  Sound Event Localization and Detection refers to the problem of identifying
the presence of independent or temporally-overlapped sound sources, correctly
identifying to which sound class it belongs, estimating their spatial
directions while they are active. In the last years, neural networks have
become the prevailing method for sound Event Localization and Detection task,
with convolutional recurrent neural networks being among the most used systems.
This paper presents a system submitted to the Detection and Classification of
Acoustic Scenes and Events 2020 Challenge Task 3. The algorithm consists of a
convolutional recurrent neural network using rectangular filters, specialized
in recognizing significant spectral features related to the task. In order to
further improve the score and to generalize the system performance to unseen
data, the training dataset size has been increased using data augmentation. The
technique used for that is based on channel rotations and reflection on the xy
plane in the First Order Ambisonic domain, which allows improving Direction of
Arrival labels keeping the physical relationships between channels. Evaluation
results on the development dataset show that the proposed system outperforms
the baseline results, considerably improving Error Rate and F-score for
location-aware detection.


  A perturbative calculation of the three-nucleon ground-state energy shift in
a finite volume is carried out within the non-relativistic effective theory.
The energy shift is evaluated up to and including $\mathcal{O}(L^{-6})$, where
$L$ is the size of a cubic box. The convergence of the perturbative series at
physical values of the scattering lengths is studied numerically.


  In this work, we consider a class of dynamical systems described by ordinary
differential equations under the assumption that the global asymptotic
stability (GAS) of equilibrium points is established based on the Lyapunov
stability theory with the help of quadratic Lyapunov functions. We employ the
Micken's methodology to construct a family of explicit nonstandard finite
difference (NSFD) methods preserving any given quadratic Lyapunov function $V$,
i.e. they admit $V$ as a discrete Lyapunov function. Here, the proposed NSFD
methods are derived from a novel non-local approximation for the zero vector
function.
  Through rigorous mathematical analysis, we show that the constructed NSFD
methods have the ability to preserve any given quadratic Lyapunov functions
regardless of the values of the step size. As an important consequence, they
are dynamically consistent with respect to the GAS of continuous-time dynamical
systems. On the other hand, the positivity of the proposed NSFD methods is
investigated. It is proved that they can also preserve the positivity of
solutions of continuous-time dynamical systems.
  Finally, the theoretical findings are supported by a series of illustrative
numerical experiments, in which advantages of the NSFD methods are
demonstrated.


  A search for the non-resonant decays $B^0_s \rightarrow
\mu^{+}\mu^{-}\mu^{+}\mu^{-}$ and $B^0 \rightarrow
\mu^{+}\mu^{-}\mu^{+}\mu^{-}$ is presented. The measurement is performed using
the full Run 1 data set collected in proton-proton collisions by the LHCb
experiment at the LHC. The data correspond to integrated luminosities of $1$
and $2~\mathrm{fb}^{-1}$ collected at centre-of-mass energies of $7$ and
$8~\mathrm{TeV}$, respectively. No signal is observed and upper limits on the
branching fractions of the non-resonant decays at $95\%$ confidence level are
determined to be
  \mathcal{B}(B^0_s \rightarrow \mu^{+}\mu^{-}\mu^{+}\mu^{-}) & < 2.5 \times
10^{-9}
  \mathcal{B}(B^0 \rightarrow \mu^{+}\mu^{-}\mu^{+}\mu^{-}) & < 6.9 \times
10^{-10}.


  We present a set of detailed, self-consistent, isotropic dynamical models for
disc galaxies. We start from the hypothesis that each galaxy can be decomposed
in a bulge, following the r^{1/4} law, and a disc with an exponential projected
density profile; and that the isodensity surfaces of each component can be
represented by similar concentric spheroids. Under these conditions we produce
the rotational velocity and velocity dispersion profiles, after taking into
account both the asymmetric drift effects and the integration along the line of
sight. The model is successfully applied to reproduce the stellar kinematic and
photometry of the bulge of the 4 lenticular and early-type spiral galaxies NGC
4565, NGC 7814, NGC 5866 and NGC 4594. For these galaxies detailed stellar
kinematical data are available at different positions across the galaxy. The
application of our models shows that: 1) For all the galaxies considered in
this work, an isotropic model is able to reproduce the whole dynamical data.
This is surprising in the special case of NGC 4565, for which previous models
were unable to reproduce the velocity distribution without introducing strong
anisotropies. 2) We do not need a dark mass halo to reproduce these data. This
do not mean that a dark halo is not present, but just that its dynamical
effects in these inner regions of the galaxies are negligible.


  We show that a generic vector field on an affine space of positive
characteristic admits an invariant algebraic hypersurface. This contrast with
Jouanolou's Theorem that shows that in characteristic zero the situation is
completely opposite. That is a generic vector field in the complex plane does
not admit any invariant algebraic curve.


  Database research has always given limited attention to optimizing predicates
with disjunctions. What little past work there is, has mostly focused on
optimizations for traditional row-oriented databases. However, a key difference
between how row-oriented and column-oriented engines evaluate predicates is
that while row-oriented engines apply predicates to a single tuple at a time,
column-oriented engines apply predicates to sets of tuples, adding another
dimension to the problem. As such, row-oriented engines focus only on the best
order to apply predicates in to "short-circuit" the overall predicate
expression, but column-oriented engines must additionally decide on the input
sets of tuples for each predicate application. This is important, since smaller
inputs lead to faster runtimes, and nontrivial, since the results of earlier
predicates can be used to reduce the inputs to later predicates and predicates
may be combined via disjunctions in the predicate expression. In this work, we
formally analyze the predicate evaluation problem for column-oriented engines
and present BestD/Update, the first ever polynomial-time, provably optimal
algorithms to deduce the minimum input sets for each predicate application.
BestD/Update's optimality is guaranteed under a wide range of cost models,
representing different real-world scenarios. Furthermore, when combined with
the predicate ordering algorithm Hanani, BestD/Update reduce into EvalPred, a
simple O(n log^2 n) algorithm, which we recommend for practical use and optimal
for all predicate expressions of nested depth 2 or less. Our evaluation shows,
thanks to its optimality and polynomial planning time, EvalPred outperforms not
implementing any disjunction optimizations and exiting optimal algorithms by up
to 2.6x and 28x respectively for synthetic workloads and by up to 1.3x and 100x
respectively for queries from TPC-H and the CH-benchmark.


  A generalized translational invariant noncommutative field theory is analyzed
in detail, and a complete description of translational invariant noncommutative
structures is worked out. The relevant gauge theory is described, and the
planar and nonplanar axial anomalies are obtained.


  Managers regularly face a complex ethical dilemma over how to best govern
online communities by evaluating the effectiveness of different social or
technical strategies. What ethical considerations should guide researchers and
managers when they employ causal research methods that make different community
members bear different risks and benefits, under different levels of consent?
We introduce a structural framework for evaluating the flows of risks and
benefits in social systems with multiple interacting parties. This framework
has implications for understanding the governmentality of managing
socio-technical systems, for making research ethics discussions more
commensurable, and for enumerating alternative goals researchers might pursue
with interventions.


  Clock generators are an essential and critical building block of any
communication link, whether it be wired or wireless, and they are increasingly
critical given the push for lower I/O power and higher bandwidth in
Systems-on-Chip (SoCs) for the Internet-of-Things (IoT). One recurrent issue
with clock generators is multiple-phase generation, especially for low-power
applications; several methods of phase generation have been proposed, one of
which is phase interpolation. We propose a phase interpolator (PI) that employs
the concept of constant-slope operation. Consequently, a low-power
highly-linear operation is coupled with the wide dynamic range (i.e. phase
wrapping) capabilities of a PI. Furthermore, the PI is powered by a low-dropout
regulator (LDO) supporting fast transient operation. Implemented in 65-nm CMOS
technology, it consumes 350$\mu$W at a 1.2-V supply and a 0.5-GHz clock; it
achieves energy efficiency 4$\times$-15$\times$ lower than state-of-the-art
(SoA) digital-to-time converters (DTCs) and an integral non-linearity (INL) of
2.5$\times$-3.1$\times$ better than SoA PIs, striking a good balance between
linearity and energy efficiency.


