  Topological phase imprinting is a unique technique for vortex formation in a
Bose-Einstein condensate (BEC) of alkali metal gas, in that it does not involve
rotation: BEC is trapped in a quadrupole field with a uniform bias field which
is reversed adiabatically leading to vortex formation at the center of the
magnetic trap. The scenario has been experimentally verified by MIT group
employing $^{23}$Na atoms. Recently similar experiments have been conducted at
Kyoto University, in which BEC of $^{87}$Rb atoms has been used. In the latter
experiments they found that the fine-tuning of the field reverse time $T_{\rm
rev}$ is required to achieve stable vortex formation. Otherwise, they often
observed vortex fragmentations or a condensate without a vortex. It is shown in
this paper that this behavior is attributed to the heavy mass of the Rb atom.
The confining potential, which depends on the eigenvalue $m_B$ of the hyperfine
spin $\bv{F}$ along the magnetic field, is now shifted by the gravitational
field perpendicular to the vortex line. Then the positions of two
weak-field-seeking states with $m_B=1$ and 2 deviate from each other. This
effect is more prominent for BEC with a heavy atomic mass, for which the
deviation is greater and, moreover, the Thomas-Fermi radius is smaller. We
found, by solving the Gross-Pitaevskii equation numerically, that two
condensates interact in a very complicated way leading to fragmentation of
vortices, unless $T_{\rm rev}$ is properly tuned.


  Organizing professional conferences online has never been more timely.
Responding to the new challenges raised by COVID-19, the organizers of the
ACM/SPEC International Conference on Performance Engineering 2020 had to
address the question: How should we organize these conferences online? This
article summarizes their successful answer.


  In this paper, we focus on unsupervised representation learning for
clustering of images. Recent advances in deep clustering and unsupervised
representation learning are based on the idea that different views of an input
image (generated through data augmentation techniques) must be close in the
representation space (exemplar consistency), and/or similar images must have
similar cluster assignments (population consistency). We define an additional
notion of consistency, consensus consistency, which ensures that
representations are learned to induce similar partitions for variations in the
representation space, different clustering algorithms or different
initializations of a single clustering algorithm. We define a clustering loss
by executing variations in the representation space and seamlessly integrate
all three consistencies (consensus, exemplar and population) into an end-to-end
learning framework. The proposed algorithm, consensus clustering using
unsupervised representation learning (ConCURL), improves upon the clustering
performance of state-of-the-art methods on four out of five image datasets.
Furthermore, we extend the evaluation procedure for clustering to reflect the
challenges encountered in real-world clustering tasks, such as maintaining
clustering performance in cases with distribution shifts. We also perform a
detailed ablation study for a deeper understanding of the proposed algorithm.
The code and the trained models are available at
https://github.com/JayanthRR/ConCURL_NCE.


  The supercurrent components of the N=1, D=4 Super-Yang-Mills theory in the
Wess-Zumino gauge are coupled to the components of a background
supergravitation field in the ``new minimal'' representation, in order to
describe the various conservation laws in a functional way through the Ward
identities for the diffeomorphisms and for the local supersymmetry, Lorentz and
R-transformations. We also incorporate in the same functional formalism the
supertrace identities, which leads however to a slight modification of the new
minimal representation for supergravity, thus leading to a conformal version of
it. The most general classical action obeying all the symmetry constraints is
constructed.


  Rate-Splitting Multiple Access (RSMA) for multi-user downlink operates by
splitting the message for each user equipment (UE) into a private message and a
set of common messages, which are simultaneously transmitted by means of
superposition coding. The RSMA scheme can enhance throughput and connectivity
as compared to conventional multiple access techniques by optimizing the
rate-splitting ratios along with the corresponding downlink beamforming
vectors. This work examines the impact of erroneous channel state information
(CSI) on the performance of RSMA in cell-free multiple-input multiple-output
(MIMO) systems. An efficient robust optimization algorithm is proposed by using
closed-form lower bound expressions on the expected data rates. Extensive
numerical results show the importance of robust design in the presence of CSI
errors and how the performance gain of RSMA over conventional schemes is
affected by CSI imperfection.


  Federated Learning (FL) is one of the hot research topics, and it utilizes
Machine Learning (ML) in a distributed manner without directly accessing
private data on clients. However, FL faces many challenges, including the
difficulty to obtain high accuracy, high communication cost between clients and
the server, and security attacks related to adversarial ML. To tackle these
three challenges, we propose an FL algorithm inspired by evolutionary
techniques. The proposed algorithm groups clients randomly in many clusters,
each with a model selected randomly to explore the performance of different
models. The clusters are then trained in a repetitive process where the worst
performing cluster is removed in each iteration until one cluster remains. In
each iteration, some clients are expelled from clusters either due to using
poisoned data or low performance. The surviving clients are exploited in the
next iteration. The remaining cluster with surviving clients is then used for
training the best FL model (i.e., remaining FL model). Communication cost is
reduced since fewer clients are used in the final training of the FL model. To
evaluate the performance of the proposed algorithm, we conduct a number of
experiments using FEMNIST dataset and compare the result against the random FL
algorithm. The experimental results show that the proposed algorithm
outperforms the baseline algorithm in terms of accuracy, communication cost,
and security.


  Investigation of external flows past arbitrary objects requires access to the
information in the boundary layer and the inviscid flow to paint a full picture
of their characteristics. However, in laser diagnostic techniques such as
particle image velocimetry (PIV), limitations like the size of the sample,
field of view and magnification of the camera, and the size of the area of
interest restrict access to some or part of this information. Here, we present
a variation on the two-dimensional, two-component (2D-2C) PIV to access flows
past samples larger than the field of view of the camera. We introduce an
optical setup to use one laser to create a double-light-sheet illumination to
access both sides of a non-transparent sample and employ a Computer Numerically
Controlled (CNC) carrier to move the camera in consecutive-overlapping steps to
perform the measurements. As a case study, we demonstrate the capability of
this approach in the study of the boundary layer over a finite-size slender
plate. We discuss how access to micro-scale details of a macro-scale flow can
be used to explore the local behavior of the flow in terms of velocity profiles
and the shear stress distribution. The boundary layers are not fully captured
by the Blasius theory and are affected by a distribution of pressure gradient
which in comparison results in regions of more attached or detached profiles.
Ultimately, we show that the measurements can also be used to investigate the
forces experienced by the body and decompose their effects into different
components.


  The baryon mass content of dark matter halos in the early Universe depends on
global factors - e.g. ionising ultraviolet (UV) radiation background - and
local factors - e.g. star formation efficiency and assembly history. We use a
lightweight semi-analytical model to investigate how local and global factors
impact halo baryon mass content at redshifts of $z\geq 5$. Our model
incorporates a time delay between when stars form and when they produce
feedback, which drive bursts of star formation, and a mass and redshift
dependent UV background, which captures the influence of cosmological
reionization on gas accretion onto halos. We use statistically representative
halo assembly histories and assume that the cosmological gas accretion rate is
proportional to the halo mass accretion rate. Delayed feedback leads to
oscillations in gas mass with cosmic time, behaviour that cannot be captured
with instantaneous feedback. Highly efficient star formation drives stronger
oscillations, while strong feedback impacts when oscillations occur; in
contrast, inefficient star formation and weak feedback produce similar
long-term behaviour to that observed in instantaneous feedback models. If the
delayed feedback timescale is too long, a halo retains its gas reservoir but
the feedback suppresses star formation. Our model predicts that lower mass
systems ($\leq 10^7 \text{M}_\odot$) at $z \leq 10$ should be strongly gas
deficient, whereas higher mass systems retain their gas reservoirs because they
are sufficiently massive to continue accreting gas through cosmological
reionization. Interestingly, in higher mass halos, the median
$m_\star/(m_\star+m_\text{g}) \simeq 0.01-0.05$, but is a factor of 3-5 smaller
when feedback is delayed. Our model does not include seed supermassive black
hole feedback, which is necessary to explain massive quenched galaxies in the
early Universe.


  In this paper we propose \texttt{GIFAIR-FL}: a framework that imposes
\textbf{G}roup and \textbf{I}ndividual \textbf{FAIR}ness to \textbf{F}ederated
\textbf{L}earning settings. By adding a regularization term, our algorithm
penalizes the spread in the loss of client groups to drive the optimizer to
fair solutions. Our framework \texttt{GIFAIR-FL} can accommodate both global
and personalized settings. Theoretically, we show convergence in non-convex and
strongly convex settings. Our convergence guarantees hold for both $i.i.d.$ and
non-$i.i.d.$ data. To demonstrate the empirical performance of our algorithm,
we apply our method to image classification and text prediction tasks. Compared
to existing algorithms, our method shows improved fairness results while
retaining superior or similar prediction accuracy.


  Diffusion is a fundamental phenomenon that occurs ubiquitously in nature and
remains the subject of continuous research interest. Understanding diffusion is
a key to understanding leaving systems. In this Chapter, I discuss diffusion of
macromolecules under crowding conditions inside living cells. I describe
briefly how to characterize, model and measure diffusion properties. The focus
is on physics and simulations, with a particular emphasis on the effects
important for crowded, biologically relevant systems.


  This paper investigates the effect of learning a forward model on the
performance of a statistical forward planning agent. We transform Conway's Game
of Life simulation into a single-player game where the objective can be either
to preserve as much life as possible or to extinguish all life as quickly as
possible.
  In order to learn the forward model of the game, we formulate the problem in
a novel way that learns the local cell transition function by creating a set of
supervised training data and predicting the next state of each cell in the grid
based on its current state and immediate neighbours. Using this method we are
able to harvest sufficient data to learn perfect forward models by observing
only a few complete state transitions, using either a look-up table, a decision
tree or a neural network.
  In contrast, learning the complete state transition function is a much harder
task and our initial efforts to do this using deep convolutional auto-encoders
were less successful.
  We also investigate the effects of imperfect learned models on prediction
errors and game-playing performance, and show that even models with significant
errors can provide good performance.


  We compute the capitulation kernel for the module of Bertrandias-Payan in an
arbitrary $\ell$-extension of number fields which satisfies the Leopoldt
conjecture. As a consequence we relate the existence of extensions with trivial
such module to the classical problem of locally cyclotomic towers.


