  Blame attribution is one of the key aspects of accountable decision making,
as it provides means to quantify the responsibility of an agent for a decision
making outcome. In this paper, we study blame attribution in the context of
cooperative multi-agent sequential decision making. As a particular setting of
interest, we focus on cooperative decision making formalized by Multi-Agent
Markov Decision Processes (MMDPs), and we analyze different blame attribution
methods derived from or inspired by existing concepts in cooperative game
theory. We formalize desirable properties of blame attribution in the setting
of interest, and we analyze the relationship between these properties and the
studied blame attribution methods. Interestingly, we show that some of the well
known blame attribution methods, such as Shapley value, are not
performance-incentivizing, while others, such as Banzhaf index, may over-blame
agents. To mitigate these value misalignment and fairness issues, we introduce
a novel blame attribution method, unique in the set of properties it satisfies,
which trade-offs explanatory power (by under-blaming agents) for the
aforementioned properties. We further show how to account for uncertainty about
agents' decision making policies, and we experimentally: a) validate the
qualitative properties of the studied blame attribution methods, and b) analyze
their robustness to uncertainty.


  We study the question of when cyclic branched covers of knots admit taut
foliations, have left-orderable fundamental group, and are not L-spaces.


  The wide availability of specific courses together with the flexibility of
academic plans in university studies reveal the importance of Recommendation
Systems (RSs) in this area. These systems appear as tools that help students to
choose courses that suit to their personal interests and their academic
performance. This paper presents a hybrid RS that combines Collaborative
Filtering (CF) and Content-based Filtering (CBF) using multiple criteria
related both to student and course information to recommend the most suitable
courses to the students. A Genetic Algorithm (GA) has been developed to
automatically discover the optimal RS configuration which include both the most
relevant criteria and the configuration of the rest of parameters. The
experimental study has used real information of Computer Science Degree of
University of Cordoba (Spain) including information gathered from students
during three academic years, counting on 2500 entries of 95 students and 63
courses. Experimental results show a study of the most relevant criteria for
the course recommendation, the importance of using a hybrid model that combines
both student information and course information to increase the reliability of
the recommendations as well as an excellent performance compared to previous
models.


  We consider distributed smooth nonconvex unconstrained optimization over
networks, modeled as a connected graph. We examine the behavior of distributed
gradient-based algorithms near strict saddle points. Specifically, we establish
that (i) the renowned Distributed Gradient Descent (DGD) algorithm likely
converges to a neighborhood of a Second-order Stationary (SoS) solution; and
(ii) the more recent class of distributed algorithms based on gradient
tracking--implementable also over digraphs--likely converges to exact SoS
solutions, thus avoiding (strict) saddle-points. Furthermore, new convergence
rate results to first-order critical points is established for the latter class
of algorithms.


  In this paper, we describe the asymptotic behaviour of globally defined
solutions and of bounded solutions blowing up in finite time of the radial
energy-critical focusing non-linear wave equation in three space dimension.


  The noncentral $t$-distribution is a generalization of the Student's
$t$-distribution. In this paper we suggest an alternative approach for
computing the cumulative distribution function (CDF) of the noncentral
$t$-distribution which is based on a direct numerical integration of a well
behaved function. With a double-precision arithmetic, the algorithm provides
highly precise and fast evaluation of the extreme tail probabilities of the
noncentral $t$-distribution, even for large values of the noncentrality
parameter $\delta$ and the degrees of freedom $\nu$. The implementation of the
algorithm is available at the MATLAB Central, File Exchange:
http://www.mathworks.com/matlabcentral/fileexchange/41790-nctcdfvw.


  We compare systematically several classes of stochastic volatility models of
stock market fluctuations. We show that the long-time return distribution is
either Gaussian or develops a power-law tail, while the short-time return
distribution has generically a stretched-exponential form, but can assume also
an algebraic decay, in the family of models which we call ``GARCH''-type. The
intermediate regime is found in the exponential Ornstein-Uhlenbeck process. We
calculate also the decay of the autocorrelation function of volatility.


  We enumerate a certain class of monomino-domino coverings of square grids,
which conform to the \emph{tatami} restriction; no four tiles meet. Let
$\mathbf T_{n}$ be the set of monomino-domino tatami coverings of the $n\times
n$ grid with the maximum number, $n$, of monominoes, oriented so that they have
a monomino in each of the top left and top right corners. We give an algorithm
for exhaustively generating the coverings in $\mathbf T_{n}$ with exactly $v$
vertical dominoes in constant amortized time, and an explicit formula for
counting them. The polynomial that generates these counts has the factorisation
{align*} P_n(z)\prod_{j\ge 1} S_{\lfloor \frac{n-2}{2^j} \rfloor}(z), {align*}
where $S_n(z) = \prod_{i=1}^{n} (1 + z^i)$, and $P_n(z)$ is an irreducible
polynomial, at least for ${1 < n < 200}$. We present some compelling properties
and conjectures about $P_n(z)$. For example $P_n(1) = n2^{\nu(n-2)-1}$ for all
$n \ge 2$, where $\nu(n)$ is the number of 1s in the binary representation of
$n$ and deg$(P_n(z)) = \sum_{k=1}^{n-2} Od(k)$, where $Od(k)$ is the largest
odd divisor of $k$.


  We propose a Calculus of Mobility and Communication (CMC) for the modelling
of mobility, communication and context-awareness in the setting of ubiquitous
computing. CMC is an ambient calculus with the in and out capabilities of
Cardelli and Gordon's Mobile Ambients. The calculus has a new form of global
communication similar to that in Milner's CCS. In CMC an ambient is tagged with
a set of ports that agents executing inside the ambient are allowed to
communicate on. It also has a new context-awareness feature that allows
ambients to query their location. We present reduction semantics and labelled
transition system semantics of CMC and prove that the semantics coincide. A new
notion of behavioural equivalence is given by defining capability barbed
bisimulation and congruence which is proved to coincide with barbed
bisimulation congruence. The expressiveness of the calculus is illustrated by
two case studies.


  Crowdfunding is a new funding method through which founders request small
amounts of funding from a large number of people through an online platform.
Crowdfunding facilitates a new type of social capital and exhibits a unique
form of social dynamics, thus attracting the interest of sociologists and other
social scientists. Previous studies have focused on social relationships in
crowdfunding such as direct reciprocity and consider how they contribute to the
success of funding. The social structure of crowdfunding, however, involves
more complex social relationships and it may contribute to the success of a new
venture or project in many ways. In this study, we focus on a specific type of
triadic social structure, the buddy relation, which can be described as a
relationship through which project founder x, who previously backed another
founder z's project, receives financial backing from the other backers of z's
project. We found that the buddy relation occurs significantly more often than
randomly, concluding that this structure facilitates the gathering of financial
backing and may contribute to the success of a crowdfunded project.


  We investigate magnetic excitations in an $S=3/2$ Heisenberg model
representing two-dimensional antiferromagnet ${\rm Ba_2CoGe_2O_7}$. In
terahertz absorption experiment of the compound, Goldstone mode as well as
novel magnetic excitations, conventional magnetic resonance at 2 meV and both
electric- and magnetic-active excitation at 4 meV, have been observed. By
introducing a hard uniaxial anisotropy term $\Lambda (S^z)^2$, three modes can
be explained naturally. We also indicate that, via the spin-dependent
metal-ligand hybridization mechanism, the 4 meV excitation is an
electric-active mode through the coupling between spin and electric-dipole.
Moreover, at 4 meV excitation, an interference between magnetic and electric
responses emerges as a cross correlated effect. Such cross correlation effects
explain the non-reciprocal linear directional dichroism observed in ${\rm
Ba_2CoGe_2O_7}$.


  Huge diagrams have unique properties for organizations and research, such as
client linkages in informal organizations and customer evaluation lattices in
social channels. They necessitate a lot of financial assets to maintain because
they are large and frequently continue to expand. Owners of large diagrams may
need to use cloud resources due to the extensive arrangement of open cloud
resources to increase capacity and computation flexibility. However, the
cloud's accountability and protection of schematics have become a significant
issue. In this study, we consider calculations for security savings for
essential graph examination practices: schematic extraterrestrial examination
for outsourcing graphs in the cloud server. We create the security-protecting
variants of the two proposed Eigen decay computations. They are using two
cryptographic algorithms: additional substance homomorphic encryption (ASHE)
strategies and some degree homomorphic encryption (SDHE) methods. Inadequate
networks also feature a distinctively confidential info adaptation convention
to allow the trade-off between secrecy and data sparseness. Both dense and
sparse structures are investigated. According to test results, calculations
with sparse encoding can drastically reduce information. SDHE-based strategies
have reduced computing time, while ASHE-based methods have reduced stockpiling
expenses.


  In this paper, we present a new approach to interpret deep learning models.
By coupling mutual information with network science, we explore how information
flows through feedforward networks. We show that efficiently approximating
mutual information allows us to create an information measure that quantifies
how much information flows between any two neurons of a deep learning model. To
that end, we propose NIF, Neural Information Flow, a technique for codifying
information flow that exposes deep learning model internals and provides
feature attributions.


  Background: Sub-optimal code is prevalent in software systems. Developers may
write low-quality code due to many reasons, such as lack of technical
knowledge, lack of experience, time pressure, management decisions, and even
unhappiness. Once sub-optimal code is unknowingly (or knowingly) integrated
into the codebase of software systems, its accumulation may lead to large
maintenance costs and technical debt. Stack Overflow is a popular website for
programmers to ask questions and share their code snippets. The crowdsourced
and collaborative nature of Stack Overflow has created a large source of
programming knowledge that can be leveraged to assist developers in their
day-to-day activities. Objective: In this paper, we present an exploratory
study to evaluate the usefulness of recommending code improvements based on
Stack Overflow answers' edits. Method: We propose Matcha, a code recommendation
tool that leverages Stack Overflow code snippets with version history and code
clone search techniques to identify sub-optimal code in software projects and
suggest their optimised version. By using SOTorrent and GitHub datasets, we
will quali-quantitatively investigate the usefulness of recommendations given
by \textsc{Matcha} to developers using manual categorisation of the
recommendations and acceptance of pull-requests to open-source projects.


