrm{R}_E$,\ and
a planetary density of $\rho_p = 3.27_{-0.91}^{+0.97}\,\mathrm{g.cm^{-3}}$.
From internal structure modelling of the planet, we conclude that the planet
has either a water-rich envelope, a gas-rich envelope, or a mixture of both. We
have performed evaporation modelling of the planet. If we assume the planet has
a gas-rich envelope, we find that the planet has lost a significant fraction of
its envelope and its radius has shrunk. Furthermore, we estimate it will lose
all its remaining gaseous envelope in $\sim 0.52\,$ Gyr. Otherwise, the planet
could have already lost all its primordial gas and is now a bare ocean planet.
Further observations of its possible atmosphere and/or mass-loss rate would
allow us to distinguish between these two hypotheses. Such observations would
determine if the planet remains above the radius gap or if it will shrink and
be below the gap.