  Flow structures beneath a moving disturbance along a water free surface in
the weakly nonlinear weakly dispersive regime in a sheared channel with finite
depth and constant vorticity are investigated. We compute the exact two
branches of steady solutions in the disturbance moving frame. The velocity
field in the bulk fluid is approximated which allows us to compute the flow
structures beneath the free surface including stagnation points and Kelvin
cat-eyes structures. We show that stagnation points exist only in one branch of
solutions. The bifurcation of the flow is analyzed according to the intensity
of the vorticity and the speed of the moving disturbance. Differently from the
unforced problem, stagnation points can arise for small values of the vorticity
as long as the moving disturbance travels sufficiently fast.


  In this paper we present a new and flexible method to show that, in one
dimension, various self-repellent random walks converge to self-repellent
Brownian motion in the limit of weak interaction after appropriate space-time
scaling. Our method is based on cutting the path into pieces of an
appropriately scaled length, controlling the interaction between the different
pieces, and applying an invariance principle to the single pieces. In this way
we show that the self-repellent random walk large deviation rate function for
the empirical drift of the path converges to the self-repellent Brownian motion
large deviation rate function after appropriate scaling with the interaction
parameters. The method is considerably simpler than the approach followed in
our earlier work, which was based on functional analytic arguments applied to
variational representations and only worked in a very limited number of
situations.
  We consider two examples of a weak interaction limit: (1) vanishing
self-repellence, (2) diverging step variance. In example (1), we recover our
earlier scaling results for simple random walk with vanishing self-repellence
and show how these can be extended to random walk with steps that have zero
mean and a finite exponential moment. Moreover, we show that these scaling
results are stable against adding self-attraction, provided the self-repellence
dominates. In example (2), we prove a conjecture by Aldous for the scaling of
self-avoiding walk with diverging step variance. Moreover, we consider
self-avoiding walk on a two-dimensional horizontal strip such that the steps in
the vertical direction are uniform over the width of the strip and find the
scaling as the width tends to infinity.


  Background: Time-dependent techniques in nuclear theory often rely on
mean-field or Hartree-Fock descriptions. Beyond mean-field dynamical
calculations within the time-dependent density matrix (TDDM) theory have often
invoked symmetry restrictions and ignored the connection between the mean-field
and the induced interaction. Purpose: We study the ground states obtained in a
TDDM approach for nuclei from $A=12$ to $A=24$, including examples of even and
odd-even nuclei with and without intrinsic deformation. We overcome previous
limitations using three-dimensional simulations and employ density-independent
Skyrme interactions self-consistently. Methods: The correlated ground states
are found starting from the Hartree-Fock solution, by adiabatically including
the beyond-mean-field terms in real time. Results: We find that, within this
approach, correlations are responsible for $\approx 4-5 \%$ of the total
energy. Radii are generally unaffected by the introduction of beyond mean-field
correlations. Large nuclear correlation entropies are associated to large
correlation energies. By all measures, $^{12}$C is the most correlated isotope
in the mass region considered. Conclusions: Our work is the starting point of a
consistent implementation of the TDDM technique for applications into nuclear
reactions. Our results indicate that correlation effects in structure are
small, but beyond-mean-field dynamical simulations could provide new insight
into several issues of interest.


  In this work, the behavior of test particles near a domain wall of a stable
false vacuum bubble is studied. It is shown that matter is naturally trapped in
the vicinity of a static domain wall, and also, that there is a discontinuity
in the test particle's velocity when crossing the domain wall. The latter is
unexpected as it stands in contrast to Newtonian theory, where infinite forces
are not allowed. The weak field limit is defined in order to show that there is
no conflict with the non-relativistic behavior of gravitational fields and
particle motions under these conditions.


  Technology has evolved over the years, making our lives easier. It has
impacted the healthcare sector, increasing the average life expectancy of human
beings. Still, there are gaps that remain unaddressed. There is a lack of
transparency in the healthcare system, which results in inherent trust problems
between patients and hospitals. In the present day, a patient does not know
whether he or she will get the proper treatment from the hospital for the fee
charged. A patient can claim reimbursement of the medical bill from any
insurance company. However, today there is minimal scope for the Insurance
Company to verify the validity of such bills or medical records. A patient can
provide fake details to get financial benefits from the insurance company.
Again, there are trust issues between the patient (i.e., the insurance claimer)
and the insurance company. Blockchain integrated with the smart contract is a
well-known disruptive technology that builds trust by providing transparency to
the system. In this paper, we propose a blockchain-enabled Secure and Smart
HealthCare System. Fairness of all the entities: patient, hospital, or
insurance company involved in the system is guaranteed with no one trusting
each other. Privacy and security of patients' medical data are ensured as well.
We also propose a method for privacy-preserving sharing of aggregated data with
the research community for their own purpose. Shared data must not be
personally identifiable, i.e, no one can link the acquired data to the identity
of any patient or their medical history. We have implemented the prototype in
the Ethereum platform and Ropsten test network, and have included the analysis
as well.


  Borcherds-Zagier bases of the spaces of weakly holomorphic modular forms of
weights 1/2 and 3/2 share the Fourier coefficients which are traces of singular
moduli. Recently, Duke, Imamo\={g}lu, and T\'{o}th have constructed a basis of
the space of weight 1/2 mock modular forms, each member in which has Zagier's
generating series of traces of singular moduli as its shadow. They also showed
that Fourier coefficients of their mock modular forms are sums of cycle
integrals of the $j$-function which are real quadratic analogues of singular
moduli. In this paper, we prove the Fourier coefficients of a basis of the
space of weight 3/2 mock modular forms are sums of cycle integrals of a
sesqui-harmonic Maass form of weight zero whose image under hyperbolic
Laplacian is the $j$-function. Furthermore, we express these sums as
regularized inner products of weakly holomorphic modular forms of weight 1/2.


  I present the theoretical evidence which suggests that gravity is an emergent
phenomenon like gas dynamics or elasticity with the gravitational field
equations having the same status as, say, the equations of fluid
dynamics/elasticity. This paradigm views a wide class of gravitational theories
- including Einstein's theory - as describing the thermodynamic limit of the
statistical mechanics of "atoms of spacetime". The evidence for this paradigm
is hidden in several classical features of the gravitational theories and
depends on just one quantum mechanical input, viz. the existence of
Davies-Unruh temperature of horizons. I discuss several conceptual ingredients
of this approach.


  Multiderivative time integrators have a long history of development for
ordinary differential equations, and yet to date, only a small subset of these
methods have been explored as a tool for solving partial differential equations
(PDEs). This large class of time integrators include all popular (multistage)
Runge-Kutta as well as single-step (multiderivative) Taylor methods. (The
latter are commonly referred to as Lax-Wendroff methods when applied to PDEs.)
In this work, we offer explicit multistage multiderivative time integrators for
hyperbolic conservation laws. Like Lax-Wendroff methods, multiderivative
integrators permit the evaluation of higher derivatives of the unknown in order
to decrease the memory footprint and communication overhead. Like traditional
Runge-Kutta methods, multiderivative integrators admit the addition of extra
stages, which introduce extra degrees of freedom that can be used to increase
the order of accuracy or modify the region of absolute stability. We describe a
general framework for how these methods can be applied to two separate spatial
discretizations: the discontinuous Galerkin (DG) method and the finite
difference essentially non-oscillatory (FD-WENO) method. The two proposed
implementations are substantially different: for DG we leverage techniques that
are closely related to generalized Riemann solvers; for FD-WENO we construct
higher spatial derivatives with central differences. Among multiderivative time
integrators, we argue that multistage two-derivative methods have the greatest
potential for multidimensional applications, because they only require the flux
function and its Jacobian, which is readily available. Numerical results
indicate that multiderivative methods are indeed competitive with popular
strong stability preserving time integrators.


  We study text reuse related to Wikipedia at scale by compiling the first
corpus of text reuse cases within Wikipedia as well as without (i.e., reuse of
Wikipedia text in a sample of the Common Crawl). To discover reuse beyond
verbatim copy and paste, we employ state-of-the-art text reuse detection
technology, scaling it for the first time to process the entire Wikipedia as
part of a distributed retrieval pipeline. We further report on a pilot analysis
of the 100 million reuse cases inside, and the 1.6 million reuse cases outside
Wikipedia that we discovered. Text reuse inside Wikipedia gives rise to new
tasks such as article template induction, fixing quality flaws due to
inconsistencies arising from asynchronous editing of reused passages, or
complementing Wikipedia's ontology. Text reuse outside Wikipedia yields a
tangible metric for the emerging field of quantifying Wikipedia's influence on
the web. To foster future research into these tasks, and for reproducibility's
sake, the Wikipedia text reuse corpus and the retrieval pipeline are made
freely available.


  Textual health records of cancer patients are usually protracted and highly
unstructured, making it very time-consuming for health professionals to get a
complete overview of the patient's therapeutic course. As such limitations can
lead to suboptimal and/or inefficient treatment procedures, healthcare
providers would greatly benefit from a system that effectively summarizes the
information of those records. With the advent of deep neural models, this
objective has been partially attained for English clinical texts, however, the
research community still lacks an effective solution for languages with limited
resources. In this paper, we present the approach we developed to extract
procedures, drugs, and diseases from oncology health records written in
European Portuguese. This project was conducted in collaboration with the
Portuguese Institute for Oncology which, besides holding over $10$ years of
duly protected medical records, also provided oncologist expertise throughout
the development of the project. Since there is no annotated corpus for
biomedical entity extraction in Portuguese, we also present the strategy we
followed in annotating the corpus for the development of the models. The final
models, which combined a neural architecture with entity linking, achieved
$F_1$ scores of $88.6$, $95.0$, and $55.8$ per cent in the mention extraction
of procedures, drugs, and diseases, respectively.