  We study the long- and short-term effects of telecommuting (TLC), staggered
work hours (SWH), and their combined scheme on peak-period congestion and
location patterns. In order to enable a unified comparison of the schemes'
long- and short-term effects, we develop a novel equilibrium analysis approach
that consistently synthesizes the long-term equilibrium (location and
percentage of telecommuting choice) and short-term equilibrium (preferred
arrival time and departure time choice). By exploiting their special
mathematical structures similar to optimal transport problems, we derive the
closed-form solution to the long- and short-term equilibrium while explicitly
considering their interaction. These closed-form solutions elucidate the
discrepancies between the effects of each scheme and uncover a paradoxical
finding: the introduction of SWH, in conjunction with TLC, may increase the
total commuting costs compared to the scenario with only TLC, without yielding
any improvement in worker utility.


  X-ray binaries in outburst typically show two canonical X-ray spectral
states, i.e. hard and soft states, in which the physical properties of the
accretion flow and of the jet are known to change. Recently, the JED-SAD
paradigm has been proposed for black hole X-ray binaries, aimed to address the
accretion-ejection interplay in these systems. According to this model, the
accretion flow is composed by an outer standard Shakura-Sunyaev disk (SAD) and
an inner hot Jet Emitting Disk (JED). The JED produces both the hard X-ray
emission, effectively playing the role of the hot corona, and the radio jets.
In this paper, we use the JED-SAD model to describe the evolution of the
accretion flow in the black hole transient MAXI J1820+070 during its hard and
hard-intermediate states. Contrarily to the previous applications of this
model, the Compton reflection component has been taken into account. We use
eight broadband X-rays spectra, including NuSTAR, NICER and Swift data,
providing a total spectral coverage of 0.8-190 keV. The data were directly
fitted with the JED-SAD model. Our results suggest that the optically thick
disk (i.e. the SAD) does not extend down to the ISCO in any of the considered
epochs. In particular, as the system evolves towards the hard/intermediate
state, we find that the inner radius decreases from $\sim$60 R$_{\rm G}$ in the
first observation down to $\sim$30 R$_{\rm G}$ in the last one. This trend is
accompanied by an increase of the mass-accretion rate. In all hard-intermediate
state observations, two reflection components, characterized by different
values of ionization, are required to adequately explain the data. These
components likely originate from different regions of the SAD. We show that a
flared outer disk could, in principle, explain the double reflection component.


  We present results of deep 153 ks Chandra observations of the hot, 11 keV,
galaxy cluster associated with the radio galaxy 3C 438. By mapping the
morphology of the hot gas and analyzing its surface brightness and temperature
distributions, we demonstrate the presence of a merger bow shock. We identify
the presence of two jumps in surface brightness and in density located at
$\sim$400 kpc and $\sim$800 kpc from the cluster's core. At the position of the
inner jump, we detect a factor of $2.3\pm 0.2$ density jump, while at the
location of the outer jump, we detect a density drop of a factor of $3.5 \pm
0.7$. Combining this with the temperature distribution within the cluster, we
establish that the pressure of the hot gas is continuous at the 400 kpc jump,
while there is a factor of $6.2 \pm 2.8$ pressure discontinuity at 800 kpc
jump. From the magnitude of the outer pressure discontinuity, using the
Rankine-Hugoniot jump conditions, we determine that the sub-cluster is moving
at $M = 2.3\pm 0.5$, or approximately $2600\pm 565$ km/s through the
surrounding intracluster medium, creating the conditions for a bow shock. Based
on these findings, we conclude that the pressure discontinuity is likely the
result of an ongoing major merger between two massive clusters. Since few
observations of bow shocks in clusters have been made, this detection can
contribute to the study of the dynamics of cluster mergers, which offers
insight on how the most massive clusters may have formed.


  In this work, we investigate the interplay between dissipation and
symmetry-protected topological order. We considered the one-dimensional spin-1
Affleck-Kennedy-Lieb-Tasaki model interacting with an environment where the
dissipative dynamics are described by the Lindladian master equation. The
Markovian dynamics is solved by the implementation of a tensor network
algorithm for mixed states in the thermodynamic limit. We observe that, for
time-reversal symmetric dissipation, the resulting steady state has topological
signatures even if being a mixed state. This is seen in finite string-order
parameters as well as in the degeneracy pattern of singular values in the
tensor network decomposition of the reduced density matrix. We also show that
such features do not appear for non-symmetric dissipation. Our work opens the
way toward a generalized and more practical definition of symmetry-protected
topological order for mixed states induced by dissipation.


  Using first principles calculations in the framework of Density Functional
Theory, we investigated the electronic and transport properties of
metal(II)-phthalocyanine (M(II)Pc) systems, both in a single molecule
configuration and in a model-device geometry. In particular, using the
Copper(II)- and Manganese(II)-Pc as prototypical examples, we studied how
electronic correlations on the central metal-ion influence the analysis of the
electronic structure of the system and we demonstrated that the choice of the
exchange-correlation functional, also beyond the standard local or gradient
corrected level, is of crucial importance for a correct interpretation of the
data. Finally, our electronic transport simulations have shown that
M(II)Pc-based devices can act selectively as molecular conductors, as in the
case of Copper, or as spin valves, as in the case of Manganese, demonstrating
once more the great potential of these systems for molecular nanoelectronics
applications.


  A classification of the $IJ^{PC}=00^{++}$ mesons is performed on the basis of
the K-matrix analysis of meson spectra in the reactions: (i) GAMS data on $\pi
p\to \pi^0\pi^0 n$, $\eta\eta n$, $\eta\eta' n$; (ii) Crystal Barrel data on
$p\bar p~(at~rest)\to \pi^0\pi^0\pi^0$, $\pi^0\pi^0\eta$, $\pi^0\eta\eta$;
(iii) CERN-M\"unich data on $\pi p\to \pi^+\pi^- n$; (iiii) BNL data on $\pi
N\to K\bar K N$. The analysis points to the existence of four comparatively
narrow scalar resonances which correspond to the following poles of the
scattering amplitude (in MeV): $(1015\pm 15)- i(43\pm 8)$, $(1300\pm
20)-i(120\pm 20)$, $(1499\pm 8)-i(65\pm 10)$ and $(1780\pm 30)-i(125\pm 70)$.
The scattering amplitude also has a fifth pole $f_0(1530^{+90}_{-250})$ at the
complex mass $(1530^{+90}_{-250}) - i(560\pm 140)$. The masses of the K-matrix
poles (bare states) are at $720\pm 100$ MeV, $1230\pm 50$ MeV, $1260\pm 30$
MeV, $1600\pm 50$ MeV and $1810\pm 30$ MeV. The quark content of the bare
states is analyzed using the values of their couplings to the $\pi\pi$, $K\bar
K$, $\eta\eta$ and $\eta\eta'$. It is shown that one of the bare states in the
mass region 1200-1600 MeV is superfluous for the $q\bar q$ classification and
should be considered as the lightest glueball.


  Short-term forecasting is an important tool in understanding environmental
processes. In this paper, we incorporate machine learning algorithms into a
conditional distribution estimator for the purposes of forecasting tropical
cyclone intensity. Many machine learning techniques give a single-point
prediction of the conditional distribution of the target variable, which does
not give a full accounting of the prediction variability. Conditional
distribution estimation can provide extra insight on predicted response
behavior, which could influence decision-making and policy. We propose a
technique that simultaneously estimates the entire conditional distribution and
flexibly allows for machine learning techniques to be incorporated. A smooth
model is fit over both the target variable and covariates, and a logistic
transformation is applied on the model output layer to produce an expression of
the conditional density function. We provide two examples of machine learning
models that can be used, polynomial regression and deep learning models. To
achieve computational efficiency we propose a case-control sampling
approximation to the conditional distribution. A simulation study for four
different data distributions highlights the effectiveness of our method
compared to other machine learning-based conditional distribution estimation
techniques. We then demonstrate the utility of our approach for forecasting
purposes using tropical cyclone data from the Atlantic Seaboard. This paper
gives a proof of concept for the promise of our method, further computational
developments can fully unlock its insights in more complex forecasting and
other applications.


  Recent works have proven the effectiveness of k-nearest-neighbor machine
translation(a.k.a kNN-MT) approaches to produce remarkable improvement in
cross-domain translations. However, these models suffer from heavy retrieve
overhead on the entire datastore when decoding each token. We observe that
during the decoding phase, about 67% to 84% of tokens are unvaried after
searching over the corpus datastore, which means most of the tokens cause
futile retrievals and introduce unnecessary computational costs by initiating
k-nearest-neighbor searches. We consider this phenomenon is explainable in
linguistics and propose a simple yet effective multi-layer perceptron (MLP)
network to predict whether a token should be translated jointly by the neural
machine translation model and probabilities produced by the kNN or just by the
neural model. The results show that our method succeeds in reducing redundant
retrieval operations and significantly reduces the overhead of kNN retrievals
by up to 53% at the expense of a slight decline in translation quality.
Moreover, our method could work together with all existing kNN-MT systems.


  Observations of the intra-cluster medium (ICM) in galactic globular clusters
(GCs) show a systematic deficiency in ICM mass as compared to that expected
from accumulation of stellar winds in the time available between galactic plane
crossings. In this paper, we reexamine the original hypothesis of Scott and
Durisen that hydrogen-rich explosions on accreting white dwarfs, classical
novae (CNe), will sweep out the ICM from the cluster more frequently than
galactic plane crossings. From the CNe rate and stellar mass-loss rate, this
clearing mechanism predicts that ~ 0.03 M_sun should be present in <= 10^5
M_sun GCs. We model the expanding remnant made from the 10^-4 M_sun nova ejecta
and show that it escapes long before it has cooled. We discuss the few positive
ICM measurements and use a Monte-Carlo simulation of the accumulation and CNe
recurrence times to reveal the possible variance in the ICM masses for the
higher mass (> 5x10^5 M_sun) GCs. We find that nova shells are effective at
clearing the ICM in low-mass GCs (<= 10^5 M_sun), whereas higher-mass clusters
may experience a quiescent time between novae long enough to prevent the next
nova shell from escaping. The nova clearing mechanism will also operate in
ultra-faint Milky Way satellites, where many upper limits on gas masses are
available.


  Monodromy groups, i.e. the groups of isometries of the intersection lattice
L_X:=H_2/torsion generated by the monodromy action of all deformation families
of a given surface, have been computed in math.AG/0006231 for any minimal
elliptic surface with p_g>q=0. New and refined methods are now employed to
address the cases of minimal elliptic surfaces with p_g+1>q>0. To this end we
find explicit families such that any isometry is in the group generated by
their monodromies or does not respect the invariance of the canonical class or
the spinor norm. The monodromy action is moreover shown to act by the full
symplectic group on the first homology modulo torsion.


