  The magnetic activity of late-type MS stars is characterized by different
regimes, and their activity levels are well described by Ro, the ratio between
P_rot and the convective turnover time. Very young PMS stars show, similarly to
MS stars, intense magnetic activity. However they do not show clear
activity-rotation trends, and it still debated which stellar parameters
determine their magnetic activity levels. To bridge the gap between MS and PMS
stars, we studied the activity-rotation relation in the young cluster h Per, a
~13 Myr old cluster, that contains both fast and slow rotators, whose members
have ended their accretion phase and have already developed a radiative core.
It offers us the opportunity to study the activity level of intermediate-age
PMS stars with different rotational velocities, excluding any interactions with
the circumstellar environment. We constrained the magnetic activity levels of h
Per members measuring their X-ray emission from a Chandra observation, while
P_rot were obtained by Moraux et al. (2013). We collected a final catalog of
414 h Per members with known P_rot, T_eff, M_star, with 169 of them having also
detected X-ray emission. We found that h Per members, with 1.0 M_sun < M_star <
1.4 M_sun, display different activity regimes: fast rotators show
supersaturation, while slower rotators have activity levels compatible to the
non-saturated regime. At 13 Myr h Per is therefore the youngest cluster showing
activity-rotation regimes analogous to that of MS stars, indicating that, at
this age, magnetic field production is likely regulated by the alpha-Omega type
dynamo. Moreover we observed that supersaturation is better described by P_rot
than Ro, and that the observed patterns are compatible with the hypothesis of
centrifugal stripping. In this scenario we inferred that coronae can produce
structures as large as ~2 R_star above the stellar surface.


  In addition to their central role in quantum information processing, qubits
have proven to be useful tools in a range of other applications such as
enhanced quantum sensing and as spectrometers of quantum noise. Here we show
that a superconducting qubit strongly coupled to a nonlinear resonator can act
as a probe of quantum fluctuations of the intra-resonator field. Building on
previous work [M. Boissoneault et al. Phys. Rev. A 85, 022305 (2012)], we
derive an effective master equation for the qubit which takes into account
squeezing of the resonator field. We show how sidebands in the qubit excitation
spectrum that are predicted by this model can reveal information about
squeezing and quantum heating. The main results of this paper have already been
successfully compared to experimental data [F. R. Ong et al. Phys. Rev. Lett.
110, 047001 (2013)] and we present here the details of the derivations.


  Proximity effect at superconductor/antiferromagnet (S/AF) interfaces, which
manifests itself as generation of Neel-type triplet correlations, leads to
sensitivity of the superconducting critical temperature to the mutual
orientation of the AF Neel vectors in AF/S/AF trilayers, which is called the
spin-valve effect. Here we predict that the spin-valve effect in AF/S/AF
heterostructures crucially depends on the value of the chemical potential of
the superconducting interlayer due to the occurrence of the finite-momentum
Neel triplet correlations. In addition we investigate equal-spin triplet
correlations, which appear in AF/S/AF structures for non-aligned Neel vectors
of the AFs, and their role in the nonmonotonic dependence of the
superconducting critical temperature of the AF/S/AF structure on the mutual
orientation of the AF Neel vectors. The influence of impurities on the
spin-valve effect is also investigated.


  In Jurek 1985 and 1988 the random integral representations conjecture was
stated. It claims that (some) limit laws can be written as probability
distributions of random integrals of the form $\int_{(a,b]}h(t)dY_{\nu}(r(t))$,
for some deterministic functions $h$, $r$ and a L\'evy process $Y_{\nu}(t),t\ge
0$. Here we review situations where a such claim holds true. Each theorem is
followed by a remark which gives references to other related papers, results as
well as some historical comments. Moreover, some open questions are stated.


  In this paper, we study the cooperative card game, The Crew: The Quest for
Planet Nine from the viewpoint of algorithmic combinatorial game theory. The
Crew: The Quest for Planet Nine, is a game based on traditional trick-taking
card games, like bridge or hearts. In The Crew, players are dealt a hand of
cards, with cards being from one of $c$ colors and having a value between 1 to
$n$. Players also draft objectives, which correspond to a card in the current
game that they must collect in order to win. Players then take turns each
playing one card in a trick, with the player who played the highest value card
taking the trick and all cards played in it. If all players complete all of
their objectives, the players win. The game also forces players to not talk
about the cards in their hand and has a number of "Task Tokens" which can
modify the rules slightly. In this work, we introduce and formally define a
perfect-information model of this problem, and show that the general unbounded
version is computationally intractable. However, we also show that three
bounded versions of this decision problem - deciding whether or not all players
can complete their objectives - can be solved in polynomial time.


  Improving user retention with reinforcement learning~(RL) has attracted
increasing attention due to its significant importance in boosting user
engagement. However, training the RL policy from scratch without hurting users'
experience is unavoidable due to the requirement of trial-and-error searches.
Furthermore, the offline methods, which aim to optimize the policy without
online interactions, suffer from the notorious stability problem in value
estimation or unbounded variance in counterfactual policy evaluation. To this
end, we propose optimizing user retention with Decision Transformer~(DT), which
avoids the offline difficulty by translating the RL as an autoregressive
problem. However, deploying the DT in recommendation is a non-trivial problem
because of the following challenges: (1) deficiency in modeling the numerical
reward value; (2) data discrepancy between the policy learning and
recommendation generation; (3) unreliable offline performance evaluation. In
this work, we, therefore, contribute a series of strategies for tackling the
exposed issues. We first articulate an efficient reward prompt by weighted
aggregation of meta embeddings for informative reward embedding. Then, we endow
a weighted contrastive learning method to solve the discrepancy between
training and inference. Furthermore, we design two robust offline metrics to
measure user retention. Finally, the significant improvement in the benchmark
datasets demonstrates the superiority of the proposed method.


  We investigate the possibility of measuring the H^\pm W^\mp Z^0 vertex from
the single $H^\pm$ production process via WZ fusion at the CERN Large Hadron
Collider (LHC). This vertex strongly depends on the structure of the Higgs
sector in various new physics scenarios, so that its measurement can be useful
to distinguish the models. A signal and background simulation under the
expected detector performance at the LHC is done for the processes of pp \to
W^\pm Z^0 X \to H^\pm X \to tbX and pp \to W^\pm Z^0 X \to H^\pm X \to W^\pm
Z^0 X, and the required magnitudes of the H^\pm W^\mp Z^0 vertex for
observation are evaluated. It is found that although the loop induced H^\pm
W^\pm Z^0 vertex in multi-Higgs doublet models cannot be measurable, the latter
process can be useful to test the model with a real and a complex triplets.


  An evaporating droplet is a dynamic system in which flow is spontaneously
generated to minimize the surface energy, dragging particles to the borders and
ultimately resulting in the so-called "coffee-stain effect". The situation
becomes more complex at the droplet's surface, where surface tension gradients
of different nature can compete with each other yielding different scenarios.
With careful experiments and with the aid of 3D particle tracking techniques,
we are able to show that different types of surfactants turn the droplet's
surface either rigid or elastic, which alters the evaporating fluid flow,
either enhancing the classical coffee-stain effect or leading to a total flow
inversion. Our measurements lead to unprecedented and detailed measurements of
the surface tension difference along an evaporating droplet's surface with good
temporal and spatial resolution.


  Andersons groundbreaking discovery that the presence of stochastic
imperfections in a crystal may result in a sudden breakdown of conductivity
revolutionized our understanding of disordered media. After stimulating decades
of lively studies, Anderson localization has found intriguing applications in
various areas of physics, such mesoscopic physics, strongly-correlated systems,
light localization, cavity quantum electrodynamics, random lasers, and
topological phases of matter. However, a fundamental assumption in Andersons
treatment is that no energy is exchanged with the environment, in contrast to
the common knowledge that every real system is subject to dissipation.
Recently, a growing number of theoretical studies has addressed disordered
media with dissipation. In particular it has been predicted that in such
systems all eigenstates exponentially localize, similar to the original case
without dissipation that Anderson considered. However, in dissipative systems
an eigenstate analysis is insufficient for characterizing the transport
dynamics of wave, in stark contrast to Hermitian systems, where the
localization of all eigenstates necessarily suppresses transport. In our work,
we show in theory and experiment that systems with dissipative disorder allow
for a new type of spatial transport, despite the fact that all eigenstates are
exponentially localized. This Anderson transport is characterized by
super-diffusive spreading and ultra-far spatial jumps between localized states.
We anticipate our findings to mark the starting point towards novel phenomena
in dissipative media, which are subject to general non-Hermitian disorder.


  We consider the Dirichlet-to-Neumann map associated to the Schr\"odinger
equation with a potential in a bounded Lipschitz domain in three or more
dimensions. We show that the integral of the potential over a two-plane is
determined by the Cauchy data of certain exponentially growing solutions on any
neighborhood of the intersection of the two-plane with the boundary.


  It has been shown that the Dimopoulos-Wilczek (or missing-VEV) mechanism for
doublet-triplet splitting can be implemented in $SU(5) \times SU(5)$ models,
which requires no adjoint Higgs fields. This is an advantage from the point of
view of string theory construction. Here the stability of the gauge hierarchy
is examined in detail, and it is shown that it can be guaranteed much more
simply than in $SO(10)$. In fact a $Z_2$ symmetry ensures the stability of the
DW form of the expectation values to all orders in GUT-scale VEVs. It is also
shown that models based on $SO(10) \times SU(5)$ have the advantages of $SU(5)
\times SU(5)$ while permitting complete quark-lepton unification as in
$SO(10)$.