  In our previous studies, we developed discrete-space Birth, Death and
Innovation Models (BDIM) of genome evolution. These models explain the origin
of the characteristic Pareto distribution of paralogous gene family sizes in
genomes, and model parameters that provide for the evolution of these
distributions within a realistic timeframe have been identified. Here we
develop the diffusion version of BDIM whose dynamics is described by the
Fokker-Plank equation and the stationary solution could be any specified Pareto
function. The diffusion models have time-dependent solutions of a special kind,
namely, the generalized self-similar solutions, which describe the transition
from one stationary distribution of the system to another; this provides for
the possibility of examining the temporal dynamics of genome evolution.
Analysis of the generalized self-similar solutions of the diffusion BDIM
reveals a biphasic curve of genome growth in which the initial, relatively
short, self-accelerating phase is followed by a prolonged phase of slow
deceleration. In biological terms, this regime of evolution can be tentatively
interpreted as a punctuated-equilibrium-like phenomenon such that whereby
evolutionary transitions are accompanied by rapid gene amplification and
innovation, followed by slow relaxation to a new stationary state.


  We show that a topological superconductor made of four chains of
superconducting spinless fermions characterized by four Majorana edge states
can adiabatically be deformed into a trivial band insulator. To unwind this
time-reversal invariant topological superconductor, interactions to spinful
fermions are switched on along an adiabatic path. Thereby, we couple modes
which belong to two different representations of the time-reversal symmetry
operator T with T^2 = 1 and T^2 = -1, respectively. This observation can be
understood by investigating how the relevant symmetries act on the entanglement
spectrum giving rise to four instead of eight different topological phases with
Majorana edge modes. We also show that a simple level crossing of doubly and
singly degenerate states occurs in the entanglement spectrum upon deforming the
quantum state.


  Existing works in few-shot action recognition mostly fine-tune a pre-trained
image model and design sophisticated temporal alignment modules at feature
level. However, simply fully fine-tuning the pre-trained model could cause
overfitting due to the scarcity of video samples. Additionally, we argue that
the exploration of task-specific information is insufficient when relying
solely on well extracted abstract features. In this work, we propose a simple
but effective task-specific adaptation method (Task-Adapter) for few-shot
action recognition. By introducing the proposed Task-Adapter into the last
several layers of the backbone and keeping the parameters of the original
pre-trained model frozen, we mitigate the overfitting problem caused by full
fine-tuning and advance the task-specific mechanism into the process of feature
extraction. In each Task-Adapter, we reuse the frozen self-attention layer to
perform task-specific self-attention across different videos within the given
task to capture both distinctive information among classes and shared
information within classes, which facilitates task-specific adaptation and
enhances subsequent metric measurement between the query feature and support
prototypes. Experimental results consistently demonstrate the effectiveness of
our proposed Task-Adapter on four standard few-shot action recognition
datasets. Especially on temporal challenging SSv2 dataset, our method
outperforms the state-of-the-art methods by a large margin.


  Self-similarity was recently introduced as a measure of inter-class
congruence for classification of actions. Herein, we investigate the dual
problem of intra-class dissimilarity for classification of action styles. We
introduce self-dissimilarity matrices that discriminate between same actions
performed by different subjects regardless of viewing direction and camera
parameters. We investigate two frameworks using these invariant style
dissimilarity measures based on Principal Component Analysis (PCA) and Fisher
Discriminant Analysis (FDA). Extensive experiments performed on IXMAS dataset
indicate remarkably good discriminant characteristics for the proposed
invariant measures for gender recognition from video data.


  NetHack is known as the frontier of reinforcement learning research where
learning-based methods still need to catch up to rule-based solutions. One of
the promising directions for a breakthrough is using pre-collected datasets
similar to recent developments in robotics, recommender systems, and more under
the umbrella of offline reinforcement learning (ORL). Recently, a large-scale
NetHack dataset was released; while it was a necessary step forward, it has yet
to gain wide adoption in the ORL community. In this work, we argue that there
are three major obstacles for adoption: resource-wise, implementation-wise, and
benchmark-wise. To address them, we develop an open-source library that
provides workflow fundamentals familiar to the ORL community: pre-defined
D4RL-style tasks, uncluttered baseline implementations, and reliable evaluation
tools with accompanying configs and logs synced to the cloud.


  The geologically active south pole of Enceladus generates a plume of
micron-sized particles, which likely form Saturn's tenuous E-ring extending
from the orbit of Mimas to Titan. Interactions between these particles and
satellites have been suggested, though only as very thin surficial phenomena.
We scrutinize high-resolution images with a newly developed numerical shape
model of Helene and find that the leading hemisphere of Helene is covered by
thick deposits of E-ring particles, which occasionally collapse to form
gully-like depressions. The depths of the resultant gullies and near-absence of
small craters on the leading hemisphere indicate that the deposit is tens to
hundreds of meters thick. The ages of the deposits are less than several tens
of My, which coincides well with similar deposits found on Telesto and Calypso.
Our findings as well as previous theoretical work collectively indicate that
the cryovolcanic activity currently occurring on Enceladus is ephemeral.


  There is chiral like symmetry for 4-component massless fermion in
(2+1)-dimensional gauge theory.Since QED$_{3}$ with Chern-Simons term contains
vortex solution for vector potential,one may expect vortex driven phase
transition as Kosterlitz-Thouless type where chiral condensate is washed away
at zero temperature.To study this possibility,we evaluate the fermion
propagator by Dyson-Schwinger equation numerically and spectral function
analytically in the Landau gauge.For quenched case we adopt Ball-Chiu vertex to
keep gauge invariance of the results.The critical value of topological
mass,above which chiral condensate washed away, turned out to be
$O(10^{-2})e^{2}$ at least for weak coupling in both cases.


  We introduce a new problem formulation, Double-Deck Multi-Agent Pickup and
Delivery (DD-MAPD), which models the multi-robot shelf rearrangement problem in
automated warehouses. DD-MAPD extends both Multi-Agent Pickup and Delivery
(MAPD) and Multi-Agent Path Finding (MAPF) by allowing agents to move beneath
shelves or lift and deliver a shelf to an arbitrary location, thereby changing
the warehouse layout. We show that solving DD-MAPD is NP-hard. To tackle
DD-MAPD, we propose MAPF-DECOMP, an algorithmic framework that decomposes a
DD-MAPD instance into a MAPF instance for coordinating shelf trajectories and a
subsequent MAPD instance with task dependencies for computing paths for agents.
We also present an optimization technique to improve the performance of
MAPF-DECOMP and demonstrate how to make MAPF-DECOMP complete for well-formed
DD-MAPD instances, a realistic subclass of DD-MAPD instances. Our experimental
results demonstrate the efficiency and effectiveness of MAPF-DECOMP, with the
ability to compute high-quality solutions for large-scale instances with over
one thousand shelves and hundreds of agents in just minutes of runtime.


  The Cramer-Rao lower bound for the estimation of the affine transformation
parameters in a multivariate heteroscedastic errors-in-variables model is
derived. The model is suitable for feature-based image registration in which
both sets of control points are localized with errors whose covariance matrices
vary from point to point. With focus given to the registration of fluorescence
microscopy images, the Cramer-Rao lower bound for the estimation of a feature's
position (e.g. of a single molecule) in a registered image is also derived. In
the particular case where all covariance matrices for the localization errors
are scalar multiples of a common positive definite matrix (e.g. the identity
matrix), as can be assumed in fluorescence microscopy, then simplified
expressions for the Cramer-Rao lower bound are given. Under certain simplifying
assumptions these expressions are shown to match asymptotic distributions for a
previously presented set of estimators. Theoretical results are verified with
simulations and experimental data.


  In this work, we demonstrate the generation of sidebands in
electromagnetically induced transparency (EIT) signal due to frequency
modulation of Rydberg states in $^{87}\text{Rb}$ thermal vapor. An oscillating
radio-frequency (RF) electric field generated through a pair of parallel-placed
copper plates leads to modulation of the Rydberg state by virtue of Stark
effect where the frequency and strength of the sidebands quantify the RF
electric field present. The variation of the strength of the EIT peak and the
sidebands with RF voltage is observed to vary as a function of the Bessel
function of modulation index. It provides an estimate of the atomic
polarizability in the medium, which for $54S_{1/2}$ state, is found to be
$(h)\cdot1.01\times10^8$ $\text{Hz/(V/cm)}^2$.


  Probabilistic Logic Programming (PLP) languages enable programmers to specify
systems that combine logical models with statistical knowledge. The inference
problem, to determine the probability of query answers in PLP, is intractable
in general, thereby motivating the need for approximate techniques. In this
paper, we present a technique for approximate inference of conditional
probabilities for PLP queries. It is an Adaptive Markov Chain Monte Carlo
(MCMC) technique, where the distribution from which samples are drawn is
modified as the Markov Chain is explored. In particular, the distribution is
progressively modified to increase the likelihood that a generated sample is
consistent with evidence. In our context, each sample is uniquely characterized
by the outcomes of a set of random variables. Inspired by reinforcement
learning, our technique propagates rewards to random variable/outcome pairs
used in a sample based on whether the sample was consistent or not. The
cumulative rewards of each outcome is used to derive a new "adapted
distribution" for each random variable. For a sequence of samples, the
distributions are progressively adapted after each sample. For a query with
"Markovian evaluation structure", we show that the adapted distribution of
samples converges to the query's conditional probability distribution. For
Markovian queries, we present a modified adaptation process that can be used in
adaptive MCMC as well as adaptive independent sampling. We empirically evaluate
the effectiveness of the adaptive sampling methods for queries with and without
Markovian evaluation structure.


  The heaviest chemical elements are naturally produced by the rapid
neutron-capture process (r-process) during neutron star mergers or supernovae.
The r-process production of elements heavier than uranium (transuranic nuclei)
is poorly understood and inaccessible to experiments, so must be extrapolated
using nucleosynthesis models. We examine element abundances in a sample of
stars that are enhanced in r-process elements. The abundances of elements Ru,
Rh, Pd, and Ag (atomic numbers Z = 44 to 47, mass numbers A = 99 to 110)
correlate with those of heavier elements (63 <= Z <= 78, A > 150). There is no
correlation for neighboring elements (34 <= Z <= 42 and 48 <= Z <= 62). We
interpret this as evidence that fission fragments of transuranic nuclei
contribute to the abundances. Our results indicate that neutron-rich nuclei
with mass numbers >260 are produced in r-process events.