  The nature of the phase diagrams of HTSC is clarified by discussing two kinds
of phase diagrams, that of the host crystalline lattice, and that of the dopant
glass. The latter is associated with changes in the electronic properties,
while the former is much more accessible to direct experimental identification,
by diffraction, of nanoscale phase separation. Careful examination of
electronic properties in both the normal and superconductive states reveals
that there are several electronic miscibility gaps in YBa2Cu3Ox and
La2-xSrxCuO4 that have been previously overlooked. Recent experiments on the
pseudogap in Bi2Sr1.6La0.4CuOy also reveal an electronic miscibility gap.


  We study the asymptotic behavior of the $N$-dimensional colored Jones
polynomial evaluated at $\exp(\xi/N)$ for a real number $\xi$ greater than a
certain constant. We prove that, from the asymptotic behavior, we can extract
the $\rm{SL}(2;\mathbb{C})$ Chern--Simons invariant and the Reidemeister
torsion twisted by the adjoint action both associated with a representation
determined by $\xi$.


  We present AMIGOS-- A dataset for Multimodal research of affect, personality
traits and mood on Individuals and GrOupS. Different to other databases, we
elicited affect using both short and long videos in two social contexts, one
with individual viewers and one with groups of viewers. The database allows the
multimodal study of the affective responses, by means of neuro-physiological
signals of individuals in relation to their personality and mood, and with
respect to the social context and videos' duration. The data is collected in
two experimental settings. In the first one, 40 participants watched 16 short
emotional videos. In the second one, the participants watched 4 long videos,
some of them alone and the rest in groups. The participants' signals, namely,
Electroencephalogram (EEG), Electrocardiogram (ECG) and Galvanic Skin Response
(GSR), were recorded using wearable sensors. Participants' frontal HD video and
both RGB and depth full body videos were also recorded. Participants emotions
have been annotated with both self-assessment of affective levels (valence,
arousal, control, familiarity, liking and basic emotions) felt during the
videos as well as external-assessment of levels of valence and arousal. We
present a detailed correlation analysis of the different dimensions as well as
baseline methods and results for single-trial classification of valence and
arousal, personality traits, mood and social context. The database is made
publicly available.


  In this study, the interaction between the gas molecules, including H2O, N2,
CO, NO, NO2 and N2O, with the WSe2 monolayer containing a Se vacancy (denoted
as VSe) is theoretically studied. It is found that H2O and N2 molecules are
highly prone to be physisorbed on the VSe surface. The presence of the Se
vacancy should significantly enhance the sensing ability of the WSe2 monolayer
toward the H2O and N2 molecules. In contrast, CO and NO molecules highly prefer
to be molecularly chemisorbed on the VSe surface with the non-oxygen atom
occupying the Se site. Further, the exposed O atoms of the molecularly
chemisorbed CO or NO can be reacted with the additional CO or NO molecule, to
produce the C-doped or N-doped WSe2 monolayer. Our electronic structure
calculations show that the WSe2 monolayers are p-doped by the CO and NO
molecules, as well as the C and N atoms. The calculated energies suggest that
the filling of the CO or NO molecule and the removal of the exposed O atom by
the additional CO or NO molecule are both energetically and dynamically
favorable. However, only the NO molecule and N atom doped WSe2 monolayers
exhibit significantly improved electronic structures compared with the
defective WSe2 monolayer. The NO2 and N2O molecules will dissociate directly to
form a O-doped WSe2 with a physisorbed NO or N2. After desorbing the
physisorbed NO or N2, the O-doped WSe2 monolayer can be obtained, for which the
defect levels due to the Se vacancy can be completely removed. The calculated
energies suggest that although the dissociation processes for NO2 and N2O
molecules are highly endothermic, the N2O dissociation may need to operate at
an elevated temperature compared with the room temperature, due to its large
energy barrier of ~ 1 eV.


  This paper addresses the learning task of estimating driver drowsiness from
the signals of car acceleration sensors. Since even drivers themselves cannot
perceive their own drowsiness in a timely manner unless they use burdensome
invasive sensors, obtaining labeled training data for each timestamp is not a
realistic goal. To deal with this difficulty, we formulate the task as a weakly
supervised learning. We only need to add labels for each complete trip, not for
every timestamp independently. By assuming that some aspects of driver
drowsiness increase over time due to tiredness, we formulate an algorithm that
can learn from such weakly labeled data. We derive a scalable stochastic
optimization method as a way of implementing the algorithm. Numerical
experiments on real driving datasets demonstrate the advantages of our
algorithm against baseline methods.


  We introduce a novel technique within the Nested Sampling framework to
enhance efficiency of the computation of Bayesian evidence, a critical
component in scientific data analysis. In higher dimensions, Nested Sampling
relies on Markov Chain-based likelihood-constrained prior samplers, which
generate numerous 'phantom points' during parameter space exploration. These
points are too auto-correlated to be used in the standard Nested Sampling
scheme and so are conventionally discarded, leading to waste. Our approach
discovers a way to integrate these phantom points into the evidence
calculation, thereby improving the efficiency of Nested Sampling without
sacrificing accuracy. This is achieved by ensuring the points within the live
set remain asymptotically i.i.d. uniformly distributed, allowing these points
to contribute meaningfully to the final evidence estimation. We apply our
method on several models, demonstrating substantial enhancements in sampling
efficiency, that scales well in high-dimension. Our findings suggest that this
approach can reduce the number of required likelihood evaluations by at least a
factor of 5. This advancement holds considerable promise for improving the
robustness and speed of statistical analyses over a wide range of fields, from
astrophysics and cosmology to climate modelling.


  This paper describes the algorithms used by the CMS experiment to reconstruct
and identify tau to hadrons + tau neutrino decays during Run 1 of the LHC. The
performance of the algorithms is studied in proton-proton collisions recorded
at a centre-of-mass energy of 8 TeV, corresponding to an integrated luminosity
of 19.7 inverse femtobarns. The algorithms achieve an identification efficiency
of 50-60%, with misidentification rates for quark and gluon jets, electrons,
and muons between per mille and per cent levels.


  We show that C-odd gluelumps can be successfully described as bound states of
a single transverse constituent gluon evolving in the flux-tube-like potential
generated by a static color-octet source. The use of a helicity degree of
freedom rather than a spin one for the constituent gluon forbids the states
that are not observed in lattice QCD. Our model leads to a gluelump mass
spectrum in remarkable agreement with the available lattice data provided that
an additional parity-splitting mass term is introduced. We argue that such a
term is due to instanton-induced interactions in gluelumps.


  The Polynomial Freiman-Ruzsa conjecture is one of the central open problems
in additive combinatorics. If true, it would give tight quantitative bounds
relating combinatorial and algebraic notions of approximate subgroups. In this
note, we restrict our attention to subsets of Euclidean space. In this regime,
the original conjecture considers approximate algebraic subgroups as the set of
lattice points in a convex body. Green asked in 2007 whether this can be
simplified to a generalized arithmetic progression, while not losing more than
a polynomial factor in the underlying parameters. We give a negative answer to
this question, based on a recent reverse Minkowski theorem combined with
estimates for random lattices.


  Training automated agents to complete complex tasks in interactive
environments is challenging: reinforcement learning requires careful
hand-engineering of reward functions, imitation learning requires specialized
infrastructure and access to a human expert, and learning from intermediate
forms of supervision (like binary preferences) is time-consuming and extracts
little information from each human intervention. Can we overcome these
challenges by building agents that learn from rich, interactive feedback
instead? We propose a new supervision paradigm for interactive learning based
on "teachable" decision-making systems that learn from structured advice
provided by an external teacher. We begin by formalizing a class of
human-in-the-loop decision making problems in which multiple forms of
teacher-provided advice are available to a learner. We then describe a simple
learning algorithm for these problems that first learns to interpret advice,
then learns from advice to complete tasks even in the absence of human
supervision. In puzzle-solving, navigation, and locomotion domains, we show
that agents that learn from advice can acquire new skills with significantly
less human supervision than standard reinforcement learning algorithms and
often less than imitation learning.


  In this article we develop new methods for exhibiting convex semialgebraic
sets that are not spectrahedral shadows. We characterize when the set of
nonnegative polynomials with a given support is a spectrahedral shadow in terms
of sums of squares. As an application of this result we prove that the cone of
copositive matrices of size $n\geq5$ is not a spectrahedral shadow, answering a
question of Scheiderer. Our arguments are based on the model theoretic
observation that any formula defining a spectrahedral shadow must be preserved
by every unital $\mathbb{R}$-linear completely positive map $R\to R$ on a real
closed field extension $R$ of $\mathbb{R}$.


  Crop field boundaries aid in mapping crop types, predicting yields, and
delivering field-scale analytics to farmers. Recent years have seen the
successful application of deep learning to delineating field boundaries in
industrial agricultural systems, but field boundary datasets remain missing in
smallholder systems due to (1) small fields that require high resolution
satellite imagery to delineate and (2) a lack of ground labels for model
training and validation. In this work, we combine transfer learning and weak
supervision to overcome these challenges, and we demonstrate the methods'
success in India where we efficiently generated 10,000 new field labels. Our
best model uses 1.5m resolution Airbus SPOT imagery as input, pre-trains a
state-of-the-art neural network on France field boundaries, and fine-tunes on
India labels to achieve a median Intersection over Union (IoU) of 0.86 in
India. If using 4.8m resolution PlanetScope imagery instead, the best model
achieves a median IoU of 0.72. Experiments also show that pre-training in
France reduces the number of India field labels needed to achieve a given
performance level by as much as $20\times$ when datasets are small. These
findings suggest our method is a scalable approach for delineating crop fields
in regions of the world that currently lack field boundary datasets. We
publicly release the 10,000 labels and delineation model to facilitate the
creation of field boundary maps and new methods by the community.


  In this work, we discuss the generalized Einstein-Maxwell-Dilaton gravity
theory with a nonminimal coupling between the Maxwell field and scalar field.
Considering different geometric properties of black hole horizon structure, the
charged dilaton Lifshitz black hole solutions are presented in 4-dimensional
spacetimes. Later, utilizing the Wald Formalism, we derive the thermodynamic
first law of black hole and conserved quantities. According to the relationship
between the heat capacity and the local stability of black hole, we study the
stability of charged Lifshitz black holes and identify the thermodynamic stable
region of black holes that meet the criteria.


