  Mutual Information (MI) based feature selection makes use of MI to evaluate
each feature and eventually shortlists a relevant feature subset, in order to
address issues associated with high-dimensional datasets. Despite the
effectiveness of MI in feature selection, we notice that many state-of-the-art
algorithms disregard the so-called unique relevance (UR) of features, and
arrive at a suboptimal selected feature subset which contains a non-negligible
number of redundant features. We point out that the heart of the problem is
that all these MIBFS algorithms follow the criterion of Maximize Relevance with
Minimum Redundancy (MRwMR), which does not explicitly target UR. This motivates
us to augment the existing criterion with the objective of boosting unique
relevance (BUR), leading to a new criterion called MRwMR-BUR. Depending on the
task being addressed, MRwMR-BUR has two variants, termed MRwMR-BUR-KSG and
MRwMR-BUR-CLF, which estimate UR differently. MRwMR-BUR-KSG estimates UR via a
nearest-neighbor based approach called the KSG estimator and is designed for
three major tasks: (i) Classification Performance. (ii) Feature
Interpretability. (iii) Classifier Generalization. MRwMR-BUR-CLF estimates UR
via a classifier based approach. It adapts UR to different classifiers, further
improving the competitiveness of MRwMR-BUR for classification performance
oriented tasks. The performance of both MRwMR-BUR-KSG and MRwMR-BUR-CLF is
validated via experiments using six public datasets and three popular
classifiers. Specifically, as compared to MRwMR, the proposed MRwMR-BUR-KSG
improves the test accuracy by 2% - 3% with 25% - 30% fewer features being
selected, without increasing the algorithm complexity. MRwMR-BUR-CLF further
improves the classification performance by 3.8%- 5.5% (relative to MRwMR), and
it also outperforms three popular classifier dependent feature selection
methods.


  We use analytic bootstrap techniques for a CFT with an interface or a
boundary. Exploiting the analytic structure of the bulk and boundary conformal
blocks we extract the CFT data. We further constrain the CFT data by applying
the equation of motion to the boundary operator expansion. The method presented
in this paper is general, and it is illustrated in the context of perturbative
Wilson-Fisher theories. In particular, we find constraints on the OPE
coefficients for the conformal interface CFT in $4 - \epsilon$ dimensions (upto
order $\mathcal{O}(\epsilon^2)$) with $\phi^4$-interactions in the bulk. We
also compute the corresponding coefficients for the non-unitary $\phi^3$-theory
in $6 - \epsilon$ dimensions in the presence of a conformal boundary equipped
with either Dirichlet or Neumann boundary conditions upto order
$\mathcal{O}(\epsilon)$, or an interface upto order
$\mathcal{O}(\sqrt{\epsilon})$.


  A new method to determine the bias parameter of galaxies relative to matter
is suggested. The method is based on the assumption that gravity is the
dominating force which determines the formation of the structure in the
Universe. Due to gravitational instability the galaxy formation is a threshold
process: in low-density environments galaxies do not form and matter remains in
primordial form. We investigate the influence of the presence of void and
clustered populations to the power spectrum of matter and galaxies. The power
spectrum of galaxies is similar to the power spectrum of matter; the fraction
of total matter in the clustered population determines the difference between
amplitudes of fluctuations of matter and galaxies, i.e. the bias factor. To
determine the fraction of matter in voids and clustered population we perform
numerical simulations. The fraction of matter in galaxies at the present epoch
is found using a calibration through the sigma_8 parameter.


  Post-hoc model-agnostic interpretation methods such as partial dependence
plots can be employed to interpret complex machine learning models. While these
interpretation methods can be applied regardless of model complexity, they can
produce misleading and verbose results if the model is too complex, especially
w.r.t. feature interactions. To quantify the complexity of arbitrary machine
learning models, we propose model-agnostic complexity measures based on
functional decomposition: number of features used, interaction strength and
main effect complexity. We show that post-hoc interpretation of models that
minimize the three measures is more reliable and compact. Furthermore, we
demonstrate the application of these measures in a multi-objective optimization
approach which simultaneously minimizes loss and complexity.


  The quantity R_K = Gamma(K -> e nu)/Gamma(K -> mu nu) studied by the
experiment NA62 at CERN is known to probe lepton-flavour violating (LFV)
parameters of the Minimal Supersymmetric Standard Model (MSSM). A non-zero
parameter delta_{RR}^{13} can open the decay channel K -> e nu_tau and enhance
R_K over its Standard-Model value. In the region of the parameter space probed
by NA62 the contribution from a bino-stau loop diagram is numerically dominant
and the mixing between left-handed and right-handed staus is important. For
large values of the stau mixing angle theta_tau the commonly adopted mass
insertion approximation is not accurate. We therefore express the
supersymmetric contribution to R_K in terms of the mass of the lightest stau
eigenstate, the mixing angle theta_tau, and other relevant MSSM parameter such
as tan(beta) and the charged-Higgs boson mass M_H and plot the parameter
regions constrained by R_K. We further study to which extent R_K can be
depleted through MSSM contributions interfering destructively with the SM
amplitude for K -> e nu_e. This lepton-flavour conserving (LFC) mechanism
involves the parameter combination |delta_{LL}^{13} delta_{RR}^{13}|, which can
be constrained with a naturalness consideration for the electron mass or with
the measurement of the anomalous magnetic moment of the electron. The LFC
effect on R_K is marginal, an NA62 measurement of R_K significantly below the
Standard Model expectation would indicate physics beyond the MSSM.


  One of the main research areas in Bayesian Nonparametrics is the proposal and
study of priors which generalize the Dirichlet process. Here we exploit
theoretical properties of Poisson random measures in order to provide a
comprehensive Bayesian analysis of random probabilities which are obtained by
an appropriate normalization. Specifically we achieve explicit and tractable
forms of the posterior and the marginal distributions, including an explicit
and easily used description of generalizations of the important
Blackwell-MacQueen P\'olya urn distribution. Such simplifications are achieved
by the use of a latent variable which admits quite interesting interpretations
which allow to gain a better understanding of the behaviour of these random
probability measures. It is noteworthy that these models are generalizations of
models considered by Kingman (1975) in a non-Bayesian context. Such models are
known to play a significant role in a variety of applications including
genetics, physics, and work involving random mappings and assemblies. Hence our
analysis is of utility in those contexts as well. We also show how our results
may be applied to Bayesian mixture models and describe computational schemes
which are generalizations of known efficient methods for the case of the
Dirichlet process. We illustrate new examples of processes which can play the
role of priors for Bayesian nonparametric inference and finally point out some
interesting connections with the theory of generalized gamma convolutions
initiated by Thorin and further developed by Bondesson.


  Two structures $A$ and $B$ are $n$-equivalent if player II has a winning
strategy in the $n$-move Ehrenfeucht-Fra\"iss\'e game on $A$ and $B$. In
earlier papers we studied $n$-equivalence classes of ordinals and coloured
ordinals. In this paper we similarly treat a class of scattered order-types,
focussing on monomials and sums of monomials in $\omega$ and its reverse
$\omega^*$.


  Let R be a commutative ring with unity, M be an unitary R-module and {\Gamma}
be a simple graph. This research article is an interplay of combinatorial and
algebraic properties of M . We show a combinatorial object completely
determines an algebraic object and characterise all finite abelian groups. We
discuss the correspondence between essential ideals of R, submodules of M and
vertices of graphs arising from M . We examine various types of equivalence
relations on objects of M. We study essential ideals corresponding to elements
of an object over hereditary and regular rings. Further, we study isomorphism
of annihilating graphs arising from M and tensor product.


  In this paper we show how we can transform quadratic systems into new
quadratic systems after some kind of birational transformations, the quadratic
plane Cremona maps. We afterwards apply these transformations to the families
of quadratic differential systems having an algebraic limit cycle. As a
consequence, we provide a new family of quadratic systems having an algebraic
limit cycle of degree 5. Moreover we show how the known families of quadratic
differential systems having an algebraic limit cycle of degree greater than
four are obtained using these transformations. We also provide the phase
portraits on the Poincar\'e disk of all the families of quadratic differential
systems having algebraic limit cycles.


  State-of-the-art deep learning models for tabular data have recently achieved
acceptable performance to be deployed in industrial settings. However, the
robustness of these models remains scarcely explored. Contrary to computer
vision, there are no effective attacks to properly evaluate the adversarial
robustness of deep tabular models due to intrinsic properties of tabular data,
such as categorical features, immutability, and feature relationship
constraints. To fill this gap, we first propose CAPGD, a gradient attack that
overcomes the failures of existing gradient attacks with adaptive mechanisms.
This new attack does not require parameter tuning and further degrades the
accuracy, up to 81% points compared to the previous gradient attacks. Second,
we design CAA, an efficient evasion attack that combines our CAPGD attack and
MOEVA, the best search-based attack. We demonstrate the effectiveness of our
attacks on five architectures and four critical use cases. Our empirical study
demonstrates that CAA outperforms all existing attacks in 17 over the 20
settings, and leads to a drop in the accuracy by up to 96.1% points and 21.9%
points compared to CAPGD and MOEVA respectively while being up to five times
faster than MOEVA. Given the effectiveness and efficiency of our new attacks,
we argue that they should become the minimal test for any new defense or robust
architectures in tabular machine learning.


  In recent years we have witnessed an increasing interest in applying Deep
Neural Networks (DNNs) to improve the rate-distortion performance in image
compression. However, the existing approaches either train a post-processing
DNN on the decoder side, or propose learning for image compression in an
end-to-end manner. This way, the trained DNNs are required in the decoder,
leading to the incompatibility to the standard image decoders (e.g., JPEG) in
personal computers and mobiles. Therefore, we propose learning to improve the
encoding performance with the standard decoder. In this paper, We work on JPEG
as an example. Specifically, a frequency-domain pre-editing method is proposed
to optimize the distribution of DCT coefficients, aiming at facilitating the
JPEG compression. Moreover, we propose learning the JPEG quantization table
jointly with the pre-editing network. Most importantly, we do not modify the
JPEG decoder and therefore our approach is applicable when viewing images with
the widely used standard JPEG decoder. The experiments validate that our
approach successfully improves the rate-distortion performance of JPEG in terms
of various quality metrics, such as PSNR, MS-SSIM and LPIPS. Visually, this
translates to better overall color retention especially when strong compression
is applied. The codes are available at
https://github.com/YannickStruempler/LearnedJPEG.