  We study the half-filled Hubbard model on the triangular lattice with
spin-dependent Kitaev-like hopping. Using the variational cluster approach, we
identify five phases: a metallic phase, a non-coplanar chiral magnetic order, a
$120^\circ$ magnetic order, a nonmagnetic insulator (NMI), and an interacting
Chern insulator (CI) with a nonzero Chern number. The transition from CI to NMI
is characterized by the change of the charge gap from an indirect band gap to a
direct Mott gap. Based on the slave-rotor mean-field theory, the NMI phase is
further suggested to be a gapless Mott insulator with a spinon Fermi surface or
a fractionalized CI with nontrivial spinon topology, depending on the strength
of Kitaev-like hopping. Our work highlights the rising field that interesting
phases emerge from the interplay of band topology and Mott physics.


  We analyse the orbital motion of a light anti D6-brane in the presence of a
stack of heavy, distant D6-branes in ten dimensions, taking account of possible
time-variations in the background moduli fields. The Coulomb-like central
potential arising through brane-antibrane interactions is then modified to
include time-dependent prefactors, which generally preclude the existence of
stable elliptical orbits.


  Imputation of missing attribute values in medical datasets for extracting
hidden knowledge from medical datasets is an interesting research topic of
interest which is very challenging. One cannot eliminate missing values in
medical records. The reason may be because some tests may not been conducted as
they are cost effective, values missed when conducting clinical trials, values
may not have been recorded to name some of the reasons. Data mining researchers
have been proposing various approaches to find and impute missing values to
increase classification accuracies so that disease may be predicted accurately.
In this paper, we propose a novel imputation approach for imputation of missing
values and performing classification after fixing missing values. The approach
is based on clustering concept and aims at dimensionality reduction of the
records. The case study discussed shows that missing values can be fixed and
imputed efficiently by achieving dimensionality reduction. The importance of
proposed approach for classification is visible in the case study which assigns
single class label in contrary to multi-label assignment if dimensionality
reduction is not performed.


  For a non-cyclic free group $F$, the second homology of its pronilpotent
completion $H_2(\widehat F)$ is not a cotorsion group.


  We present lightcurves, shapes, and a 3D convex spin-axis model for the
main-belt asteroid (2501) Lohja. The models were obtained with the lightcurve
inversion process using combined dense photometric data from the apparitions
between years 2006 and 2017. The analysis found a sidereal period of
3.80835\,hours, and a possible ecliptic pole solution (J2000.0), the prograde
sense of rotation and main ratios of the ellipsoidal model.


  We consider a simple system with a local synchronous generator and a load
whose power consumption is a random process. The most probable scenario of
system failure (synchronization loss) is considered, and it is argued that its
knowledge is virtually enough to estimate the probability of failure per unit
time. We discuss two numerical methods to obtain the "optimal" evolution
leading to failure.


  We present measurements from Brookhaven Experiment 864 of neutron invariant
multiplicity in 11.5 A GeV/c Au+Pb collisions. The measurements span a rapidity
range from center-of-mass to beam rapidity (y(beam)=3.2) and are presented as a
function of event centrality. The results are compared with E864 measurements
of proton invariant multiplicity and an average n/p ratio at hadronic
freeze-out of 1.19+-.08 is determined for the rapidity range y=1.6 to y=2.4. We
discuss briefly the implications of this ratio within a simple equilibrium
model of the collision system.


  Improving the future of healthcare starts by better understanding the current
actual practices in hospitals. This motivates the objective of discovering
typical care pathways from patient data. Revealing homogeneous groups of care
pathways can be achieved through clustering. The difficulty in clustering care
pathways, represented by sequences of timestamped events, lies in defining a
semantically appropriate metric and clustering algorithms.
  In this article, we adapt two methods developed for time series to time
sequences: the drop-DTW metric and the DBA approach for the construction of
averaged time sequences. These methods are then applied in clustering
algorithms to propose original and sound clustering algorithms for timed
sequences.
  This approach is experimented with and evaluated on synthetic and real use
cases.


  Fractional (in time and in space) evolution equations defined on Dirichlet
regular bounded open domains, driven by fractional integrated in time Gaussian
spatiotemporal white noise, are considered here. Sufficient conditions for the
definition of a weak-sense Gaussian solution, in the mean-square sense, are
derived. The temporal, spatial and spatiotemporal H\"older continuity, in the
mean-square sense, of the formulated solution is obtained, under suitable
conditions, from the asymptotic properties of the Mittag-Leffler function, and
the asymptotic order of the eigenvalues of a fractional polynomial of the
Dirichlet negative Laplacian operator on such bounded open domains.


  The Chiral Perturbation Theory (ChPT) has been very successful in describing
low-energy hadronic properties in the non-perturbative regime of Quantum
Chromodynamics. The results of ChPT, many of which are currently under active
experimental investigation, provide stringent predictions of many fundamental
properties of hadrons, including quantities such as electromagnetic
polarizabilities. The paper outlines our semi-automated calculations in ChPT,
the corresponding results for the electric and magnetic polarizabilities of the
proton and our predictions for Compton differential cross sections.


  For a field F with discrete valuation and residue field $k$ we relate the
third homology of SL_2(F) with half-integral coefficients to the third homology
of SL_2(k) and a certain refined scissors congruence group of k. As an
application, we obtain explicit calculations of the third homology of SL_2 of
certain higher-dimensional local fields in terms of scissors congruence groups,
in the sense of Dupont and Sah, of the residue fields of the associated
valuations.


  Due to uncertainty in demand, sometimes management may delay their planning
of shifts later in the horizon when they have more information. We consider a
novel dynamic and flexible system for scheduling personnel for work shifts
planned at the last minute called 'on-call' shifts. The flexibility in the
system allows employees to freely select or reject shifts offered to them.
Senior employees may also replace or bump junior employees from the current
schedule if no other shift of their choice is available provided certain
conditions are met. The operator seeks to quickly assign all available shifts
to employees while ensuring minimum total bumps. We develop several
easy-to-implement policy approximations as well as deep reinforcement
learning-based policies to efficiently operate the system. A comparison of all
the developed policies is made using real data from our industrial partner. We
identify the best policies under different settings and significantly improve
upon the current practices.


  A major challenge in stroke research and stroke recovery predictions is the
determination of a stroke lesion's extent and its impact on relevant brain
systems. Manual segmentation of stroke lesions from 3D magnetic resonance (MR)
imaging volumes, the current gold standard, is not only very time-consuming,
but its accuracy highly depends on the operator's experience. As a result,
there is a need for a fully automated segmentation method that can efficiently
and objectively measure lesion extent and the impact of each lesion to predict
impairment and recovery potential which might be beneficial for clinical,
translational, and research settings. We have implemented and tested a fully
automatic method for stroke lesion segmentation which was developed using eight
different 2D-model architectures trained via transfer learning (TL) and mixed
data approaches. Additionally, the final prediction was made using a novel
ensemble method involving stacking and agreement window. Our novel method was
evaluated in a novel in-house dataset containing 22 T1w brain MR images, which
were challenging in various perspectives, but mostly because they included T1w
MR images from the subacute (which typically less well defined T1 lesions) and
chronic stroke phase (which typically means well defined T1-lesions).
Cross-validation results indicate that our new method can efficiently and
automatically segment lesions fast and with high accuracy compared to ground
truth. In addition to segmentation, we provide lesion volume and weighted
lesion load of relevant brain systems based on the lesions' overlap with a
canonical structural motor system that stretches from the cortical motor region
to the lowest end of the brain stem.