  In this work, we explore two mechanisms that explain non-Gaussian behaviour
of power-grid frequency recordings in the South African grid. We make use of a
Fokker-Planck approach to power-grid frequency that yields a direct relation
between common model parameters such as inertia, damping, and noise amplitude
and non-parametric estimations of the same directly from power-grid frequency
recordings. We propose two explanations for the non-Gaussian leptokurtic
distributions in South Africa: The first based on multiplicative noise in
power-grid frequency recordings, which we observe in South Africa; The second
based on the well-known scheduled and unscheduled load shedding and rolling
blackouts that beset South Africa. For the first we derive an analytic
expression of the effects of multiplicative noise that permits the estimation
of all statistical moments - and discuss drawbacks in comparison with the data;
For the second we employ a simple numerical analysis with a modular power grid
of South Africa. Both options help understand the statistics of power-grid
frequency in South Africa - particularly the presence of heavy tails.


  Modern single image super-resolution (SISR) system based on convolutional
neural networks (CNNs) achieves fancy performance while requires huge
computational costs. The problem on feature redundancy is well studied in
visual recognition task, but rarely discussed in SISR. Based on the observation
that many features in SISR models are also similar to each other, we propose to
use shift operation to generate the redundant features (i.e., ghost features).
Compared with depth-wise convolution which is time-consuming on GPU-like
devices, shift operation can bring a practical inference acceleration for CNNs
on common hardwares. We analyze the benefits of shift operation on SISR task
and make the shift orientation learnable based on Gumbel-Softmax trick.
Besides, a clustering procedure is explored based on pre-trained models to
identify the intrinsic filters for generating intrinsic features. The ghost
features will be derived by moving these intrinsic features along a specific
orientation. Finally, the complete output features are constructed by
concatenating the intrinsic and ghost features together. Extensive experiments
on several benchmark models and datasets demonstrate that both the non-compact
and lightweight SISR models embedded with the proposed method can achieve a
comparable performance to that of their baselines with a large reduction of
parameters, FLOPs and GPU inference latency. For instance, we reduce the
parameters by 46%, FLOPs by 46% and GPU inference latency by 42% of $\times2$
EDSR network with basically lossless performance.


  We establish several contraction formulas for Kirchhoff index. We relate
Kirchhoff index with some other metrized graph invariants. By applying our
contraction formulas successively when the graph is a tree, we derive new
formulas for Wiener index and obtain some previously known Wiener index
formulas with new proofs.


  We discuss recent progress towards the establishment of important
structure-property-function relationships in eumelanins - key functional
bio-macromolecular systems responsible for photo-protection and immune response
in humans, and implicated in the development of melanoma skin cancer. We focus
on the link between eumelanin's secondary structure and optical properties such
as broad band UV-visible absorption and strong non-radiative relaxation; both
key features of the photo-protective function. We emphasise the insights gained
through a holistic approach combining optical spectroscopy with first
principles quantum chemical calculations, and advance the hypothesis that the
robust functionality characteristic of eumelanin is related to extreme chemical
and structural disorder at the secondary level. This inherent disorder is a low
cost natural resource, and it is interesting to speculate as to whether it may
play a role in other functional bio-macromolecular systems.


  In this paper, we study the relations between the numerical structure of the
optimal solutions of a convex programming problem defined on the edge set of a
simple graph and the stability number (i.e. the maximum size of a subset of
pairwise non-adjacent vertices) of the graph. Our analysis shows that the
stability number of every graph G can be decomposed in the sum of the stability
number of a subgraph containing a perfect 2-matching (i.e. a system of
vertex-disjoint odd-cycles and edges covering the vertex-set) plus a term
computable in polynomial time. As a consequence, it is possible to bound from
above and below the stability number in terms of the matching number of a
subgraph having a perfect 2-matching and other quantities computable in
polynomial time. Our results are closely related to those by Lorentzen,
Balinsky, Spielberg, and Pulleyblank on the linear relaxation of the
vertex-cover problem. Moreover, The convex programming problem involved has
important applications in information theory and extremal set theory where, as
a graph capacity formula, has been used to answer a longstanding open question
about qualitatively independet sets in the sense of Renyi (L. Gargano, J.
K{\"o}rner, and U. Vaccaro, "Sperner capacities", Graphs and combinatorics,
9:31-46, 1993).


  Optimal actuator design for a vibration control problem is calculated. The
actuator shape is optimized according to the closed-loop performance of the
resulting linear-quadratic regulator and a penalty on the actuator size. The
optimal actuator shape is found by means of shape calculus and a topological
derivative of the linear-quadratic regulator (LQR) performance index. An
abstract framework is proposed based on the theory for infinite-dimensional
optimization of both the actuator shape and the associated control problem. A
numerical realization of the optimality condition is presented for the actuator
shape using a level-set method for topological derivatives. A Numerical example
illustrating the design of actuator for Euler-Bernoulli beam model is provided.


  The moving sofa problem, posed by L. Moser in 1966, asks for the planar shape
of maximal area that can move around a right-angled corner in a hallway of unit
width, and is conjectured to have as its solution a complicated shape derived
by Gerver in 1992. We extend Gerver's techniques by deriving a family of six
differential equations arising from the area-maximization property. We then use
this result to derive a new shape that we propose as a possible solution to the
"ambidextrous moving sofa problem," a variant of the problem previously studied
by Conway and others in which the shape is required to be able to negotiate a
right-angle turn both to the left and to the right. Unlike Gerver's
construction, our new shape can be expressed in closed form, and its boundary
is a piecewise algebraic curve. Its area is equal to $X+\arctan Y$, where $X$
and $Y$ are solutions to the cubic equations $x^2(x+3)=8$ and $x(4x^2+3)=1$,
respectively.


  We establish Harnack inequality and shift Harnack inequality for stochastic
differential equation driven by $G$-Brownian motion. As applications, the
uniqueness of invariant linear expectations and estimates on the $\sup$-kernel
are investigated, where the $\sup$-kernel is introduced in this paper for the
first time.


  The relative equilibria of a symmetric Hamiltonian dynamical system are the
critical points of the so-called augmented Hamiltonian. The underlying
geometric structure of the system is used to decompose the critical point
equations and construct a collection of implicitly defined functions and
reduced equations describing the set of relative equilibria in a neighborhood
of a given relative equilibrium. The structure of the reduced equations is
studied in a few relevant situations. In particular, a persistence result of
Lerman and Singer [LS98] is generalized to the framework of Abelian proper
actions. Also, a Hamiltonian version of the Equivariant Branching Lemma and a
study of bifurcations with maximal isotropy are presented. An elementary
example is presented to illustrate the use of this approach.


  The generalized connections of the (anti)-de Sitter space symmetry algebra,
which are differential forms of arbitrary degree with values in any irreducible
(spin)-tensor representation of the (anti)-de Sitter algebra, are studied. It
is shown that arbitrary-spin gauge field in (anti)-de Sitter space, massless or
partially-massless, can be described by a single connection. A 'one-to-one'
correspondence between the connections of the (anti)-de Sitter algebra and the
gauge fields is established. The gauge symmetry is manifest and auxiliary
fields are automatically included in the formalism.


  3D printing or additive manufacturing is a revolutionary technology that
enables the creation of physical objects from digital models. However, the
quality and accuracy of 3D printing depend on the correctness and efficiency of
the G-code, a low-level numerical control programming language that instructs
3D printers how to move and extrude material. Debugging G-code is a challenging
task that requires a syntactic and semantic understanding of the G-code format
and the geometry of the part to be printed. In this paper, we present the first
extensive evaluation of six state-of-the-art foundational large language models
(LLMs) for comprehending and debugging G-code files for 3D printing. We design
effective prompts to enable pre-trained LLMs to understand and manipulate
G-code and test their performance on various aspects of G-code debugging and
manipulation, including detection and correction of common errors and the
ability to perform geometric transformations. We analyze their strengths and
weaknesses for understanding complete G-code files. We also discuss the
implications and limitations of using LLMs for G-code comprehension.


  We show that the weight 0 part of the first cohomology of a complex algebraic
variety $X$ is a topological invariant, and give an explicit description of its
dimension using a topological construction of the normalization of $X$, where
$X$ can be reducible, but must be equidimensional. The first assertion is known
in the $X$ compact case by A. Weber, where intersection cohomology is used.
Note that the weight 1 or 2 part of the first cohomology is not a topological
(or even analytic) invariant in the non-compact case by Serre's example.


  Sensing technologies have been under research and development for their
varied applications from microelectronics to space exploration. With the end of
Moores law in sight, there is growing demand for shrinking materials and
improving sensitivity and range of sensing of sensors. Carbon nanotubes (CNTs)
offer an excellent combination of small size (in the order of nanometers in two
dimensions and micrometers in the third dimension), varied current conductivity
(from insulating to metallic), flexibility, mechanical strength and feasibility
of mass production. Here we used CNTs to fabricate pressure sensors to sense
static loads of pressure and studied the characteristics of different methods
of building the sensors. We offer an adhesive-absorption technique of
fabrication of pressure sensors that tackles the issue of endurance of the
sensors to repeated operation. We demonstrate a significant change in
resistance of a vertically aligned forest of nanotubes upon application of
static loads. This study will enable building of better pressure sensors for
several applications.


  While separately leveraging monocular 3D object detection and 2D multi-object
tracking can be straightforwardly applied to sequence images in a
frame-by-frame fashion, stand-alone tracker cuts off the transmission of the
uncertainty from the 3D detector to tracking while cannot pass tracking error
differentials back to the 3D detector. In this work, we propose jointly
training 3D detection and 3D tracking from only monocular videos in an
end-to-end manner. The key component is a novel spatial-temporal information
flow module that aggregates geometric and appearance features to predict robust
similarity scores across all objects in current and past frames. Specifically,
we leverage the attention mechanism of the transformer, in which self-attention
aggregates the spatial information in a specific frame, and cross-attention
exploits relation and affinities of all objects in the temporal domain of
sequence frames. The affinities are then supervised to estimate the trajectory
and guide the flow of information between corresponding 3D objects. In
addition, we propose a temporal
  -consistency loss that explicitly involves 3D target motion modeling into the
learning, making the 3D trajectory smooth in the world coordinate system.
Time3D achieves 21.4\% AMOTA, 13.6\% AMOTP on the nuScenes 3D tracking
benchmark, surpassing all published competitors, and running at 38 FPS, while
Time3D achieves 31.2\% mAP, 39.4\% NDS on the nuScenes 3D detection benchmark.