  We review recent developments in the study of the AdS/CFT correspondence in
lower dimensions. We start summarising the classification of AdS$_3\times$S$^2$
solutions in massive Type IIA supergravity with (0,4) supersymmetries, and the
construction of their 2d dual quiver CFTs. These theories are the seed for
further developments, that we review next. First, we construct a new class of
AdS$_3$ solutions in M-theory that describe M-strings in M5-brane
intersections. Second, we generate a new class of AdS$_2\times$S$^3$ solutions
in massive IIA with 4 supercharges that we interpret as describing backreacted
baryon vertices within the 5d $\mathcal{N}=1$ QFT living in D4-D8 branes.
Third, we construct two classes of AdS$_2$ solutions in Type IIB. The first are
dual to discrete light-cone quantised quantum mechanics living in null
cylinders. The second class is interpreted as dual to backreacted baryon
vertices within 4d $\mathcal{N}=2$ QFT living in D3-D7 branes. Explicit dual
quiver field theories are given for all classes of solutions. These are used to
compute the central charges of the CFTs, that are shown to agree with the
holographic expressions.


  Reading scientific research papers is a skill that many students do not learn
before entering PhD programs, but it is critical to their success. This paper
describes our diagramming technique for teaching this skill, which helps them
identify the structure and the scientific argument of the paper. This has made
our students more effective readers.


  This paper develops a framework for analyzing and designing dynamic networks
comprising different classes of nodes that coexist and interact in one shared
environment. We consider {\em ad hoc} (i.e., nodes can leave the network
unannounced, and no node has any global knowledge about the class identities of
other nodes) {\em preferentially grown networks}, where different classes of
nodes are characterized by different sets of local parameters used in the
stochastic dynamics that all nodes in the network execute. We show that
multiple scale-free structures, one within each class of nodes, and with
tunable power-law exponents (as determined by the sets of parameters
characterizing each class) emerge naturally in our model. Moreover, the
coexistence of the scale-free structures of the different classes of nodes can
be captured by succinct phase diagrams, which show a rich set of structures,
including stable regions where different classes coexist in heavy-tailed and
light-tailed states, and sharp phase transitions. Finally, we show how the
dynamics formulated in this paper will serve as an essential part of {\em
ad-hoc networking protocols}, which can lead to the formation of robust and
efficiently searchable networks (including, the well-known Peer-To-Peer (P2P)
networks) even under very dynamic conditions.


  We study the implications at the LHC for the minimal (least) version of the
supersymmetric standard model. In this model supersymmetry is broken by gravity
and extra gauge interactions effects, providing a spectrum similar in several
aspects to that in natural supersymmetric scenarios. Having the first two
generations of sparticles partially decoupled means that any significant signal
can only involve gauginos and the third family of sfermions. In practice, the
signals are dominated by gluino production with subsequent decays into the stop
sector. As we show, for gluino masses below 2300 GeV, a discovery at the LHC is
possible at \sqrt{s}=14 TeV, but will require large integrated luminosities.


  We give an exact solution for the static force between two black holes at the
turning points in their binary motion. The results are derived by Gibbs'
principle and the Bekenstein-Hawking entropy applied to the apparent horizon
surfaces in time-symmetric initial data. New power laws are derived for the
entropy jump in mergers, while Newton's law is shown to derive from a new
adiabatic variational principle for the Hilbert action in the presence of
apparent horizon surfaces. In this approach, entropy is strictly monotonic such
that gravity is attractive for all separations including mergers, and the
Bekenstein entropy bound is satisfied also at arbitrarily large separations,
where gravity reduces to Newton's law. The latter is generalized to point
particles in the Newtonian limit by application of Gibbs' principle to
world-lines crossing light cones.


  Deep neural networks have become prevalent in human analysis, boosting the
performance of applications, such as biometric recognition, action recognition,
as well as person re-identification. However, the performance of such networks
scales with the available training data. In human analysis, the demand for
large-scale datasets poses a severe challenge, as data collection is tedious,
time-expensive, costly and must comply with data protection laws. Current
research investigates the generation of \textit{synthetic data} as an efficient
and privacy-ensuring alternative to collecting real data in the field. This
survey introduces the basic definitions and methodologies, essential when
generating and employing synthetic data for human analysis. We conduct a survey
that summarises current state-of-the-art methods and the main benefits of using
synthetic data. We also provide an overview of publicly available synthetic
datasets and generation models. Finally, we discuss limitations, as well as
open research problems in this field. This survey is intended for researchers
and practitioners in the field of human analysis.


  The still-unsolved problem of determining the set of eigenvalues realized by
$n$-by-$n$ doubly stochastic matrices, those matrices with row sums and column
sums equal to $1$, has attracted much attention in the last century. This
problem is somewhat algebraic in nature, due to a result of Birkhoff
demonstrating that the set of doubly stochastic matrices is the convex hull of
the permutation matrices. Here we are interested in a general matrix group $G
\subseteq GL_n(\mathbb{C})$ and the hull spectrum $\text{HS}(G)$ of eigenvalues
realized by convex combinations of elements of $G$. We show that hull spectra
of matrix groups share many nice properties. Moreover, we give bounds on the
hull spectra of matrix groups, determine $\text{HS}(G)$ exactly for important
classes of matrix groups, and study the hull spectra of representations of
abstract groups.


  Previous top-down studies of jet stopping in strongly-coupled QCD-like
plasmas with gravity duals have been in the infinite 't Hooft coupling limit
lambda -> infinity. They have found that, though a wide range of jet stopping
distances are possible depending on initial conditions, the maximum jet
stopping distance l_max scales with energy as E^(1/3) at large energy. But it
has always been unclear whether the large-coupling and high-energy limits
commute. In this paper, we use the string alpha' expansion in AdS-CFT to study
the corrections to the lambda=infinity result in powers of 1/lambda. For the
particular type of "jets" that we study, we find that (i) the naive expansion
in 1/lambda breaks down for certain initial conditions but (ii) the relative
corrections to the maximum stopping distance are small when 1/lambda is small.
More specifically, we find that the expansion in 1/lambda is well behaved for
jets whose stopping distance l_stop is in the range lambda^(-1/6) l_max <<
l_stop <~ l_max, but the expansion breaks down (and the fate of lambda=infinity
results is uncertain) for jets created in such a way that l_stop <<
lambda^(-1/6) l_max. The analysis requires assessing the effects of all
higher-derivative corrections to the supergravity action for the gravity dual.


  In this paper, we prove a quantitative spectral inequality for the second
Robin eigenvalue in non-compact rank-1 symmetric spaces. In particular, this
shows that for bounded domains in non-compact rank-1 symmetric spaces, the
geodesic ball maximises the second Robin eigenvalue among domains of the same
volume, with negative Robin parameter in the regime connecting the first
nontrivial Neumann and Steklov eigenvalues. This result generalises the work of
Freitas and Laugesen in the Euclidean setting [FL21] as well as our previous
work in the hyperbolic space [LWW20].


  We propose an experiment for detecting Axion-Like Particles (ALPs) based on
the axion-photon interaction in the presence of a non-uniform magnetic field.
The impact of virtual ALPs on the polarization of the photons inside a cavity
is studied and a detection scheme is proposed. We find that the cavity normal
modes are dispersed differently owing to their coupling to the ALPs in the
presence of a background magnetic field. This birefringence, in turn, can be
observed as a phase difference between the cavity polarization modes. The
signal is considerably enhanced for a squeezed light source. We argue that the
amplified signal allows for exclusion of a range of axion mass
$6\times10^{-4}\text{eV}\lesssim m_{a}\lesssim 6\times10^{-3}\text{eV}$ even at
very small axion-photon coupling constant with the potential to reach
sensitivity to the QCD axion. Our scheme allows for the exclusion of a range of
axion masses that has not yet been covered by other experimental techniques.


  Narrow-band imaging surveys aimed at detecting the faint emission from the
5007 [O III] line of intracluster planetary nebulae in Virgo also probe high
redshift z=3.1 Ly alpha emitters. Here we report on the spectroscopic
identification of 9 Ly alpha emitters at z=3.13, obtained with the FORS
spectrograph at Unit 1 of the ESO Very Large Telescope (VLT UT1). The spectra
of these high redshift objects show a narrow, isolated Ly alpha emission with
very faint (frequently undetected) continuum, indicating a large equivalent
width. No other features are visible in our spectra. Our Ly alpha emitters are
quite similar to those found by Hu (1998), Cowie & Hu (1998) and Hu et al.
(1998). Using simple population synthesis models, on the assumption that these
sources are regions of star formation, we conclude that the nebulae are nearly
optically thick and must have a very low dust content, in order to explain the
high observed Ly alpha equivalent widths. For the cosmological and star
formation parameters we adopted, the total stellar mass produced would seem to
correspond to the formation of rather small galaxies, some of which are perhaps
destined to merge. The implied star formation density in our sampled comoving
volume is probably somewhat smaller than, but of the same order of magnitude as
the star formation density at z=3 derived by other authors from Lyman-break
galaxy surveys. This result agrees with the expectation that the Ly alpha
emitters are a low-metallicity (or low-dust) tail in a distribution of star
forming regions at high redshifts. Finally, the Ly alpha emitters may
contribute as many H-ionizing photons as QSOs at z=3.


  The phase transition for a finite volume system that incorporates the
Polyakov loops and maintains the colorless state is explored using the
Polyakov-loop extended Nambu-Jona-Lasinio (PNJL) model. The order parameter for
Polyakov loops is demonstrated to signal the appearance of a transition for
$SU(3)_{c}$ analogous to Gross-Witten (GW-) phase transition instead of the
deconfinement phase transition to quark-gluon plasma. The asymptotic
restoration of Polyakov loops is conjectured to be a threshold production for
meta-stable Hagedorn (or semi-QGP) states and this does not imply a direct
deconfinement phase transition. In this context, the GW-like point is the point
where the colorless states switches from the low-lying hadronic states to the
meta-stable high-lying Hagedorn states. The chiral phase transition takes place
within an extended GW-like point depending on the fireball's size. The
deconfinement phase transition is determined by Hagedorn's temperature above
GW-like temperature.