  Strengthening by grain refinement via the Hall-Petch mechanism and softening
by nanograin formation via the inverse Hall-Petch mechanism have been the
subject of argument for decades, particularly for ultrafine-grained materials.
In this study, the Hall-Petch relationship is examined for ultrafine-grained
magnesium, aluminum, copper, and iron produced by severe plastic deformation in
the literature. Magnesium, aluminum, copper, and their alloys follow the
Hall-Petch relationship with a low slope, but an up-break appears when the
grain sizes are reduced below 500-1000 nm. This extra strengthening, which is
mainly due to the enhanced contribution of dislocations, is followed by a
down-break for grain sizes smaller than 70-150 nm due to the diminution of the
dislocation contribution and an enhancement of thermally-activated phenomena.
For pure iron with a lower dislocation mobility, the Hall-Petch breaks are not
evident, but the strength at the nanometer grain size range is lower than the
expected Hall-Petch trend in the submicrometer range. The strength of
nanograined iron can be increased to the expected trend by stabilizing grain
boundaries via impurity atoms. Detailed analyses of the data confirm that grain
refinement to the nanometer level is not necessarily a solution to achieve
extra strengthening, but other strategies such as microstructural stabilization
by segregation or precipitation are required.


  The discrete distribution of the length of longest increasing subsequences in
random permutations of $n$ integers is deeply related to random matrix theory.
In a seminal work, Baik, Deift and Johansson provided an asymptotics in terms
of the distribution of the scaled largest level of the large matrix limit of
GUE. As a numerical approximation, however, this asymptotics is inaccurate for
small $n$ and has a slow convergence rate, conjectured to be just of order
$n^{-1/3}$. Here, we suggest a different type of approximation, based on
Hayman's generalization of Stirling's formula. Such a formula gives already a
couple of correct digits of the length distribution for $n$ as small as $20$
but allows numerical evaluations, with a uniform error of apparent order
$n^{-2/3}$, for $n$ as large as $10^{12}$; thus closing the gap between a table
of exact values (compiled for up to $n=1000$) and the random matrix limit.
Being much more efficient and accurate than Monte-Carlo simulations, the
Stirling-type formula allows for a precise numerical understanding of the first
few finite size correction terms to the random matrix limit. From this we
derive expansions of the expected value and variance of the length, exhibiting
several more terms than previously put forward.


  When one agent interacts with a multi-agent environment, it is challenging to
deal with various opponents unseen before. Modeling the behaviors, goals, or
beliefs of opponents could help the agent adjust its policy to adapt to
different opponents. In addition, it is also important to consider opponents
who are learning simultaneously or capable of reasoning. However, existing work
usually tackles only one of the aforementioned types of opponents. In this
paper, we propose model-based opponent modeling (MBOM), which employs the
environment model to adapt to all kinds of opponents. MBOM simulates the
recursive reasoning process in the environment model and imagines a set of
improving opponent policies. To effectively and accurately represent the
opponent policy, MBOM further mixes the imagined opponent policies according to
the similarity with the real behaviors of opponents. Empirically, we show that
MBOM achieves more effective adaptation than existing methods in a variety of
tasks, respectively with different types of opponents, i.e., fixed policy,
na\"ive learner, and reasoning learner.


  The production cross sections of $^{68,69}$Ge and $^{66,67}$Ga by
\alpha-induced reactions on $^{nat}$Zn have been measured using the
stacked-foil activation method and off-line \gamma-ray spectrometry from their
threshold energies to 50.7 MeV. The derived cross sections were compared with
the previous experimental data and the calculated values in the TENLD-2017
library. Our result shows a slightly larger amplitude than the previous data at
the peak, though the peak energy is consistent with them.


  The millisecond pulsar J1823-3021A is a very active giant pulse emitter in
the globular cluster NGC 6624. New observations with the MeerKAT radio
telescope have revealed 14350 giant pulses over 5 hours of integration time,
with an average wait time of about 1 second between giant pulses. The giant
pulses occur in phases compatible with the ordinary radio emission, follow a
power-law distribution with an index of -2.63 $\pm$ 0.02 and contribute 4
percent of the total integrated flux. The spectral index of the giant pulses
follows a Gaussian distribution centered around -1.9 with a standard deviation
of 0.6 and is on average flatter than the integrated emission, which has a
spectral index of -2.81 $\pm$ 0.02. The waiting times between the GPs are
accurately described by a Poissonian distribution, suggesting that the time of
occurrence of a GP is independent from the times of occurrence of other GPs. 76
GPs show multiple peaks within the same rotation, a rate that is also
compatible with the mutual independence of the GP times of occurrence. We
studied the polarization properties of the giant pulses finding, on average,
linear polarization only at the 1 percent level and circular polarization at
the 3 percent level, similar to the polarization percentages of the total
integrated emission. In 4 cases it was possible to measure the RM of the GPs
which are highly variable and, in two cases, is inconsistent with the mean RM
of the total integrated pulsar signal.


  The Gordon and Betty Moore Foundation ran an Investigator Competition as part
of its Data-Driven Discovery Initiative in 2014. We received about 1,100
applications and each applicant had the opportunity to list up to five
influential works in the general field of "Big Data" for scientific discovery.
We collected nearly 5,000 references and 53 works were cited at least six
times. This paper contains our preliminary findings.


  This paper proposes a new preconditioning scheme for a linear system with a
saddle-point structure arising from a hybrid approximation scheme on the
sphere, an approximation scheme that combines (local) spherical radial basis
functions and (global) spherical polynomials. Making use of a recently derived
inf-sup condition [13] and the Brezzi stability and convergence theorem for
this approximation scheme, we show that the linear system can be optimally
preconditioned with a suitable block-diagonal preconditioner. Numerical
experiments with a non-uniform distribution of data points support the
theoretical conclusions.


  Deep learning, which is especially formidable in handling big data, has
achieved great success in various fields, including bioinformatics. With the
advances of the big data era in biology, it is foreseeable that deep learning
will become increasingly important in the field and will be incorporated in
vast majorities of analysis pipelines. In this review, we provide both the
exoteric introduction of deep learning, and concrete examples and
implementations of its representative applications in bioinformatics. We start
from the recent achievements of deep learning in the bioinformatics field,
pointing out the problems which are suitable to use deep learning. After that,
we introduce deep learning in an easy-to-understand fashion, from shallow
neural networks to legendary convolutional neural networks, legendary recurrent
neural networks, graph neural networks, generative adversarial networks,
variational autoencoder, and the most recent state-of-the-art architectures.
After that, we provide eight examples, covering five bioinformatics research
directions and all the four kinds of data type, with the implementation written
in Tensorflow and Keras. Finally, we discuss the common issues, such as
overfitting and interpretability, that users will encounter when adopting deep
learning methods and provide corresponding suggestions. The implementations are
freely available at \url{https://github.com/lykaust15/Deep_learning_examples}.


  Recently, gas disks have been discovered around main sequence stars well
beyond the usual protoplanetary disk lifetimes (i.e., > 10 Myrs), when planets
have already formed. These gas disks, mainly composed of CO, carbon, and oxygen
seem to be ubiquitous in systems with planetesimal belts (similar to our Kuiper
belt), and can last for hundreds of millions of years. Planets orbiting in
these gas disks will accrete a large quantity of gas that will transform their
primordial atmospheres into new secondary atmospheres with compositions similar
to that of the parent gas disk. Here, we quantify how large a secondary
atmosphere can be created for a variety of observed gas disks and for a wide
range of planet types. We find that gas accretion in this late phase is very
significant and an Earth's atmospheric mass of gas is readily accreted on
terrestrial planets in very tenuous gas disks. In slightly more massive disks,
we show that massive CO atmospheres can be accreted, forming planets with up to
sub-Neptune-like pressures. Our new results demonstrate that new secondary
atmospheres with high metallicities and high C/O ratios will be created in
these late gas disks, resetting their primordial compositions inherited from
the protoplanetary disk phase, and providing a new birth to planets that lost
their atmosphere to photoevaporation or giant impacts. We therefore propose a
new paradigm for the formation of atmospheres on low-mass planets, which can be
tested with future observations (JWST, ELT, ARIEL). We also show that this late
accretion would show a very clear signature in Sub-Neptunes or cold
exo-Jupiters. Finally, we find that accretion creates cavities in late gas
disks, which could be used as a new planet detection method, for low mass
planets a few au to a few tens of au from their host stars.


  We consider both discrete and continuous time finite state-action stochastic
games. In discrete time stochastic games, it is known that a stationary
Blackwell-Nash equilibrium (BNE) exists for a single controller additive reward
(SC-AR) stochastic game which is a special case of a general stochastic game.
We show that, in general, the additive reward condition is needed for the
existence of a BNE. We give an example of a single controller stochastic game
which does not satisfy additive reward condition. We show that this example
does not have a stationary BNE. For a general discrete time discounted
stochastic game we give two different sets of conditions and show that a
stationary Nash equilibrium that satisfies any set of conditions is a BNE. One
of these sets of conditions weakens a set of conditions available in the
literature. For continuous time stochastic games, we give an example that does
not have a stationary BNE. In fact, this example is a single controller
continuous time stochastic game. Then, we introduce a continuous time SC-AR
stochastic game. We show that there always exists a stationary deterministic
BNE for continuous time SC-AR stochastic game. For a general continuous time
discounted stochastic game we give two different sets of conditions and show
that a Nash equilibrium that satisfies any set of conditions is a BNE.


  A local expression of the Diederich--Fornaess exponent of complements of
Levi-flat real hypersurfaces is exhibited. This expression describes the
correspondence between pseudoconvexity of their complements and positivity of
their normal bundles, which was suggested in a work of Brunella, in a
quantitative way. As an application, a connection between the
Diederich--Fornaess exponent and the exponent of conformal harmonic measures is
discussed.


  We study the dependence of thermal conductivity of single walled nanotubes
(SWNT) on chirality and isotope impurity by nonequilibrium molecular dynamics
method with accurate potentials. It is found that, contrary to electronic
conductivity, the thermal conductivity is insensitive to the chirality. The
isotope impurity, however, can reduce the thermal conductivity up to 60% and
change the temperature dependence behavior. We also study the dependence of
thermal conductivity on tube length for tubes of different radius at different
temperatures.


  There are two aspects of machine learning and artificial intelligence: (1)
interpreting information, and (2) inventing new useful information. Much
advance has been made for (1) with a focus on pattern recognition techniques
(e.g., interpreting visual data). This paper focuses on (2) with intelligent
duplication (ID) for invention. We explore the possibility of learning a
specific individual's creative reasoning in order to leverage the learned
expertise and talent to invent new information. More specifically, we employ a
deep learning system to learn from the great composer Beethoven and capture his
composition ability in a hash-based knowledge base. This new form of knowledge
base provides a reasoning facility to drive the music composition through a
novel music generation method.