  Widely studied quasiparticle models for quark gluon plasma is revisited here
to understand the statistical mechanics and thermodynamics of the system. We
investigate the statistical mechanics and thermodynamics inconsistencies
involved in these models and their consequences in the observables.
Quasiparticle model is a phenomenological model with few parameters and by
adjusting them all models fit the results of lattice gauge simulation of gluon
plasma \cite{bo.1}. However, after fixing 2 of the 3 parameters of the model by
physical arguments, only one quasiparticle model, which is consistent with both
statistical mechanics and thermodynamics, fits the Bielefeld lattice data
\cite{bo.1}. The same model also fits the recent lattice results of
Wuppertal-Budapest group \cite{fo.1}, which deals with precision SU(3)
thermodynamics for a large temperature range, reasonably well.


  The widespread adoption of large language models such as ChatGPT and Bard has
led to unprecedented demand for these technologies. The burgeoning cost of
inference for ever-increasing model sizes coupled with hardware shortages has
limited affordable access and poses a pressing need for efficiency approaches
geared towards high throughput and performance. Multi-input multi-output (MIMO)
algorithms such as data multiplexing, offer a promising solution with a
many-fold increase in throughput by performing inference for multiple inputs at
the cost of a single input. Yet these approaches are not currently performant
enough to be deployed in modern systems. We change that by developing MUX-PLMs,
a class of high throughput pre-trained language models (PLMs) trained with data
multiplexing, that can be fine-tuned for any downstream task to yield
high-throughput high-performance. Our novel multiplexing and demultiplexing
modules proficiently entangle and disentangle inputs, and enable
high-performance high throughput \muxplms{} that are competitive with vanilla
PLMs while achieving 2x/5x inference speedup with only a $1-4\%$ drop on a
broad suite of tasks.


  With the help of the methods developed in our previous article [Schmitz, to
appear in "Annales de l'I.H.P. Prob. & Stat.], we highlight condition (T) as a
source of new examples of 'ballistic' diffusions in a random environment when
d>1 ('ballistic' means that a strong law of large numbers with non-vanishing
limiting velocity holds). In particular we are able to treat the case of
non-constant diffusion coefficients, a feature that causes problems. Further we
recover the ballistic character of two important classes of diffusions in a
random environment by simply checking condition (T). This not only points out
to the broad range of examples where condition (T) can be checked, but also
fortifies our belief that condition (T) is a natural contender for the
characterisation of ballistic diffusions in a random environment when d>1.


  The Stirling number of a simple graph is the number of partitions of its
vertex set into a specific number of non-empty independent sets. In 2015,
Engbers et al. showed that the coefficients in the normal ordering of a word
$w$ in the alphabet $\{x,D\}$ subject to the relation $Dx=xD+1$ are equal to
the Stirling number of certain graphs constructed from $w$. In this paper, we
introduce graphical versions of the Stirling numbers of the first kind and the
Lah numbers and show how they occur as coefficients in other normal ordering
settings. Identities involving their $q$-analogues are also obtained.


  We investigate a simple variation of the series RLC circuit in which
anti-parallel diodes replace the resistor. This results in a damped harmonic
oscillator with a nonlinear damping term that is maximal at zero current and
decreases with an inverse current relation for currents far from zero. A set of
nonlinear differential equations for the oscillator circuit is derived and
integrated numerically for comparison with circuit measurements. The agreement
is very good for both the transient and steady-state responses. Unlike the
standard RLC circuit, the behavior of this circuit is amplitude dependent. In
particular for the transient response the oscillator makes a transition from
under-damped to over-damped behavior, and for the driven oscillator the
resonance response becomes sharper and stronger as drive source amplitude
increases. The equipment is inexpensive and common to upper level physics labs.


  Spiking neural networks (SNNs), which are inspired by the human brain, have
recently gained popularity due to their relatively simple and low-power
hardware for transmitting binary spikes and highly sparse activation maps.
However, because SNNs contain extra time dimension information, the SNN
accelerator will require more buffers and take longer to infer, especially for
the more difficult high-resolution object detection task. As a result, this
paper proposes a sparse compressed spiking neural network accelerator that
takes advantage of the high sparsity of activation maps and weights by
utilizing the proposed gated one-to-all product for low power and highly
parallel model execution. The experimental result of the neural network shows
71.5$\%$ mAP with mixed (1,3) time steps on the IVS 3cls dataset. The
accelerator with the TSMC 28nm CMOS process can achieve 1024$\times$576@29
frames per second processing when running at 500MHz with 35.88TOPS/W energy
efficiency and 1.05mJ energy consumption per frame.


  In this paper we consider $N \times N $ matrices $D_{N}$ with i.i.d. entries
all following an $a-$stable law divided by $N^{1/a}$. We prove that the least
singular value of $D_{N}$, multiplied by $N$, tends to the same law as in the
Gaussian case, for almost all $a \in (0,2)$. This is proven by considering the
symmetrization of the matrix $D_{N}$ and using a version of the three step
strategy, a well known strategy in the random matrix theory literature. In
order to apply the three step strategy, we also prove an isotropic local law
for the symmetrization of matrices after slightly perturbing them by a Gaussian
matrix with a similar structure. The isotropic local law is proven for a
general class of matrices that satisfy some regularity assumption. We also
prove the complete delocalization for the left and right singular vectors of
$D_{N}$ at small energy, i.e., for energies at a small interval around $0$.


  New expressions are derived for photoemission, which allow experimental
electron energy loss data to be used for estimating losses in photoemission.
The derivation builds on new results for dielectric response and mean free
paths of strongly correlated systems of two dimensional layers. Numerical
evaluations are made for $Bi_{2}Sr_{2}CaCu_{2}O_{8}$ (Bi2212) by using a
parametrized loss function. The mean free path for Bi2212 is calculated and
found to be substantially larger than obtained by Norman et al in a recent
paper. The photocurrent is expressed as the convolution of the intrinsic
approximation for the current from a specific 2D layer with an effective loss
function. The observed current is the sum of such currents from the first few
layers. The photo electron from a specific $CuO$ layer is found to excite low
energy acoustic plasmon modes due to the coupling between the $CuO$ layers.
These modes give rise to an asymmetric power law broadening of the photo
current an isolated two dimensional layer would have given. We define an
asymmetry index where a contribution from a Luttinger lineshape is additive to
the contribution from our broadening function. Already the loss effect
considered here gives broadening comparable to what is observed experimentally.
A superconductor with a gapped loss function is predicted to have a
peak-dip-hump lineshape similar to what has been observed, and with the same
qualitative behavior as predicted in the recent work by Campuzano et al.


  We consider self-adjoint extensions of differential operators of the type $
(-\frac{d^2}{dr^2} + \frac{l(l+1)}{r^2})^3 $ on the real semi-axis for l=1,2
with two kinds of boundary conditions: first that nullify the value of a
function and its first derivative and second that nullify the 4th (l=1) or the
3rd (l=2) derivative. We calculate the expressions for the correponding
resolvents and derive spectral decompositions. These types of boundary
conditions are interesting from the physical point of view, especially the
second ones, which give an example of emergence of long-range action in
exchange for a singularity at the origin.


  Because of DNA appealing features as perfect material, including minuscule
size, defined structural repeat and rigidity, programmable DNA-mediated
processing is a promising computing paradigm, which employs DNAs as information
storing and processing substrates to tackle the computational problems. The
massive parallelism of DNA hybridization exhibits transcendent potential to
improve multitasking capabilities and yield a tremendous speed-up over the
conventional electronic processors with stepwise signal cascade. As an example
of multitasking capability, we present an in vitro programmable DNA-mediated
optimal route planning processor as a functional unit embedded in contemporary
navigation systems. The novel programmable DNA-mediated processor has several
advantages over the existing silicon-mediated methods, such as conducting
massive data storage and simultaneous processing via much fewer materials than
conventional silicon devices.


  Evolutionary game theory, encompassing discrete, continuous, and mixed
strategies, is pivotal for understanding cooperation dynamics. Discrete
strategies involve deterministic actions with a fixed probability of one,
whereas continuous strategies employ intermediate probabilities to convey the
extent of cooperation and emphasize expected payoffs. Mixed strategies, though
akin to continuous ones, calculate immediate payoffs based on the action chosen
at a given moment within intermediate probabilities. Although previous research
has highlighted the distinct impacts of these strategic approaches on fostering
cooperation, the reasons behind the differing levels of cooperation among these
approaches have remained somewhat unclear. This study explores how these
strategic approaches influence cooperation in the context of the prisoner's
dilemma game, particularly in networked populations with varying clustering
coefficients. Our research goes beyond existing studies by revealing that the
differences in cooperation levels between these strategic approaches are not
confined to finite populations; they also depend on the clustering coefficients
of these populations. In populations with nonzero clustering coefficients, we
observed varying degrees of stable cooperation for each strategic approach
across multiple simulations, with mixed strategies showing the most
variability, followed by continuous and discrete strategies. However, this
variability in cooperation evolution decreased in populations with a clustering
coefficient of zero, narrowing the differences in cooperation levels among the
strategies. These findings suggest that in more realistic settings, the
robustness of cooperation systems may be compromised, as the evolution of
cooperation through mixed and continuous strategies introduces a degree of
unpredictability.


  We introduce a training method for both better word representation and
performance, which we call GROVER (Gradual Rumination On the Vector with
maskERs). The method is to gradually and iteratively add random noises to word
embeddings while training a model. GROVER first starts from conventional
training process, and then extracts the fine-tuned representations. Next, we
gradually add random noises to the word representations and repeat the training
process from scratch, but initialize with the noised word representations.
Through the re-training process, we can mitigate some noises to be compensated
and utilize other noises to learn better representations. As a result, we can
get word representations further fine-tuned and specialized on the task. When
we experiment with our method on 5 text classification datasets, our method
improves model performances on most of the datasets. Moreover, we show that our
method can be combined with other regularization techniques, further improving
the model performance.