  Given the success of IoT platforms, more developers and companies want to
include the technology in their portfolio. However, in the case of single board
microcontrollers, the support for networking operations is not ideal, and
different IoT platforms allow access to the networking submodule via different
libraries and system calls, leading to a steeper learning curve. Code
generators for API clients can enhance productivity, but they tend to generate
universal purpose code, and on the other hand the networking primitives of IoT
devices are platform specific, especially when security mechanisms such as
Transport Layer Security are part of the picture. This paper presents
\texttt{cpp-tiny-client}, an API client generator developed as a plugin for the
OpenAPI Generator project, which can tailor the generated code based on the IoT
platform specified by the user. Our work allows to generate correct code for
API clients for IoT devices, and thus can empower a developer with more
productivity and a faster time-to-market for its own applications. By combining
together mainstream technologies only, \texttt{cpp-tiny-client} offers a gentle
learning curve. Moreover, experiments show that the generated code has a
reasonable footprint, at least with respect to the IoT devices that were used
in the validation of the work. The code related to this work is available
through the OpenAPI Generator project~\cite{OpenAPIGenerator}. This technical
report is an extension of~\cite{acmsac22}, and it integrates the information
presented at the ACM SAC 2022 conference.


  A Cremona transformation of five-dimensional projective space is constructed.
The degree of the transformation is 7. The inequalities of Fano are not
fulfilled for this transformation.


  In this paper we study the index of reducibility of powers of a standard
parameter ideal. An explicit formula is proved for the extremely case. We apply
the main result to compute Hilbert polynomials of socle ideals of standard
parameter ideals.


  We analytically derive and experimentally demonstrate a method for the
simultaneous measurement of deflection for large arrays of cantilevers. The
Fresnel diffraction patterns of a cantilever independently reveals tilt,
curvature, cubic and higher order bending of the cantilever. It provides a
calibrated absolute measurement of the polynomial coefficients describing the
cantilever shape, without careful alignment and could be applied to several
cantilevers simultaneously with no added complexity. We show that the method is
easily implemented, works in both liquid mediums and in air, for a broad range
of displacements and is especially suited to the requirements for multi-marker
biosensors.


  This article summarizes the talk given at the LCWS 2021 conference on the
status and news of the WHIZARD Monte Carlo event generator. We presented its
features relevant for the physics program of future lepton and especially
linear colliders as well as recent developments towards including NLO
perturbative corrections and a UFO interface to study models beyond the
Standard Model. It takes as reference the version 3.0.0$\beta$ released in
August 2020 and additionally discusses the developments that will be included
in the next major version 3.0.0 to be released in April 2021.


  The common approach to radial distortion is by the means of polynomial
approximation, which introduces distortion-specific parameters into the camera
model and requires estimation of these distortion parameters. The task of
estimating radial distortion is to find a radial distortion model that allows
easy undistortion as well as satisfactory accuracy. This paper presents a new
piecewise radial distortion model with easy analytical undistortion formula.
The motivation for seeking a piecewise radial distortion model is that, when a
camera is resulted in a low quality during manufacturing, the nonlinear radial
distortion can be complex. Using low order polynomials to approximate the
radial distortion might not be precise enough. On the other hand, higher order
polynomials suffer from the inverse problem. With the new piecewise radial
distortion function, more flexibility is obtained and the radial undistortion
can be performed analytically. Experimental results are presented to show that
with this new piecewise radial distortion model, better performance is achieved
than that using the single function. Furthermore, a comparable performance with
the conventional polynomial model using 2 coefficients can also be
accomplished.


  High resolution, high S/N spectra have been obtained for a sample of 90 F and
G main-sequence disk stars covering the metallicity range -1.0 < [Fe/H] < +0.1,
and have been analysed in a parallel way to the work of Edvardsson et al.
(1993). Effective temperatures are based on the Alonso et al. (1996)
calibration of color indices and surface gravities are calculated from
Hipparcos parallaxes, which also allow more accurate ages to be calculated. In
addition, more reliable kinematical parameters are derived from Hipparcos
distances and proper motions. Finally, a larger spectral coverage, 5600 - 8800
A, makes it possible to improve the abundance accuracy by studying more lines
and to discuss several elements not included in the work of Edvardsson et al.
The present paper provides the data and discusses some general results of the
abundance survey. A group of stars in the metallicity range of -1.0 < [Fe/H] <
-0.6 having a small mean Galactocentric distance in the stellar orbits, Rm < 7
kpc, are shown to be older than the other disk stars and probably belong to the
thick disk. Excluding these stars, a slight decreasing trend of [Fe/H] with
increasing Rm and age is found, but a large scatter in [Fe/H] (up to 0.5 dex)
is present at a given age and Rm. The derived trends of O, Mg, Si, Ca, Ti, Ni
and Ba as a function of [Fe/H] agree rather well with those of Edvardsson et
al., but the overabundance of Na and Al for metal-poor stars found in their
work is not confirmed. Furthermore, the Galactic evolution of elements not
included in Edvardsson et al., K, V and Cr, is studied.


  Local Differential Privacy (LDP) protocols enable an untrusted server to
perform privacy-preserving, federated data analytics. Various LDP protocols
have been developed for different types of data such as categorical data,
numerical data, and key-value data. Due to their distributed settings, LDP
protocols are fundamentally vulnerable to poisoning attacks, in which fake
users manipulate the server's analytics results via sending carefully crafted
data to the server. However, existing poisoning attacks focused on LDP
protocols for simple data types such as categorical and numerical data, leaving
the security of LDP protocols for more advanced data types such as key-value
data unexplored.
  In this work, we aim to bridge the gap by introducing novel poisoning attacks
to LDP protocols for key-value data. In such a LDP protocol, a server aims to
simultaneously estimate the frequency and mean value of each key among some
users, each of whom possesses a set of key-value pairs. Our poisoning attacks
aim to simultaneously maximize the frequencies and mean values of some
attacker-chosen target keys via sending carefully crafted data from some fake
users to the sever. Specifically, since our attacks have two objectives, we
formulate them as a two-objective optimization problem. Moreover, we propose a
method to approximately solve the two-objective optimization problem, from
which we obtain the optimal crafted data the fake users should send to the
server. We demonstrate the effectiveness of our attacks to three LDP protocols
for key-value data both theoretically and empirically. We also explore two
defenses against our attacks, which are effective in some scenarios but have
limited effectiveness in other scenarios. Our results highlight the needs for
new defenses against our poisoning attacks.


  The possible mechanisms of communications between distant bio-systems by
means of optical and UV photons are studied. It is argued that their main
production mechanism is owed to the biochemical reactions, occurring during the
cell division.. In the proposed model the bio-systems perform such
communications, radiating the photons in form of short periodic bursts, which
were observed experimentally for fish and frog eggs1. For experimentally
measured photon rates the communication algorithm is supposedly similar to the
exchange of binary encoded data in computer net via optical channels


  The Stochastic Block Model (SBM) is a popular probabilistic model for random
graphs. It is commonly used for clustering network data by aggregating nodes
that share similar connectivity patterns into blocks. When fitting an SBM to a
network which is partially observed, it is important to take into account the
underlying process that generates the missing values, otherwise the inference
may be biased. This paper introduces missSBM, an R-package fitting the SBM when
the network is partially observed, i.e., the adjacency matrix contains not only
1's or 0's encoding presence or absence of edges but also NA's encoding missing
information between pairs of nodes. This package implements a set of algorithms
for fitting the binary SBM, possibly in the presence of external covariates, by
performing variational inference adapted to several observation processes. Our
implementation automatically explores different block numbers to select the
most relevant model according to the Integrated Classification Likelihood (ICL)
criterion. The ICL criterion can also help determine which observation process
better corresponds to a given dataset. Finally, missSBM can be used to perform
imputation of missing entries in the adjacency matrix. We illustrate the
package on a network data set consisting of interactions between political
blogs sampled during the French presidential election in 2007.


  Context. Transient astronomical events that exhibit no discernible
association with a host galaxy are commonly referred to as hostless. These rare
phenomena are associated with extremely energetic events, and they can offer
unique insights into the properties and evolution of stars and galaxies.
However, the sheer number of transients captured by contemporary high-cadence
astronomical surveys renders the manual identification of all potential
hostless transients impractical. Therefore, creating a systematic
identification tool is crucial for studying these elusive events. Aims. We
present the ExtragaLactic alErt Pipeline for Hostless AstroNomical Transients
(ELEPHANT), a framework for filtering hostless transients in astronomical data
streams. Methods. We used Fink to access all the ZTF alerts produced between
January/2022 and December/2023, selecting only those associated with
extragalactic transients. We then processed the associated stamps using a
sequence of image analysis techniques to retrieve hostless candidates. Results.
We find that less than 2% of all analyzed transients are potentially hostless.
Among them, approximately 10% have a spectroscopic class reported on TNS, with
Type Ia supernova being the most common class, followed by SLSN. Among the
hostless candidates retrieved by our pipeline, there was SN 2018ibb, which has
been proposed to be a PISN candidate; and SN 2022ann, one of only five known
SNe Icn. When no class is reported on TNS, the dominant classes are QSO and SN
candidates, the former obtained from SIMBAD and the latter inferred using the
Fink ML classifier. Conclusions. ELEPHANT represents an effective strategy to
filter extragalactic events within large and complex astronomical alert
streams. There are many applications for which this pipeline will be useful,
ranging from transient selection for follow-up to studies of transient
environments.


  This paper has been withdrawn by the author due to an error in Lemma 3,
making the (bijective) proof of Theorem 4 and Corollary 5 invalid (symmetry of
k-nonnesting and k-noncrossing set partitions).


  Recent developments in the aerospace industry have led to a dramatic
reduction in the manufacturing and launch costs of low Earth orbit satellites.
The new trend enables the paradigm shift of satellite-terrestrial integrated
networks with global coverage. In particular, the integration of 5G
communication systems and satellites has the potential to restructure
next-generation mobile networks. By leveraging the network function
virtualization and network slicing, the orbital 5G core networks will
facilitate the coordination and management of network functions in
satellite-terrestrial integrated networks. We are the first to deploy a
lightweight 5G core network on a real-world satellite to investigate its
feasibility. We conducted experiments to validate the onboard 5G core network
functions. The validated procedures include registration and session setup
procedures. The results show that the 5G core network can function normally and
generate correct signaling.