  We study system design problems stated as parameterized stochastic programs
with a chance-constraint set. We adopt a Bayesian approach that requires the
computation of a posterior predictive integral which is usually intractable. In
addition, for the problem to be a well-defined convex program, we must retain
the convexity of the feasible set. Consequently, we propose a variational
Bayes-based method to approximately compute the posterior predictive integral
that ensures tractability and retains the convexity of the feasible set. Under
certain regularity conditions, we also show that the solution set obtained
using variational Bayes converges to the true solution set as the number of
observations tends to infinity. We also provide bounds on the probability of
qualifying a true infeasible point (with respect to the true constraints) as
feasible under the VB approximation for a given number of samples.


  The mean-field limit in a weakly interacting stochastic many-particle system
for multiple population species in the whole space is proved. The limiting
system consists of cross-diffusion equations, modeling the segregation of
populations. The mean-field limit is performed in two steps: First, the
many-particle system leads in the large population limit to an intermediate
nonlocal diffusion system. The local cross-diffusion system is then obtained
from the nonlocal system when the interaction potentials approach the Dirac
delta distribution. The global existence of the limiting and the intermediate
diffusion systems is shown for small initial data, and an error estimate is
given.


  We investigate the influence of Rashba spin-orbit coupling in a parabolic
nanowire modulated by longitudinal periodic potential. The modulation potential
can be obtained from realistically grown supperlattices (SLs). Our study shows
that the Rashba spin-orbit interaction induces the level crossing point in the
parabolic nanowire SLs. We estimate large anticrossing width (approximately 117
$\mu eV$) between singlet-triplet states. We study the phonon and
electromagnetic field mediated spin transition rates in the parabolic nanowire
SLs. We report that the phonon mediated spin transition rate is several order
of magnitude larger than the electromagnetic field mediated spin transition
rate. Based on the Feynman disentangling technique, we find the exact spin
transition probability. For the case wave vector $k=0$, we report that the
transition probability can be tuned in the form of resonance at fixed time
interval. For the general case ($k\neq 0$), we solve the Riccati equation and
find that the arbitrary values of $k$ induces the damping in the transition
probability. At large value of Rashba spin-orbit coupling coefficients for
($k\neq 0$), spin transition probability freezes.


  We perform an analytical study of the correspondence between a classical
oscillator with frequency perturbed by a coloured noise and the one-dimensional
Anderson-type model with correlated diagonal disorder. It is rigorously shown
that localisation of electronic states in the quantum model corresponds to
exponential divergence of nearby trajectories of the classical random
oscillator. We discuss the relation between the localisation length for the
quantum model and the rate of energy growth for the stochastic oscillator.
Finally, we examine the problem of electron transmission through a finite
disordered barrier by considering the evolution of the classical oscillator.


  The Cauchy-Kowalevski theorem is applied to the solutions of Einstein's
equations and to cosmology. Three fundamental requirements of the theorem: the
use of analytic series; the existence of the boundary surfaces; and the setting
of the independent initial data are revised, using methods of geometric
analysis. It is shown that during its relativistic phase, the standard model of
the universe is governed by Einstein's gravitation described as a massless
spin-2 field, but it is necessarily complemented by massive spin-2 field, which
responds for the dark sector of the universe. On the other hand, at the
inflationary phase, the exponential growth of the volume of the universe is
shown to be consistent with a thermal, non-relativistic expansion. These two
phases are separated by the last inflationary surface.


  This thesis takes Brady's construction of $K(\pi,1)$s for the braid groups as
a starting point. It is widely known that this construction can - with the
right ingredients - be generalized to Artin groups of finite type. Results of
Bessis as well as Brady and Watt are used to establish the general construction
for Artin groups of finite type. The non-crossing partition lattice in finite
Coxeter groups is identified and used to generate the so called poset group.
With the help of this poset group, which turns out to be isomorphic to the
Artin group, a simplicial complex is constructed on which the poset group acts.
It is shown that the complex itself is the universal cover of the $K(\pi,1)$ of
the Artin group of finite type and the quotient under the action is the desired
$K(\pi,1)$.


  We present a method of obtaining a lower bound estimate of the curvatures of
the Bergman metric without using the regularity of the kernel function on the
boundary. As an application, we prove the existence of an uniform lower bound
of the bisectional curvatures of the Bergman metric of a smooth bounded
pseudoconvex domain near the boundary with constant Levi rank.


  We present a straightforward scheme for creating macroscopic superpositions
of different superfluid flow states of Bose-Einstein condensates trapped in
optical lattices. This scheme has the great advantage that all the techniques
required are achievable with current experiments. Furthermore, the relative
difficulty of creating cats scales favorably with the size of the cat. This
means that this scheme may be well-suited to creating superpositions involving
large numbers of particles. Such states may have interesting technological
applications such as making quantum-limited measurements of angular momentum.


  This manuscript contains nothing new, but synthesizes known results: For the
theoretical population geneticist with a probabilistic background, we provide a
summary of some key results on stochastic differential equations. For the
evolutionary game theorist, we give a new perspective on the derivations of
results obtained when using discrete birth-death processes. For the theoretical
biologist familiar with deterministic modeling, we outline how to derive and
work with stochastic versions of classical ecological and evolutionary
processes.


  Anomalous X-ray pulsars (AXPs) and soft gamma-ray repeaters (SGRs) are
recognized as the most promising magnetar candidates, as indicated by their
energetic bursts and rapid spin-downs. It is expected that the strong magnetic
field leaves distinctive imprints on the emergent radiation both by affecting
the radiative processes in atmospheres of magnetars and by scattering in the
upper magnetospheres. We construct a self-consistent physical model that
incorporates emission from the magnetar surface and its reprocessing in the
three-dimensional (3D) twisted magnetosphere using a Monte Carlo technique. The
synthetic spectra are characterized by four parameters: surface temperature kT,
surface magnetic field strength $B$, magnetospheric twist angle $\Delta\phi$,
and the normalized electron velocity $\beta$. We also create a tabular model
(STEMS3D) and apply it to a large sample of XMM-Newton spectra of magnetars.
The model successfully fits nearly all spectra, and the obtained magnetic field
for 7 out of the 11 sources are consistent with the values inferred from the
spin-down rates. We conclude that the continuum-fitting by our model is a
robust method to measure the magnetic field strength and magnetospheric
configuration of AXPs and SGRs. Investigating the multiple observations of
variable sources, we also study the mechanism of their spectral evolution. Our
results suggest that the magnetospheres in these sources are highly twisted
($\Delta\phi > 1$), and the behavior of magnetospheric twisting and untwisting
is revealed in the 2002 outburst of 1E 2259+586.


  The dynamical generation of a transverse gauge field mass is studied by means
of gap equations for pure gauge theory and the gauged non-linear sigma model in
three dimensions. Non-trivial solutions to the gap equations are found
indicating that this type of mass generation is a feature of the static sector
of finite temperature field theory. In the case of the non-linear sigma model
the transverse mass can be defined in a manifestly gauge invariant manner. Its
value is determined to be m=0.28g^2.


  Within the domain of texture classification, a lot of effort has been spent
on local descriptors, leading to many powerful algorithms. However,
preprocessing techniques have received much less attention despite their
important potential for improving the overall classification performance. We
address this question by proposing a novel, simple, yet very powerful
biologically-inspired filtering (BF) which simulates the performance of human
retina. In the proposed approach, given a texture image, after applying a DoG
filter to detect the "edges", we first split the filtered image into two "maps"
alongside the sides of its edges. The feature extraction step is then carried
out on the two "maps" instead of the input image. Our algorithm has several
advantages such as simplicity, robustness to illumination and noise, and
discriminative power. Experimental results on three large texture databases
show that with an extremely low computational cost, the proposed method
improves significantly the performance of many texture classification systems,
notably in noisy environments. The source codes of the proposed algorithm can
be downloaded from https://sites.google.com/site/nsonvu/code.


  We propose a scalable design of entangling quantum gates for large ion
crystals with the following desirable features: 1) The gate design is universal
and applicable for large ion crystals of arbitrary sizes; 2) The gate has no
speed limitation and can work outside of the Lamb-Dicke region; 3) The gate
operates by driving from either continuous-wave or pulsed laser beams; 4) The
gate is insensitive to slow variation of the laser optical phase and works
under a thermal state for the ions' motion; 5) The intrinsic gate infidelity
can be reduced to a level well below the threshold for fault-tolerant quantum
computation under realistic experimental parameters. Different from the
previous gate schemes, here we propose a gate design based on driving of the
local oscillation mode of the ions instead of the collective normal modes and
develop a formalism based on the Heisenberg equations to deal with the
many-body quantum dynamics outside of the Lamb-Dicke region.


  We analyze the BGG Category $\mathcal{O}$ over a large class of generalized
Weyl algebras (henceforth termed GWAs). Given such a "triangular" GWA for which
Category $\mathcal{O}$ decomposes into a direct sum of subcategories, we study
in detail the homological properties of blocks with finitely many simples. As
consequences, we show that the endomorphism algebra of a projective generator
of such a block is quasi-hereditary, finite-dimensional, and graded Koszul. We
also classify all tilting modules in the block, as well as all submodules of
all projective and tilting modules. Finally, we present a novel connection
between blocks of triangular GWAs and Young tableaux, which provides a
combinatorial interpretation of morphisms and extensions between objects of the
block.


  The plasma panel sensor is an ionizing photon and particle radiation detector
derived from PDP technology with high gain and nanosecond response.
Experimental results in detecting cosmic ray muons and beta particles from
radioactive sources are described along with applications including high energy
and nuclear physics, homeland security and cancer therapeutics


  We investigate the structure of scientific collaboration networks. We
consider two scientists to be connected if they have authored a paper together,
and construct explicit networks of such connections using data drawn from a
number of databases, including MEDLINE (biomedical research), the Los Alamos
e-Print Archive (physics), and NCSTRL (computer science). We show that these
collaboration networks form "small worlds" in which randomly chosen pairs of
scientists are typically separated by only a short path of intermediate
acquaintances. We further give results for mean and distribution of numbers of
collaborators of authors, demonstrate the presence of clustering in the
networks, and highlight a number of apparent differences in the patterns of
collaboration between the fields studied.