  Reinforcement learning (RL) is a branch of machine learning which is employed
to solve various sequential decision making problems without proper
supervision. Due to the recent advancement of deep learning, the newly proposed
Deep-RL algorithms have been able to perform extremely well in sophisticated
high-dimensional environments. However, even after successes in many domains,
one of the major challenge in these approaches is the high magnitude of
interactions with the environment required for efficient decision making.
Seeking inspiration from the brain, this problem can be solved by incorporating
instance based learning by biasing the decision making on the memories of high
rewarding experiences. This paper reviews various recent reinforcement learning
methods which incorporate external memory to solve decision making and a survey
of them is presented. We provide an overview of the different methods - along
with their advantages and disadvantages, applications and the standard
experimentation settings used for memory based models. This review hopes to be
a helpful resource to provide key insight of the recent advances in the field
and provide help in further future development of it.


  The structure of many real networks is not locally tree-like and hence,
network analysis fails to characterise their bond percolation properties. In a
recent paper [P. Mann, V. A. Smith, J. B. O. Mitchell, and S. Dobson,
Percolation in random graphs with higher-order clustering, arXiv e-prints, p.
arXiv:2006.06744, June 2020.], we developed analytical solutions to the
percolation properties of random networks with homogeneous clustering (clusters
whose nodes are degree-equivalent). In this paper, we extend this model to
investigate networks that contain clusters whose nodes are not
degree-equivalent, including multilayer networks. Through numerical examples we
show how this method can be used to investigate the properties of random
complex networks with arbitrary clustering, extending the applicability of the
configuration model and generating function formulation.


  We give a general approach to infinite dimensional non-Gaussian analysis
which generalizes the work \cite{KSWY95}. For given measure we construct a
family of biorthogonal systems. We study their properties and their Gel'fand
triples that they generate. As an example we consider the measures of Poisson
type.


  I review selected topics in supersymmetry, including: effects of
non-universality, high $\tanb$ and phases on SUSY signals; a heavy gluino as
the LSP; gauge-mediated SUSY signals involving delayed decays; R-parity
violation and the very worst case for SUSY discovery; some topics regarding
Higgs bosons in supersymmetry; and doubly-charged Higgs and higgsinos in
supersymmetric left-right symmetric models. I emphasize scenarios in which
detectionof supersymmetric particles and/or the SUSY Higgs bosons might require
special experimental and/or analysis techniques.


  We have performed a molecular line search toward the flaring 6.7-GHz masers
G24.33+0.13 and G359.62-0.24 using the Australia Telescope Compact Array. We
present spectra of the 6.7-GHz class~II methanol and 22.2-GHz water masers
toward these sources and provide comparison with other recent flaring events
these sources have experienced. We also detect the fourth example of a 23.4-GHz
class~I methanol maser, and the eleventh example of a 4.8-GHz formaldehyde
maser toward G24.33+0.13. Alongside these results, we observe the previously
detected ammonia (3,3) emission and report upper limits on the presence of
various other cm-wavelength methanol, ammonia and OH transitions. Our results
are consistent with the flaring of G24.33+0.13 being driven by a variable
accretion rate in the host high-mass young stellar object.


  I prove that "quantum" and "Einsteinian gravity" are incompatible concepts.
Accordingly, the graviton is a mere object of science fiction.


  We investigate statistical equilibrium of neutral and singly-ionized
strontium in late-type stellar atmospheres. Particular attention is given to
the completeness of the model atom, which includes new energy levels,
transition probabilities, photoionization and electron-impact excitation
cross-sections computed with the R-matrix method. The NLTE model is applied to
the analysis of Sr I and Sr II lines in the spectra of the Sun, Procyon,
Arcturus, and HD 122563, showing a significant improvement in the ionization
balance compared to LTE line formation calculations, which predict abundance
discrepancies of up to 0.5 dex. The solar Sr abundance is log A = 2.93 \pm 0.04
dex, in agreement with the meteorites. A grid of NLTE abundance corrections for
Sr I and Sr II lines covering a large range of stellar parameters is presented.


  We apply the technique of sequential deconfinement to the four dimensional
$\mathcal{N}\!=\!1$ $Usp(2N)$ gauge theory with an antisymmetric field and $2F$
fundamentals. The fully deconfined frame is a length-$N$ quiver. We use this
deconfined frame to prove the known self-duality of $Usp(2N)$ with an
antisymmetric field and $8$ fundamentals. Along the way we encounter a
subtlety: in certain quivers with degenerate holomorphic operators, a naive
application of Seiberg duality rules leads to an incorrect superpotential or
chiral ring.
  We also consider the reduction to $3d$ $\mathcal{N}\!=\!2$ theories,
recovering known fully deconfined duals of $Usp(2N)$ and $U(N)$ gauge theories,
and obtaining new ones.


  The first image of black hole in M87 galaxy taken by Event Horizon Telescope
shows that directed observation of supermassive black holes would be a
promising way to test general relativity in strong gravity field regime. In
order to calculate shadow of rotating black holes with respect to observers
located at finite distance, orthonormal tetrads have been introduced in
previous works. However, it is noticed that different choice of tetrads does
not lead to the same shape of shadow for observers in near regions. In this
paper, we alternatively use formula of astrometric observables for calculating
the shadow of a general rotating black hole with respect to these observers.
For the sake of intuitive, we also consider Kerr-de Sitter black holes as a
representative example. In this space-time, size and shape of Kerr-de Sitter
black hole shadows are expressed as functions of distance between the black
hole and observer. It is forecasted that the distortion of shadow would
increase with distance.


  For arbitrary undirected graph $G$, we are designing SATISFIABILITY problem
(SAT) for HCP, using tools of Boolean algebra only. The obtained SAT be the
logic formulation of conditions for Hamiltonian cycle existence, and use $m$
Boolean variables, where $m$ is the number of graph edges. This Boolean
expression is true if and only if an initial graph is Hamiltonian. That is,
each satisfying assignment of the Boolean variables determines a Hamiltonian
cycle of $G$, and each Hamiltonian cycle of $G$ corresponds to a satisfying
assignment of the Boolean variables. In common case, the obtained Boolean
expression may has an exponential length (the number of Boolean literals).


  A cavity quantum electrodynamical (QED) system beyond the strong-coupling
regime is expected to exhibit intriguing quantum phenomena. Here we report a
direct measurement of the photon-dressed qubit transition frequencies up to
four photons by harnessing the same type of state transitions in an
ultrastrongly coupled circuit-QED system realized by inductively coupling a
superconducting flux qubit to a coplanar-waveguide resonator. This demonstrates
a convincing observation of the photon-dressed Bloch-Siegert shift in the
ultrastrongly coupled quantum system. Moreover, our results show that the
photon-dressed Bloch-Siegert shift becomes more pronounced as the photon number
increases, which is a characteristic of the quantum Rabi model.


  So far most of the analysis of coronavirus 2020 epidemic data has been
focusing on a short-time window and consequently a quantitative test of
statistical physical laws of Coronavirus Epidemics with Containment Measures
(CEwCM) is currently lacking. Here we report a quantitative analysis of CEwCM
over 230 days, covering the full-time lapse of the first epidemic wave. We use
a 3D phase diagram tracking the simultaneous evolution of the doubling time
Td(t) and reproductive number Rt(t) showing that this expanded parameter space
is needed for biological physics of CEwCP. We have verified that in the
supercritical [Rt(t)>1, Td(t)<40 days] regime i) the curve Z(t) of total
infected cases follows the growth rate called Ostwald law; ii) the doubling
time follows the exponential law Td(t)=A exp((t-t0)/s) as a function of time
and iii) the power law Td(t)=C(Rt(t)-1)^-n is verified with the exponent n
depending on the definition of Rt(t). The log-log plots Td(t) versus (Rt-1) of
the second 2020 epidemic wave unveil in the subcritical regime [Td(t)>100 days]
arrested metastable phases with Rt>1 where Td(t) was kept constant followed by
its explosion and its containment following the same power law as in the first
wave


  A model of discrete spacetime on a microscopic level is considered. It is a
directed acyclic dyadic graph. This is the particular case of a causal set. The
goal of this model is to describe particles as some repetitive symmetrical
self-organized structures of the graph without any reference to continuous
spacetime. The dynamics of the model is considered. This dynamics is stochastic
sequential additions of new vertexes. Growth of the graph is a Markovian
process. This dynamics is a consequence of a causality principle.


  We count pseudoholomorphic curves in the higher-dimensional Heegaard Floer
homology of disjoint cotangent fibers of a two dimensional disk. We show that
the resulting algebra is isomorphic to the Hecke algebra associated to the
symmetric group.


  Measurements of the accelerations of stars enabled by time-series
extreme-precision spectroscopic observations, from pulsar timing, and from
eclipsing binary stars in the Solar Neighborhood offer insights into the mass
distribution of the Milky Way that do not rely on traditional equilibrium
modeling. Given the measured accelerations, we can determine a total mass
density, and from this, by accounting for the mass in stars, gas, and dust, we
can infer the amount of dark matter. Leveraging the FIRE-2 simulations of Milky
Way-mass galaxies, we compare vertical acceleration profiles between cold dark
matter (CDM) and self-interacting dark matter (SIDM) with constant
cross-section of 1 cm$^2$ g$^{-1}$ across three halos with diverse assembly
histories. Notably, significant asymmetries in vertical acceleration profiles
near the midplane at fixed radii are observed in both CDM and SIDM,
particularly in halos recently affected by mergers with satellites of
Sagittarius/SMC-like masses or greater. These asymmetries offer a unique window
into exploring the merger history of a galaxy. We show that SIDM halos
consistently exhibit higher local stellar and dark matter densities and steeper
vertical acceleration gradients, up to 30% steeper near the Solar Neighborhood.
SIDM halos also manifest a more oblate halo shape in the Solar Neighborhood.
Furthermore, enhanced precision in acceleration measurements and larger
datasets promise to provide better constraints on the local dark matter
density, complementing our understanding from kinematic analysis of their
distribution within galaxies.


  We propose to apply stimulated adiabatic passage to transfer atoms from their
ground state into Rydberg excited states. Atoms a few micrometers apart
experience a dipole-dipole interaction among Rydberg states that is strong
enough to shift the atomic resonance and inhibit excitation of more than a
single atom. We show that the adiabatic passage in the presence of this
interaction between two atoms leads to robust creation of maximally entangled
states and to two-bit quantum gates. For many atoms, the excitation blockade
leads to an effective implementation of collective-spin and
Jaynes-Cummings-like Hamiltonians, and we show that the adiabatic passage can
be used to generate collective Jx=0 eigenstates and Greenberger-Horne-Zeilinger
states of tens of atoms.