  In this paper, we use the empirical likelihood method to construct the
confidence regions for the difference between the parameters of a two-phases
nonlinear model with random design. We show that the empirical likelihood ratio
has an asymptotic chi-squared distribution. The result is a nonparametric
version of Wilk's theorem. Empirical likelihood method is also used to
construct the confidence regions for the difference between the parameters of a
two-phases nonlinear model with response variables missing at randoms (MAR). In
order to construct the confidence regions of the parameter in question, we
propose three empirical likelihood statistics : Empirical likelihood based on
complete-case data, weighted empiri- cal likelihood and empirical likelihood
with imputed values. We prove that all three empirical likelihood ratios have
asymptotically chi-squared distributions. The effectiveness of the proposed
approaches in aspects of coverage probability and interval length is
demonstrated by a Monte-Carlo simulations.


  Designed at the Crimean Astrophysical Observatory the SL-method of stellar
magnetic field measurement is based on the calculation of the distance between
the centers of gravity of the left and right circularly polarized components of
the spectral line. The SL-method is free from the limitations of the
LSD-method, namely its requirement for weak magnetic fields, weak spectral
lines, the same shape of polarized profiles. Also, this method is free from
model limitations of the PCA, NDD and ZCD methods also. The SL-method involves
the calculation of the magnetic field using individual spectral lines and the
subsequent formation of uniform arrays of the magnetic field measurements. The
formation of the arrays is based on different criteria, e.g. using spectral
lines of a chemical element, assumption of the same physical conditions in the
areas of the spectral line formation, a statistically significant difference
between the values of the magnetic field calculated from different arrays of
spectral lines etc. If there are several arrays of spectral lines, it is
necessary to obtain normalized polarized profiles using predetermined
parameters for each array: the magnitude of the field, wavelength for
normalization, Lande factor and the central residual intensity of the line. In
this paper, we present a method of normalization of polarized profiles based on
the dependence of the residual intensity of the observed polarized profiles on
their central residual intensity (COntour Algorithm of the Line Approximation:
COALA). The SL and COALA methods strong depend on the signal-to-noise ratio and
a number of used unblended spectral lines. In this work the application of the
proposed method is demonstrated for synthetic polarized spectra and for
circularly polarized spectra of the yellow subgiant $\beta$ Aql (Sp G8 IV)
obtained on 2014 October 06.


  We present a technique to explore the radio sky into the nanoJansky regime by
employing image stacking using the FIRST survey. We first discuss the
non-intuitive relationship between the mean and median values of a distribution
that is dominated by noise, followed by an analysis of the systematic effects
present in FIRST's 20cm VLA snapshot images. Image stacking allows us to
recover the properties of source populations with fluxes a factor of 30 or more
below the rms noise level. Mean estimates of radio flux density, luminosity,
etc., are derivable for any source class having arcsecond positional accuracy.
  We use this technique to compute the mean radio properties for 41,295 quasars
from the SDSS DR3 catalog. There is a tight correlation between optical and
radio luminosity, with the radio luminosity increasing as the 0.85 power of
optical luminosity. This implies declining radio-loudness with optical
luminosity: the most luminous objects (M=-28.5) have average radio-to-optical
ratios 3 times lower than the least luminous objects (M=-20). There is also a
striking correlation between optical color and radio loudness: quasars that are
either redder or bluer than the norm are brighter radio sources, with objects
0.8 magnitudes redder than the SDSS composite spectrum having radio-loudness
ratios that are higher by a factor of 10. We explore the longstanding question
of whether a radio-loud/radio-quiet dichotomy exists in quasars, finding that
optical selection effects probably dominate the distribution function of radio
loudness, which has at most a modest (~20%) inflection between the radio-loud
and radio-quiet ends of the distribution. We also find, surprisingly, that
broad absorption line quasars have higher mean radio flux densities, with the
greatest disparity arising in the rare low-ionization BAL subclass.


  Among the first observations released to the public from the James Webb Space
Telescope (JWST) was a section of the star-forming region NGC 3324 known
colloquially as the "Cosmic Cliffs." We build a photometric catalog of the
region and test the ability of using the Probabilistic Random Forest machine
learning method to identify its Young Stellar Objects (YSOs). We find 450
candidate YSOs (cYSOs) out of 19~497 total objects within the field, 413 of
which are cYSOs not found in previous works. These classifications are verified
with several different metrics, including recall and precision. Using the
obtained probabilities of objects being YSOs, we employ a Monte Carlo approach
to determine the surface density of cYSOs in the Cosmic Cliffs, which we find
to be largely coincident with column densities derived from Herschel data, up
to a column density of 1.37 $\times$ 10$^{22}$ cm$^{-2}$. The newly determined
number and spatial distribution of YSOs in the Cosmic Cliffs demonstrate that
JWST is far more capable of detecting YSOs in dusty regions than Spitzer.
Comparisons of the observed colors and brightness of faint cYSOs with those of
pre-main-sequence models suggest JWST has detected a significant population of
sub-stellar YSOs in the Cosmic Cliffs. The size of this population further
suggests previous estimates of star formation efficiencies in molecular clouds
have been systematically low.


  A high-quality single crystal of Ce3Pt23Si11 has been grown using the
Czochralski method. The crystal structure is presented and the chemical
composition has been checked using an electron microprobe analyzer.
Measurements of the electrical resistivity and magnetic susceptibility
performed at low temperature show a ferromagnetic transition at Tc = 0.44 K.


  Morphological tasks use large multi-lingual datasets that organize words into
inflection tables, which then serve as training and evaluation data for various
tasks. However, a closer inspection of these data reveals profound
cross-linguistic inconsistencies, that arise from the lack of a clear
linguistic and operational definition of what is a word, and that severely
impair the universality of the derived tasks. To overcome this deficiency, we
propose to view morphology as a clause-level phenomenon, rather than
word-level. It is anchored in a fixed yet inclusive set of features, that
encapsulates all functions realized in a saturated clause. We deliver
MightyMorph, a novel dataset for clause-level morphology covering 4
typologically-different languages: English, German, Turkish and Hebrew. We use
this dataset to derive 3 clause-level morphological tasks: inflection,
reinflection and analysis. Our experiments show that the clause-level tasks are
substantially harder than the respective word-level tasks, while having
comparable complexity across languages. Furthermore, redefining morphology to
the clause-level provides a neat interface with contextualized language models
(LMs) and allows assessing the morphological knowledge encoded in these models
and their usability for morphological tasks. Taken together, this work opens up
new horizons in the study of computational morphology, leaving ample space for
studying neural morphology cross-linguistically.


  Kekul\'e phases are Peierls-like lattice distortions in graphene that are
predicted to host novel electronic states beyond graphene (1-8). Although the
Kekul\'e phases are realized in graphene through introducing electron-electron
interactions at high magnetic fields (9-11) or adatom superlattices (12-15), it
is still an extremely challenge to obtain large-area graphene Kekul\'e phases
in experiment. Here we demonstrate that sample-wide Kekul\'e distortions in
graphene can be stabilized by using transition metal dichalcogenides (TMDs) as
substrates and the induced Kekul\'e orders are quite robust in the whole
graphene/TMDs heterostructures with different twist angles. The commensurate
structures of the heterostructures provide periodic scattering centers that
break the translational symmetry of graphene and couple electrons of the two
valleys in graphene, which tips the graphene toward global Kekul\'e density
wave phases. Unexpectedly, three distinct Kekul\'e bond textures stabilized at
various energies are directly imaged in every graphene/TMDs heterostructure.
Our results reveal an unexpected sensitivity of electronic properties in
graphene to the supporting substrates and provide an attractive route toward
designing novel phases in graphene/TMDs heterostructures.


  In this work it is presented some considerations about entrepreneurship. Most
of these questions are linked with Portuguese context. Portugal has some
particularities, namely because the asymmetries between the littoral and the
interior. This situation carried out some problems that complicate and prevent
the appearance of new innovated business. In a situation of crisis like that we
have today this context can become a really problem to solve some questions.


  We study superconductor-ferromagnet bi-layers, not only for s-wave but also
for d-wave superconductors. We observe oscillations of the critical temperature
when varying the thickness of the ferromagnetic layer for both s-wave and
d-wave superconductors. However, for a rotated d-wave order parameter the
critical temperature differs considerably from that for the unrotated case. In
addition we calculate the density of states for different thicknesses of the
ferromagnetic layer; the results reflect the oscillatory behaviour of the
superconducting correlations.


  We first prove rigidity results for pseudo-Anosov flows in prototypes of
toroidal 3-manifolds: we show that a pseudo-Anosov flow in a Seifert fibered
manifold is up to finite covers topologically equivalent to a geodesic flow and
we show that a pseudo-Anosov flow in a solv manifold is topologically
equivalent to a suspension Anosov flow. Then we study the interaction of a
general pseudo-Anosov flow with possible Seifert fibered pieces in the torus
decomposition: if the fiber is associated with a periodic orbit of the flow, we
show that there is a standard and very simple form for the flow in the piece
using Birkhoff annuli. This form is strongly connected with the topology of the
Seifert piece. We also construct a large new class of examples in many graph
manifolds, which is extremely general and flexible. We construct other new
classes of examples, some of which are generalized pseudo-Anosov flows which
have one prong singularities and which show that the above results in Seifert
fibered and solvable manifolds do not apply to one prong pseudo-Anosov flows.
Finally we also analyse immersed and embedded incompressible tori in optimal
position with respect to a pseudo-Anosov flow.


  In this article we give an explicit algorithm which will determine, in a
discrete and computable way, whether a finite piecewise Euclidean complex is
non-positively curved. In particular, given such a complex we show how to
define a boolean combination of polynomial equations and inequalities in real
variables, i.e. a real semi-algebraic set, which is empty if and only if the
complex is non-positively curved. Once this equivalence has been shown, the
main result follows from a standard theorem in real algebraic geometry.


  We develop a new method to determine the linear mass power spectrum using the
mass function of galaxy clusters. We obtain the rms mass fluctuation sigma (M)
using the expression for the mass function in the Press & Schechter (1974),
Sheth, Mo & Tormen (2001) and Jenkins et al. (2001) formalisms. We apply
different techniques to recover the adimensional power spectrum Delta ^{2}(k)
from sigma (M) namely the k_{eff} approximation, the singular value
decomposition and the linear regularization method. The application of these
techniques to the tCDM and LCDM GIF simulations shows a high efficiency in
recovering the theoretical power spectrum over a wide range of scales. We
compare our results with those derived from the power spectrum of the spatial
distribution of the same sample of clusters in the simulations obtained by
application of the classical Feldman, Kaiser & Peacock (1994), FKP, method. We
find that the mass function based method presented here can provide a very
accurate estimate of the linear power spectrum, particularly for low values of
k. This estimate is comparable, or even better behaved, than the FKP solution.
The principal advantage of our method is that it allows the determination of
the linear mass power spectrum using the joint information of objects of a wide
range of masses without dealing with specific assumptions on the bias relative
to the underlying mass distribution.