  We present a model to estimate the synchrotron radio emission generated in
microquasar (MQ) jets due to secondary pairs created via decay of charged pions
produced in proton-proton collisions between stellar wind ions and jet
relativistic protons. Signatures of electrons/positrons are obtained from
consistent particle energy distributions that take into account energy losses
due to synchrotron and inverse Compton (IC) processes, as well as adiabatic
expansion. The space parameter for the model is explored and the corresponding
spectral energy distributions (SEDs) are presented. We conclude that secondary
leptonic emission represents a significant though hardly dominant contribution
to the total radio emission in MQs, with observational consequences that can be
used to test some still unknown processes occurring in these objects as well as
the nature of the matter outflowing in their jets.


  While previous work has found success in deploying visualizations as museum
exhibits, it has not investigated whether museum context impacts visitor
behaviour with these exhibits. We present an interactive Deep-time Literacy
Visualization Exhibit (DeLVE) to help museum visitors understand deep time
(lengths of extremely long geological processes) by improving proportional
reasoning skills through comparison of different time periods. DeLVE uses a new
visualization idiom, Connected Multi-Tier Ranges, to visualize curated datasets
of past events across multiple scales of time, relating extreme scales with
concrete scales that have more familiar magnitudes and units. Museum staff at
three separate museums approved the deployment of DeLVE as a digital kiosk, and
devoted time to curating a unique dataset in each of them. We collect data from
two sources, an observational study and system trace logs. We discuss the
importance of context: similar museum exhibits in different contexts were
received very differently by visitors. We additionally discuss differences in
our process from Sedlmair et al.'s design study methodology which is focused on
design studies triggered by connection with collaborators rather than the
discovery of a concept to communicate. Supplemental materials are available at:
https://osf.io/z53dq/


  We use Pseudo Quantum Electrodynamics (PQED) in order to describe the full
electromagnetic interaction of the p-electrons of graphene in a consistent 2D
formulation. We first consider the effect of this interaction in the vacuum
polarization tensor or, equivalently, in the current correlator. This allows us
to obtain the dc conductivity after a smooth zero-frequency limit is taken in
Kubo's formula.Thereby, we obtain the usual expression for the minimal
conductivity plus corrections due to the interaction that bring it closer to
the experimental value. We then predict the onset of an interaction-driven
spontaneous Quantum Valley Hall effect (QVHE) below a critical temperature of
the order of $0.05$ K. The transverse (Hall) valley conductivity is evaluated
exactly and shown to coincide with the one in the usual Quantum Hall effect.
Finally, by considering the effects of PQED, we show that the electron
self-energy is such that a set of P- and T- symmetric gapped electron energy
eigenstates are dynamically generated, in association with the QVHE.


  We have used UVES on VLT-UT2 to take spectra of 15 red giants in the
Sculptor, Fornax, Carina and Leo I dwarf spheroidal galaxies. We measure the
abundances of alpha, iron peak, s and r-process elements. No dSph giants in our
sample show the deep mixing abundance pattern seen in nearly all globular
clusters. At a given metallicity, the dSph giants exhibit lower [el/Fe]
abundance ratios for the alpha elements than stars in the Galactic halo. This
can be caused by a slow star formation rate and contribution from Type Ia SN,
and/or a small star formation event (low total mass) and mass dependent Type II
SN yields. Differences in the even-Z [el/Fe] ratios between these galaxies, as
well as differences in the evolution of the s&r-process elements are
interpreted in terms of their star formation histories. Comparison of the dSph
abundances with those of the Galactic halo reveals some consistencies. In
particular, we find stars that mimic the abundance pattern found by Nissen &
Shuster (1997) for metal-rich, high R_max, high z_max halo stars, supporting
their suggestion that disrupted dSph's may explain up to 50% of the metal-rich
halo. A comparison with the metal-poor Galactic halo reveal no consistencies
suggesting that the majority of the metal-poor Galactic halo could not have
been formed from objects similar to these dSph. Finally, we use the dSph
abundances to place new constraints on the nucleosynthetic origins, including
Cu and Mn which are more consistent with metallicity dependent SN yields than
the more commonly assumed SN Ia production.


  We revisit the concept of "adversary" in online learning, motivated by
solving robust optimization and adversarial training using online learning
methods. While one of the classical setups in online learning deals with the
"adversarial" setup, it appears that this concept is used less rigorously,
causing confusion in applying results and insights from online learning.
Specifically, there are two fundamentally different types of adversaries,
depending on whether the "adversary" is able to anticipate the exogenous
randomness of the online learning algorithms. This is particularly relevant to
robust optimization and adversarial training because the adversarial sequences
are often anticipative, and many online learning algorithms do not achieve
diminishing regret in such a case.
  We then apply this to solving robust optimization problems or (equivalently)
adversarial training problems via online learning and establish a general
approach for a large variety of problem classes using imaginary play. Here two
players play against each other, the primal player playing the decisions and
the dual player playing realizations of uncertain data. When the game
terminates, the primal player has obtained an approximately robust solution.
This meta-game allows for solving a large variety of robust optimization and
multi-objective optimization problems and generalizes the approach of
arXiv:1402.6361.


  We consider the evolution of critical temperature both for the formation of a
pion condensate as well as for the chiral transition, from the perspective of
the linear sigma model, in the background of a magnetic field. We developed the
discussion for the pion condensate in one loop approximation for the effective
potential getting magnetic catalysis for high values of B, i.e. a raising of
the critical temperature with the magnetic field. For the analysis of the
chiral restoration, we go beyond this approximation, by taking one loop
thermo-magnetic corrections to the couplings as well as plasma screening
effects for the boson's masses, expressed through the resumation of ring
diagrams. Here we found the opposite behavior, i.e. inverse magnetica
catalysis, i.e. a decreasing of the chiral critical temperature as function of
the intensity of the magnetic field, which seems to be in agreement with recent
results form the lattice community.


  The non-Abelian plane waves, first found in flat spacetime by Coleman and
subsequently generalized to give pp-waves in Einstein-Yang-Mills theory, are
shown to be 1/2 supersymmetric solutions of a wide variety of N=1 supergravity
theories coupled to scalar and vector multiplets, including the theory of SU(2)
Yang-Mills coupled to an axion \sigma and dilaton \phi recently obtained as the
reduction to four-dimensions of the six-dimensional Salam-Sezgin model. In this
latter case they provide the most general supersymmetric solution. Passing to
the Riemannian formulation of this theory we show that the most general
supersymmetric solution may be constructed starting from a self-dual Yang-Mills
connection on a self-dual metric and solving a Poisson equation for e^\phi. We
also present the generalization of these solutions to non-Abelian AdS pp-waves
which allow a negative cosmological constant and preserve 1/4 of supersymmetry.


  A Learning Model Predictive Controller (LMPC) for iterative tasks is
presented. The controller is reference-free and is able to improve its
performance by learning from previous iterations. A safe set and a terminal
cost function are used in order to guarantee recursive feasibility and
non-increasing performance at each iteration. The paper presents the control
design approach, and shows how to recursively construct terminal set and
terminal cost from state and input trajectories of previous iterations.
Simulation results show the effectiveness of the proposed control logic.


  Bad metals display transport behavior that differs from what is commonly seen
in ordinary metals. One of the most significant differences is a resistivity
that is linear in temperature and rises to well above the Ioffe-Regel limit
(where the mean-free path is equal to the lattice spacing). Using an exact Kubo
formula, we show that a linear resistivity naturally occurs for many systems
when they are in an incoherent intermediate-temperature state. We verify the
analytic arguments with numerical calculations for a simplified version of the
Hubbard model which is solved with dynamical mean-field theory. Similar
features have also been seen in Hubbard models, where they can begin at even
lower temperatures due to the formation of resilient quasiparticles.


  The Legendre-Hadamard necessary condition for energy minimizers is derived in
the framework of Cosserat elasticity theory.


  The issue of fairness in machine learning stems from the fact that historical
data often displays biases against specific groups of people which have been
underprivileged in the recent past, or still are. In this context, one of the
possible approaches is to employ fair representation learning algorithms which
are able to remove biases from data, making groups statistically
indistinguishable. In this paper, we instead develop a fair representation
learning algorithm which is able to map individuals belonging to different
groups in a single group. This is made possible by training a pair of
Normalizing Flow models and constraining them to not remove information about
the ground truth by training a ranking or classification model on top of them.
The overall, ``chained'' model is invertible and has a tractable Jacobian,
which allows to relate together the probability densities for different groups
and ``translate'' individuals from one group to another. We show experimentally
that our methodology is competitive with other fair representation learning
algorithms. Furthermore, our algorithm achieves stronger invariance w.r.t. the
sensitive attribute.


  We present a dynamical theory which incorporates the electron-electron
correlations and the effects of external magnetic fields for an electron
escaping from a helium surface. Analytical expressions for the escape rate can
be obtained in various limits. In particular, the tunneling rate with a
parallel magnetic field is presented explicitly.


  This paper describes a method of fitting total intensity and polarization
profiles in VLBI images of astrophysical jets to profiles predicted by a
theoretical model. As an example, the method is used to fit profiles of the jet
in the Active Galactic Nucleus Mrk501 with profiles predicted by a model in
which a cylindrical jet of synchrotron plasma is threaded by a magnetic field
with helical and disordered components. This fitting yields model Stokes Q
profiles that agree with the observed profiles to within the 1-2 \sigma
uncertainties; the I model and observed profiles are overall not in such good
agreement, with the model I profiles being generally more symmetrical than the
observed profiles. Consistent fitting results are obtained for profiles derived
from 6cm VLBI images at two distances from the core, and also for profiles
obtained for different wavelengths at a single location in the VLBI jet. The
most striking success of the model is its ability to reproduce the spine-sheath
polarization structure observed across the jet. Using the derived viewing angle
in the jet rest frame, \delta' approximately 83 degrees, together with a
superluminal speed reported in the literature, \beta apparent = 3.3, yields a
solution for the viewing angle and velocity of the jet in the observer's frame
\delta degrees and \beta approximately 0.96. Although these results for Mrk501
must be considered tentative, the combined analysis of polarization profiles
and apparent component speeds holds promise as a means of further elucidating
the magnetic field structures and other parameters of parsec-scale AGN jets.