  In this paper we study the first eigenvalue of the Laplacian on a compact
Kaehler manifold using stable bundles and balanced bases.


  It is well known that if G is a finite group then the group of endotrivial
modules is finitely generated. In this paper we prove that for an arbitrary
finite group scheme G, and for any fixed integer n > 0, there are only finitely
many isomorphism classes of endotrivial modules of dimension n. This provides
evidence to support the speculation that the group of endotrivial modules for a
finite group scheme is always finitely generated. The result also has some
applications to questions about lifting and twisting the structure of
endotrivial modules in the case that G is an infinitesimal group scheme
associated to an algebraic group.


  For any rank-one convex projective manifold with a compact convex core, we
prove that there exists a unique probability measure of maximal entropy on the
set of unit tangent vectors whose geodesic is contained in the convex core, and
that it is mixing. We use this to establish asymptotics for the number of
closed geodesics. In order to construct the measure of maximal entropy, we
develop a theory of Patterson--Sullivan densities for general rank-one convex
projective manifolds. In particular, we establish a
Hopf--Tsuji--Sullivan--Roblin dichotomy, and prove that, when it is finite, the
measure on the unit tangent bundle induced by a Patterson--Sullivan density is
mixing under the action of the geodesic flow.


  The impact of the lateral distribution of light in extensive air showers on
the detection and reconstruction of shower profiles is investigated for the
Auger fluorescence telescopes. Based on three-dimensional simulations, the
capability of the Auger telescopes to measure the lateral distribution of light
is evaluated. The ability to infer the actual lateral distribution is confirmed
by the comparison of detailed simulations with real data. The contribution of
pixels located far from the axis of the shower image is calculated and the
accepted signal is rescaled in order to reconstruct a correct shower profile.
The analysis presented here shows that: (a) the Auger telescopes are able to
observe the lateral distribution of showers and (b) the energy corrections to
account for the signal in outlying pixels can exceed 10%, depending on shower
geometry.


  We study the dispersion of light particles floating on a flat shear-free
surface of an open channel in which the flow is turbulent. This configuration
mimics the motion of buoyant matter (e.g. phytoplankton, pollutants or
nutrients) in water bodies when surface waves and ripples are smooth or absent.
We perform direct numerical simulation of turbulence coupled with Lagrangian
particle tracking, considering different values of the shear Reynolds number
(Re{\tau} = 171 and 509) and of the Stokes number (0.06 < St < 1 in viscous
units). Results show that particle buoyancy induces clusters that evolve
towards a long-term fractal distribution in a time much longer than the
Lagrangian integral fluid time scale, indicating that such clusters over-live
the surface turbulent structures which produced them. We quantify cluster
dynamics, crucial when modeling dispersion in free-surface flow turbulence, via
the time evolution of the cluster correlation dimension.


  In this paper, the notion of operator means in the setting of JB-algebras is
introduced and their properties are studied. Many identities and inequalities
are established, most of them have origins from operators on Hilbert space but
they have different forms and connotations, and their proofs require techniques
in JB-algebras.


  Based on the many-particle-number-state treatment for transport through a
pair of Majorana zero modes (MZMs) which are coupled to the leads via two
quantum dots, we identify that the reason for zero cross correlation of
currents at uncoupling limit between the MZMs is from a degeneracy of the
teleportation and the Andreev process channels. We then propose a scheme to
eliminate the degeneracy by introducing finite charging energy on the Majorana
island which allows for coexistence of the two channels. We find nonzero cross
correlation established even in the Majorana uncoupling limit (and also in the
small charging energy limit), which demonstrates well the teleportation or
nonlocal nature of the MZMs. More specifically, the characteristic structure of
coherent peaks in the power spectrum of the cross correlation is analyzed to
identify the nonlocal and coherent coupling mechanism between the MZMs and the
quantum dots. We also display the behaviors of peak shift with variation of the
Majorana coupling energy, which can be realized by modulating parameters such
as the magnetic field.


  We compare the Hilbert series approach with explicit constructions of
higher-order Lagrangians for the $O(N)$ nonlinear sigma model. We use the
Hilbert series to find the number and type of operators up to mass dimension
16, for spacetime dimension $D$ up to 12 and $N$ up to 12, and further classify
the operators into spacetime parity and parity of the internal symmetry group
$O(N)$. The explicit construction of operators is done up to mass dimension 12
for both parities even and dimension 10 for the other three cases. The results
of the two methods are in full agreement. This provides evidence for the
Hilbert series conjecture regarding co-closed but not co-exact $k$-forms, which
takes into account the integration-by-parts relations.


  A general approach to consider spatially extended stochastic systems with
correlations between additive and multiplicative noises subject to nonlinear
damping is developed. Within modified cumulant expansion method, we derive an
effective Fokker-Planck equation whose stationary solutions describe a
character of ordered state. We find that fluctuation cross-correlations lead to
a symmetry breaking of the distribution function even in the case of the
zero-dimensional system. In general case, continuous, discontinuous and
reentrant noise induced phase transitions take place. It is appeared the
cross-correlations play a role of bias field which can induce a chain of phase
transitions being different in nature. Within mean field approach, we give an
intuitive explanation of the system behavior through an effective potential of
thermodynamic type. This potential is written in the form of an expansion with
coefficients defined by temperature, intensity of spatial coupling, auto- and
cross-correlation times and intensities of both additive and multiplicative
noises.


  We observe that a system of irreducible, fiber-linear, first class
constraints on T*M is equivalent to the definition of a foliation Lie algebroid
over M. The BFV formulation of the constrained system is given by the
Hamiltonian lift of the Vaintrob description (E[1],Q) of the Lie algebroid to
its cotangent bundle T*E[1]. Affine deformations of the constraints are
parametrized by the first Lie algebroid cohomology H^1_Q and lead to
irreducible constraints also for much more general Lie algebroids such as Dirac
structures; the modified BFV function follows by the addition of a
representative of the deformation charge.
  Adding a Hamiltonian to the system corresponds to a metric g on M. Evolution
invariance of the constraint surface introduces a connection nabla on E and one
reobtains the compatibility of g with (E,rho,nabla) found previously in the
literature. The covariantization of the Hamiltonian to a function on T*E[1]
serves as a BFV-Hamiltonian, iff, in addition, this connection is compatible
with the Lie algebroid structure, turning (E, rho, [ , ], nabla) into a
Cartan-Lie algebroid. The BV formulation of the system is obtained from BFV by
a (time-dependent) AKSZ procedure.


  The downlink of a single cell base station (BS) equipped with large-scale
multiple-input multiple-output (MIMO) system is investigated in this paper. As
the number of antennas at the base station becomes large, the power consumed at
the RF chains cannot be anymore neglected. So, a circuit power consumption
model is introduced in this work. It involves that the maximal sum-rate is not
obtained when activating all the available RF chains. Hence, the aim of this
work is to find the optimal number of activated RF chains that maximizes the
sum-rate. Computing the optimal number of activated RF chains must be
accompanied by an adequate antenna selection strategy. First, we derive
analytically the optimal number of RF chains to be activated so that the
average sum-rate is maximized under received equal power. Then, we propose an
efficient greedy algorithm to select the sub-optimal set of RF chains to be
activated with regards to the system sum-rate. It allows finding the balance
between the power consumed at the RF chains and the transmitted power. The
performance of the proposed algorithm is compared with the optimal performance
given by brute force search (BFS) antenna selection. Simulations allow to
compare the performance given by greedy, optimal and random antenna selection
algorithms.


  In this paper we extend the result obtained in \cite{AKR97} (see also \cite
{AKR96}) on the representation of the intrinsic pre- Dirichlet form
$\mathcal{E}_{\pi_{\sigma}}^{\Gamma}$ of the Poisson measure $\pi_{\sigma}$ in
terms of the extrinsic one $\mathcal{E}_{\pi_{\sigma},H_{\sigma}^{X}}^{P}$.
More precisely, replacing $\pi_{\sigma}$ by a Gibbs measure $\mu$ on the
configuration space $\Gamma_{X}$ we derive a relation between the intrinsic
pre-Dirichlet form $\mathcal{E}_{\mu}^{\Gamma}$ of the measure $\mu$ and the
extrinsic one $\mathcal{E}_{\mu, H_{\sigma}^{X}}^{P}$. As a consequence we
prove the closability of $\mathcal{E}_{\mu}^{\Gamma}$ on
$L^{2}(\Gamma_{X},\mu)$ under very general assumptions on the interaction
potential of the Gibbs measures $\mu$.


  We study the vacuum Rabi splitting of a qubit ultrastrongly coupled to a
high-$Q$ cavity mode and a radiation reservoir. Three methods are employed: a
numerically exact variational approach with a multiple Davydov ansatz, the
rotating-wave approximation (RWA), and the transformed RWA. Agreement between
the variational results and the transformed RWA results is found in the regime
of validity of the latter, where the RWA breaks down completely. We illustrate
that the Lamb shift plays an essential role in modifying the vacuum Rabi
splitting in the ultrastrong coupling regime, leading to off-resonant
qubit-cavity coupling even though the cavity frequency equals to the bare
transition frequency of the qubit. Specifically, the emission spectrum exhibits
one broad low-frequency peak and one narrow high-frequency peak in the presence
of relatively weak cavity-qubit coupling. As the cavity-qubit coupling
increases, the low-frequency peak narrows while the high-frequency peak
broadens until they have similar widths.


  The disclosure of basic nonlinear optical properties of graphene-like
nanostructures with correlated electron-hole nonlinear dynamics over a wide
range of frequencies and pump field intensities is of great importance for both
graphene fundamental physics and for expected novel applications of 2D
hexagonal nanostructures in extreme nonlinear optics. In the current paper, the
nonlinear interaction of 2D hexagonal nanostructures with the bichromatic
infrared driving field taking into account many-body Coulomb interaction is
investigated. Numerical investigation in the scope of the Bloch equations
within the Houston basis that take into account $e-e$ and $e-h$ interactions in
the Hartree-Fock approximation reveals significant excitonic effects in the
high harmonic generation process in 2D hexagonal nanostructures such as
graphene and silicene. It is shown that due to the correlated electron-hole
nonlinear dynamics around the van Hove singularity, spectral caustics in the
high harmonic generation spectrum are induced near the saddle point excitonic
resonances.