  The optical observations of wide fields of view encounter the problem of
selection of best exposure time. As there are usually plenty of objects
observed simultaneously, the quality of photometry of the brightest ones is
always better than of the dimmer ones. Frequently all of them are equally
interesting for the astronomers and thus it is desired to have all of them
measured with the highest possible accuracy.
  In this paper we present a novel optimization algorithm dedicated for the
division of exposure time into sub-exposures, which allows to perform
photometry with more balanced noise budget. Thanks to the proposed technique,
the photometric precision of dimmer objects is increased at the expense of the
measurement fidelity of the brightest ones. We tested the method on real
observations using two telescope setups demonstrating its usefulness and good
agreement with the theoretical expectations. The main application of our
approach is a wide range of sky surveys, including the ones performed by the
space telescopes. The method can be applied for planning virtually any
photometric observations, in which the objects of interest show a wide range of
magnitudes.


  We consider an N=1 U(N) gauge theory with matter in the antisymmetric
representation and its conjugate, with a tree level superpotential containing
at least quartic interactions for these fields. We obtain the effective
glueball superpotential in the classically unbroken case, and show that it has
a non-trivial N-dependence which does not factorize. We also recover additional
contributions starting at order S^N from the dynamics of Sp(0) factors. This
can also be understood by a precise map of this theory to an Sp(2N-2) gauge
theory with antisymmetric matter.


  Debate topic for Effective Field Theory (EFT) is the choice of a "basis" for
$\mrdim = 6$ operators Clearly all bases are equivalent as long as they are a
"basis", containing a minimal set of operators after the use of equations of
motion and respecting gauge invariance. From a more formal point of view a
basis is characterized by its closure with respect to renormalization.
Equivalence of bases should always be understood as a statement for the
S-matrix and not for the Lagrangian, as dictated by the equivalence theorem.
Any phenomenological approach that misses one of these ingredients is still
acceptable for a preliminar analysis, as long as it does not pretend to be an
EFT. Here we revisit the equivalence theorem and its consequences for EFT when
two sets of higher dimensional operators are connected by a set of non-linear,
noninvariant, field reparametrizations.


  We consider the following document ranking problem: We have a collection of
documents, each containing some topics (e.g. sports, politics, economics). We
also have a set of users with diverse interests. Assume that user u is
interested in a subset I_u of topics. Each user u is also associated with a
positive integer K_u, which indicates that u can be satisfied by any K_u topics
in I_u. Each document s contains information for a subset C_s of topics. The
objective is to pick one document at a time such that the average satisfying
time is minimized, where a user's satisfying time is the first time that at
least K_u topics in I_u are covered in the documents selected so far. Our main
result is an O({\rho})-approximation algorithm for the problem, where {\rho} is
the algorithmic integrality gap of the linear programming relaxation of the set
cover instance defined by the documents and topics. This result generalizes the
constant approximations for generalized min-sum set cover and ranking with
unrelated intents and the logarithmic approximation for the problem of ranking
with submodular valuations (when the submodular function is the coverage
function), and can be seen as an interpolation between these results. We
further extend our model to the case when each user may interest in more than
one sets of topics and when the user's valuation function is XOS, and obtain
similar results for these models.


  We propose an algorithm for low rank matrix completion for matrices with
binary entries which obtains explicit binary factors. Our algorithm, which we
call TBMC (\emph{Tiling for Binary Matrix Completion}), gives interpretable
output in the form of binary factors which represent a decomposition of the
matrix into tiles. Our approach is inspired by a popular algorithm from the
data mining community called PROXIMUS: it adopts the same recursive
partitioning approach while extending to missing data. The algorithm relies
upon rank-one approximations of incomplete binary matrices, and we propose a
linear programming (LP) approach for solving this subproblem. We also prove a
$2$-approximation result for the LP approach which holds for any level of
subsampling and for any subsampling pattern. Our numerical experiments show
that TBMC outperforms existing methods on recommender systems arising in the
context of real datasets.


  One-dimensional Fisher-Wright diffusion process on the interval $(0,1)$ with
mutations is considered. This is a widely known model in population genetics.
The goal of the paper is an exponential recurrence of the process, which also
implies exponential rate of convergence towards the invariant measure.


  We demonstrate instrumentation and methods to enable fluorescence-detected
photothermal infrared (F-PTIR) microscopy, then demonstrate the utility of
F-PTIR to characterize the composition within phase-separated domains of model
amorphous solid dispersions (ASDs) induced by water sorption. In F-PTIR,
temperature-dependent changes in fluorescence quantum efficiency are shown to
sensitively report on highly localized absorption of mid-infrared radiation.
The spatial resolution with which infrared spectroscopy can be performed is
dictated by fluorescence microscopy, rather than the infrared wavelength.
Following proof of concept F-PTIR demonstration on model systems of
polyethylene glycol (PEG) and silica gel, F-PTIR enabled the characterization
of chemical composition within inhomogeneous ritonavir /
polyvinylpyrrolidone-vinyl acetate (PVPVA) amorphous dispersions. Phase
separation is implicated in the observation of critical behaviors in ASD
dissolution kinetics, with the results of F-PTIR supporting the formation of
phase-separated drug-rich domains upon water absorption in spin-cast films.


  Frequencies of volcanic eruptions in the past 270 years are compared with
variations of solar activity and summary curve of principal components of the
solar background magnetic field (SBMF).Frequency analysis with Morlet wavelet
reveals the most pronounced period of volcanic eruptions of 22 years. There is
a strong correlation (0.84) between volcanic frequencies and the summary curve
of SBMF for 11 cycles after 1868. The maxima of volcanic eruptions are shown to
occur during solar activity cycles with the southern magnetic polarity. The
next anticipated maximum of volcanic eruptions is expected to occur during
cycle 26, when SBMF have a southern magnetic polarity.


  Given two images, we can estimate the relative camera pose between them by
establishing image-to-image correspondences. Usually, correspondences are
2D-to-2D and the pose we estimate is defined only up to scale. Some
applications, aiming at instant augmented reality anywhere, require
scale-metric pose estimates, and hence, they rely on external depth estimators
to recover the scale. We present MicKey, a keypoint matching pipeline that is
able to predict metric correspondences in 3D camera space. By learning to match
3D coordinates across images, we are able to infer the metric relative pose
without depth measurements. Depth measurements are also not required for
training, nor are scene reconstructions or image overlap information. MicKey is
supervised only by pairs of images and their relative poses. MicKey achieves
state-of-the-art performance on the Map-Free Relocalisation benchmark while
requiring less supervision than competing approaches.


  For set $A\subset {\mathbb {F}_p}^*$ define by ${\mathsf{sf}}(A)$ the size of
the largest sum--free subset of $A.$ Alon and Kleitman showed that
${\mathsf{sf}} (A) \ge |A|/3+O(|A|/p).$ We prove that if ${\mathsf{sf}}
(A)-|A|/3$ is small then the set $A$ must be uniformly distributed on cosets of
each large multiplicative subgroup. Our argument relies on irregularity of
distribution of multiplicative subgroups on certain intervals in ${\mathbb
{F}_p}$.


  Bounds on the expansion coefficients of the strangeness changing $K\pi$ form
factors were derived recently from analyticity and unitarity, using as input
suitable correlation functions calculated by perturbative QCD in the Euclidian
region. We investigate two types of invariant amplitudes and their
corresponding dispersion relations, and show that they lead to similar results
for the shape of the vector and scalar $K_{\ell 3}$ form factors.


  A random intersection graph is constructed by independently assigning a
subset of a given set of objects $W,$ to each vertex of the vertex set $V$ of a
simple graph $G.$ There is an edge between two vertices of $V,$ iff their
respective subsets(in $W$,) have at least one common element. The strong
threshold for the connectivity between any two arbitrary vertices of vertex set
$V,$ is derived. Also we determine the almost sure probability bounds for the
vertex degree of a typical vertex of graph $G.$


  Binary black hole simulations starting from quasi-circular (i.e., zero radial
velocity) initial data have orbits with small but non-zero orbital
eccentricities. In this paper the quasi-equilibrium initial-data method is
extended to allow non-zero radial velocities to be specified in binary black
hole initial data. New low-eccentricity initial data are obtained by adjusting
the orbital frequency and radial velocities to minimize the orbital
eccentricity, and the resulting ($\sim 5$ orbit) evolutions are compared with
those of quasi-circular initial data. Evolutions of the quasi-circular data
clearly show eccentric orbits, with eccentricity that decays over time. The
precise decay rate depends on the definition of eccentricity; if defined in
terms of variations in the orbital frequency, the decay rate agrees well with
the prediction of Peters (1964). The gravitational waveforms, which contain
$\sim 8$ cycles in the dominant l=m=2 mode, are largely unaffected by the
eccentricity of the quasi-circular initial data. The overlap between the
dominant mode in the quasi-circular evolution and the same mode in the
low-eccentricity evolution is about 0.99.


  The Qatar Exoplanet Survey (QES) is discovering hot Jupiters and aims to
discover hot Saturns and hot Neptunes that transit in front of relatively
bright host stars. QES currently operates a robotic wide-angle camera system to
identify promising transiting exoplanet candidates among which are the
confirmed exoplanets Qatar 1b and 2b. This paper describes the first generation
QES instrument, observing strategy, data reduction techniques, and follow-up
procedures. The QES cameras in New Mexico complement the SuperWASP cameras in
the Canary Islands and South Africa, and we have developed tools to enable the
QES images and light curves to be archived and analysed using the same methods
developed for the SuperWASP datasets. With its larger aperture, finer pixel
scale, and comparable field of view, and with plans to deploy similar systems
at two further sites, the QES, in collaboration with SuperWASP, should help to
speed the discovery of smaller radius planets transiting bright stars in
northern skies.


  We investigate quantum pumping of massless Dirac fermions in an ideal
(impurity free) double layer of graphene. The pumped current is generated by
adiabatic variation of two gate voltages in the contact regions to a weakly
doped double graphene sheet. At the Dirac point and for a wide bilayer with
width W much larger then length L, we find that the pumped current scales
linearly with the interlayer coupling length l_{perp} for L/l_{perp} << 1, is
maximal for L/l_{perp} ~ 1, and crosses over to a
log(L/l_{perp})/(L/l_{perp})-dependence for L/l_{perp} >> 1. We compare our
results with the behavior of the conductance in the same system and discuss
their experimental feasibility.