  Many neurological diseases are characterized by gradual deterioration of
brain structure and function. Large longitudinal MRI datasets have revealed
such deterioration, in part, by applying machine and deep learning to predict
diagnosis. A popular approach is to apply Convolutional Neural Networks (CNN)
to extract informative features from each visit of the longitudinal MRI and
then use those features to classify each visit via Recurrent Neural Networks
(RNNs). Such modeling neglects the progressive nature of the disease, which may
result in clinically implausible classifications across visits. To avoid this
issue, we propose to combine features across visits by coupling feature
extraction with a novel longitudinal pooling layer and enforce consistency of
the classification across visits in line with disease progression. We evaluate
the proposed method on the longitudinal structural MRIs from three neuroimaging
datasets: Alzheimer's Disease Neuroimaging Initiative (ADNI, N=404), a dataset
composed of 274 normal controls and 329 patients with Alcohol Use Disorder
(AUD), and 255 youths from the National Consortium on Alcohol and
NeuroDevelopment in Adolescence (NCANDA). In all three experiments our method
is superior to other widely used approaches for longitudinal classification
thus making a unique contribution towards more accurate tracking of the impact
of conditions on the brain. The code is available at
https://github.com/ouyangjiahong/longitudinal-pooling.


  Universality of the weak interactions is reviewed, with special emphasis on
the origin of the Cabibbo theory of strange particles beta-decays and its role
in the discovery of the unified Electroweak Theory. Achievements and present
challenges of the Standard Theory of particles interactions are briefly
illustrated. As an homage to Nicola Cabibbo, his leading role in the Roma
school of theoretical physics and in the italian science in general is
reviewed.


  Following the emergence of COVID-19 in Spring 2020, undergraduate in-person
physics laboratory courses at a R1 public university were adapted for remote
learning to accommodate the subsequent campus closure. Video lectures and
web-based virtual experiments were utilized to provide students enrolled in
these laboratories with required learning materials on a weekly basis. During
the fall semester of the 2021-2022 academic year, optional Kahoot! quizzes were
offered in addition, serving to incentivize participation and to provide
self-efficacy opportunities to students. This study sought to explore the
intersection of self-efficacy growth, self-regulatory behaviors, and
intelligence mindsets (i.e., having fixed or growth mindsets) for students and
to examine the impact of these remote learning methods. Using a modified
version of the Colorado Learning Attitudes About Science Survey (CLASS),
students' physics self-efficacy was measured at the beginning and end of the
semester. The analysis revealed that participation in Kahoot! alone did not
correspond to greater self-efficacy scores or greater self-efficacy change.
However, a strong correlation was observed between intelligence mindset and
self-efficacy for both pre-and post-surveys. Pre-survey intelligence mindset
scores were not related to average Kahoot! performance, while post-survey
intelligence mindsets were. Finally, positive self-efficacy change $\langle c
\rangle$ was measured for the class, but was not statistically significant.


  In this work, we investigate upper limits on the global escape fraction of
ionizing photons ($f_{\rm esc/global}^{\rm abs}$) from a sample of galaxies
probed for Lyman-continuum (LyC) emission characterized as non-LyC and LyC
leakers. We present a sample of 9 clean non-contaminated (by low redshift
interlopers, CCD problems and internal reflections of the instrument) galaxies
which do not show significant ($>$ $3\sigma$) LyC flux between 880\AA\
$<\lambda_{rest}<$ 910\AA. The 9 galaxy stacked spectrum reveals no significant
LyC flux with an upper limit of $f_{\rm esc}^{\rm abs} \leq 0.06$. In the next
step of our analysis, we join all estimates of $f_{\rm esc}^{\rm abs}$ upper
limits derived from different samples of $2\lesssim z < 6$ galaxies from the
literature reported in last $\sim$20 years and include the sample presented in
this work. We find the $f_{\rm esc}^{\rm abs}$ upper limit $\leq$ 0.084 for the
galaxies recognized as non-LyC leakers. After including all known detections
from literature $f_{\rm esc/global}^{\rm abs}$ upper limit $\leq$ 0.088 for all
galaxies examined for LyC flux. Furthermore, $f_{\rm esc}^{\rm abs}$ upper
limits for different groups of galaxies indicate that the strongest LyC
emitters could be galaxies classified as Lyman alpha emitters. We also discuss
the possible existence of a correlation among the observed flux density ratio
$(F_{\nu}^{LyC}/F_{\nu}^{UV})_{\rm obs}$ and Lyman alpha equivalent width
EW(Ly$\alpha)$, where we confirm the existence of moderately significant
correlation among galaxies classified as non-LyC leakers.


  We inspect the coupled dependence of physical parameters of the SDSS galaxies
on the small-scale (distance to and morphology of the nearest neighbor galaxy)
and the large-scale (background density smoothed over 20 nearby galaxies)
environments. The impacts of interaction on galaxy properties are detected at
least out to the neighbor separation corresponding to the virial radius of
galaxies, which is typically between 200 and 400 h^{-1}kpc for the galaxies in
our sample. To detect these long-range interaction effects it is crucial to
divide galaxy interactions into four cases dividing the morphology of target
and neighbor galaxies into early and late types. We show that there are two
characteristic neighbor-separation scales where the galaxy interactions cause
abrupt changes in the properties of galaxies. The first scale is the virial
radius of the nearest neighbor galaxy r_{vir,nei}. Many physical parameters
start to deviate from those of extremely isolated galaxies at the projected
neighbor separation r_p of about r_{vir,nei}. The second scale is at r_p
\approx 0.05 r_{vir,nei} = 10--20 h^{-1} kpc, and is the scale at which the
galaxies in pairs start to merge. We find that late-type neighbors enhance the
star formation activity of galaxies while early-type neighbors reduce it, and
that these effects occur within r_{vir,nei}. The hot halo gas and cold disk gas
must be participating in the interactions at separations less than the virial
radius of the galaxy plus dark halo system. Our results also show that the role
of the large-scale density in determining galaxy properties is minimal once
luminosity and morphology are fixed. We propose that the weak residual
dependence of galaxy properties on the large-scale density is due to the
dependence of the halo gas property on the large-scale density.


  Helioseismology has revealed many details of solar differential rotation and
also its time variation, known as torsional oscillations. So far there is no
generally accepted theoretical explanation for torsional oscillations, even
though a close relation to the solar activity cycle is evident. On the
theoretical side non-kinematic dynamo models (including the Lorentz force
feedback on differential rotation) have been used to explain torsional
oscillations. In this paper we use a slightly different approach by forcing
torsional oscillations in a mean field differential rotation model. Our aim is
not a fully self-consistent model but rather to point out a few general
properties of torsional oscillations and their possible origin that are
independent from a particular dynamo model. We find that the poleward
propagating high latitude branch of the torsional oscillations can be explained
as a response of the coupled differential rotation / meridional flow system to
periodic forcing in mid-latitudes, of either mechanical (Lorentz force) or
thermal nature. The speed of the poleward propagation sets constraints on the
value of the turbulent viscosity in the solar convection zone to be less than
3x10^8 m^2/s. We also show that the equatorward propagating low latitude branch
is very unlikely a consequence of mechanical forcing (Lorentz force) alone, but
rather of thermal origin due to the Taylor-Proudman theorem.


  The Tsallis relative entropy $S_q (\hat{\rho},\hat{\sigma})$ measures the
distance between two arbitrary density matrices $\hat{\rho}$ and
$\hat{\sigma}$. In this work the approximation to this quantity when
$q=1+\delta$ ($\delta\ll 1$) is obtained. It is shown that the resulting series
is equal to the von Neumann relative entropy when $\delta=0$. Analyzing the von
Neumann relative entropy for arbitrary $\hat{\rho}$ and a thermal equilibrium
state $\hat{\sigma}=e^{- \beta \hat{H}}/{\rm Tr}(e^{- \beta \hat{H}})$ is
possible to define a new inequality relating the energy, the entropy, and the
partition function of the system. From this inequality, a parameter that
measures the distance between the two states is defined. This distance is
calculated for a general qubit system and for an arbitrary unimodal Gaussian
state. In the qubit case, the dependence on the purity of the system is studied
for $T \geq 0$ and also for $T<0$. In the Gaussian case, the general partition
function given a unimodal quadratic Hamiltonian is calculated and the
comparison of the thermal light state as a thermal equilibrium state of the
parametric amplifier is presented.


  A new physics scenario shows that four-fermion operators of
Nambu-Jona-Lasinio (NJL) type have a strong-coupling UV fixed point, where
composite fermions $F$ (bosons $\Pi$) form as bound states of three (two) SM
elementary fermions and they couple to their constituents via effective contact
interactions at the composite scale $\Lambda \approx {\cal O} $(TeV). We
present a phenomenological study to investigate such composite particles at the
LHC by computing the production cross sections and decay widths of composite
fermions in the context of the relevant experiments at the LHC with $pp$
collisions at $\sqrt{s}={\rm 13}$ TeV and $\sqrt{s}={\rm 14}$ TeV.
Systematically examining all the different composite particles $F$ and the
signatures with which they can manifest, we found a vast spectrum of composite
particles $F$ that has not yet been explored at the LHC. Recasting the recent
CMS results of the resonant channel $pp\rightarrow e^+F \rightarrow e^+e^-
q\bar{q}'$, we find that the composite fermion mass $m_F$ below 4.25 TeV is
excluded for $\Lambda$/$m_F$ = 1. We further highlight the region of parameter
space where this specific composite particle $F$ can appear using 3 ab$^{-1}$,
expected by the High-Luminosity LHC, computing 3 and 5 $\sigma$ contour plots
of its statistical significance.


  In this work, we unify several expected generalization error bounds based on
random subsets using the framework developed by Hellstr\"om and Durisi [1].
First, we recover the bounds based on the individual sample mutual information
from Bu et al. [2] and on a random subset of the dataset from Negrea et al.
[3]. Then, we introduce their new, analogous bounds in the randomized subsample
setting from Steinke and Zakynthinou [4], and we identify some limitations of
the framework. Finally, we extend the bounds from Haghifam et al. [5] for
Langevin dynamics to stochastic gradient Langevin dynamics and we refine them
for loss functions with potentially large gradient norms.