  We study the properties of the avoided or hidden quantum critical point
(AQCP) in three dimensional Dirac and Weyl semi-metals in the presence of short
range potential disorder. By computing the averaged density of states (along
with its second and fourth derivative at zero energy) with the kernel
polynomial method (KPM) we systematically tune the effective length scale that
eventually rounds out the transition and leads to an AQCP. We show how to
determine the strength of the avoidance, establishing that it is not controlled
by the long wavelength component of the disorder. Instead, the amount of
avoidance can be adjusted via the tails of the probability distribution of the
local random potentials. A binary distribution with no tails produces much less
avoidance than a Gaussian distribution. We introduce a double Gaussian
distribution to interpolate between these two limits. As a result we are able
to make the length scale of the avoidance sufficiently large so that we can
accurately study the properties of the underlying transition (that is
eventually rounded out), unambiguously identify its location, and provide
accurate estimates of the critical exponents $\nu=1.01\pm0.06$ and
$z=1.50\pm0.04$. We also show that the KPM expansion order introduces an
effective length scale that can also round out the transition in the scaling
regime near the AQCP.


  We investigate collective spin excitations of graphene electrons with
short-ranged interactions and subject to the external Zeeman magnetic field. We
find that in addition to the familiar Silin spin wave, a collective spin-flip
excitation that reduces to the uniform precession when the wave's momentum
approaches zero, the magnetized graphene supports another collective mode
visible in the transverse spin susceptibility: a collective spin-current mode.
Unlike the Silin wave, this mode is not dictated by the spin-rotational
symmetry but rather owns its existence to the pseudo-spin structure of the
graphene lattice. We find the new collective excitation to become sharply
defined in a finite interval of wave's momenta, the range of which is
determined by the interaction and the magnetization.


  Dynamic Monte Carlo simulations are used to study coupled transport
(co-transport) through sub-nanometer-diameter pores. In this classic
Hodgkin-Keynes mechanism, an ion species uses the large flux of an abundant ion
species to move against its concentration gradient. The efficiency of
co-transport is examined for various pore parameters so that synthetic
nanopores can be engineered to maximize this effect. In general, the pore must
be narrow enough that ions cannot pass each other and the charge of the pore
large enough to attract many ions so that they exchange momentum. Co-transport
efficiency increases as pore length increases, but even very short pores
exhibit co-transport, in contradiction to the usual perception that long pores
are necessary. The parameter ranges where co-transport occurs is consistent
with current and near-future synthetic nanopore geometry parameters, suggesting
that co-transport of ions may be a new application of nanopores.


  We consider Einstein gravity with positive cosmological constant coupled with
higher spin interactions and calculate Euclidean path integral perturbatively.
We confine ourselves to the static patch of the 3 dimensional de Sitter space.
This geometry, when Euclideanlized is equivalent to 3-sphere. However, infinite
number of topological quotients of this space by discrete subgroups of the
isometry group are valid Euclidean saddles as well. Pure Einstein gravity is
known to diverge, when all saddles are included as contribution to the thermal
partition functions (also interpreted as the Hartle Hawking state in the
cosmological scenario). We show how higher spins, described by metric-Fronsdal
fields help making the partition function finite. Counter-intuitively, this
convergence is not achieved by mere inclusion of spin-3, but requires spin-4
interactions.


  The exciton lifetimes $T_1$ in arrays of InAs/GaAs vertically coupled quantum
dot pairs have been measured by time-resolved photoluminescence. A considerable
reduction of $T_1$ by up to a factor of $\sim$ 2 has been observed as compared
to a quantum dots reference, reflecting the inter-dot coherence. Increase of
the molecular coupling strength leads to a systematic decrease of $T_1$ with
decreasing barrier width, as for wide barriers a fraction of structures shows
reduced coupling while for narrow barriers all molecules appear to be well
coupled. The coherent excitons in the molecules gain the oscillator strength of
the excitons in the two separate quantum dots halving the exciton lifetime.
This superradiance effect contributes to the previously observed increase of
the homogeneous exciton linewidth, but is weaker than the reduction of $T_2$.
This shows that as compared to the quantum dots reference pure dephasing
becomes increasingly important for the molecules.


  We present here a very simple, short and new proof which shows that no
trapped surface is ever formed in spherical gravitational collapse of isolated
bodies. Although this derivation is of purely mathematical nature and without
any assumption, it is shown, in the Appendix, that, physically, trapped
surfaces do not form in order that the 3 speed of the fluid as measured by an
observer at a fixed circumference coordinate $R$ (a scalar), is less than the
speed of light $c$. The consequence of this result is that, mathematically,
even if there would be Schwarzschild Black Holes, they would have unique
gravitational mass M=0. Recall that Schwazschild BHs may be considered as a
special case of rotating Kerr BHs with rotation parameter a=0. If one would
derive the Boyer-Lindquist metric [1] in a straight forward manner by using the
Backlund transformation[2], one would obtain a= M sin phi where phi is the
azimuth angle. This relation demands that BHs have unique mass M=0 (along with
a=0) which in turn confirms that there cannot be any trapped surface in
realistic gravitational collapse where the fluid has real pressure and density.
Since there is no trapped surface and no horizon there is no Information
Paradox in the first place.


  We present evidence for rotational modulation of X-ray flares by an analysis
of four outbursts on late-type stars. Three of these flares have been observed
on T Tauri Stars and one on Algol.
  The structure of the X-ray lightcurves observed in this selection of flare
events is untypical in that the maximum emission extends over several hours
producing a round hump in the lightcurve instead of a sharp peak. We explain
this deviation from the standard shape of a flare lightcurve as the result of a
flare erupting on the back side of the star and gradually moving into the line
of sight due to the star's rotation. An estimate for the decay timescale and
the size of the X-ray emitting volume is derived. Spectral information supports
our proposition that changes of the visible volume are responsible for the
observed time development of these flares.


  The Jensen-Shannon divergence has been successfully applied as a segmentation
tool for symbolic sequences, that is to separate the sequence into subsequences
with the same symbolic content. In this work, we propose a method, based on the
the Jensen-Shannon divergence, for segmentation of what we call \textit{quantum
generated sequences}, which consist in symbolic sequences generated from
measuring a quantum system. For one-qubit and two-qubit systems, we show that
the proposed method is adequate for segmentation.


  Construction and training principles have been proposed and tested for an
artificial neural network based on metal-oxide thin-film nanostructures
possessing bipolar resistive switching (memristive) effect. Experimental
electronic circuit of neural network is implemented as a double-layer
perceptron with a weight matrix composed of 32 memristive devices. The network
training algorithm takes into account technological variations of the
parameters of memristive nanostructures. Despite the limited size of weight
matrix the developed neural network model is well scalable and capable of
solving nonlinear classification problems.


  Based on MUSE integral-field data we present evidence for a radial variation
at the low-mass end of the stellar initial-mass function (IMF) in the central
regions of the giant early-type galaxy NGC4486 (M87). We used state-of-the-art
stellar population models and the observed strength of various IMF-sensitive
absorption-line features to solve for the best low-mass tapered "bimodal" form
of the IMF, while accounting also for radial variations in stellar metallicity,
the overall $\alpha$-elements abundance, and the abundance of individual
elements such as Ti, O, Na and Ca. Our analysis reveals a strong IMF gradient
in M87, corresponding to an exceeding fraction of low-mass stars compared to
the case of the Milky Way toward the center of M87 that drops to nearly
Milky-way levels by 0.4 $R_e$. This IMF gradient is found to correlate well
with both the radial profile for stellar metallicity and for $\alpha$-elements
abundance but not with stellar velocity dispersion. Such IMF variations
correspond to over a factor two increase in stellar mass-to-light M/L ratio
compared to the case of a Milky-way like IMF, consistent with other
investigations into IMF gradients in early-type galaxies, including recent
dynamical constraints on M/L radial variations in M87 by Oldham & Auger. In
addition to constraining the IMF in M87 we also looked into the abundance of
Sodium, which turned up to be super-Solar over the entire radial range of our
MUSE observations and to exhibit a considerable negative gradient. These
findings suggest an additional role of metallicity in boosting the Na-yields in
the central, metal-rich regions of M87 during its early and brief
star-formation history. Our work adds the case of M87 to the few objects that
as of today have radial constraints on their IMF or [Na/Fe] abundance, while
also illustrating the accuracy that MUSE could bring to this kind of
investigations.


  Aerodynamic optimization is ubiquitous in the design of most engineering
systems interacting with fluids. A common approach is to optimize a performance
function defined by a choice of an aerodynamic model, e.g., turbulence RANS
model, and at nominal operating conditions. Practical experience indicates that
such a deterministic approach may result in considerably sub-optimal designs
when the adopted aerodynamic model does not lead to accurate flow predictions
or when the actual operating conditions differ from those considered in the
design. One approach to address this shortcoming is to consider an average or
robust design, wherein the statistical moments of the performance function,
given the uncertainty in the operating conditions and the aerodynamic model, is
optimized. However, when the number of uncertain inputs is large or the
performance function exhibits significant variability, an accurate evaluation
of these moments may require a large number of forward and/or adjoint solves,
at each iteration of a gradient-based scheme. This, in turn, renders the design
computationally expensive, if not infeasible. To tackle this difficulty, we
consider a variant of the stochastic gradient descent method where, in each
optimization iteration, a stochastic approximation of the objective,
constraints, and their gradients are generated. This is done via a small number
of forward/adjoint solves corresponding to random selections of the uncertain
parameters and aerodynamic model. The methodology is applied to the robust
optimization of the standard NACA-0012 subject to parametric and turbulence
model uncertainty. With a cost that is a small factor larger than that of the
deterministic approach, the stochastic gradient approach significantly improves
the performance (mean and variance) of the aerodynamic design for a wide range
of operating conditions and turbulence models.


  While deep learning has received a surge of interest in a variety of fields
in recent years, major deep learning models barely use complex numbers.
However, speech, signal and audio data are naturally complex-valued after
Fourier Transform, and studies have shown a potentially richer representation
of complex nets. In this paper, we propose a Complex Transformer, which
incorporates the transformer model as a backbone for sequence modeling; we also
develop attention and encoder-decoder network operating for complex input. The
model achieves state-of-the-art performance on the MusicNet dataset and an
In-phase Quadrature (IQ) signal dataset.