  Let $G=(V,E)$ be a locally finite connected weighted graph, $\Delta$ be the
usual graph Laplacian. In this paper, we study the blow-up problems for the
nonlinear parabolic equation $u_t=\Delta u + f(u)$ on $G$. The blow-up
phenomenons of the equation are discussed in terms of two cases: (i) an initial
condition is given; (ii) a Dirichlet boundary condition is given. We prove that
if $f$ satisfies appropriate conditions, then the solution of the equation
blows up in a finite time.


  We combine few- and many-body degrees of freedom in a model applicable to
both bound and continuum states and adaptable to different subfields of
physics. We formulate a self-consistent three-body model for a core-nucleus
surrounded by two valence nucleons. We treat the core in the mean-field
approximation and use the same effective Skyrme interaction between both core
and valence nucleons. We apply the model to $^{26}$O where we reproduce the
known experimental data as well as phenomenological models with more
parameters. The decay of the ground state is found to proceed directly into the
continuum without effect of the virtual sequential decay through the well
reproduced $d_{3/2}$-resonance of $^{25}$O.


  We use the general formalism of squeezed rotated states to calculate baryon
asymmetry in the wake of inflation through parametric amplification. We base
our analysis on a B and CP violating Lagrangian in an isotropically expanding
universe. The B and CP violating terms originate from the coupling of complex
fields with non-zero baryon number to a complex background inflaton field. We
show that a differential amplification of particle and anti-particle modes
gives rise to baryon asymmetry.


  The first of $N$ identical independently distributed (i.i.d.) Brownian
trajectories that arrives to a small target, sets the time scale of activation,
which in general is much faster than the arrival to the target of only a single
trajectory. Analytical asymptotic expressions for the minimal time is
notoriously difficult to compute in general geometries. We derive here
asymptotic laws for the probability density function of the first and second
arrival times of a large number of i.i.d. Brownian trajectories to a small
target in 1,2, and 3 dimensions and study their range of validity by stochastic
simulations. The results are applied to activation of biochemical pathways in
cellular transduction.


  We provide experimental and theoretical evidence for a novel type of
pressure-induced insulator-metal transition characterized by site-selective
delocalization of the electrons. M\"ossbauer spectroscopy, X-ray diffraction
and electrical transport measurements on Fe$_2$O$_3$ to 100 GPa, along with
dynamical mean-field theory (DFT+DMFT) calculations, reveal this site-selective
Mott transition between 50 and 68 GPa, such that the metallization can be
described by ($^\rm{VI}$Fe$^{3+\rm{HS}}$)$_2$O$_3$ [$R\bar{3}c$ structure]
$\overrightarrow{\tiny\rm{50~GPa}}$
($^\rm{VIII}$Fe$^{3+\rm{HS~VI}}$Fe$^\rm{M}$)O$_3$ [$P2_1/n$ structure]
$\overrightarrow{\tiny\rm{68~GPa}}$ ($^\rm{VI}$Fe$^\rm{M}$)$_2$O$_3$ [$Aba2$
structure]. Within the $P2_1/n$ crystal structure, characterized by two
distinct coordination sites (VI and VIII), we observe equal abundances of
ferric ions (Fe$^{3+}$) and ions having delocalized electrons (Fe$^\rm{M}$),
and only at higher pressures is a fully metallic $Aba2$ structure obtained, all
at room temperature. The transition is characterized by
delocalization/metallization of the $3d$ electrons on half the Fe sites, with a
site-dependent collapse of local moments. Above $\sim$50 GPa, Fe$_2$O$_3$ is a
strongly correlated metal with reduced electron mobility (large band
renormalizations) of m*/m$\sim$4 and 6 near the Fermi level. Upon
decompression, we observe a site-selective (metallic) to conventional Mott
insulator phase transition ($^\rm{VIII}$Fe$^{3+\rm{HS~VI}}$Fe$^\rm{M}$)O$_3$
$\overrightarrow{\tiny\rm{50~GPa}}$ ($^\rm{VIII}$Fe$^{3+\rm{HS~VI}}$Fe$^{3+
\rm{HS}}$)O$_3$ within the same $P2_1/n$ structure, indicating a decoupling of
the electronic and lattice degrees of freedom, characteristic of a true Mott
transition. Our results show that the interplay of electronic correlations and
lattice may result in rather complex behavior of the electronic structure and
magnetic state.


  We investigate robustness of deep feed-forward neural networks when input
data are subject to random uncertainties. More specifically, we consider
regularization of the network by its Lipschitz constant and emphasize its role.
We highlight the fact that this regularization is not only a way to control the
magnitude of the weights but has also a coupling effect on the network weights
accross the layers. We claim and show evidence on a dataset that this coupling
effect brings a tradeoff between robustness and expressiveness of the network.
This suggests that Lipschitz regularization should be carefully implemented so
as to maintain coupling accross layers.


  Mass public quarantining, colloquially known as a lock-down, is a
non-pharmaceutical intervention to check spread of disease. This paper presents
ESOP (Epidemiologically and Socio-economically Optimal Policies), a novel
application of active machine learning techniques using Bayesian optimization,
that interacts with an epidemiological model to arrive at lock-down schedules
that optimally balance public health benefits and socio-economic downsides of
reduced economic activity during lock-down periods. The utility of ESOP is
demonstrated using case studies with VIPER
(Virus-Individual-Policy-EnviRonment), a stochastic agent-based simulator that
this paper also proposes. However, ESOP is flexible enough to interact with
arbitrary epidemiological simulators in a black-box manner, and produce
schedules that involve multiple phases of lock-downs.


  We consider the immediate or near-term experimental opportunities offered by
some scenarios that could explain the new diphoton excess at the LHC. If the
excess is due to a new particle $X_s$ at 750 GeV, additional new particles are
required, providing further signals. If connected with naturalness, the $X_s$
may be produced in top partner decays. Then a $t'\bar t'$ signal, with $t'\to t
X_s$ and $X_s\to gg$ dominantly, might be discovered by reinterpreting 13 TeV
SUSY searches in multijet events with low MET and/or a lepton. If $X_s$ is a
bound state of quirks, the signal events may be accompanied by an unusual
number of soft tracks or soft jets. Other resonances including dilepton and
photon+jet as well as dijet may lie at or above this mass, and signatures of
hidden glueballs might also be observable. If the "photons" in the excess are
actually long-lived particles decaying to photon pairs or to electron pairs,
there are opportunities for detecting overlapping photons and/or unusual
patterns of apparent photon-conversions in either $X_s$ or 125 GeV Higgs
decays. There is also the possibility of events with a hard "photon" recoiling
against a narrow isolated HCAL-only "jet", which, after the jet's energy is
corrected for its electromagnetic origin, would show a peak at 750 GeV.


  A medium modified gluon propagator is used to evaluate the scattering cross
section for the process gg-->gg in the QCD medium by performing an explicit sum
over the polarizations of the gluons. We incorporate a magnetic sreening mass
from a non-perturbative study. It is shown that the medium modified cross
section is finite, divergence free, and is independent of any ad-hoc momentum
transfer cut-off parameters. The medium modified finite cross sections are
necessary for a realistic investigation of the production and equilibration of
the minijet plasma expected at RHIC and LHC.


  We consider phenomenological implications of susy models with spontaneously
broken CP-symmetry. In particular, we analyze CP-asymmetries in B decays and
find that the predictions of these models are vastly different from those of
the SM. These features are common to NMSSM-like models with an arbitrary number
of sterile superfields and the MSSM with broken R-parity.


  Let $E$ be an elliptic curve of rank $\text{rk}(E) \geq 1$, and let
$E(\mathbb{F}_p)$ be the elliptic group of order $\#E(\mathbb{F}_p)=n$. The
number of primes $p\leq x$ such that $n$ is prime is expected to be
$\pi(x,E)=\delta(E)x/\log^2 x+o(x/\log^2 x)$, where $\delta(E)\geq 0$ is the
density constant. This note proves a lower bound $\pi(x,E) \gg x/\log^2 x$.


  We study four-dimensional Einstein-Maxwell fields for which any higher-order
corrections to the field equations effectively reduces to just a rescaling of
the gravitational and the cosmological constant. These configurations are thus
simultaneous solutions of (virtually) any modified theory of gravity coupled
(possibly non-minimally) to any electrodynamics. In the case of non-null
electromagnetic fields we provide a full characterization of such universal
solutions, which correspond to a family of gravitational waves propagating in
universes of the Levi-Civita--Bertotti--Robinson type. For null fields we first
obtain a set of general necessary conditions, and then a full characterization
for a special subfamily, which turns out to represent electromagnetic waves
accompanied by pure radiation in the (anti-)Nariai background. The results are
exemplified for the case of Born-Infeld, ModMax and Horndeski electrodynamics.


  We discuss how a background bath of gravitons can induce decoherence of
quantum systems. The mechanism is dephasing, the loss of phase coherence due to
quantum geometry fluctuations caused by the gravitons. This effect is
illustrated in a simple analog model of quantum particles in a cavity whose
walls undergo position fluctuations, and create the same effect expected from
spacetime geometry fluctuations. We obtain an explicit result for the
decoherence rate in the limit where the graviton wavelength is large compared
to the size of the quantum system, and make some estimates for this rate.


  New heavy charged lepton production and decay signatures at future
electron-positron colliders are investigated at $\sqrt {s}=500$ GeV. The
consequences of model dependence for vector singlets and vector doublets are
studied. Distributions are calculated including hadronization effects and
experimental cuts that suppress the standard model background. The final state
leptonic energy distributions are shown to give a very clear signature for
heavy charged leptons.


  A pair of clauses in a CNF formula constitutes a conflict if there is a
variable that occurs positively in one clause and negatively in the other. A
CNF formula without any conflicts is satisfiable. The Lovasz Local Lemma
implies that a k-CNF formula is satisfiable if each clause conflicts with at
most 2^k/e-1 clauses. It does not, however, give any good bound on how many
conflicts an unsatisfiable formula has globally. We show here that every
unsatisfiable k-CNF formula requires 2.69^k conflicts and there exist
unsatisfiable k-CNF formulas with 3.51^k conflicts.