  The nonequilibrium fluctuation relation is a cornerstone of quantum
thermodynamics. It is widely believed that the system-bath heat exchange obeys
the famous Jarzynski-W\'{o}jcik fluctuation theorem. However, this theorem is
established in the Born-Markovian approximation under the weak-coupling
condition. Via studying the energy exchange between a harmonic oscillator and
its coupled bath in the non-Markovian dynamics, we establish a generalized
quantum fluctuation theorem for energy exchange being valid for arbitrary
coupling strength. The Jarzynski-W\'{o}jcik fluctuation theorem is recovered in
the weak-coupling limit. We also find the average energy exchange exhibits rich
nonequilibrium characteristics when different numbers of system-bath bound
states are formed, which suggests a useful way to control the quantum heat.
Deepening our understanding of the fluctuation relation in quantum
thermodynamics, our result lays the foundation to design high-efficiency
quantum heat engines.


  A given subset $A$ of natural numbers is said to be complete if every element
of $\mathbb{N}$ is the sum of distinct terms taken from $A$. This topic is
strongly connected to the knapsack problem which is known to be NP complete.
Interestingly if $A$ and $B$ are complete sequences then $A\times B$ is not
necessarily complete in $\mathbb{N}^2$. In this paper we consider a modular
version of this problem, motivated by the communication complexity problem of
[2].


  Introducing a spherical, steady, self-supported pair-plasma pressure mediated
shock surface around a Schwarzschild black hole as the effective physical
atmosphere which may be responsible for the generation of astrophysical mass
outflows from relativistic quasi-spherical accretion, we calculate the mass
outflow rate $R_{\dot m}$ by simultaneously solving the set of equations
governing transonic polytropic accretion and isothermal winds. $R_{\dot m}$ is
computed in terms of {\it only three} inflow parameters, which, as we believe,
has been done for the first time in our work. We then study the dependence of
$R_{\dot m}$ on various inflow as well as shock parameters and establish the
fact that the outflow rate is essentially controlled by the post-shock proton
temperature.


  Experimentalists have come to temperatures very close to absolute zero at
which physics that was once ordinary becomes extraordinary. In such a regime
quantum effects and fluctuations start to play a dominant role. In this context
we study the simplest open quantum system, namely, a free quantum Brownian
particle coupled to thermal vacuum, i.e. thermostat in the limiting case of
absolute zero temperature. We analyze the average energy $E=E(c)$ of the
particle from a weak to strong interaction strength $c$ between the particle
and thermal vacuum. The impact of various dissipation mechanisms is considered.
In the weak coupling regime the energy tends to zero as $E(c) \sim c\,
\ln{(1/c)}$ while in the strong coupling regime it diverges to infinity as
$E(c) \sim \sqrt{c}$. We demonstrate it for selected examples of the
dissipation mechanisms defined by the memory kernel $\gamma(t)$ of the
Generalized Langevin Equation. We reveal how at a fixed value of $c$ the energy
$E(c)$ depends on the dissipation model: one has to compare values of the
derivative $\gamma'(t)$ of the dissipation function $\gamma(t)$ at time $t=0$
or at the memory time $t=\tau_c$ which characterizes the degree of
non-Markovianity of the Brownian particle dynamics. The impact of low
temperature is also presented.


  Two different kinds of synchronization have been applied to cryptography:
Synchronization of chaotic maps by one common external signal and
synchronization of neural networks by mutual learning. By combining these two
mechanisms, where the external signal to the chaotic maps is synchronized by
the nets, we construct a hybrid network which allows a secure generation of
secret encryption keys over a public channel. The security with respect to
attacks, recently proposed by Shamir et al, is increased by chaotic
synchronization.


  We prove that any compact selfdual Einstein 4-orbifold of positive scalar
curvature whose isometry group contains a 2-torus is, up to an orbifold
covering, a quaternion Kaehler quotient of (k-1)-dimensional quaternionic
projective space by a (k-2)-torus for some $k\geq 2$. We also obtain a
topological classification in terms of the intersection form of the 4-orbifold.


  A thin thread of viscous fluid falling onto a moving belt generates a
surprising variety of patterns depending on the belt speed, fall height, flow
rate, and fluid properties. Here we simulate this experiment numerically using
the Discrete Viscous Threads method that can predict the non-steady dynamics of
thin viscous filaments, capturing the combined effects of inertia, stretching,
bending and twisting. Our simulations successfully reproduce nine out of ten
different patterns previously seen in the laboratory, and agree closely with
the experimental phase diagram of Morris et al.\ (2008). We propose a new
classification of the patterns based on the Fourier spectra of the longitudinal
and transverse motion of the point of contact of the thread with the belt.
These frequencies appear to be locked in most cases to simple ratios of the
frequency $\Omega_c$ of steady coiling obtained in the limit of zero belt
speed. In particular the intriguing `alternating loops' pattern is produced by
combining the first five multiples of $\Omega_c/3$.


  Historically, the Natural Language Processing area has been given too much
attention by many researchers. One of the main motivation beyond this interest
is related to the word prediction problem, which states that given a set words
in a sentence, one can recommend the next word. In literature, this problem is
solved by methods based on syntactic or semantic analysis. Solely, each of
these analysis cannot achieve practical results for end-user applications. For
instance, the Latent Semantic Analysis can handle semantic features of text,
but cannot suggest words considering syntactical rules. On the other hand,
there are models that treat both methods together and achieve state-of-the-art
results, e.g. Deep Learning. These models can demand high computational effort,
which can make the model infeasible for certain types of applications. With the
advance of the technology and mathematical models, it is possible to develop
faster systems with more accuracy. This work proposes a hybrid word suggestion
model, based on Naive Bayes and Latent Semantic Analysis, considering
neighbouring words around unfilled gaps. Results show that this model could
achieve 44.2% of accuracy in the MSR Sentence Completion Challenge.


  The two-flavor Gross-Neveu model with U(2)$_L\times$U(2)$_R$ chiral symmetry
in 1+1 dimensions is used to construct a novel variant of four-fermion theories
with O(2)$_L\times$O(2)$_R$ chiral symmetry. The spontaneous breaking of the
group O(2), a continuous group with two connected components (rotations and
reflections), gives rise to new phenomena. It is ideally suited to describe a
situation where two distinct kinds of condensation compete, in particular
chiral symmetry breaking (particle-hole condensation) and Cooper pairing
(particle-particle condensation). After solving the O(2) chiral Gross-Neveu
model in detail, we demonstrate that it is dual to another classically
integrable model due to Zakharov and Mikhailov. The duality enables us to solve
the quantum version of this model in the large $N$ limit with semiclassical
methods, supporting its integrability at the quantum level. The resulting model
is the unique four-fermion theory sharing the full Pauli-G\"ursey symmetry with
free, massless fermions (``perfect Gross-Neveu model") and provides us with a
solvable model for competing chiral and Cooper pair condensates, including
explicit soliton dynamics and the phase diagram.


  We report charge sensing measurements of a silicon metal-oxide-semiconductor
quantum dot using a single-electron transistor as a charge sensor with dynamic
feedback control. Using digitallycontrolled feedback, the sensor exhibits
sensitive and robust detection of the charge state of the quantum dot, even in
the presence of charge drifts and random charge rearrangements. The sensor
enables the occupancy of the quantum dot to be probed down to the single
electron level.


  Power grids, across the world, play an important societal and economical role
by providing uninterrupted, reliable and transient-free power to several
industries, businesses and household consumers. With the advent of renewable
power resources and EVs resulting into uncertain generation and highly dynamic
load demands, it has become ever so important to ensure robust operation of
power networks through suitable management of transient stability issues and
localize the events of blackouts. In the light of ever increasing stress on the
modern grid infrastructure and the grid operators, this paper presents a
reinforcement learning (RL) framework, PowRL, to mitigate the effects of
unexpected network events, as well as reliably maintain electricity everywhere
on the network at all times. The PowRL leverages a novel heuristic for overload
management, along with the RL-guided decision making on optimal topology
selection to ensure that the grid is operated safely and reliably (with no
overloads). PowRL is benchmarked on a variety of competition datasets hosted by
the L2RPN (Learning to Run a Power Network). Even with its reduced action
space, PowRL tops the leaderboard in the L2RPN NeurIPS 2020 challenge
(Robustness track) at an aggregate level, while also being the top performing
agent in the L2RPN WCCI 2020 challenge. Moreover, detailed analysis depicts
state-of-the-art performances by the PowRL agent in some of the test scenarios.


  We discover a simple factorization law describing how multipartite
entanglement of a composite quantum system evolves when one of the subsystems
undergoes an arbitrary physical process. This multipartite entanglement decay
is determined uniquely by a single factor we call the entanglement resilient
factor (ERF). Since the ERF is a function of the quantum channel alone, we find
that multipartite entanglement evolves in exactly the same way as bipartite
(two qudits) entanglement. For the two qubits case, our factorization law
reduces to the main result of Nature Physics 4, 99 (2008). In addition, for a
permutation $P$, we provide an operational definition of $P$-asymmetry of
entanglement, and find the conditions when a permuted version of a state can be
achieved by local means.


  This paper considers the problem of voltage regulation in distribution
networks. The primary motivation is to keep voltages within preassigned
operating limits by commanding the reactive power output of distributed energy
resources (DERs) deployed in the grid. We develop a framework for developing
local Volt/Var control that comprises two main steps. In the first, by
exploiting historical data and for each DER, we learn a function representing
the desirable equilibrium points for the power network. These points
approximate solutions of an Optimal Power Flow (OPF) problem. In the second, we
propose a control scheme for steering the network towards these favorable
configurations. Theoretical conditions are derived to formally guarantee the
stability of the developed control scheme, and numerical simulations illustrate
the effectiveness of the proposed approach.


  An excess of events of unknown origin at low energies below 1 keV has been
observed in multiple low-threshold dark matter detectors. Depending on the
target material, nuclear recoil events at these energies may cause lattice
defects, in which case a part of the true recoil energy is stored in the defect
and not observed in the phonon detector. If the threshold for defect creation
is sharp, this effect leads to a prominent feature in the observed recoil
spectrum. Electronic recoils at low energies do not create defects and
therefore the feature in the observed spectrum is not expected in that case. We
propose to use the sharp defect creation threshold of diamond to test if the
low energy events are due to nuclear recoils. Based on simulated data we expect
the nuclear recoil bump in the observed spectrum to be visible in diamond with
just ~0.1 gram days of exposure.