  Deep reinforcement learning algorithms require large and diverse datasets in
order to learn successful policies for perception-based mobile navigation.
However, gathering such datasets with a single robot can be prohibitively
expensive. Collecting data with multiple different robotic platforms with
possibly different dynamics is a more scalable approach to large-scale data
collection. But how can deep reinforcement learning algorithms leverage such
heterogeneous datasets? In this work, we propose a deep reinforcement learning
algorithm with hierarchically integrated models (HInt). At training time, HInt
learns separate perception and dynamics models, and at test time, HInt
integrates the two models in a hierarchical manner and plans actions with the
integrated model. This method of planning with hierarchically integrated models
allows the algorithm to train on datasets gathered by a variety of different
platforms, while respecting the physical capabilities of the deployment robot
at test time. Our mobile navigation experiments show that HInt outperforms
conventional hierarchical policies and single-source approaches.


  A classical problem in Software Engineering is how to certify that every
system requirement is correctly implemented by source code. This problem,
albeit well studied, can still be considered an open one, given the problems
faced by software development organizations. Trying to solve this problem,
Behavior-Driven Development (BDD) is a specification technique that
automatically certifies that all functional requirements are treated properly
by source code, through the connection of the textual description of these
requirements to automated tests. However, in some areas, such as Enterprise
Information Systems, requirements are identified by Business Process Modeling -
which uses graphical notations of the underlying business processes. Therefore,
the aim of this paper is to present Business Language Driven Development
(BLDD), a method that aims to extend BDD, by connecting business process models
directly to source code, while keeping the expressiveness of text descriptions
when they are better fitted than graphical artifacts.


  The purpose of this paper is to investigate the global categorical symmetries
that arise when gauging finite higher groups in three or more dimensions. The
motivation is to provide a common perspective on constructions of
non-invertible global symmetries in higher dimensions and a precise description
of the associated symmetry categories. This paper focusses on gauging finite
groups and split 2-groups in three dimensions. In addition to topological
Wilson lines, we show that this generates a rich spectrum of topological
surface defects labelled by 2-representations and explain their connection to
condensation defects for Wilson lines. We derive various properties of the
topological defects and show that the associated symmetry category is the
fusion 2-category of 2-representations. This allows us to determine the full
symmetry categories of certain gauge theories with disconnected gauge groups. A
subsequent paper will examine gauging more general higher groups in higher
dimensions.


  We present a detailed experimental study of the velocity distribution of
atoms cooled in an optical lattice. Our results are supported by full-quantum
numerical simulations. Even though the Sisyphus effect, the responsible cooling
mechanism, has been used extensively in many cold atom experiments, no detailed
study of the velocity distribution has been reported previously. For the
experimental as well as for the numerical investigation, it turns out that a
Gaussian function is not the one that best reproduce the data for all
parameters. We also fit the data to alternative functions, such as Lorentzians,
Tsallis functions and double Gaussians. In particular, a double Gaussian
provides a more precise fitting to our results.


  In the unconstrained MSSM, we reanalyze the constraints on the phases of
supersymmetric flavour conserving couplings that follow from the electron and
neutron electric dipole moments (EDM). We find that the constraints become weak
if at least one exchanged superpartner mass is >O(1 TeV) or if we accept large
cancellations among different contributions. However, such cancellations have
no evident underlying symmetry principle. For light superpartners, models with
small phases look like the easiest solution to the experimental EDM
constraints. This conclusion becomes stronger the larger is the value of
$\tan\beta$. We discuss also the dependence of $\epsilon_K$, $\Delta m_B$ and
$b\to s\gamma$ decay on those phases.


  In the present analysis of Procyon-A and Eta-Bootis, we use the local-wave
formalism which, despite its lack of precision inherent to any semi-analytical
method, uses directly the model profile without any modification when
calculating the acoustic mode eigenfrequencies. These two solar-like stars
present steep variations toward the center due to the convective core
stratification, and toward the surface due to the very thin convective zone.
Based on different boundary conditions, the frequencies obtained with this
formalism are different from that of the classical numerical calculation. We
point out that (1) the frequencies calculated with the local-wave formalism
seem to agree better with observational ones. All the frequencies detected with
a good confident level including those classified as 'noise' find an
identification, (2) some frequencies can be clearly identified here as
indications of the core limit.


  A final-state observability result in the Banach space setting for
non-autonomous observation problems is obtained that covers and extends all
previously known results in this context, while providing a streamlined proof
that follows the established Lebeau-Robbiano strategy.


  Motivated by simulations of carbon nanocones (see Jordan and Crespi, Phys.
Rev. Lett., 2004), we consider a variational plate model for an elastic cone
under compression in the direction of the cone symmetry axis. Assuming radial
symmetry, and modeling the compression by suitable Dirichlet boundary
conditions at the center and the boundary of the sheet, we identify the energy
scaling law in the von-K\'arm\'an plate model. Specifically, we find that three
different regimes arise with increasing indentation $\delta$: initially the
energetic cost of the logarithmic singularity dominates, then there is a linear
response corresponding to a moderate deformation close to the boundary of the
cone, and for larger $\delta$ a localized inversion takes place in the central
region. Then we show that for large enough indentations minimizers of the
elastic energy cannot be radially symmetric. We do so by an explicit
construction that achieves lower elastic energy than the minimum amount
possible for radially symmetric deformations.


  The $Q$-function is a central quantity in many Reinforcement Learning (RL)
algorithms for which RL agents behave following a (soft)-greedy policy w.r.t.
to $Q$. It is a powerful tool that allows action selection without a model of
the environment and even without explicitly modeling the policy. Yet, this
scheme can only be used in discrete action tasks, with small numbers of
actions, as the softmax cannot be computed exactly otherwise. Especially the
usage of function approximation, to deal with continuous action spaces in
modern actor-critic architectures, intrinsically prevents the exact computation
of a softmax. We propose to alleviate this issue by parametrizing the
$Q$-function implicitly, as the sum of a log-policy and of a value function. We
use the resulting parametrization to derive a practical off-policy deep RL
algorithm, suitable for large action spaces, and that enforces the softmax
relation between the policy and the $Q$-value. We provide a theoretical
analysis of our algorithm: from an Approximate Dynamic Programming perspective,
we show its equivalence to a regularized version of value iteration, accounting
for both entropy and Kullback-Leibler regularization, and that enjoys
beneficial error propagation results. We then evaluate our algorithm on classic
control tasks, where its results compete with state-of-the-art methods.


  The Fisher matrix (FM) has been generally used to predict the accuracy of the
gravitational wave parameter estimation. Although a limitation of the FM has
been well known, it is still mainly used due to its very low computational cost
compared to the Monte Carlo simulations. Recently, Rodriguez et al. [Phys. Rev.
D 88, 084013 (2013)] performed Markov chain Monte Carlo (MCMC) simulations for
nonspinning binary systems with total masses $M \leq 20 M_{\odot}$, they found
systematic differences between the predictions from FM and MCMC for $M>10
M_{\odot}$. On the other hand, an effective Fisher matrix (eFM) was recently
introduced by Cho et al. [Phys. Rev. D 87, 24004 (2013)]. The eFM is a
semi-analytic approach to the standard FM, in which the partial derivative is
taken by a quadratic fitting function to the local overlap surface. In this
work, we apply the eFM method to several nonspinning binary systems and find
that the error bounds in eFM are qualitatively in good agreement with the MCMC
results of Rodriguez et al. in all mass regions. In particular, we provide
concrete examples showing an importance of taking into account the
template-dependent frequency cutoff of the inspiral waveforms.


  I present results for soft anomalous dimensions in single top-quark
production processes through three loops. I first discuss the cusp anomalous
dimension with one massive and one massless line at three loops. I then
calculate the three-loop soft anomalous dimension for $tW$ production and for
related processes with new physics such as $tZ$, $t\gamma$, and $tH^-$
production. This is followed by results at two and three loops for the soft
anomalous dimension matrix in $s$-channel single-top production and, finally,
in $t$-channel single-top production. These new results are needed for
soft-gluon resummation at N$^3$LL accuracy and for calculations of soft-gluon
corrections at N$^3$LO.