  Complex autonomous control systems are subjected to sensor failures,
cyber-attacks, sensor noise, communication channel failures, etc. that
introduce errors in the measurements. The corrupted information, if used for
making decisions, can lead to degraded performance. We develop a framework for
using adversarial deep reinforcement learning to design observer strategies
that are robust to adversarial errors in information channels. We further show
through simulation studies that the learned observation strategies perform
remarkably well when the adversary's injected errors are bounded in some sense.
We use neural network as function approximator in our studies with the
understanding that any other suitable function approximating class can be used
within our framework.


  Let ${\mathcal{M} = (M_i \colon i\in K)}$ be a finite or infinite family
consisting of matroids on a common ground set $E$ each of which may be finitary
or cofinitary. We prove the following Cantor-Bernstein-type result: If there is
a collection of bases, one for each $M_i$, which covers the set $E$, and also a
collection of bases which is pairwise disjoint, then there is a collection of
bases which partitions $E$. We also show that the failure of this
Cantor-Bernstein-type statement for arbitrary matroid families is consistent
relative to the axioms of set theory ZFC.


  The principal minors of the Toeplitz matrix $\left( x_{i-j+1}\right)_{1\le
i,j,\le n}$, where $x_0=1, x_k=0$ if $k\le -1$, directly determine an
involution of the polynomial ring $R[x_1, ... ,x_n]$ over any commutative ring
$R$.


  The status of lattice determinations of quarkonia and hybrid meson spectra is
presented and compared with experiment. Both quenched and unquenched results
are discussed. Hybrid meson decays are considered.


  A general matrix approach to study entangled states is presented, based on
operator completeness relations. Bases of unitary operators are considered,
with focus on irreducible representations of groups. Bell measurements for
teleportation are considered, and robustness of teleportation to various kinds
of non idealities is shown.


  Homology theories categorifying quantum group link invariants are known to be
governed by the representation theory of quiver Hecke algebras, also called
KLRW algebras. Here we show that certain cylindrical KLRW algebras, relevant in
particular for cylindrical generalizations of link homology theories, can be
realized by Lagrangian Floer homology in multiplicative Coulomb branches. This
confirms a homological mirror symmetry prediction of the first author.


  A Neutrino Factory requires an intense and well-cooled (in transverse phase
space) muon beam. We discuss a hybrid approach for a linear 4D cooling channel
consisting of high-pressure gas-filled RF cavities- potentially allowing high
gradients without breakdown- and discrete LiH absorbers to provide the
necessary energy loss that results in the required muon beam cooling. We report
simulations of the channel performance and its comparison with the vacuum case;
we also briefly discuss technical and safety issues associated with cavities
filled with high-pressure hydrogen gas. Even with additional windows that might
be needed for safety reasons, the channel performance is comparable to that of
the original, all-vacuum Feasibility Study 2a channel on which our design is
based. If tests demonstrate that the gas-filled RF cavities can operate
effectively with an intense beam of ionizing particles passing through them,
our approach would be an attractive way of avoiding possible breakdown problems
with a vacuum RF channel.


  Lattice Boltzmann Models (LBM) and Phase Field Models (PFM) are two of the
most widespread approaches for the numerical study of multicomponent fluid
systems. Both methods have been successfully employed by several authors but,
despite their popularity, still remains unclear how to properly compare them
and how they perform on the same problem. Here we present a unified framework
for the direct (one-to-one) comparison of the multicomponent LBM against the
PFM. We provide analytical guidelines on how to compare the Shan-Chen (SC)
lattice Boltzmann model for non-ideal multicomponent fluids with a
corresponding free energy (FE) lattice Boltzmann model. Then, in order to
properly compare the LBM vs. the PFM, we propose a new formulation for the free
energy of the Cahn-Hilliard/Navier-Stokes equations. Finally, the LBM model is
numerically compared with the corresponding phase field model solved by means
of a pseudo-spectral algorithm. This work constitute a first attempt to set the
basis for a quantitative comparison between different algorithms for
multicomponent fluids. We limit our scope to the few of the most common
variants of the two most widespread methodologies, namely the lattice Boltzmann
model (SC and FE variants) and the Phase Field Model.


  Next big commercial applications of drones require to fly the drone beyond
the visual line of sight (BVLOS). This inevitable ability to fly BVLOS will
also necessitate the ability to keep track of the drone's location, in order to
ensure successful completion of the intended service. In this context, we
explore the fundamental limits of 3D localization of drones in conjunction with
the effects of the 3D antenna radiation patterns. Although the localization of
drone/unmanned aerial vehicle (UAV) is a well-studied topic in the literature,
its relationship to the antenna effects remains mostly unexplored. In this
paper, we investigate the impact of antenna radiation pattern on the accuracy
of time-difference-of-arrival (TDOA)-based localization of the UAV.
Specifically, we consider a scenario where a fixed number of radio-frequency
(RF) sensors, placed at some known locations on the ground, collect the TDOA
measurements from the signals transmitted from the UAV and estimate the
location of the UAV from these observations. In order to study the impact of
the antenna effects on the fundamental limits of the TDOA-based positioning
scheme, we develop a simple analytical model to approximate the total antenna
gains experienced by an air-to-ground (A2G) link, for various orientations of
the antennas. We then derive the Cramer-Rao lower bound for the TDOA based
localization scheme, for all three combinations of the transmit and the receive
antenna orientations: vertical-vertical (VV), horizontal-horizontal (HH), and
vertical-horizontal (VH).


  The description of the inspiral of a stellar-mass compact object into a
massive black hole sitting at a galactic centre is a problem of major relevance
for the future space-based gravitational-wave observatory LISA (Laser
Interferometer Space Antenna), as the signals from these systems will be buried
in the data stream and accurate gravitational-wave templates will be needed to
extract them. The main difficulty in describing these systems lies in the
estimation of the gravitational effects of the stellar-mass compact object on
his own trajectory around the massive black hole, which can be modeled as the
action of a local force, the self-force. In this paper, we present a new
time-domain numerical method for the computation of the self-force in a
simplified model consisting of a charged scalar particle orbiting a nonrotating
black hole. We use a multi-domain framework in such a way that the particle is
located at the interface between two domains so that the presence of the
particle and its physical effects appear only through appropriate boundary
conditions. In this way we eliminate completely the presence of a small length
scale associated with the need of resolving the particle. This technique also
avoids the problems associated with the impact of a low differentiability of
the solution in the accuracy of the numerical computations. The spatial
discretization of the field equations is done by using the pseudospectral
collocation method and the time evolution, based on the method of lines, uses a
Runge-Kutta solver. We show how this special framework can provide very
efficient and accurate computations in the time domain, which makes the
technique amenable for the intensive computations required in the
astrophysically-relevant scenarios for LISA.


  The monaural beat is known that it can modulate brain and personal states.
However, which changes in brain waves are related to changes in state is still
unclear. Therefore, we aimed to investigate the effects of monaural beats and
find the relationship between them. Ten participants took part in five separate
random sessions, which included a baseline session and four sessions with
monaural beats stimulation: one audible session and three inaudible sessions.
Electroencephalogram (EEG) were recorded and participants completed pre- and
post-stimulation questionnaires assessing mood and sleepiness. As a result,
audible session led to increased arousal and positive mood compared to other
conditions. From the neurophysiological analysis, statistical differences in
frontal-central, central-central, and central-parietal connectivity were
observed only in the audible session. Furthermore, a significant correlation
was identified between sleepiness and EEG power in the temporal and occipital
regions. These results suggested a more detailed correlation for stimulation to
change its personal state. These findings have implications for applications in
areas such as cognitive enhancement, mood regulation, and sleep management.


  Cosmology models predict that external accretion shocks form in the outer
region of galaxy clusters due to supersonic gas infall from filaments and voids
in the cosmic web. They are characterized by high sonic and Alfv\'enic Mach
numbers, $M_s\sim10-10^2$ and $M_A\sim10^2-10^3$, and propagate into weakly
magnetized plasmas of $\beta\equiv P_g/P_B\gtrsim10^2$. Although strong
accretion shocks are expected to be efficient accelerators of cosmic rays
(CRs), nonthermal signatures of shock-accelerated CRs around clusters have not
been confirmed, and detailed acceleration physics at such shocks has yet to be
understood. In this study, we first establish through two-dimensional
particle-in-cell simulations that at strong high-$\beta$ shocks electrons can
be pre-energized via stochastic Fermi acceleration owing to the ion-Weibel
instability in the shock transition region, possibly followed by injection into
diffusive shock acceleration. Hence, we propose that the models derived from
conventional thermal leakage injection may be employed for the acceleration of
electrons and ions at accretion shocks as well. Applying these analytic models
to numerical shock zones identified in structure formation simulations, we
estimate nonthermal radiation, such as synchrotron and inverse-Compton (IC)
emission due to CR electrons, and $\pi^0$-decay $\gamma$-rays due to CR
protons, around simulated clusters. Our models with the injection parameter,
$Q\approx3.5-3.8$, predict synthetic synchrotron maps, which seem consistent
with recent radio observations of the Coma cluster. However, the detection of
nonthermal IC X-rays and $\gamma$-rays from accretion shocks would be quite
challenging. We suggest that the proposed analytic models may be adopted as
generic recipes for CR production at cosmological shocks.


  With its immensity and numerous mysteries waiting to be solved, the cosmos
has always captivated humankind. A ground-breaking field that has given us a
profound understanding of the mysteries of the cosmos is radio astronomy. This
paper presents a comprehensive overview of radio astronomy, exploring its
techniques, discoveries, and the profound insights it offers into celestial
objects. Radio astronomy, which uses radio waves to analyse celestial
phenomena, has completely changed how we think about the universe. This field
has given us crucial information about the formation of stars, galaxies, and
other celestial objects through the analysis of radio emissions. Radio
astronomy has enabled researchers to study cosmic processes that are
undetectable to the human eye by penetrating the furthest reaches of space. We
explore radio astronomy techniques in this article, revealing how it can be
used to see through interstellar dust and collect signals from the universe's
furthest reaches. Pulsars, quasars, and cosmic microwave background radiation
are significant discoveries that have helped astronomers understand dark matter
and dark energy in great detail. We also look into how radio astronomy might be
used in cosmology and astrophysics. In conclusion, radio astronomy has become a
potent tool for solving the cosmos' riddles. Its capacity for the detection and
analysis of radio emissions has produced a fundamental understanding of the
beginnings and evolution of the universe. Radio astronomy continues to advance
our understanding of the cosmos and arouses interest in additional cosmic
research by shedding light on celestial objects that are invisible to the human
eye.