  Comet 41P/Tuttle-Giacobini-Kresak (41P), a Jupiter family comet with three
discoveries over about 100 years, is in a short-periodic orbit around the Sun
with the perihelion close to the Earth distance. The 2017 apparition of 41P
offered a long-lasting visibility of the comet at a close distance to Earth.
The four month-long imaging campaign with the 2 m telescope at the Mount
Wendelstein Observatory was aimed at characterizing dust activity and nucleus
properties of the comet. Using a new analysis method of the inner coma flux, we
derived a small mean equivalent radius of about 600 m for the nucleus with an
unusual body axes ratio that is higher than two. The nucleus rotation axis was
determined from the geometric appearance of coma structures, which were
enhanced in the images. A long-lasting coma fan was produced by an extended
region at high latitudes on the slowly rotating nucleus, whereas isolated jets
originated from narrow, low latitude active regions on the nucleus. The dust
activity of 41P, despite being difficult to quantify exactly because of an
unknown phase function correction for the comet, indicates a steep radial
profile that falls off with an increasing distance from the Sun. Colors and
flux profiles provide evidence for dust fragmentation in the inner coma of the
comet. A singular outburst event created various dust structures in the coma.
The outburst came from an extended region on the nucleus and was due to either
a landslide on the nucleus or a sudden material release from a subsurface
pocket of volatile ice.


  Flip and exchange symmetric (FES) many-qubit states, which can be obtained
from a state with the same symmetries by means of invertible local operations
(ILO), constitute a one-parameter family of curves in the Hilbert space.
Eigenstates of FES ILOs correspond to vectors that cannot be transformed to
other FES states. This means equivalence classes of states under ILO can be
determined in a systematic way for an arbitrary number of qubits. More
important, for entangled states, at the boundaries of neighboring equivalence
classes, one can show that when the fidelity between the final state after an
ILO and a state of the neighboring class approaches unity, probability of
success decreases to zero. Therefore, the classes are stable under ILOs.


  Tree-based models are used in many high-stakes application domains such as
finance and medicine, where robustness and interpretability are of utmost
importance. Yet, methods for improving and certifying their robustness are
severely under-explored, in contrast to those focusing on neural networks.
Targeting this important challenge, we propose deterministic smoothing for
decision stump ensembles. Whereas most prior work on randomized smoothing
focuses on evaluating arbitrary base models approximately under input
randomization, the key insight of our work is that decision stump ensembles
enable exact yet efficient evaluation via dynamic programming. Importantly, we
obtain deterministic robustness certificates, even jointly over numerical and
categorical features, a setting ubiquitous in the real world. Further, we
derive an MLE-optimal training method for smoothed decision stumps under
randomization and propose two boosting approaches to improve their provable
robustness. An extensive experimental evaluation on computer vision and tabular
data tasks shows that our approach yields significantly higher certified
accuracies than the state-of-the-art for tree-based models. We release all code
and trained models at https://github.com/eth-sri/drs.


  We perform hydro- and magnetohydrodynamical general relativistic simulations
of a tidal disruption of a $0.1\,M_\odot$ red dwarf approaching a
$10^5\,M_\odot$ non-rotating massive black hole on a close (impact parameter
$\beta=10$) elliptical (eccentricity $e=0.97$) orbit. We track the debris
self-interaction, circularization, and the accompanying accretion through the
black hole horizon. We find that the relativistic precession leads to the
formation of a self-crossing shock. The dissipated kinetic energy heats up the
incoming debris and efficiently generates a quasi-spherical outflow. The
self-interaction is modulated because of the feedback exerted by the flow on
itself. The debris quickly forms a thick, almost marginally bound disc that
remains turbulent for many orbital periods. Initially, the accretion through
the black hole horizon results from the self-interaction, while in the later
stages it is dominated by the debris originally ejected in the shocked region,
as it gradually falls back towards the hole. The effective viscosity in the
debris disc stems from the original hydrodynamical turbulence, which dominates
over the magnetic component. The radiative efficiency is very low because of
low energetics of the gas crossing the horizon and large optical depth that
results in photon trapping.


  We construct multi-charged $AdS_3\times{\Sigma}\times\Sigma_{\mathfrak{g}}$
and $AdS_2\times{\Sigma}\times\Sigma_{\mathfrak{g}}$ solutions from M5-branes
and D4-branes wrapped on a direct product of spindle, ${\Sigma}$, and Riemann
surface, $\Sigma_{\mathfrak{g}}$. Employing uplift formula, we obtain these
solutions by uplifting the multi-charged $AdS_3\times{\Sigma}$ and
$AdS_2\times{\Sigma}$ solutions to seven and six dimensions, respectively. We
further uplift the solutions to eleven-dimensional and massive type IIA
supergravity and calculate the holographic central charge and the
Bekenstein-Hawking entropy, respectively. We perform the gravitational block
calculations and, for the $AdS_3\times{\Sigma}\times\Sigma_{\mathfrak{g}}$
solutions, the result precisely matches the holographic central charge from the
supergravity solutions.


  The technology required for eikonal scattering amplitude calculations in
Matrix theory is developed. Using the entire supersymmetric completion of the
v^4/r^7 Matrix theory potential we compute the graviton-graviton scattering
amplitude and find agreement with eleven dimensional supergravity at tree
level.


  We present a protocol for quantum cryptography in which the data obtained for
mismatched bases are used in full for the purpose of quantum state tomography.
Eavesdropping on the quantum channel is seriously impeded by requiring that the
outcome of the tomography is consistent with unbiased noise in the channel. We
study the incoherent eavesdropping attacks that are still permissible and
establish under which conditions a secure cryptographic key can be generated.
The whole analysis is carried out for channels that transmit quantum systems of
any finite dimension.


  All machine learning algorithms use a loss, cost, utility or reward function
to encode the learning objective and oversee the learning process. This
function that supervises learning is a frequently unrecognized hyperparameter
that determines how incorrect outputs are penalized and can be tuned to improve
performance. This paper shows that training speed and final accuracy of neural
networks can significantly depend on the loss function used to train neural
networks. In particular derivative values can be significantly different with
different loss functions leading to significantly different performance after
gradient descent based Backpropagation (BP) training. This paper explores the
effect on performance of using new loss functions that are also convex but
penalize errors differently compared to the popular Cross-entropy loss. Two new
classification loss functions that significantly improve performance on a wide
variety of benchmark tasks are proposed. A new loss function call smooth
absolute error that outperforms the Squared error, Huber and Log-Cosh losses on
datasets with significantly many outliers is proposed. This smooth absolute
error loss function is infinitely differentiable and more closely approximates
the absolute error loss compared to the Huber and Log-Cosh losses used for
robust regression.


  Multiscale plasmonic systems e.g. extended metallic nanostructures with
sub-nanometer inter-distances) play a key role in the development of
next-generation nano-photonic devices.
  An accurate modeling of the optical interactions in these systems requires an
accurate description of both quantum effects and far-field properties.
  Classical electromagnetism can only describe the latter, while Time-Dependent
Density Functional Theory (TD-DFT) can provide a full first-principles quantum
treatment.
  However, TD-DFT becomes computationally prohibitive for sizes that exceed few
nanometers, which are irrelevant for practical applications. In this article,
we introduce a method based on the quantum hydrodynamic theory (QHT), which
includes non-local contributions of the kinetic energy and the correct
asymptotic description of the electron density.
  We show that our QHT method can predict both plasmon energy and spill-out
effects in metal nanoparticles in excellent agreement with TD-DFT predictions,
thus allowing a reliable and efficient calculations of both quantum and
far-field properties in multiscale plasmonic systems.


  We investigate the invariance properties of general wavelet coorbit spaces
and Besov-type decomposition spaces under dilations by matrices. We show that
these matrices can be characterized by quasi-isometry properties with respect
to a certain metric in frequency domain. We formulate versions of this
phenomenon both for the decomposition and coorbit space settings.
  We then apply the general results to a particular class of dilation groups,
the so-called shearlet dilation groups. We present a general, algebraic
characterization of matrices the are coorbit compatible with a given shearlet
dilation group. We determine the groups of compatible dilations for a variety
of concrete examples.


  The application of Natural Language Processing (NLP) has achieved a high
level of relevance in several areas. In the field of software engineering (SE),
NLP applications are based on the classification of similar texts (e.g.
software requirements), applied in tasks of estimating software effort,
selection of human resources, etc. Classifying software requirements has been a
complex task, considering the informality and complexity inherent in the texts
produced during the software development process. The pre-trained embedding
models are shown as a viable alternative when considering the low volume of
textual data labeled in the area of software engineering, as well as the lack
of quality of these data. Although there is much research around the
application of word embedding in several areas, to date, there is no knowledge
of studies that have explored its application in the creation of a specific
model for the domain of the SE area. Thus, this article presents the proposal
for a contextualized embedding model, called BERT_SE, which allows the
recognition of specific and relevant terms in the context of SE. The assessment
of BERT_SE was performed using the software requirements classification task,
demonstrating that this model has an average improvement rate of 13% concerning
the BERT_base model, made available by the authors of BERT. The code and
pre-trained models are available at https://github.com/elianedb.


  Axions are hypothesized particles believed to potentially resolve two major
puzzles in modern physics: the strong CP problem and the nature of dark matter.
Cavity-based axion haloscopes represent the most sensitive tools for probing
their theoretically favored couplings to photons in the microelectronvolt
range. However, as the search mass (or frequency) increases, the detection
efficiency decreases, largely due to a decrease in cavity volume. Despite the
potential of higher-order resonant modes to preserve experimental volume, their
practical application in searches has been limited by the challenge of
maintaining a high form factor over a reasonably wide search bandwidth. We
introduce an innovative tuning method that uses the unique properties of
auxetic materials, designed to effectively tune higher modes. This approach was
applied to the TM_020 mode for a dark matter axion search exploring a mass
range from 21.38 to 21.79 ueV, resulting in the establishment of new exclusion
limits for axion-photon coupling greater than approximately 10^-13 GeV^-1.
These findings signify a breakthrough, demonstrating that our tuning mechanism
facilitates the practical utilization of higher-order modes for cavity
haloscope searches.


  We review some recent results on the construction in superspace of 3D N=2 AdS
supergravities and on the formulation of rigid supersymmetric theories in (1,1)
and (2,0) AdS superspaces.