  A unified evolutionary scheme that includes post-AGB systems, barium stars,
symbiotics, and related systems, explaining their similarites as well as
differences. Can we construct it? We compare these various classes of objects
in order to construct a consistent picture. Special attention is given to the
comparison of the barium pollution and symbiotic phenomena. Finally, we outline
a `transient torus' evolutionary scenario that makes use of the various
observational and theoretical hints and aims at explaining the observed
characteristics of the relevant systems.


  Collisional and radiative dynamics of a plasma is exposed by so-called
Collisional Radiative Models [1] that simplify the chemical kinetics by
quasi-steady state assignment on certain types of particles. The assignment is
conventionally based on the classification of the plasma species by the ratio
of the transport to the local destruction frequencies. We show that the
classification is not exact due to the role of the time-dependent local
production, and a measure is necessary to confirm the validity of the
assignment. The main goal of this study is to evaluate a measure on the
quasi-steady state assumptions of these models. Inspired by a chemical
reduction technique called Intrinsic Low Dimensional Manifolds [2, 3], an
estimate local source is provided at the transport time-scale. This source is a
deviation from the quasi-steady state for the particle and its value is
assigned as an error of the quasi-steady state assumption. The propagation of
this error on the derived quantities is formulated in the Collisional Radiative
Models. Based on the error a novel technique is proposed to discriminate the
quasi-steady states. The developed analysis is applied to mercury and argon
fluorescent lamps separately and the corresponding errors are presented. We
observe that the novel and conventional technique agrees for most of the
excited levels but disagrees for a few low energy excited states.


  The elastic anomaly observed in the coherent Kondo state of Ce heavy fermion
compounds is analyzed by using the Anderson lattice model simulating the energy
level scheme of CeTe. The $\Gamma_7$ doublets and $\Gamma_8$ quartets of the 4f
states are considered in the model. We solve the mean field equations to derive
the temperature dependences of elastic constants, using the random phase
approximation like expression for the interaction between the elastic strain
and the crystalline field splitting. We compare the calculation with the
$(c_{11} - c_{12})/2$ and $c_{44}$ constants of CeTe. The presence of the
downward dip and the observed overall temperature variations of the two
constants are well described by the present theory. The origin of the dip is
the coupling between the elastic strain and the splitting of the $\Gamma_8$
quartets.


  To operate in an urban environment, an automated vehicle must be capable of
accurately estimating its position within a global map reference frame. This is
necessary for optimal path planning and safe navigation. To accomplish this
over an extended period of time, the global map requires long-term maintenance.
This includes the addition of newly observable features and the removal of
transient features belonging to dynamic objects. The latter is especially
important for the long-term use of the map as matching against a map with
features that no longer exist can result in incorrect data associations, and
consequently erroneous localisation. This paper addresses the problem of
removing features from the map that correspond to objects that are no longer
observable/present in the environment. This is achieved by assigning a single
score which depends on the geometric distribution and characteristics when the
features are re-detected (or not) on different occasions. Our approach not only
eliminates ephemeral features, but also can be used as a reduction algorithm
for highly dense maps. We tested our approach using half a year of weekly
drives over the same 500-metre section of road in an urban environment. The
results presented demonstrate the validity of the long-term approach to map
maintenance.


  This article is concerned with the fluctuation analysis and the stability
properties of a class of one-dimensional Riccati diffusions. These
one-dimensional stochastic differential equations exhibit a quadratic drift
function and a non-Lipschitz continuous diffusion function. We present a novel
approach, combining tangent process techniques, Feynman-Kac path integration,
and exponential change of measures, to derive sharp exponential decays to
equilibrium. We also provide uniform estimates with respect to the time
horizon, quantifying with some precision the fluctuations of these diffusions
around a limiting deterministic Riccati differential equation. These results
provide a stronger and almost sure version of the conventional central limit
theorem. We illustrate these results in the context of ensemble Kalman-Bucy
filtering. To the best of our knowledge, the exponential stability and the
fluctuation analysis developed in this work are the first results of this kind
for this class of nonlinear diffusions.


  We formulate a density functional theory that describes the phase behavior of
hard rods and depleting polymers, as realized in recent experiments on
suspensions of \emph{fd} virus and non-adsorbing polymer. The theory predicts
the relative stability of nematic droplets, stacked smectic columns, and a
recently discovered phase of isolated monolayers of rods, or colloidal
membranes. We find that a minimum rod aspect ratio is required for stability of
colloidal membranes and that collective protrusion undulations are the dominant
effect that stabilizes this phase. The theoretical predictions are shown to be
qualitatively consistent with experimental and computational results.


  Piecewise Barrier Tubes (PBT) is a new technique for flowpipe
overapproximation for nonlinear systems with polynomial dynamics, which
leverages a combination of barrier certificates. PBT has advantages over
traditional time-step based methods in dealing with those nonlinear dynamical
systems in which there is a large difference in speed between trajectories,
producing an overapproximation that is time independent. However, the existing
approach for PBT is not efficient due to the application of interval methods
for enclosure-box computation, and it can only deal with continuous dynamical
systems without uncertainty. In this paper, we extend the approach with the
ability to handle both continuous and hybrid dynamical systems with uncertainty
that can reside in parameters and/or noise. We also improve the efficiency of
the method significantly, by avoiding the use of interval-based methods for the
enclosure-box computation without loosing soundness. We have developed a C++
prototype implementing the proposed approach and we evaluate it on several
benchmarks. The experiments show that our approach is more efficient and
precise than other methods in the literature.


  We consider generalized time-fractional evolution equations of the form
$$u(t)=u_0+\int_0^tk(t,s)Lu(s)ds$$ with a fairly general memory kernel $k$ and
an operator $L$ being the generator of a strongly continuous semigroup. In
particular, $L$ may be the generator $L_0$ of a Markov process $\xi$ on some
state space $Q$, or $L:=L_0+b\nabla+V$ for a suitable potential $V$ and drift
$b$, or $L$ generating subordinate semigroups or Schr\"{o}dinger type groups.
This class of evolution equations includes in particular time- and space-
fractional heat and Schr\"odinger type equations. We show that a subordination
principle holds for such evolution equations and obtain Feynman-Kac formulae
for solutions of these equations with the use of different stochastic
processes, such as subordinate Markov processes and randomly scaled Gaussian
processes. In particular, we obtain some Feynman-Kac formulae with generalized
grey Brownian motion and other related self-similar processes with stationary
increments.


  In this paper we use the Nuclear Magnetic Resonance (NMR) to write eletronic
states of a ferromagnetic system into a high-temperature paramagnetic nuclear
spins. Through the control of phase and duration of radiofrequency pulses we
set the NMR density matrix populations, and apply the technique of quantum
state tomography to experimentally obtain the matrix elements of the system,
from which we calculate the temperature dependence of magnetization for
different magnetic fields. The effects of the variation of temperature and
magnetic field over the populations can be mapped in the angles of spins
rotations, carried out by the RF pulses. The experimental results are compared
to the Brillouin functions of ferromagnetic ordered systems in the mean field
approximation for two cases: the mean field is given by (i) $B=B_0+\lambda M$
and (ii) $B=B_0+\lambda M + \lambda^\prime M^3$, where $B_0$ is the external
magnetic field, and $\lambda, \lambda^\prime$ are mean field parameters. The
first case exhibits second order transition, whereas the second case has first
order transition with temperature hysteresis. The NMR simulations are in good
agreement with the magnetic predictions.


  In this work, we show that a factored hybrid hidden Markov model (FH-HMM)
which is defined without any phonetic state-tying outperforms a
state-of-the-art hybrid HMM. The factored hybrid HMM provides a link to
transducer models in the way it models phonetic (label) context while
preserving the strict separation of acoustic and language model of the hybrid
HMM approach. Furthermore, we show that the factored hybrid model can be
trained from scratch without using phonetic state-tying in any of the training
steps. Our modeling approach enables triphone context while avoiding phonetic
state-tying by a decomposition into locally normalized factored posteriors for
monophones/HMM states in phoneme context. Experimental results are provided for
Switchboard 300h and LibriSpeech. On the former task we also show that by
avoiding the phonetic state-tying step, the factored hybrid can take better
advantage of regularization techniques during training, compared to the
standard hybrid HMM with phonetic state-tying based on classification and
regression trees (CART).