  We investigate a problem of finding the minimum, in which each user has a
real value and we want to estimate the minimum of these values under the local
differential privacy constraint. We reveal that this problem is fundamentally
difficult, and we cannot construct a mechanism that is consistent in the worst
case. Instead of considering the worst case, we aim to construct a private
mechanism whose error rate is adaptive to the easiness of estimation of the
minimum. As a measure of easiness, we introduce a parameter $\alpha$ that
characterizes the fatness of the minimum-side tail of the user data
distribution. As a result, we reveal that the mechanism can achieve
$O((\ln^6N/\epsilon^2N)^{1/2\alpha})$ error without knowledge of $\alpha$ and
the error rate is near-optimal in the sense that any mechanism incurs
$\Omega((1/\epsilon^2N)^{1/2\alpha})$ error. Furthermore, we demonstrate that
our mechanism outperforms a naive mechanism by empirical evaluations on
synthetic datasets. Also, we conducted experiments on the MovieLens dataset and
a purchase history dataset and demonstrate that our algorithm achieves
$\tilde{O}((1/N)^{1/2\alpha})$ error adaptively to $\alpha$.


  This paper investigates how a thermostatically controlled load can deliver
flexibility either in form of manual frequency restoration reserves (mFRR) or
load shifting, and which one is financially more appealing to such a load. A
supermarket freezer is considered as a representative flexible load, and a
grey-box model describing its temperature dynamics is developed using real data
from a supermarket in Denmark. Taking into account price and activation
uncertainties, a two-stage stochastic mixed-integer linear program is
formulated to maximize the flexibility value from the freezer. For practical
reasons, we propose a linear policy to determine regulating power bids, and
then linearize the mFRR activation conditions through the McCormick relaxation
approach. For computational ease, we develop a decomposition technique,
splitting the problem to a set of smaller subproblems, one per scenario.
Examined on an out-of-sample simulation based on real Danish spot and balancing
market prices in 2022, load shifting shows to be more profitable than mFRR
provision, but is also more consequential for temperature deviations in the
freezer.


  Neutrino energy losses through neutral weak currents in the triplet-spin
superfluid neutron liquid are studied for the case of condensate involving
several magnetic quantum numbers. Low-energy excitations of the multicomponent
condensate in the timelike domain of the energy and momentum are analyzed.
Along with the well-known excitations in the form of broken Cooper pairs, the
theoretical analysis predicts the existence of collective waves of spin density
at very low energy. Because of a rather small excitation energy of spin waves,
their decay leads to a substantial neutrino emission at the lowest
temperatures, when all other mechanisms of neutrino energy loss are killed by a
superfluidity. Neutrino energy losses caused by the pair recombination and
spin-wave decays are examined in all of the multicomponent phases that might
represent the ground state of the condensate, according to modern theories, and
for the case when a phase transition occurs in the condensate at some
temperature. Our estimate predicts a sharp increase in the neutrino energy
losses followed by a decrease, along with a decrease in the temperature, that
takes place more rapidly than it would without the phase transition. We
demonstrate the important role of the neutrino radiation caused by the decay of
spin waves in the cooling of neutron stars.


  We consider twisted standard filtrations of Soergel bimodules associated to
arbitrary Coxeter groups and show that the graded multiplicities in these
filtrations can be interpreted as structure constants in the Hecke algebra.
This corresponds to the positivity of the polynomials occurring when expressing
an element of the canonical basis in a generalized standard basis twisted by a
biclosed set of roots in the sense of Dyer, and comes as a corollary of
Soergel's conjecture. We then show the positivity of the corresponding inverse
polynomials in case the biclosed set is an inversion set of an element or its
complement by generalizing a result of Elias and Williamson on the linearity of
the Rouquier complexes associated to lifts of these basis elements in the
Artin-Tits group. These lifts turn out to be generalizations of mikado braids
as introduced in a joint work with Digne. This second positivity property
generalizes a result of Dyer and Lehrer from finite to arbitrary Coxeter
groups.


  A general class of inverse-moment sum rules was previously derived by the
authors in a chiral perturbation theory (ChPT) study at two-loop order of the
isospin and hypercharge vector-current propagators. Here, we address the
evaluation of the inverse-moment sum rules in terms of existing data and
theoretical constraints. Two kinds of sum rules are seen to occur, those which
contain as-yet undetermined ${\cal O}(q^6)$ counterterms and those free of such
quantities. We use the former to obtain phenomenological evaluations of two
${\cal O}(q^6)$ counterterms. Light is shed on the important but difficult
issue regarding contributions of higher orders in the ChPT expansion.


  We prove new results on splitting Brauer classes by genus 1 curves, settling
in particular the case of degree 7 classes over global fields. Though our
method is cohomological in nature, and proceeds by considering the more
difficult problem of splitting $\mu_N$-gerbes, we use crucial input from the
arithmetic of modular curves and explicit $N$-descent on elliptic curves.


  We present the results of adaptive-optics imaging of the z=0.2923 QSO PG
1700+518 in the J and H bands. The extension to the north of the QSO is clearly
seen to be a discrete companion with a well-defined tidal tail, rather than a
feature associated with the host galaxy of PG 1700+518 itself. On the other
hand, an extension to the southwest of the QSO (seen best in deeper, but
lower-resolution, optical images) does likely comprise tidal material from the
host galaxy. The SED derived from images in J, H, and two non-standard optical
bands indicates the presence of dust intermixed with the stellar component. We
use our previously reported Keck spectrum of the companion, the SED found from
the imaging data, and updated spectral-synthesis models to constrain the
stellar populations in the companion and to redetermine the age of the
starburst. While our best-fit age of 0.085 Gyr is nearly the same as our
earlier determination, the fit of the new models is considerably better. This
age is found to be remarkably robust with respect to different assumptions
about the nature of the older stellar component and the effects of dust.


  The integrated 21 cm H I emission profile of a galaxy encodes valuable
information on the kinematics, spatial distribution, and dynamical state of its
cold interstellar medium. The line width, in particular, reflects the rotation
velocity of the galaxy, which, in combination with a size scale, can be used to
constrain the dynamical mass of the system. We introduce a new method based on
the concept of the curve of growth to derive a set of robust parameters to
characterize the line width, asymmetry, and concentration of the integrated H I
spectra. We use mock spectra to evaluate the performance of our method, to
estimate realistic systematic uncertainties for the proposed parameters, and to
correct the line widths for the effects of instrumental resolution and
turbulence broadening. Using a large sample of nearby galaxies with available
spatially resolved kinematics, we demonstrate that the newly defined line
widths can predict the rotational velocities of galaxies to within an accuracy
of $\lesssim 30$ km s$^{-1}$. We use the calibrated line widths, in conjunction
with the empirical relation between the size and mass of H I disks, to
formulate a prescription for estimating the dynamical mass within the H
I-emitting region of gas-rich galaxies. Our formalism yields dynamical masses
accurate to $\sim 0.3$ dex based solely on quantities that can be derived
efficiently and robustly from current and future extragalactic H I surveys. We
further extend the dynamical mass calibration to the scale of the dark matter
halo.


  We review possible tests of the left-right electroweak model at future linear
colliders, concentrating on signatures of the central predictions of the model,
i.e. right-handed currents, massive neutrinos and triplet Higgs bosons. We
analyse processes in e+e-, e-e- and e-\gamma collision modes. We present the
mass reaches for the new particles at linear collider and sensitivities to
their couplings.


  The Ehrhart ring of the edge polytope $\mathcal{P}_G$ for a connected simple
graph $G$ is known to coincide with the edge ring of the same graph if $G$
satisfies the odd cycle condition. This paper gives for a graph which does not
satisfy the condition, a generating set of the defining ideal of the Ehrhart
ring of the edge polytope, described by combinatorial information of the graph.
From this result, two factoring properties of the Ehrhart series are obtained;
the first one factors out bipartite biconnected components, and the second one
factors out a even cycle which shares only one edge with other part of the
graph. As an application of the factoring properties, the root distribution of
Ehrhart polynomials for bipartite polygon trees is determined.


  We investigate the equivalence of Sobolev inequalities and the conjunction of
Gaussian upper heat kernel bounds and volume doubling on large scales on
graphs. For the normalizing measure, we obtain their equivalence up to
constants by imposing comparability of small balls and the vertex degree at
their centers. If arbitrary measures are considered, we incorporate a new local
regularity condition. Furthermore, new correction functions for the Gaussian,
doubling, and Sobolev dimension are introduced. For the Gaussian and doubling,
the variable correction functions always tend to one at infinity. Moreover, the
variable Sobolev dimension can be related to the doubling dimension and the
vertex degree growth.


  We present optical and X-ray identifications for the sixty-four radio sources
in the GOODS-S ACS field revealed in the ATCA 1.4 GHz survey of the Chandra
Deep Field South. Optical identifications are made using the ACS images and
catalogs, while the X-ray view is provided by the Chandra X-ray Observatory 1
Ms observations. Redshifts for the identified sources are drawn from publicly
available catalogs of spectroscopic observations and multi-band
photometric-based estimates. Using this multiwavelength information we provide
a first characterization of the faint radio source population in this region.
The sample contains a mixture of star-forming galaxies and active galactic
nuclei, as identified by their X-ray properties and optical spectroscopy. A
large number of morphologically disturbed galaxies is found, possibly related
to the star-formation phenomena. In spite of the very deep optical data
available in this field, seven of the sixty-four radio sources have no optical
identification to z(850)~28 mag. Only one of these is identified in the X-rays.