  We prove holomorphy E sqcap C(I,varPi) to C(I,varPi) of the map (x,y) mapsto
x circ [id,y] where [id,y]:I owns t mapsto (t,y(t)) for a real compact interval
I, and where varPi is a complex Banach space and E is a certain locally convex
space of continuous functions x:I times varPi to varPi for which x(t,.) is
holomorphic for all t in I. We also discuss application of this result to
establishing a holomorphic solution map (xi,varphi) mapsto y for functions y:I
to varPi satisfying the ordinary differential equation y' = varphi circ [id,y]
with initial condition y(t_0) = xi .


  The connection between the rate of entropy production and the rate of phase
space contraction for thermostatted systems in nonequilibrium steady states is
discussed for a simple model of heat flow in a Lorentz gas, previously
described by Spohn and Lebowitz. It is easy to show that for the model
discussed here the two rates are not connected, since the rate of entropy
production is non-zero and positive, while the overall rate of phase space
contraction is zero. This is consistent with conclusions reached by other
workers. Fractal structures appear in the phase space for this model and their
properties are discussed. We conclude with a discussion of the implications of
this and related work for understanding the role of chaotic dynamics and
special initial conditions for an explanation of the Second Law of
Thermodynamics.


  A complex ruled surface admits an iterated blow-up encoded by a parabolic
structure with rational weights. Under a condition of parabolic stability, one
can construct a Kaehler metric of constant scalar curvature on the blow-up
according to math.DG/0412405. We present a generalization of this construction
to the case of parabolically polystable ruled surfaces. Thus we can produce
numerous examples of Kaehler surfaces of constant scalar curvature with circle
or toric symmetry.


  The $2$d orders are a sub class of causal sets, which is especially amenable
to computer simulations. Past work has shown that the $2$d orders have a first
order phase transition between a random and a crystalline phase. When coupling
the $2$d orders to the Ising model, this phase transition coincides with the
transition of the Ising model. The coupled system also shows a new phase, at
negative $\beta$, where the Ising model induces the geometric transition. In
this article we examine the phase transitions of the coupled system, to
determine their order, as well as how they scale when the system size is
changed. We find that the transition at positive $\beta$ seems to be of mixed
order, while the two transitions at negative $\beta$ appear continous/ first
order for the Ising model/ the geometry respectively. The scaling of the
observables with the system size on the other hand is fairly simple, and does,
where applicable, agree with that found for the pure $2$d orders. We find that
the location of these transitions has fractional scaling in the system size.


  Measurements of diffractive dissociation cross sections in pp collisions at
sqrt(s) = 7 TeV are presented in kinematic regions defined by the masses M[X]
and M[Y] of the two final-state hadronic systems separated by the largest
rapidity gap in the event. Differential cross sections are measured as a
function of xi[X]= M[X]^2/s in the region -5.5 < log[10]xi[X] < -2.5, for
log[10]M[Y] < 0.5, dominated by single dissociation (SD), and 0.5 < log[10]M[Y]
< 1.1, dominated by double dissociation (DD), where M[X] and M[Y] are given in
GeV. The inclusive pp cross section is also measured as a function of the width
of the central pseudorapidity gap Delta eta for Delta eta > 3, log[10]M[X] >
1.1, and log[10]M[Y] > 1.1, a region dominated by DD. The cross sections
integrated over these regions are found to be, respectively, 2.99 +/- 0.02
(stat) +0.32 -0.29 (syst) mb, 1.18 +/- 0.02 (stat) +/- 0.13 (syst) mb, and 0.58
+/- 0.01 (stat) +0.13 -0.11 (syst) mb, and are used to extract total SD and DD
cross sections. In addition, the inclusive differential cross section, d sigma
/d Delta eta[F], for events with a pseudorapidity gap adjacent to the edge of
the detector, is measured over Delta eta[F] = 8.4 units of pseudorapidity. The
results are compared to those of other experiments and to theoretical
predictions, and found compatible with slowly-rising diffractive cross sections
as a function of center-of-mass energy.


  The reduction modulo $p$ of a family of lacunary integer polynomials,
associated with the dynamical zeta function $\zeta_{\beta}(z)$ of the
$\beta$-shift, for $\beta > 1$ close to one, is investigated. We briefly recall
how this family is correlated to the problem of Lehmer. A variety of questions
is raised about their numbers of zeroes in $\mathbb{F}_p$ and their
factorizations, via Kronecker's Average Value Theorem (viewed as an analog of
classical Theorems of Uniform Distribution Theory). These questions are
partially answered using results of Schinzel, revisited by Sawin, Shusterman
and Stoll, and density theorems (Frobenius, Chebotarev, Serre, Rosen). These
questions arise from the search for the existence of integer polynomials of
Mahler measure > 1 less than the smallest Salem number 1.176280. Explicit
connection with modular forms (or modular representations) of the numbers of
zeroes of these polynomials in $\mathbb{F}_p$ is obtained in a few cases. In
general it is expected since it must exist according to the Langlands program.


  We present an analog of the phenomenon of orthogonality catastrophe in
quantum many body systems subject to a local dissipative impurity. We show that
the fidelity $F(t)$, giving a measure for distance of the time-evolved state
from the initial one, displays a universal scaling form $F(t)\propto t^\theta
e^{-\gamma t}$, when the system supports long range correlations, in a fashion
reminiscent of traditional instances of orthogonality catastrophe in condensed
matter. An exponential fall-off at rate $\gamma$ signals the onset of
environmental decoherence, which is critically slowed down by the additional
algebraic contribution to the fidelity. This picture is derived within a second
order cumulant expansion suited for Liouvillian dynamics, and substantiated for
the one-dimensional transverse field quantum Ising model subject to a local
dephasing jump operator, as well as for XY and XX quantum spin chains, and for
the two dimensional Bose gas deep in the superfluid phase with local particle
heating. Our results hint that local sources of dissipation can be used to
inspect real-time correlations and to induce a delay of decoherence in open
quantum many body systems.


  We extend results of Bongiovanni et al. on double bubbles on the line with
log-convex density to the case where the derivative of the log of the density
is bounded. We show that the tie function between the double interval and the
triple interval still exists but may blow up to infinity in finite time. For
the first time, a density is presented for which the blowup time is positive
and finite.


  The primordial spectra are calculated using dispersion relations which
deviate from the relativistic one above a certain energy scale Lambda. We
determine the properties of the leading modifications with respect to the
standard spectra when Lambda is much greater than H, the Hubble scale during
inflation. To be generic, we parameterize the lowest order deviation from the
relativistic law by alpha, the power of P/Lambda where P is the proper
momentum. When working in the asymptotic vacuum, the leading modification
scales as (H/Lambda)^alpha for all alpha, except for a discrete set where the
power is higher. Moreover, this modification is robust against introducing
higher order terms in the dispersion relation. We then algebraically deduce the
modifications of scalar and tensor power spectra in slow roll inflation from
modifications calculated in de Sitter space. The modifications do not exhibit
oscillations unless the dispersion relation induces some non-adiabaticity near
a given scale. Finally, we explore the much less studied regime where H and
Lambda are comparable. Our results indicate that the project of reconstructing
the inflaton potential cannot be pursued without making some hypothesis about
the dispersion relation of the fluctuation modes.


  We propose a convolutional recurrent sparse auto-encoder model. The model
consists of a sparse encoder, which is a convolutional extension of the learned
ISTA (LISTA) method, and a linear convolutional decoder. Our strategy offers a
simple method for learning a task-driven sparse convolutional dictionary (CD),
and producing an approximate convolutional sparse code (CSC) over the learned
dictionary. We trained the model to minimize reconstruction loss via gradient
decent with back-propagation and have achieved competitive results to KSVD
image denoising and to leading CSC methods in image inpainting requiring only a
small fraction of their run-time.


  Hub genes play an essential role in biological systems because of their
interaction with other genes. A vocabulary used in bioinformatics called Gene
Ontology (GO) describes how genes and proteins operate. This flexible ontology
illustrates the operation of molecular, biological, and cellular processes
(Pmol, Pbio, Pcel). There are various methodologies that can be analyzed to
determine semantic similarity. Research in this study, we employ the jack-knife
method by taking into account 4 well-liked Semantic similarity measures namely
Jaccard similarity, Cosine similarity, Pairsewise document similarity, and
Levenshtein distance. Based on these similarity values, the protein-protein
interaction network (PPI) of Malaria GO (Gene Ontology) data is built, which
causes clusters of identical or related protein complexes (Px) to form. The hub
nodes of the network are these necessary proteins. We use a variety of
centrality measures to establish clusters of these networks in order to
determine which node is the most important. The clusters' unique formation
makes it simple to determine which class of Px they are allied to.


  Results of the investigation of fluctuation phenomena in bulk type-II
superconductors obtained in 1980 - 1984 years are compared with the analogous
results obtained at the investigation of high-Tc superconductors (HTSC). It is
shown the latter repeat in the main the results obtained before the HTSC
discovery. It was discovered in 1981 year that a sharp change of resistive
properties is observed below the second critical field, at H_{c4} < H_{c2}, in
high quality samples of type-II superconductors. The same was repeated at
investigation of high quality HTSC samples and was interpreted as vortex
lattice melting. The difference of the (H_{c2} - H_{c4}) values for low- and
high-Tc superconductors corresponds with the scaling law obtained in the lowest
Landau level approximation. The linear approximation region above H_{c2}, the
critical region and the Abrikosov state are considered. It is emphasized that
the rigorous Ami-Maki theory should be used for description of paraconductivity
in HTSC and "dirty" conventional superconductors. The approximation
dependencies obtained from popular Aslamasov-Larkin, Hikami-Larkin and
Lawrence-Doniac works do not have a validity region in these superconductors
with wide critical region.


  We calculate the homotopy type of $L_1L_{K(2)}S^0$ and $L_{K(1)}L_{K(2)}S^0$
at the prime 2, where $L_{K(n)}$ is localization with respect to Morava
$K$-theory and $L_1$ localization with respect to $2$-local $K$ theory. In
$L_1L_{K(2)}S^0$ we find all the summands predicted by the Chromatic Splitting
Conjecture, but we find some extra summands as well. An essential ingredient in
our approach is the analysis of the continuous group cohomology
$H^\ast_c(\mathbb{G}_2,E_0)$ where $\mathbb{G}_2$ is the Morava stabilizer
group and $E_0 = \mathbb{W}[[u_1]]$ is the ring of functions on the height $2$
Lubin-Tate space. We show that the inclusion of the constants $\mathbb{W} \to
E_0$ induces an isomorphism on group cohomology, a radical simplification.