  We consider the oscillations of the atmosphere around a stratified back
ground density and entropy distribution under the gravitation on the flat
Earth. The atmosphere is supposed to be an ideal gas and the motion is supposed
to be governed by the compressible Euler equations. The density distribution of
the back ground equilibrium is supposed to touch the vacuum at the finite
height of the stratosphere. Considering the linearized approximation for small
perturbations, we show that time periodic oscillations with a sequence of time
periods which accumulate to infinity, say, slow and slow oscillations, so
called `g-modes', can appear when the square of the Brunt-Vaisala frequency is
positive everywhere for the considered back ground equilibrium.


  One decade ago, Bitcoin was introduced, becoming the first cryptocurrency and
establishing the concept of "blockchain" as a distributed ledger. As of today,
there are many different implementations of cryptocurrencies working over a
blockchain, with different approaches and philosophies. However, many of them
share one common feature: they require proof-of-work to support the generation
of blocks (mining) and, eventually, the generation of money. This proof-of-work
scheme often consists in the resolution of a cryptography problem, most
commonly breaking a hash value, which can only be achieved through brute-force.
The main drawback of proof-of-work is that it requires ridiculously large
amounts of energy which do not have any useful outcome beyond supporting the
currency. In this paper, we present a theoretical proposal that introduces a
proof-of-useful-work scheme to support a cryptocurrency running over a
blockchain, which we named Coin.AI. In this system, the mining scheme requires
training deep learning models, and a block is only mined when the performance
of such model exceeds a threshold. The distributed system allows for nodes to
verify the models delivered by miners in an easy way (certainly much more
efficiently than the mining process itself), determining when a block is to be
generated. Additionally, this paper presents a proof-of-storage scheme for
rewarding users that provide storage for the deep learning models, as well as a
theoretical dissertation on how the mechanics of the system could be
articulated with the ultimate goal of democratizing access to artificial
intelligence.


  Sensitivity analysis is a classical and fundamental tool to evaluate the role
of a given parameter in a given system characteristic. Because the phase
response curve is a fundamental input--output characteristic of oscillators, we
developed a sensitivity analysis for oscillator models in the space of phase
response curves. The proposed tool can be applied to high-dimensional
oscillator models without facing the curse of dimensionality obstacle
associated with numerical exploration of the parameter space. Application of
this tool to a state-of-the-art model of circadian rhythms suggests that it can
be useful and instrumental to biological investigations.


  We present a model of gamma-ray emission through neutral pion production and
decay in two-temperature accretion flows around supermassive black holes. We
refine previous studies of such a hadronic gamma-ray emission by taking into
account (1) relativistic effects in the photon transfer and (2) absorption of
gamma-ray photons in the radiation field of the flow. We use a fully general
relativistic description of both the radiative and hydrodynamic processes,
which allows us to study the dependence on the black hole spin. The spin value
strongly affects the gamma-ray emissivity within ~10 gravitational radii. The
central regions of flows with the total luminosities L < 0.001 of the Eddington
luminosity are mostly transparent to photons with energies below 10 GeV,
permitting investigation of the effects of space-time metric. For such L, an
observational upper limit on the gamma-ray (0.1-10 GeV) to X-ray (2-10 keV)
luminosity ratio of L_{0.1-10 GeV}/L_{2-10 keV} << 0.1 can rule out rapid
rotation of the black hole; on the other hand, a measurement of L_{0.1-10
GeV}/L_{2-10 keV} ~ 0.1 cannot be regarded as the evidence of rapid rotation,
as such a ratio can also result from a flat radial profile of gamma-ray
emissivity (which would occur for nonthermal acceleration of protons in the
whole body of the flow). At L > 0.01 of the Eddington luminosity, the gamma-ray
emission from the innermost region is strongly absorbed and the observed
gamma-rays do not carry information on the value of the black hole spin. We
note that if the X-ray emission observed in Centaurus A comes from an accretion
flow, the hadronic gamma-ray emission from the flow should contribute
significantly to the MeV/GeV emission observed from the core of this object,
unless it contains a slowly rotating black hole and protons in the flow are
thermal.


  In this invited talk, I discuss four important contributions of E.C.G.
Sudarshan, among many. They are the V-A theory of weak interaction, Sudarshan -
Glauber representation, tachyons and the quantum zeno effect.


  Vitrimers offer a promising sustainable alternative to conventional epoxies
due to their recyclability. Vitrimers are covalent adaptive networks where some
bonds can break and reform above the vitrimer transition temperature. While
this can lead to desirable behavior such as malleability, this also leads to
undesirable rheological behavior such as low-temperature creep. In this work,
we investigate the molecular mechanisms of the creep of vitrimers using
molecular dynamics simulations. The interplay between dynamic bonding with
mechanical loading is modeled using a topology-based reaction scheme. The creep
behavior is compared against cross-linked epoxies with dynamic reactions to
understand the unique aspects related to dynamic bonding. It is found that the
free volume that arises from tensile loads is reduced in vitrimers through
dynamic bond rearrangement. An important feature that explains the difference
in secondary creep behavior between conventional epoxies and vitrimers is the
orientation of the dynamic bonds during loading. In vitrimers, the dynamic
bonds preferentially align orthogonal to the loading axis, decreasing the axial
stiffness during secondary creep, resulting in larger creep strain compared to
epoxies. Over longer timescales, such increased strain leads to void growth,
resulting in tertiary creep. Thus, chemistry changes or additives that can
prevent the initial realignment of dynamic bonds, and therefore subsequent void
growth, can be an effective strategy to mitigate creep in vitrimers.


  In the field of autonomous driving, two important features of autonomous
driving car systems are the explainability of decision logic and the accuracy
of environmental perception. This paper introduces DME-Driver, a new autonomous
driving system that enhances the performance and reliability of autonomous
driving system. DME-Driver utilizes a powerful vision language model as the
decision-maker and a planning-oriented perception model as the control signal
generator. To ensure explainable and reliable driving decisions, the logical
decision-maker is constructed based on a large vision language model. This
model follows the logic employed by experienced human drivers and makes
decisions in a similar manner. On the other hand, the generation of accurate
control signals relies on precise and detailed environmental perception, which
is where 3D scene perception models excel. Therefore, a planning oriented
perception model is employed as the signal generator. It translates the logical
decisions made by the decision-maker into accurate control signals for the
self-driving cars. To effectively train the proposed model, a new dataset for
autonomous driving was created. This dataset encompasses a diverse range of
human driver behaviors and their underlying motivations. By leveraging this
dataset, our model achieves high-precision planning accuracy through a logical
thinking process.


  A non-destructive, gamma-spectrometric method for verifying the plutonium
content of Pu-Be neutron sources has been developed. It is also shown that the
isotopic composition and the age of plutonium (Pu) can be determined in the
intensive neutron field of these sources by the ``Multi-Group Analysis''
method. Gamma spectra were taken in the far-field of the sample, which was
assumed to be cylindrical. The isotopic composition and the age of Pu were
determined using a commercial implementation of the Multi-Group Analysis
algorithm. The Pu content of the sources was evaluated from the count rates of
the gamma-peaks of 239Pu, relying on the assumption that the gamma-rays are
coming to the detector parallel to each other. The determination of the
specific neutron yields and the problem of neutron damage to the detector are
also discussed.


  The purpose of this work is to produce a regularity theory for a class of
parabolic Isaacs equations. Our techniques are based on approximation methods
which allow us to connect our problem with a Bellman parabolic model. An
approximation regime for the coefficients, combined with a smallness condition
on the source term unlocks new regularity results in Sobolev and H\"older
spaces.