  Video prediction has been an active topic of research in the past few years.
Many algorithms focus on pixel-level predictions, which generates results that
blur and disintegrate within a few frames. In this project, we use a
hierarchical approach for long-term video prediction. We aim at estimating
high-level structure in the input frame first, then predict how that structure
grows in the future. Finally, we use an image analogy network to recover a
realistic image from the predicted structure. Our method is largely adopted
from the work by Villegas et al. The method is built with a combination of
LSTMs and analogy-based convolutional auto-encoder networks. Additionally, in
order to generate more realistic frame predictions, we also adopt adversarial
loss. We evaluate our method on the Penn Action dataset, and demonstrate good
results on high-level long-term structure prediction.


  In this paper, we obtain some new estimations of Iyengar-type inequality in
which quasi-convex(quasi-concave) functions are involved. These estimations are
improvements of some recently obtained estimations. Some error estimations for
the trapezoidal formula are given. Applications for special means are also
provided.


  We introduce a novel neural network architecture -- Spectral ENcoder for
SEnsor Independence (SEnSeI) -- by which several multispectral instruments,
each with different combinations of spectral bands, can be used to train a
generalised deep learning model. We focus on the problem of cloud masking,
using several pre-existing datasets, and a new, freely available dataset for
Sentinel-2. Our model is shown to achieve state-of-the-art performance on the
satellites it was trained on (Sentinel-2 and Landsat 8), and is able to
extrapolate to sensors it has not seen during training such as Landsat 7,
Per\'uSat-1, and Sentinel-3 SLSTR. Model performance is shown to improve when
multiple satellites are used in training, approaching or surpassing the
performance of specialised, single-sensor models. This work is motivated by the
fact that the remote sensing community has access to data taken with a hugely
variety of sensors. This has inevitably led to labelling efforts being
undertaken separately for different sensors, which limits the performance of
deep learning models, given their need for huge training sets to perform
optimally. Sensor independence can enable deep learning models to utilise
multiple datasets for training simultaneously, boosting performance and making
them much more widely applicable. This may lead to deep learning approaches
being used more frequently for on-board applications and in ground segment data
processing, which generally require models to be ready at launch or soon
afterwards.


  String dynamics near the photon sphere in Schwarzschild spacetime is
considered on the basis of a perturbative approach with respect to a rescaled
string tension as a small parameter. The perturbative string solution in the
zeroth and first approximation is presented. The perturbative solution
corresponds to a small deformations of the photon sphere in Schwarzschild
spacetime.


  We consider a modification of the Fukuyama-Nishiura texture and compare it to
the precision quark flavour data, finding that it fits the data very well but
at the cost of accidental cancelations between parameters. We then propose
different viable textures for quarks, where only the Cabibbo mixing arises from
the down sector, and extend to the charged leptons while constructing a
complementary neutrino structure that leads to viable lepton masses and mixing.


  With the advancement of Cloud Computing over the past few years, there has
been a massive shift from traditional data centers to cloud enabled data
centers. The enterprises with cloud data centers are focusing their attention
on energy savings through effective utilization of resources. In this work, we
propose algorithms which try to minimize the energy consumption in the data
center duly maintaining the SLA guarantees. The algorithms try to utilize least
number of physical machines in the data center by dynamically rebalancing the
physical machines based on their resource utilization. The algorithms also
perform an optimal consolidation of virtual machines on a physical machine,
minimizing SLA violations. In extensive simulation, our algorithms achieve
savings of about 21% in terms of energy consumption and in terms of maintaining
the SLAs, it performs 60% better than Single Threshold algorithm.


  With increasing computing capabilities of modern supercomputers, the size of
the data generated from the scientific simulations is growing rapidly. As a
result, application scientists need effective data summarization techniques
that can reduce large-scale multivariate spatiotemporal data sets while
preserving the important data properties so that the reduced data can answer
domain-specific queries involving multiple variables with sufficient accuracy.
While analyzing complex scientific events, domain experts often analyze and
visualize two or more variables together to obtain a better understanding of
the characteristics of the data features. Therefore, data summarization
techniques are required to analyze multi-variable relationships in detail and
then perform data reduction such that the important features involving multiple
variables are preserved in the reduced data. To achieve this, in this work, we
propose a data sub-sampling algorithm for performing statistical data
summarization that leverages pointwise information theoretic measures to
quantify the statistical association of data points considering multiple
variables and generates a sub-sampled data that preserves the statistical
association among multi-variables. Using such reduced sampled data, we show
that multivariate feature query and analysis can be done effectively. The
efficacy of the proposed multivariate association driven sampling algorithm is
presented by applying it on several scientific data sets.


  Using the D0 detector, we have observed events produced in pbar-p collisions
that contain W or Z bosons in conjunction with very little energy deposition
(``rapidity gaps'') in large forward regions of the detector. The fraction of W
boson events with a rapidity gap (a signature for diffraction) is
0.89\pm^{0.19}_{0.17}%, and the probability that the non-diffractive background
fluctuated to yield the observed diffractive signal is 3 x 10^{-14},
corresponding to a significance of 7.5 sigma. The Z boson sample has a gap
fraction of 1.44 \pm^{0.61}_{0.52}%, with a significance of 4.4 sigma. The
diffractive events have very similar properties to the more common
non-diffractive component.


  We propose a new field theory mechanism for generating an effective
trans-Planckian decay constant from sub-Planckian ones. Using the minimal two
axions and a hierarchy between two axion decay constants is sufficient for
realizing inflation through non-perturbative effects only and with minimal
tuning. The inflationary motion is kept entirely within a sub-Planckian domain.
We outline possible strategies of embedding the model in a string theory setup.


  In this work we consider higher dimensional thin domains with the property
that both boundaries, bottom and top, present oscillations of weak type. We
consider the Laplace operator with Neumann boundary conditions and analyze the
behavior of the solutions as the thin domains shrinks to a fixed domain
$\omega\subset \R^n$. We obtain the convergence of the resolvent of the
elliptic operators in the sense of compact convergence of operators, which in
particular implies the convergence of the spectra. This convergence of the
resolvent operators will allow us to conclude the global dynamics, in terms of
the global attractors of a reaction diffusion equation in the thin domains. In
particular, we show the upper semicontinuity of the attractors and stationary
states. An important case treated is the case of a quasiperiodic situation,
where the bottom and top oscillations are periodic but with period rationally
independent.


  This chapter covers different approaches to policy evaluation for assessing
the causal effect of a treatment or intervention on an outcome of interest. As
an introduction to causal inference, the discussion starts with the
experimental evaluation of a randomized treatment. It then reviews evaluation
methods based on selection on observables (assuming a quasi-random treatment
given observed covariates), instrumental variables (inducing a quasi-random
shift in the treatment), difference-in-differences and changes-in-changes
(exploiting changes in outcomes over time), as well as regression
discontinuities and kinks (using changes in the treatment assignment at some
threshold of a running variable). The chapter discusses methods particularly
suited for data with many observations for a flexible (i.e. semi- or
nonparametric) modeling of treatment effects, and/or many (i.e. high
dimensional) observed covariates by applying machine learning to select and
control for covariates in a data-driven way. This is not only useful for
tackling confounding by controlling for instance for factors jointly affecting
the treatment and the outcome, but also for learning effect heterogeneities
across subgroups defined upon observable covariates and optimally targeting
those groups for which the treatment is most effective.


  We construct explicitly classical and quantum supercharges satisfying the
standard N = 4 supersymmetry algebra in the supersymmetric sigma models
describing the motion over HKT (hyper-Kaehler with torsion) manifolds. One
member of the family of superalgebras thus obtained is equivalent to the
superalgebra derived and formulated earlier in the purely mathematical
framework.


  The equation of state dependence of neutron star's astrophysical features
modeling is key to our understanding of dense matter. However, there exists a
series of almost equation-of-state independent relations reported in the
literature, called quasi-universal relations, that are used to determine
neutron star radii and moments of inertia from X-ray and gravitational wave
signals. Using sets of equations of state constrained by multi-messenger
astronomy measurements and nuclear-physics theory, we discuss quasi-universal
relations in the context of future gravitational-wave detectors Cosmic Explorer
and Einstein Telescope, and X-ray detector STROBE-X. We focus on relations that
involve the moment of inertia $I$, the tidal deformability $\Lambda$ and the
compactness $C$: $C(\Lambda)$, $I(\Lambda)$ and $I(C)$. The quasi-universal
fits and their associated errors are constructed with three different
microphysics approaches which include state of the art nuclear physics theory
and astrophysical constraints. Gravitational-wave and X-ray signals are
simulated with the sensitivity of the next generation of detectors. Equation of
state inference on those simulated signals is performed to assess if
quasi-universal relations will offer a better precision on the extraction of
neutron star's macroscopic parameters than equation of state dependent
relations. We show that detections with the 3rd generation of gravitational
wave detectors and the X-ray detector STROBE-X will be sensitive to the fit
error marginalization technique. We also find that the sensitivity of those
detectors will be sufficient that using full equation of state distributions
will offer better precision on extracted parameters than quasi-universal
relations.We also note that nuclear physics theory offers a more pronounced
equation of state invariance of quasi-universal relations than current
astrophysical constraints.


  Based on the physical features of Raman amplification, we propose a
three-step modelling scheme based on neural networks (NN) and linear
regression. Higher accuracy, less data requirements and lower computational
complexity are demonstrated through simulations compared with the pure NN-based
method.