  We present a geometrical model of the distribution of luminous matter in the
universe, derived from a very simple reaction-diffusion model of turbulent
phenomena. The apparent dimension of luminous matter, $D(l)$, depends linearly
on the logarithm of the scale $l$ under which the universe is viewed: $D(l)
\sim 3\log(l/l_0)/\log(\xi/l_0)$, where $\xi$ is a correlation length.
Comparison with data from the SARS red-shift catalogue, and the LEDA database
provides a good fit with a correlation length $\xi \sim 300$ Mpc. The
geometrical interpretation is clear: At small distances, the universe is
zero-dimensional and point-like. At distances of the order of 1 Mpc the
dimension is unity, indicating a filamentary, string-like structure; when
viewed at larger scales it gradually becomes 2-dimensional wall-like, and
finally, at and beyond the correlation length, it becomes uniform.


  In spacetimes admitting Yano tensors the classical theory of the spinning
particle possesses enhanced worldline supersymmetry. Quantum mechanically
generators of extra supersymmetries correspond to operators that in the
classical limit commute with the Dirac operator and generate conserved
quantities. We show that the result is preserved in the full quantum theory,
that is, Yano symmetries are not anomalous. This was known for Yano tensors of
rank two, but our main result is to show that it extends to Yano tensors of
arbitrary rank. We also describe the conformal Yano equation and show that is
invariant under Hodge duality. There is a natural relationship between Yano
tensors and supergravity theories. As the simplest possible example, we show
that when the spacetime admits a Killing spinor then this generates Yano and
conformal Yano tensors. As an application, we construct Yano tensors on
maximally symmetric spaces: they are spanned by tensor products of Killing
vectors.


  The most widely used method for monitoring Low Voltage Distribution Grids
(LVDGs) is the three phase Weighted Least Squares (WLS) State Estimation (SE),
which was initially developed for Medium Voltage Distribution Grids (MVDGs).
This methodology is implicitly applied in LVDGs with an assumption that the
neutral conductor represents a zero potential across the whole system. However,
this assumption is often not valid for LVDGs as the consumers loads are highly
asymmetrical and the neutral conductor is usually grounded only at the MV-LV
transformer substation. Therefore, if this method is applied for the monitoring
of LVDGs it may deteriorate the performance of the WLS SE leading to inaccurate
results. In this paper, an investigation is initially conducted in order to
evaluate the performance of the conventional WLS SE methodology when applied
for the monitoring of LVDGs. The results of this study indicate that the
performance of this methodology is not consistent and is highly affected by the
operating conditions of the LVDG. In view of these results, a new methodology
is proposed and developed that takes into consideration the effect of the
neutral conductor and as a result an outstanding performance is achieved
compared to the conventional methodology.


  As machines have become more intelligent, there has been a renewed interest
in methods for measuring their intelligence. A common approach is to propose
tasks for which a human excels, but one which machines find difficult. However,
an ideal task should also be easy to evaluate and not be easily gameable. We
begin with a case study exploring the recently popular task of image captioning
and its limitations as a task for measuring machine intelligence. An
alternative and more promising task is Visual Question Answering that tests a
machine's ability to reason about language and vision. We describe a dataset
unprecedented in size created for the task that contains over 760,000 human
generated questions about images. Using around 10 million human generated
answers, machines may be easily evaluated.


  Circumbinary planets whose orbits become unstable may be ejected, accreted,
or even captured by one of the stars. We quantify the relative rates of these
channels, for a binary of secondary star's mass fraction 0.1 with an orbit of
1AU. The most common outcome is ejection, which happens ~80% of the time. If
binary systems form circumbinary planets readily and sloppily, this process may
fill the Milky Way with free-floating planets. A significant fraction of the
time, ~20%, the unstable planet strikes the primary or secondary. We tracked
whether a Jupiter-like planet would undergo tidal stripping events during close
passages, and find that these events are not strong enough to change the
trajectory of the planet, though this may be observable from a changed
structured for free-floating planets that are produced by this process.


  DC microgrids conform to distributed control of renewable energy sources
which ratifies efficacious instantaneous power sharing and sustenance of energy
access among different domestic Power Management Units (PMUs) along with
maintaining stability of the grid voltage. In this paper design metrics and
performance evaluation of a scalable DC microgrid are documented where a
look-up table of generated power of a source converter complies with the
distribution of efficient power sharing phenomenon among a set of two home
PMUs. The source converter is connected with a Photovoltaic panel of 300 W and
uses Perturb and Observation (P&O) method for executing Maximum Power Point
Tracking (MPPT). A boost average DCDC converter topology is used to enhance the
voltage level of the source converter before transmission. The load converter
consists of two parallely connected PMUs each of which is constructed with high
switching frequency based Full Bridge (FB) converter to charge an integrated
Energy Storage System (ESS). In this paper the overall system is modeled and
simulated on MATLAB/Simulink platform with ESSs in the form of Lead Acid
batteries connected to the load side of the FB converter circuits and these
batteries yield to support marginalized power utilities. The behaviour of the
system is tested in different solar insolation levels along with several
battery charging levels of 12 V and 36 V to assess the power efficiency. In
each testbed the efficiency is found to be more than 93% which affirm the
reliability of the framework and a look-up table is generated comprising the
grid and load quantities for effective control of power transmission.


  Improvement of the classical gravity with the running gravitational coupling
obtained from asymptotically safe gravity, is a good way of considering the
effects of quantum gravity. This is usually done for metric theories of
gravity. Here we investigate the effects of such an improvement for pure affine
theories of gravity. To motivate the approach, we first consider the effects of
quantum improvement on the connection using metric theory and investigate the
effects on the causal structure of black hole solution. Next in the framework
of Schr\"odinger-Eddington affine theory, the general way of affine improvement
is presented and a spherically symmetric solution is obtained and compared with
other ways of improvement.


  Charged particle beams are used in Particle Therapy (PT) to treat oncological
patients due to their selective dose deposition in tissues and to their high
biological effect in killing cancer cells with respect to photons and electrons
used in conventional radiotherapy. Nowadays, protons and carbon ions are used
in PT clinical routine but, recently, the interest on the potential application
of helium and oxygen beams is growing due to their reduced multiple scattering
inside the body and increased linear energy transfer, relative biological
effectiveness and oxygen enhancement ratio. The precision of PT demands for
online dose monitoring techniques, crucial to improve the quality assurance of
treatments. The beam range confined in the irradiated target can be monitored
thanks to the neutral or charged secondary radiation emitted by the
interactions of hadron beams with matter. Prompt photons are produced by
nuclear de-excitation processes and, at present, different dose monitoring and
beam range verification techniques based on the prompt {\gamma} detection have
been proposed. It is hence of importance to perform the {\gamma} yield
measurement in therapeutical-like conditions. In this paper we report the
yields of prompt photons produced by the interaction of helium, carbon and
oxygen ion beams with a PMMA target. The measurements were performed at the
Heidelberg Ion-beam Therapy center (HIT) with beams of different energies. A
LYSO scintillator has been used as photon detector. The obtained {\gamma}
yields for $^{12}$C ion beams are compared with results from literature, while
no other results from $^{4}$He and $^{16}$O beams have been published yet. A
discussion on the expected resolution of a slit camera detector is presented,
demonstrating the feasibility of a prompt-{\gamma} based monitoring technique
for PT treatments using helium, carbon and oxygen ion beams.


  The statefulness property of network protocol implementations poses a unique
challenge for testing and verification techniques, including Fuzzing. Stateful
fuzzers tackle this challenge by leveraging state models to partition the state
space and assist the test generation process. Since not all states are equally
important and fuzzing campaigns have time limits, fuzzers need effective state
selection algorithms to prioritize progressive states over others. Several
state selection algorithms have been proposed but they were implemented and
evaluated separately on different platforms, making it hard to achieve
conclusive findings. In this work, we evaluate an extensive set of state
selection algorithms on the same fuzzing platform that is AFLNet, a
state-of-the-art fuzzer for network servers. The algorithm set includes
existing ones supported by AFLNet and our novel and principled algorithm called
AFLNetLegion. The experimental results on the ProFuzzBench benchmark show that
(i) the existing state selection algorithms of AFLNet achieve very similar code
coverage, (ii) AFLNetLegion clearly outperforms these algorithms in selected
case studies, but (iii) the overall improvement appears insignificant. These
are unexpected yet interesting findings. We identify problems and share
insights that could open opportunities for future research on this topic.


  Mesoscopic dynamics of self-organized structures is the most important aspect
in the description of complex living systems. The Belousov--Zhabotinsky (B--Z)
reaction is in this respect a convenient testbed because it represents a
prototype of chemical self-organization with a rich variety of emergent
wave-spiral patterns. Using a multi-state stochastic hotchpotch model, we show
here that the mesoscopic behaviour of the non-stirred B--Z reaction is both
qualitatively and quantitatively susceptible to the description in terms of
stochastic multilevel cellular automata. This further implies that the
mesoscopic dynamics of the non-stirred B--Z reaction results from a delicate
interplay between a) a maximal number of available states within the elementary
time lag (i.e. a minimal time interval needed for demise of a final state) and
b) an imprecision or uncertainty in the definition of state. If either the
number of time lags is largely different from 7 or the maximal number of
available states is smaller than 20, the physicochemical conditions are
inappropriate for a formation of the wave-spiral patterns. Furthermore, a white
noise seems to be key for the formation of circular structures (target
patterns) which could not be as yet systematically explained in existing
models.


  We prove that a classical theorem of McAdam about the analytic spread of an
ideal in a Noetherian local ring is true for divisorial filtrations on an
excellent local ring $R$ which is either of equicharacteristic zero or of
dimension $\le 3$. In fact, the proof is valid whenever resolution of
singularities holds.


  In this note, we prove that a nontrivial Kinoshita-Terasaka or Conway knot
does not admit chirally cosmetic surgeries, by calculating the finite type
invariant of order 3.


  We propose a concise stochastic mechanics framework for chemical reaction
systems that allows to formulate evolution equations for three general types of
data: the probability generating functions, the exponential moment generating
functions and the factorial moment generating functions. This formulation
constitutes an intimate synergy between techniques of statistical physics and
of combinatorics. We demonstrate how to analytically solve the evolution
equations for all six elementary types of single-species chemical reactions by
either combinatorial normal-ordering techniques, or, for the binary reactions,
by means of Sobolev-Jacobi orthogonal polynomials. The former set of results in
particular highlights the relationship between infinitesimal generators of
stochastic evolution and parametric transformations of probability
distributions.