  Recent work has made significant progress in learning object meshes with weak
supervision. Soft Rasterization methods have achieved accurate 3D
reconstruction from 2D images with viewpoint supervision only. In this work, we
further reduce the labeling effort by allowing such 3D reconstruction methods
leverage unlabeled images. In order to obtain the viewpoints for these
unlabeled images, we propose to use a Siamese network that takes two images as
input and outputs whether they correspond to the same viewpoint. During
training, we minimize the cross entropy loss to maximize the probability of
predicting whether a pair of images belong to the same viewpoint or not. To get
the viewpoint of a new image, we compare it against different viewpoints
obtained from the training samples and select the viewpoint with the highest
matching probability. We finally label the unlabeled images with the most
confident predicted viewpoint and train a deep network that has a
differentiable rasterization layer. Our experiments show that even labeling
only two objects yields significant improvement in IoU for ShapeNet when
leveraging unlabeled examples. Code is available at
https://github.com/IssamLaradji/SSR.


  We present a model of fast hadronization of constituent quark matter in
relativistic heavy ion collisions based on rate equations and capture cross
sections in non-relativistic potential. We utilize a thermodynamically
consistent approach with a non-ideal equation of state including correlation
terms based on string phenomenology. We investigate strange and non-strange
particle ratios observed in CERN SPS experiments.


  The deep learning field is growing rapidly as witnessed by the exponential
growth of papers submitted to journals, conferences, and pre-print servers. To
cope with the sheer number of papers, several text mining tools from natural
language processing (NLP) have been proposed that enable researchers to keep
track of recent findings. In this context, our paper makes two main
contributions: first, we collected and annotated a dataset of papers paired by
title and sub-field from the field of artificial intelligence (AI), and,
second, we present results on how to predict a paper's AI sub-field from a
given paper title only. Importantly, for the latter, short-text classification
task we compare several algorithms from conventional machine learning all the
way up to recent, larger transformer architectures. Finally, for the
transformer models, we also present gradient-based, attention visualizations to
further explain the model's classification process. All code can be found at
\url{https://github.com/1pha/paperswithtopic}


  When subjected to a linearly polarized terahertz pulse, a mesoscopic ring
endowed with spin-orbit interaction (SOI) of the Rashba-Dresselhaus type
exhibits nonuniform azimuthal charge and spin distributions. Both types of SOI
couplings are considered linear in the electron momentum. Our results are
obtained within a formalism based on the equation of motion satisfied by the
density operator which is solved numerically for different values of the angle
$\phi$, the angle determining the polarization direction of the laser pulse.
Solutions thus obtained are later employed in determining the time-dependent
charge and spin currents, whose values are calculated in the stationary limit.
Both these currents exhibit an oscillatory behavior complicated in the case of
the spin current by a beating pattern. We explain this occurrence on account of
the two spin-orbit interactions which force the electron spin to oscillate
between the two spin quantization axes corresponding to Rashba and Dresselhaus
interactions. The oscillation frequencies are explained using the single
particle spectrum.


  A geometric interpretation of curvature and torsion of linear transports
along paths is presented. A number of (Bianchi type) identities satisfied by
these quantities are derived. The obtained results contain as special cases the
corresponding classical ones concerning curvature and torsion of linear
connections.


  In the present study the interaction of a sine-Gordon kink with a localized
inhomogeneity is considered. In the absence of dissipation, the inhomogeneity
considered is found to impose a potential energy barrier. The motion of the
kink for near-critical values of velocities separating transmission from
barrier reflection is studied. Moreover, the existence and stability properties
of the kink at the relevant saddle point are examined and its dynamics is found
to be accurately captured by effective low-dimensional models. In the case
where there is dissipation in the system, below the threshold value of the
current, a stable kink is found to exist in the immediate vicinity of the
barrier. The effective particle motion of the kink is investigated obtaining
very good agreement with the result of the original field model. Both one and
two degree-of-freedom settings are examined with the latter being more
efficient than the former in capturing the details of the kink motion.


  We give a presentation of the moduli stack of toric vector bundles with fixed
equivariant total Chern class as a quotient of a fine moduli scheme of framed
bundles by a linear group action. This fine moduli scheme is described
explicitly as a locally closed subscheme of a product of partial flag varieties
cut out by combinatorially specified rank conditions. We use this description
to show that the moduli of rank three toric vector bundles satisfy Murphy's
Law, in the sense of Vakil. The preliminary sections of the paper give a
self-contained introduction to Klyachko's classification of toric vector
bundles.


  Based on the available literature reviewed are results pertinent to the
effect of magnetism on lattice dynamics obtained by M\"ossbauer spectroscopy at
57Fe and 119Sn probe atoms. Presented and discussed are sigma-phase Fe-Cr,
Fe-V, Fe-Cr-Ni and lambda-phase (C14 Laves) NbFe2 compounds that belong to the
Frank-Kasper family of phases, metallic chromium and iron mono-arsenide, FeAs.
The common feature of all these metallic systems is itinerant character of
their magnetism. Two spectral parameters relevant to the lattice dynamics viz.
the center shift, CS, and/or the recoil-free fraction, f, are considered as
figures of merit. Their temperature dependences show anomalies at magnetic
ordering temperatures giving evidence that that vibrations in the magnetic
phase are different than those in the paramagnetic phase. The lower the
magnetic ordering temperature, the stronger the difference.


  In the era of Big Data and Deep Learning, there is a common view that machine
learning approaches are the only way to cope with the robust and scalable
information extraction and summarization. It has been recently proposed that
the CNL approach could be scaled up, building on the concept of embedded CNL
and, thus, allowing for CNL-based information extraction from e.g. normative or
medical texts that are rather controlled by nature but still infringe the
boundaries of CNL. Although it is arguable if CNL can be exploited to approach
the robust wide-coverage semantic parsing for use cases like media monitoring,
its potential becomes much more obvious in the opposite direction: generation
of story highlights from the summarized AMR graphs, which is in the focus of
this position paper.


  We give a new operator formula for Grothendieck polynomials that generalizes
Magyar's Demazure operator formula for Schubert polynomials. Our proofs are
purely combinatorial, contrasting with the geometric and representation
theoretic tools used by Magyar. We apply our formula to prove a necessary
divisibility condition for a monomial to appear in a given Grothendieck
polynomial.


  We study the de Haas-van Alphen (dHvA) oscillations in the two-dimensional
compensated metal with overtilted Dirac cones near the Lifshitz transition. We
employ the tight-binding model of $\alpha$-(BEDT-TTF)$_2$I$_3$, in which the
massless Dirac fermions are realized. When a uniaxial pressure $(P)$ along the
$y$-axis is applied above $P\simeq 0.2$ kbar in $\alpha$-(BEDT-TTF)$_2$I$_3$,
one electron pocket, which encloses the overtilted Dirac points, is changed to
two electron pockets, i.e., Lifshitz transition happens, while a hole pocket
does not change a topology. We show that the Fourier components corresponding
to the $3/2$ and $5/2$ areas of the hole pocket are anomalously enhanced in the
region of pressure where the Lifshitz transition occurs. This phenomenon will
be observed in the two-dimensional overtilted Dirac fermions near the Lifshitz
transition.


  Classification on smartphone-captured chest X-ray (CXR) photos to detect
pathologies is challenging due to the projective transformation caused by the
non-ideal camera position. Recently, various rectification methods have been
proposed for different photo rectification tasks such as document photos,
license plate photos, etc. Unfortunately, we found that none of them is
suitable for CXR photos, due to their specific transformation type, image
appearance, annotation type, etc. In this paper, we propose an innovative deep
learning-based Projective Transformation Rectification Network (PTRN) to
automatically rectify CXR photos by predicting the projective transformation
matrix. To the best of our knowledge, it is the first work to predict the
projective transformation matrix as the learning goal for photo rectification.
Additionally, to avoid the expensive collection of natural data, synthetic CXR
photos are generated under the consideration of natural perturbations, extra
screens, etc. We evaluate the proposed approach in the CheXphoto
smartphone-captured CXR photos classification competition hosted by the
Stanford University Machine Learning Group, our approach won first place with a
huge performance improvement (ours 0.850, second-best 0.762, in AUC). A deeper
study demonstrates that the use of PTRN successfully achieves the
classification performance on the spatially transformed CXR photos to the same
level as on the high-quality digital CXR images, indicating PTRN can eliminate
all negative impacts of projective transformation on the CXR photos.