  Most machine learning classifiers are designed to output posterior
probabilities for the classes given the input sample. These probabilities may
be used to make the categorical decision on the class of the sample; provided
as input to a downstream system; or provided to a human for interpretation.
Evaluating the quality of the posteriors generated by these system is an
essential problem which was addressed decades ago with the invention of proper
scoring rules (PSRs). Unfortunately, much of the recent machine learning
literature uses calibration metrics -- most commonly, the expected calibration
error (ECE) -- as a proxy to assess posterior performance. The problem with
this approach is that calibration metrics reflect only one aspect of the
quality of the posteriors, ignoring the discrimination performance. For this
reason, we argue that calibration metrics should play no role in the assessment
of posterior quality. Expected PSRs should instead be used for this job,
preferably normalized for ease of interpretation. In this work, we first give a
brief review of PSRs from a practical perspective, motivating their definition
using Bayes decision theory. We discuss why expected PSRs provide a principled
measure of the quality of a system's posteriors and why calibration metrics are
not the right tool for this job. We argue that calibration metrics, while not
useful for performance assessment, may be used as diagnostic tools during
system development. With this purpose in mind, we discuss a simple and
practical calibration metric, called calibration loss, derived from a
decomposition of expected PSRs. We compare this metric with the ECE and with
the expected score divergence calibration metric from the PSR literature and
argue, using theoretical and empirical evidence, that calibration loss is
superior to these two metrics.


  We consider the analog of visibility problems in hyperbolic plane
(represented by Poincar\'{e} half-plane model H), replacing the standard
lattice $Z\times Z$ by the orbit $z=i$ under the full modular group $z$. We
prove a visibility criterion and study orchard problem and the cardinality of
visible points in large circles


  In the recent years, social media has grown to become a major source of
information for many online users. This has given rise to the spread of
misinformation through deepfakes. Deepfakes are videos or images that replace
one persons face with another computer-generated face, often a more
recognizable person in society. With the recent advances in technology, a
person with little technological experience can generate these videos. This
enables them to mimic a power figure in society, such as a president or
celebrity, creating the potential danger of spreading misinformation and other
nefarious uses of deepfakes. To combat this online threat, researchers have
developed models that are designed to detect deepfakes. This study looks at
various deepfake detection models that use deep learning algorithms to combat
this looming threat. This survey focuses on providing a comprehensive overview
of the current state of deepfake detection models and the unique approaches
many researchers take to solving this problem. The benefits, limitations, and
suggestions for future work will be thoroughly discussed throughout this paper.


  Privacy and security challenges in Machine Learning (ML) have become
increasingly severe, along with ML's pervasive development and the recent
demonstration of large attack surfaces. As a mature system-oriented approach,
Confidential Computing has been utilized in both academia and industry to
mitigate privacy and security issues in various ML scenarios. In this paper,
the conjunction between ML and Confidential Computing is investigated. We
systematize the prior work on Confidential Computing-assisted ML techniques
that provide i) confidentiality guarantees and ii) integrity assurances, and
discuss their advanced features and drawbacks. Key challenges are further
identified, and we provide dedicated analyses of the limitations in existing
Trusted Execution Environment (TEE) systems for ML use cases. Finally,
prospective works are discussed, including grounded privacy definitions for
closed-loop protection, partitioned executions of efficient ML, dedicated
TEE-assisted designs for ML, TEE-aware ML, and ML full pipeline guarantees. By
providing these potential solutions in our systematization of knowledge, we aim
to build the bridge to help achieve a much stronger TEE-enabled ML for privacy
guarantees without introducing computation and system costs.


  We consider a relay system empowered by an unmanned aerial vehicle (UAV) that
facilitates downlink information delivery while adhering to finite blocklength
requirements. The setup involves a remote controller transmitting information
to both a UAV and an industrial Internet of Things (IIoT) or remote device,
employing the non-orthogonal multiple access (NOMA) technique in the first
phase. Subsequently, the UAV decodes and forwards this information to the
remote device in the second phase. Our primary objective is to minimize the
decoding error probability (DEP) at the remote device, which is influenced by
the DEP at the UAV. To achieve this goal, we optimize the blocklength,
transmission power, and location of the UAV. However, the underlying problem is
highly non-convex and generally intractable to be solved directly. To overcome
this challenge, we adopt an alternative optimization (AO) approach and
decompose the original problem into three sub-problems. This approach leads to
a sub-optimal solution, which effectively mitigates the non-convexity issue. In
our simulations, we compare the performance of our proposed algorithm with
baseline schemes. The results reveal that the proposed framework outperforms
the baseline schemes, demonstrating its superiority in achieving lower DEP at
the remote device. Furthermore, the simulation results illustrate the rapid
convergence of our proposed algorithm, indicating its efficiency and
effectiveness in solving the optimization problem.


  The culmination of the papers (arXiv:0905.0518, arXiv:0910.0909) was a proof
of the norm convergence in $L^2(\mu)$ of the quadratic nonconventional ergodic
averages \frac{1}{N}\sum_{n=1}^N(f_1\circ T_1^{n^2})(f_2\circ
T_1^{n^2}T_2^n)\quad\quad f_1,f_2\in L^\infty(\mu) associated to an arbitrary
probability-preserving \bbZ^2-system (X,\mu,T_1,T_2). This is a special case of
the Bergelson-Leibman conjecture on the norm convergence of polynomial
nonconventional ergodic averages.
  That proof relied on some new machinery for extending probability-preserving
$\bbZ^d$-systems to obtain simplified asymptotic behaviour for various
nonconventional averages such as the above. The engine of this machinery is
formed by some detailed structure theorems for the `characteristic factors'
that are available for some such averages after ascending to a
suitably-extended system. However, these new structure theorems underwent two
distinct phases of development, separated by the discovery of some new
technical results in Moore's cohomology theory for locally compact groups
(arXiv:1004.4937). That discovery enabled a significant improvement to the main
structure theorem (Theorem 1.1 in (arXiv:0905.0518)), which in turn afforded a
much shortened proof of convergence. However, since the proof of convergence
using the original structure theorem required some quite different ideas that
are now absent from these other papers, I have recorded it here in case it has
some independent interest.


  Explaining the behaviour of intelligent agents learned by reinforcement
learning (RL) to humans is challenging yet crucial due to their
incomprehensible proprioceptive states, variational intermediate goals, and
resultant unpredictability. Moreover, one-step explanations for RL agents can
be ambiguous as they fail to account for the agent's future behaviour at each
transition, adding to the complexity of explaining robot actions. By leveraging
abstracted actions that map to task-specific primitives, we avoid explanations
on the movement level. To further improve the transparency and explainability
of robotic systems, we propose an explainable Q-Map learning framework that
combines reward decomposition (RD) with abstracted action spaces, allowing for
non-ambiguous and high-level explanations based on object properties in the
task. We demonstrate the effectiveness of our framework through quantitative
and qualitative analysis of two robotic scenarios, showcasing visual and
textual explanations, from output artefacts of RD explanations, that are easy
for humans to comprehend. Additionally, we demonstrate the versatility of
integrating these artefacts with large language models (LLMs) for reasoning and
interactive querying.


  Magnetic properties of spin $\frac 12$ $J_1-J_2$ Heisenberg antiferromagnet
on body centered cubic lattice are investigated. By using two-time temperature
Green's functions, sublattice magnetization and critical temperature depending
on the frustration ratio $p=J_2/J_1$ are obtained in both stripe and N\'eel
phase. The analysis of ground state sublattice magnetization and phase diagram
indicates the critical end point at $J_2/J_1 =0.714$, in agreement with
previous studies.


  We develop a qualitative theory of Markov Decision Processes (MDPs) and
Partially Observable MDPs that can be used to model sequential decision making
tasks when only qualitative information is available. Our approach is based
upon an order-of-magnitude approximation of both probabilities and utilities,
similar to epsilon-semantics. The result is a qualitative theory that has close
ties with the standard maximum-expected-utility theory and is amenable to
general planning techniques.


  We study symmetries of bases and spanning sets in finite element exterior
calculus, using representation theory. We want to know which vector-valued
finite element spaces have bases invariant under permutation of vertex indices.
The permutations of vertex indices correspond to the symmetry group of the
simplex. That symmetry group is represented on simplicial finite element spaces
by the pullback action. We determine a natural notion of invariance and
sufficient conditions on the dimension and polynomial degree for the existence
of invariant bases. We conjecture that these conditions are necessary too. We
utilize Djokovi\'c and Malzan's classification of monomial irreducible
representations of the symmetric group, and show new symmetries of the
geometric decomposition and canonical isomorphisms of the finite element
spaces. Explicit invariant bases with complex coefficients are constructed in
dimensions two and three for different spaces of finite element differential
forms.


  The possibility of an expanding decelerating Universe in the distant future
is investigated in the context of a quintessence scalar field cosmology. Such a
conceivable evolution is tested against SNe Ia and $H(z)$ cosmic chronometers
data, and also through a model independent method based on Gaussian Processes.
The scalar field model is an extension of the exponential Ratra-Peebles (RP)
quintessential cosmology whose potential now depends on a pair of parameters
($\alpha, \lambda)$ and predicts a decelerated expansion in the future.
Different from RP approach the $\alpha$ parameter allows for a decelerating
cosmology in the future thereby frustrating the inevitable evolution for a de
Sitter Cosmology as predicted by the cosmic concordance model ($\Lambda$CDM).
The statistical model analysis is updated with the most recent SNe Ia and
$H(z)$ data thereby obtaining $H_0 = 68.6\pm3.7$ km/s/Mpc, $\Omega_{\Phi0} =
0.735^{+0.083}_{-0.069} $, $\alpha < 6.56$ and $\lambda< 0.879 $ (at $2\sigma$
c.l.). It is also found that the extended RP model allows for a future
deceleration both for $H(z)$ and SNe Ia data. In the (model-independent)
Gaussian Processes analysis, however, future deceleration is allowed only in
the case of $H(z)$ data.


  It has been reported in experiments that capping layers which enhance the
perpendicular magnetic anisotropy (PMA) of magnetic tunnel junctions (MTJs)
induce great impact on the tunnel magnetoresistance (TMR). To explore the
essential influence caused by capping layers, we carry out ab initio
calculations on TMR in the X(001)|CoFe(001)|MgO(001)|CoFe(001)|X(001) MTJ,
where X represents the capping layer material which can be tungsten, tantalum
or hafnium. We report TMR in different MTJs and demonstrate that tungsten is an
ideal candidate for a giant TMR ratio. The transmission spectrum in Brillouin
zone is presented. It can be seen that in the parallel condition of MTJ, sharp
transmission peaks appear in the minority-spin channel. This phenomenon is
attributed to the resonant tunnel transmission effect and we explained it by
the layer-resolved density of states (DOS). In order to explore transport
properties in MTJs, the density of scattering states (DOSS) was studied from
the point of band symmetry. It has been found that CoFe|tungsten interface
blocks scattering states transmission in the anti-parallel condition. This work
reports TMR and transport properties in MTJs with different capping layers, and
proves that tungsten is a proper capping layer material, which would benefit
the design and optimization of MTJs.