  We extend some results on the convergence of one-dimensional diffusions
killed at the boundary, conditioned on extended survival, to the case of
general killing on the interior. We show, under fairly general conditions, that
a diffusion conditioned on long survival either runs off to infinity almost
surely, or almost surely converges to a quasistationary distribution given by
the lowest eigenfunction of the generator. In the absence of internal killing,
only a sufficiently strong inward drift can keep the process close to the
origin, to allow convergence in distribution. An alternative, that arises when
general killing is allowed, is that the conditioned process is held near the
origin by a high rate of killing near infinity. We also extend, to the case of
general killing, the standard result on convergence to a quasistationary
distribution of a diffusion on a compact interval.


  In this paper, we prove Khovanov-Lauda's cyclotomic categorification
conjecture for all symmetrizable Kac-Moody algebras. Let $U_q(g)$ be the
quantum group associated with a symmetrizable Cartan datum and let $V(\Lambda)$
be the irreducible highest weight $U_q(g)$-module with a dominant integral
highest weight $\Lambda$. We prove that the cyclotomic Khovanov-Lauda-Rouquier
algebra $R^{\Lambda}$ gives a categorification of $V(\Lambda)$.


  We propose a PDE-based accelerated gradient algorithm for optimal feedback
controls of McKean-Vlasov dynamics that involve mean-field interactions both in
the state and action. The method exploits a forward-backward splitting approach
and iteratively refines the approximate controls based on the gradients of
smooth costs, the proximal maps of nonsmooth costs, and dynamically updated
momentum parameters. At each step, the state dynamics is approximated via a
particle system, and the required gradient is evaluated through a coupled
system of nonlocal linear PDEs. The latter is solved by finite difference
approximation or neural network-based residual approximation, depending on the
state dimension. We present exhaustive numerical experiments for low and
high-dimensional mean-field control problems, including sparse stabilization of
stochastic Cucker-Smale models, which reveal that our algorithm captures
important structures of the optimal feedback control and achieves a robust
performance with respect to parameter perturbation.


  Let $d, n \in \mathbb{Z}^+$ such that $1\leq d \leq n$. A $d$-code
$\mathcal{C} \subset \mathbb{F}_q^{n \times n}$ is a subset of order $n$ square
matrices with the property that for all pairs of distinct elements in
$\mathcal{C}$, the rank of their difference is greater than or equal to $d$. A
$d$-code with as many as possible elements is called a maximum $d$-code. The
integer $d$ is also called the minimum distance of the code. When $d<n$, a
classical example of such an object is the so-called generalized Gabidulin
code. There exist several classes of maximum $d$-codes made up respectively of
symmetric, alternating and hermitian matrices. In this article we focus on such
examples. Precisely, we determine their automorphism groups and solve the
equivalence issue for them. Finally, we exhibit a maximum symmetric $2$-code
which is not equivalent to the one with same parameters known so far.


  Background: Employee innovation is a crucial aspect of organizations in the
current era. Therefore, studying the factors influencing individual innovation
is vital and unavoidable. Undoubtedly, job satisfaction is a significant
variable in management sciences. Nowadays, all organizations are interconnected
with technology. Objective: This research explores the relationship between job
satisfaction and individual innovation, including its components, and the
moderating role of technostress. Research Method: This study, in terms of
purpose, is applied, and in terms of data collection method, it is a
descriptive survey. Data collection tools included the Technostress Inventory
by Tarafdar and colleagues (2007), Janssen's Individual Innovation
Questionnaire (2000), and the Job Satisfaction Survey (JSS) by Spector (1994).
The validity and reliability of these questionnaires were confirmed. The sample
size for this study was 215, and data analysis was performed using SPSS and
SMART-PLS software. Findings: Job satisfaction has a significant and positive
relationship with individual innovation, idea generation, idea promotion, and
idea implementation. Technostress moderates the relationship between job
satisfaction and individual innovation, as well as idea generation and idea
promotion. However, technostress does not play a moderating role in the
relationship between job satisfaction and idea implementation. Conclusion:
Based on the obtained results, organizations should take necessary measures to
increase job satisfaction and reduce technostress among their employees.


  We describe the effects of the crossing of the exciton levels in moire
quantum dots in external magnetic field. We demonstrate that the unpolarized
light can create significant dynamic valley polarization of interlayer excitons
in the conditions where the Zeeman energy is much smaller than the thermal
energy. The angular momentum is gained by the excitons from the nuclear spin
fluctuations. In the $n$-type moire quantum dots for the typical experimental
parameters, the unpolarized light can lead to the complete valley polarization
of the resident electrons.


  We investigate SU(2) gauge theory in a constant chromomagnetic field in three
dimensions both in the continuum and on the lattice. Using a variational method
to stabilize the unstable modes, we evaluate the vacuum energy density in the
one-loop approximation. We compare our theoretical results with the outcomes of
the numerical simulations.


  A new construction of the real number system, that is built directly upon the
additive group of integers and has its roots in the definition due to Henri
Poincar\'e of the rotation number of an orientation preserving homeomorphism of
the circle.


  Once critical substations are compromised, attack agents can coordinate among
their peers to plot for maximizing disruption using local control devices. For
defenders, it is critical to enumerate and identify all digital relays to
determine the systemic risks. Any combination of disruptive switching via the
compromised relays can result in misoperation or immediate effect to the
system. The resulting consequence of these attack's initial events would
possibly incur cascading failure to a grid. This paper quantifies the
criticality of substation protective relays with the combination of the outage
level and its corresponding severity risk index. The proposed hypothesized
outages are based on the type of protective relaying, bus configuration of a
substation, and commonly implemented relaying schemes, such as bus
differential, directional overcurrent, and distance relays, are studied. This
preliminary work also provides three approaches of determination in
probabilities for sensitivity analysis. The proposed risk indices are evaluated
using IEEE test systems.


  This work proposes a general framework for the design and simulation of
network on chip based turbo decoder architectures. Several parameters in the
design space are investigated, namely the network topology, the parallelism
degree, the rate at which messages are sent by processing nodes over the
network and the routing strategy. The main results of this analysis are: i) the
most suited topologies to achieve high throughput with a limited complexity
overhead are generalized de-Bruijn and generalized Kautz topologies; ii)
depending on the throughput requirements different parallelism degrees, message
injection rates and routing algorithms can be used to minimize the network area
overhead.


  This paper considers discounted infinite horizon mean field games by
extending the probabilistic weak formulation of the game as introduced by
Carmona and Lacker (2015). Under similar assumptions as in the finite horizon
game, we prove existence and uniqueness of solutions for the extended infinite
horizon game. The key idea is to construct local versions of the previously
considered stable topologies. Further, we analyze how sequences of finite
horizon games approximate the infinite horizon one. Under a weakened
Lasry-Lions monotonicity condition, we can quantify the convergence rate of
solutions for the finite horizon games to the one for the infinite horizon game
using a novel stability result for mean field games. Lastly, applying our
results allows to solve the invariant mean field game as well.


  An ability to precisely regulate the quantity and location of molecular flux
is of value in applications such as nanoscale 3D printing, catalysis, and
sensor design. Barrier materials containing pores with molecular dimensions
have previously been used to manipulate molecular compositions in the gas
phase, but have so far been unable to offer controlled gas transport through
individual pores. Here, we show that gas flux through discrete angstrom-sized
pores in monolayer graphene can be detected and then controlled using
nanometer-sized gold clusters, which are formed on the surface of the graphene
and can migrate and partially block a pore. In samples without gold clusters,
we observe stochastic switching of the magnitude of the gas permeance, which we
attribute to molecular rearrangements of the pore. Our molecular valves could
be used, for example, to develop unique approaches to molecular synthesis that
are based on the controllable switching of a molecular gas flux, reminiscent of
ion channels in biological cell membranes and solid state nanopores.