  We show that a properly stratified algebra is Gorenstein if and only if the
characteristic tilting module coincides with the characteristic cotilting
module. We further show that properly stratified Gorenstein algebras $A$ enjoy
strong homological properties such as all Gorenstein projective modules being
properly stratified and all endomorphism rings
$\operatorname{End}_A(\Delta(i))$ being Frobenius algebras. We apply our
results to the study of properly stratified algebras that are minimal
Auslander-Gorenstein algebras in the sense of Iyama-Solberg and calculate under
suitable conditions their Ringel duals. This applies in particular to all
centraliser algebras of nilpotent matrices.


  I give an overview of the latest development in understanding collective
phenomena in high-multiplicity hadronic final state from relativistic
nucleus-nucleus, proton-nucleus and proton-proton collisions. Upon reviewing
the experimental data and confronting them with theoretical models, a unified
paradigm in describing the observed collectivity across all hadronic collision
systems is emerging. Potential future paths toward addressing key open
questions, especially on collectivity in small systems (pp, pA), are discussed.


  In the pursuit of achieving near-term quantum advantage for combinatorial
optimization problems, the Quantum Approximate Optimization Algorithm (QAOA)
and the Variational Quantum Eigensolver (VQE) are the primary methods of
interest, but their practical effectiveness remains uncertain. Therefore, there
is a persistent need to develop and evaluate alternative variational quantum
algorithms. This study presents an investigation of the Block ENcoding Quantum
Optimizer (BENQO), a hybrid quantum solver that uses block encoding to
represent the cost function. BENQO is designed to be universally applicable
across discrete optimization problems. Beyond Maximum Cut, we evaluate BENQO's
performance in the context of the Traveling Salesperson Problem, which is of
greater practical relevance. Our findings confirm that BENQO performs
significantly better than QAOA and competes with VQE across a variety of
performance metrics. We conclude that BENQO is a promising novel hybrid
quantum-classical algorithm that should be further investigated and optimized
to realize its full potential.


  The objective of this paper is to introduce an innovative approach for the
recovery of non-stationary signal components with possibly cross-over
instantaneous frequency (IF) curves from a multi-component blind-source signal.
The main idea is to incorporate a chirp rate parameter with the time-scale
continuous wavelet-like transformation, by considering the quadratic phase
representation of the signal components. Hence-forth, even if two IF curves
cross, the two corresponding signal components can still be separated and
recovered, provided that their chirp rates are different. In other words,
signal components with the same IF value at any time instant could still be
recovered. To facilitate our presentation, we introduce the notion of
time-scale-chirp_rate (TSC-R) recovery transform or TSC-R recovery operator to
develop a TSC-R theory for the 3-dimensional space of time, scale, chirp rate.
Our theoretical development is based on the approximation of the non-stationary
signal components with linear chirps and applying the proposed adaptive TSC-R
transform to the multi-component blind-source signal to obtain fairly accurate
error bounds of IF estimations and signal components recovery. Several
numerical experimental results are presented to demonstrate the out-performance
of the proposed method over all existing time-frequency and time-scale
approaches in the published literature, particularly for non-stationary source
signals with crossover IFs.


  Medical imaging systems are commonly assessed and optimized by use of
objective-measures of image quality (IQ) that quantify the performance of an
observer at specific tasks. Variation in the objects to-be-imaged is an
important source of variability that can significantly limit observer
performance. This object variability can be described by stochastic object
models (SOMs). In order to establish SOMs that can accurately model realistic
object variability, it is desirable to use experimental data. To achieve this,
an augmented generative adversarial network (GAN) architecture called
AmbientGAN has been developed and investigated. However, AmbientGANs cannot be
immediately trained by use of advanced GAN training methods such as the
progressive growing of GANs (ProGANs). Therefore, the ability of AmbientGANs to
establish realistic object models is limited. To circumvent this, a
progressively-growing AmbientGAN (ProAmGAN) has been proposed. However,
ProAmGANs are designed for generating two-dimensional (2D) images while medical
imaging modalities are commonly employed for imaging three-dimensional (3D)
objects. Moreover, ProAmGANs that employ traditional generator architectures
lack the ability to control specific image features such as fine-scale textures
that are frequently considered when optimizing imaging systems. In this study,
we address these limitations by proposing two advanced AmbientGAN
architectures: 3D ProAmGANs and Style-AmbientGANs (StyAmGANs). Stylized
numerical studies involving magnetic resonance (MR) imaging systems are
conducted. The ability of 3D ProAmGANs to learn 3D SOMs from imaging
measurements and the ability of StyAmGANs to control fine-scale textures of
synthesized objects are demonstrated.


  Asteroseismology allows us to probe the internal structure of stars through
their global modes of oscillation. Thanks to missions such as the NASA Kepler
space observatory, we now have high-quality asteroseismic data for nearly 100
solar-type stars. In this thesis, new techniques to measure the ages, masses,
and radii of stars are presented, as well as a way to infer their internal
structure.


  Deep learning (DL) defines a new data-driven programming paradigm that
constructs the internal system logic of a crafted neuron network through a set
of training data. We have seen wide adoption of DL in many safety-critical
scenarios. However, a plethora of studies have shown that the state-of-the-art
DL systems suffer from various vulnerabilities which can lead to severe
consequences when applied to real-world applications. Currently, the testing
adequacy of a DL system is usually measured by the accuracy of test data.
Considering the limitation of accessible high quality test data, good accuracy
performance on test data can hardly provide confidence to the testing adequacy
and generality of DL systems. Unlike traditional software systems that have
clear and controllable logic and functionality, the lack of interpretability in
a DL system makes system analysis and defect detection difficult, which could
potentially hinder its real-world deployment. In this paper, we propose
DeepGauge, a set of multi-granularity testing criteria for DL systems, which
aims at rendering a multi-faceted portrayal of the testbed. The in-depth
evaluation of our proposed testing criteria is demonstrated on two well-known
datasets, five DL systems, and with four state-of-the-art adversarial attack
techniques against DL. The potential usefulness of DeepGauge sheds light on the
construction of more generic and robust DL systems.


  We review different aspects of field theory at zero and finite temperature,
related to the theory of phase transitions. We discuss different
renormalization conditions for the effective potential at zero temperature,
emphasizing in particular the MS-bar renormalization scheme. Finite temperature
field theory is discussed in the real and imaginary time formalisms, showing
their equivalence in simple examples. Bubble nucleation by thermal tunneling,
and the subsequent development of the phase transition is described in some
detail. Some attention is also devoted to the breakdown of the perturbative
expansion and the infrared problem in the finite temperature field theory.
Finally the application to baryogenesis at the electroweak phase transition is
done in the Standard Model and in the Minimal Supersymmetric Standard Model. In
all cases we have translated the condition of not washing out any previously
generated baryon asymmetry by upper bounds on the Higgs mass.


  Multimodal large language models (MLLMs) are designed to process and
integrate information from multiple sources, such as text, speech, images, and
videos. Despite its success in language understanding, it is critical to
evaluate the performance of downstream tasks for better human-centric
applications. This paper assesses the application of MLLMs with 5 crucial
abilities for affective computing, spanning from visual affective tasks and
reasoning tasks. The results show that \gpt has high accuracy in facial action
unit recognition and micro-expression detection while its general facial
expression recognition performance is not accurate. We also highlight the
challenges of achieving fine-grained micro-expression recognition and the
potential for further study and demonstrate the versatility and potential of
\gpt for handling advanced tasks in emotion recognition and related fields by
integrating with task-related agents for more complex tasks, such as heart rate
estimation through signal processing. In conclusion, this paper provides
valuable insights into the potential applications and challenges of MLLMs in
human-centric computing. Our interesting examples are at
https://github.com/EnVision-Research/GPT4Affectivity.


  Cyber-physical systems (CPSs) are widespread in critical domains, and
significant damage can be caused if an attacker is able to modify the code of
their programmable logic controllers (PLCs). Unfortunately, traditional
techniques for attesting code integrity (i.e. verifying that it has not been
modified) rely on firmware access or roots-of-trust, neither of which
proprietary or legacy PLCs are likely to provide. In this paper, we propose a
practical code integrity checking solution based on privacy-preserving black
box models that instead attest the input/output behaviour of PLC programs.
Using faithful offline copies of the PLC programs, we identify their most
important inputs through an information flow analysis, execute them on multiple
combinations to collect data, then train neural networks able to predict PLC
outputs (i.e. actuator commands) from their inputs. By exploiting the black box
nature of the model, our solution maintains the privacy of the original PLC
code and does not assume that attackers are unaware of its presence. The trust
instead comes from the fact that it is extremely hard to attack the PLC code
and neural networks at the same time and with consistent outcomes. We evaluated
our approach on a modern six-stage water treatment plant testbed, finding that
it could predict actuator states from PLC inputs with near-100% accuracy, and
thus could detect all 120 effective code mutations that we subjected the PLCs
to. Finally, we found that it is not practically possible to simultaneously
modify the PLC code and apply discreet adversarial noise to our attesters in a
way that leads to consistent (mis-)predictions.


  Apart from the qualitative features described in \cite{chm}, the
renormalization group equation derived for the rotation of the fermion mass
matrices are amenable to quantitative study. The equation depends on a coupling
and a fudge factor and, on integration, on 3 integration constants. Its
application to data analysis, however, requires the input from experiment of
the heaviest generation masses $m_t, m_b, m_\tau, m_{\nu_3}$ all of which are
known, except for $m_{\nu_3}$. Together then with the theta-angle in the QCD
action, there are in all 7 real unknown parameters. Determining these 7
parameters by fitting to the experimental values of the masses $m_c, m_\mu,
m_e$, the CKM elements $|V_{us}|, |V_{ub}|$, and the neutrino oscillation angle
$\sin^2\theta_{13}$, one can then calculate and compare with experiment the
following 12 other quantities $m_s,
  m_u/m_d, |V_{ud}|, |V_{cs}|, |V_{tb}|, |V_{cd}|, |V_{cb}|, |V_{ts}|,
|V_{td}|, J, \sin^2 2\theta_{12}, \sin^2 2\theta_{23}$, and the results all
agree reasonably well with data, often to within the stringent experimental
error now achieved. Counting the predictions not yet measured by experiment,
this means that 17 independent parameters of the standard model are now
replaced by 7 in the FSM.


  A cycle $C$ in a graph $G$ is called a Tutte cycle if, after deleting $C$
from $G$, each component has at most three neighbors on $C$. Tutte cycles play
an important role in the study of Hamiltonicity of planar graphs. Thomas and Yu
and independently Sanders proved the existence of Tutte cycles containining
three specified edges of a facial cycle in a 2-connected plane graph. We prove
a quantitative version of this result, bounding the number of components of the
graph obtained by deleting a Tutte cycle. As a corollary, we can find long
cycles in essentially 4-connected plane graphs that also contain three
prescribed edges of a facial cycle.


  With the growing need for online and iterative graph processing, software
systems that continuously process large-scale graphs become widely deployed.
With optimizations inherent as part of their design, these systems are complex,
and have unique features beyond conventional graph processing. This paper
describes CG Calculus, the first semantic foundation for continuous graph
processing. The calculus captures the essential behavior of both the backend
graph processing engine and the frontend application, with a focus on two
essential features: temporal locality optimization (TLO) and incremental
operation processing (IOP). A key design insight is that the operations
continuously applied to the graph can be captured by a semantics defined over
the operation stream flowing through the graph nodes. CG Calculus is a
systematic study on the correctness of building continuous graph processing
systems and applications. The most important result is result determinism:
despite significant non-deterministic executions introduced by TLO and IOP, the
results produced by CG Calculus are the same as conventional graph processing
without TLO or IOP. The metatheory of CG Calculus is mechanized in Coq.


  In this paper, we propose a method to design state feedback harmonic control
laws that assign the closed loop poles of a linear harmonic model to some
desired locations. The procedure is based on the solution of an
infinite-dimensional harmonic Sylvester equation under an invertibility
constraint. We provide a sufficient condition to ensure this invertibility and
show how this infinite-dimensional Sylvester equation can be solved up to an
arbitrary small error. The results are illustrated on an unstable linear
periodic system. We also provide a counterexample to illustrate the fact that,
unlike the classical finite dimensional case, the solution of the Sylvester
equation may not be invertible in the infinite dimensional case even if an
observability condition is satisfied.


  Recent JLab data of the differential cross section for the reaction
p(e,e'p)pi0 in the invariant mass region of 1.1 < W < 1.4 GeV at four-momentum
transfer squared Q^2= 2.8 and 4.0 (GeV/c)$^2$ are analyzed with two models,
both of which give an excellent description of most of the existing pion
electroproduction data below W < 1.5 GeV. We find that at up to Q^2 =
4.0(GeV/c)^2, the extracted helicity amplitudes A_{3/2} and A_{/2} remain
comparable with each other, implying that hadronic helicity is not conserved at
this range of Q^2. The ratios E_{1+}/M_{1+} obtained show, starting from a
small and negative value at the real photon point, a clear tendency to cross
zero, and to become positive with increasing Q^2. This is a possible indication
of a very slow approach toward the pQCD region.Furthermore, we find that the
helicity amplitude A_{1/2} and S_{1/2}, but not A_{3/2}, starts exhibiting the
scaling behavior at about Q^2 \ge 2.5(GeV/c)^2.


  We study precision electroweak constraints within a RS1 model with gauge
fields and fermions in the bulk. The electroweak gauge symmetry is enhanced to
SU(2)_L \times SU(2)_R \times U(1)_{B-L}, thereby providing a custodial isospin
symmetry sufficient to suppress excessive contributions to the T parameter. We
then construct complete models, complying with all electroweak constraints, for
solving the hierarchy problem, without supersymmetry or large hierarchies in
the fundamental couplings. Using the AdS/CFT correspondence our models can be
interpreted as dual to a strongly coupled conformal Higgs sector with global
custodial symmetry, gauge and fermionic matter being fundamental fields
external to the CFT. This scenario has interesting collider signals, distinct
from other RS models in the literature.


  In the present work, we tested the performance of several new functionals for
studying the mechanisms of concurrent reaction of hydroarylation and oxidative
coupling catalyzed by Ru(II) chloride carbonyls. We find that DLPNO-CCSD(T) is
an acceptable substitute for full canonical CCSD(T) calculations; that the
recent {\omega}B97X-V and {\omega}B97M-V functionals exhibit superior
performance to commonly used DFT functionals; and that the revised DSD-PBEP86
double hybrid represents an improvement over the original, even though
transition metals were not involved in its parametrization.


  Probabilistic proofs of the Johnson-Lindenstrauss lemma imply that random
projection can reduce the dimension of a data set and approximately preserve
pairwise distances. If a distance being approximately preserved is called a
success, and the complement of this event is called a failure, then such a
random projection likely results in no failures. Assuming a Gaussian random
projection, the lemma is proved by showing that the no-failure probability is
positive using a combination of Bonferroni's inequality and Markov's
inequality. This paper modifies this proof in two ways to obtain a greater
lower bound on the no-failure probability. First, Bonferroni's inequality is
applied to pairs of failures instead of individual failures. Second, since a
pair of projection errors has a bivariate gamma distribution, the probability
of a pair of successes is bounded using an inequality from Jensen (1969). If
$n$ is the number of points to be embedded and $\mu$ is the probability of a
success, then this leads to an increase in the lower bound on the no-failure
probability of $\frac{1}{2}\binom{n}{2}(1-\mu)^2$ if $\binom{n}{2}$ is even and
$\frac{1}{2}\left(\binom{n}{2}-1\right)(1-\mu)^2$ if $\binom{n}{2}$ is odd. For
example, if $n=10^5$ points are to be embedded in $k=10^4$ dimensions with a
tolerance of $\epsilon=0.1$, then the improvement in the lower bound is on the
order of $10^{-14}$. We also show that further improvement is possible if the
inequality in Jensen (1969) extends to three successes, though we do not have a
proof of this result.


  In April 2019, Aycock et al. published "Sexual harassment reported by
undergraduate female physicists" in Phys. Rev. PER. Their main finding is that
3/4 of undergraduate women in physics in the U.S. report experiencing sexual
harassment. Gender minorities experience high rates of harassment also. Many
physics departments will want to discuss these findings with the ultimate goal
of making our field harassment-free. However, there are major challenges
inherent in having these discussions, since many participants will have
experienced harassment themselves. We suggest questions for discussion
organizers to reflect on, and ideas for how departments can approach these
discussions.


  We review some results recently obtained for the conformal field theories
based on the affine Lie superalgebra osp(1|2). In particular, we study the
representation theory of the osp(1|2) current algebras and their character
formulas. By means of a free field representation of the conformal blocks, we
obtain the structure constants and the fusion rules of the model. (Lecture
delivered at the CERN-Santiago de Compostela-La Plata Meeting, "Trends in
Theoretical Physics", La Plata, Argentina, April-May 1997).


  The value of neuromorphic computers depends crucially on our ability to
program them for relevant tasks. Currently, neuromorphic computers are mostly
limited to machine learning methods adapted from deep learning. However,
neuromorphic computers have potential far beyond deep learning if we can only
make use of their computational properties to harness their full power.
Neuromorphic programming will necessarily be different from conventional
programming, requiring a paradigm shift in how we think about programming in
general. The contributions of this paper are 1) a conceptual analysis of what
"programming" means in the context of neuromorphic computers and 2) an
exploration of existing programming paradigms that are promising yet overlooked
in neuromorphic computing. The goal is to expand the horizon of neuromorphic
programming methods, thereby allowing researchers to move beyond the shackles
of current methods and explore novel directions.


  Minifilament (MF) eruption producing small jets and micro-flares is regarded
as an important source for coronal heating and the solar wind transients
through studies mostly based on coronal observations in the extreme ultraviolet
(EUV) and X-ray wavelengths. In this study, we focus on the chromospheric
plasma diagnostics of a tiny minifilament in quiet Sun located at [71'', 450'']
on 2021--08--07 at 19:11 UT observed as part of the ninth encounter of the PSP
campaign. Main data obtained are the high cadence, high resolution spectroscopy
from the Fast Imaging Solar Spectrograph (FISS) and high-resolution
magnetograms from the Near InfraRed Imaging Spectropolarimeter (NIRIS) on the
1.6~m Goode Solar Telescope (GST) at Big Bear Solar Observatory (BBSO). The
mini-filament with size $\sim$1''$\times$5'' and a micro-flare are detected in
both the H$\alpha$ line center and SDO/AIA 193, 304~\AA\ images. On the NIRIS
magnetogram, we found that the cancellation of a magnetic bipole in the
footpoints of the minifilament triggered its eruption in a sigmoidal shape. By
inversion of the \ha\ and Ca {\sc ii} spectra under the embedded cloud model,
we found a temperature increase of 3,800 K in the brightening region,
associated with rising speed average of MF increased by 18~$km~s^{-1}$. This
cool plasma is also found in the EUV images. We estimate the kinetic energy
change of the rising filament as 1.5$\times$$10^{25}$~ergs, and thermal energy
accumulation in the MF, 1.4$\times$$10^{25}$~ergs. From the photospheric
magnetograms, we find the magnetic energy change is 1.6$\times$$10^{26}$~ergs
across the PIL of converging opposite magnetic elements, which amounts to the
energy release in the chromosphere in this smallest two-ribbon flare ever
observed.


  We propose a fusion formula for AdS/CFT worldsheet boundary S-matrices. We
show that, starting from the fundamental Y=0 boundary S-matrix, this formula
correctly reproduces the two-particle bound-state boundary S-matrices.