  We consider a model mixture of hard colloidal spheres and non-adsorbing
polymer chains in a theta solvent. The polymer component is modelled as a
polydisperse mixture of effective spheres, mutually noninteracting but excluded
from the colloids, with radii that are free to adjust to allow for
colloid-induced compression. We investigate the bulk fluid demixing behaviour
of this model system using a geometry-based density-functional theory that
includes the polymer size polydispersity and configurational free energy,
obtained from the exact radius-of-gyration distribution for an ideal
(random-walk) chain. Free energies are computed by minimizing the free energy
functional with respect to the polymer size distribution. With increasing
colloid concentration and polymer-to-colloid size ratio, colloidal confinement
is found to increasingly compress the polymers. Correspondingly, the demixing
fluid binodal shifts, compared to the incompressible-polymer binodal, to higher
polymer densities on the colloid-rich branch, stabilizing the mixed phase.


  In this note, we prove pointwise decay in time of solutions to the 3D
energy-critical nonlinear Schr\"odinger equations assuming data in $L^1\cap
H^3$. The main ingredients are the boundness of the Schr\"odinger propagators
in Hardy space due to Miyachi \cite{Miyachi} and a fractional Leibniz rule in
the Hardy space. We also extend the fractional chain rule to the Hardy space.


  In this paper, we study tau-tilting modules over Nakayama algebras. We
establish bijections between tau-tilting modules, triangulations of a polygon
with a puncture and certain integer sequences. Moreover, we give an algorithm
to construct the Hasse quiver of support tau-tilting modules by using Rejection
Lemma of Drozd-Kirichenko.


  We establish that every monadic second-order logic (MSO) formula on graphs
with bounded treedepth is decidable in a constant number of rounds within the
CONGEST model. To our knowledge, this marks the first meta-theorem regarding
distributed model-checking. Various optimization problems on graphs are
expressible in MSO. Examples include determining whether a graph $G$ has a
clique of size $k$, whether it admits a coloring with $k$ colors, whether it
contains a graph $H$ as a subgraph or minor, or whether terminal vertices in
$G$ could be connected via vertex-disjoint paths. Our meta-theorem
significantly enhances the work of Bousquet et al. [PODC 2022], which was
focused on distributed certification of MSO on graphs with bounded treedepth.
Moreover, our results can be extended to solving optimization and counting
problems expressible in MSO, in graphs of bounded treedepth.


  We consider a scenario in which the global geometry of the universe is driven
by non-linear fermions obeying Heisenberg dynamics.


  The capacities of noisy quantum channels capture the ultimate rates of
information transmission across quantum communication lines, and the quantum
capacity plays a key role in determining the overhead of fault-tolerant quantum
computation platforms. In the case of bosonic systems, central to many
applications, no closed formulas for these capacities were known for bosonic
dephasing channels, a key class of non-Gaussian channels modelling, e.g., noise
affecting superconducting circuits or fiber-optic communication channels. Here
we provide the first exact calculation of the quantum, private, two-way
assisted quantum, and secret-key agreement capacities of all bosonic dephasing
channels. We prove that that they are equal to the relative entropy of the
distribution underlying the channel to the uniform distribution. Our result
solves a problem that has been open for over a decade, having been posed
originally by [Jiang & Chen, Quantum and Nonlinear Optics 244, 2010].


  Path integral Monte Carlo (PIMC) simulations are used to calculate the
momentum distribution of the homogeneous electron gas at finite temperature.
This is done by calculating the off-diagonal elements of the real-space density
matrix, represented in PIMC by open paths. It is demonstrated how the
restricted path integral Monte Carlo methods can be extended in order to deal
with open paths in fermionic systems where a sign problem is present. The
computed momentum distribution shows significant deviations for strong
correlation from free fermion results but agrees with predictions from
variational methods.


  (Abridged) Binary systems with similar components are ideal laboratories
which allow several physical processes to be tested, such as the possible
chemical pattern imprinted by the planet formation process. Aims. We explore
the probable chemical signature of planet formation in the remarkable binary
system HD 80606 - HD 80607. The star HD 80606 hosts a giant planet with 4 MJup
detected by both transit and radial velocity techniques, being one of the most
eccentric planets detected to date. We study condensation temperature Tc trends
of volatile and refractory element abundances to determine whether there is a
depletion of refractories that could be related to the terrestrial planet
formation. Methods. We carried out a high-precision abundance determination in
both components of the binary system, using a line-by-line strictly
differential approach, using the Sun as a reference and then using HD 80606 as
reference. We used an updated version of the program FUNDPAR, together with
ATLAS9 model atmospheres and the MOOG code. Conclusions. From the study of Tc
trends, we concluded that the stars HD 80606 and HD 80607 do not seem to be
depleted in refractory elements, which is different for the case of the Sun.
Then, the terrestrial planet formation would have been less efficient in the
components of this binary system than in the Sun. The lack of a trend for
refractory elements with Tc between both stars implies that the presence of a
giant planet do not neccesarily imprint a chemical signature in their host
stars, similar to the recent result of Liu et al. (2014). This is also in
agreement with Melendez et al. (2009), who suggest that the presence of
close-in giant planets might prevent the formation of terrestrial planets.
Finally, we speculate about a possible planet around the star HD 80607.


  We report on the modulation of indirect excitons (IXs) as well as their
transport by moving periodic potentials produced by surface acoustic waves
(SAWs). The potential modulation induced by the SAW strain modifies both the
band gap and the electrostatic field in the quantum wells confining the IX,
leading to changes in their energy. In addition, this potential capture and
transports IXs over several hundreds of {\mu}m. While the IX packets keep to a
great extent their spatial shape during transport by the moving potential, the
effective transport velocity is lower than the SAW group velocity and increases
with the SAW amplitude. This behavior is attributed to the capture of IXs by
traps along the transport path, thereby increasing the IX transit time. The
experimental results are well-reproduced by an analytical model for the
interaction between trapping centers and IXs during transport.


  In this paper, we analyse the role of non-Gaussianity in continuous-variable
(CV) quantum key distribution (QKD) with multimode light under mode-mismatch.
We consider entanglement-based protocol with non-Gaussian resources generated
by single-photon-subtraction and zero-photon-catalysis on a two-mode squeezed
vacuum state (TMSV). Our results indicate that, compared to the case of TMSV,
these non-Gaussian resources reasonably enhances the performance of CV-QKD,
even under the effect of noise arising due to mode-mismatch. To be specific,
while in the case of TMSV the maximum transmission distance is limited to 47
Km, single-photon subtracted TMSV and zero-photon-catalysed TMSV yield much
higher distance of 73 Km and 152 Km respectively. However, photon loss as a
practical concern in zero-photon-catalysis setup limits the transmission
distance for zero-photon-catalysed TMSV to 36 Km. This makes
single-photon-subtraction on TMSV to be the best choice for entanglement-based
CV-QKD in obtaining large transmission distance. Nonetheless, we note that the
non-Gaussianity does not improve the robustness of entanglement-based CV-QKD
scheme against detection inefficiency. We believe that our work provides a
practical view of implementing CV-QKD with multimode light under realistic
conditions.


  Kepler observations of the star HD 187091 (KID 8112039, hereafter KOI-54)
revealed a remarkable light curve exhibiting sharp periodic brightening events
every 41.8 days with a superimposed set of oscillations forming a beating
pattern in phase with the brightenings. Spectroscopic observations revealed
that this is a binary star with a highly eccentric orbit, e=0.83. We are able
to match the Kepler light curve and radial velocities with a nearly face-on
(i=5.5 degree) binary star model in which the brightening events are caused by
tidal distortion and irradiation of nearly identical A stars during their close
periastron passage. The two dominant oscillations in the light curve,
responsible for the beating pattern, have frequencies that are the 91st and
90th harmonic of the orbital frequency. The power spectrum of the light curve,
after removing the binary star brightening component, reveals a large number of
pulsations, 30 of which have a signal-to-noise ratio > 7. Nearly all of these
pulsations have frequencies that are either integer multiples of the orbital
frequency or are tidally-split multiples of the orbital frequency. This pattern
of frequencies unambiguously establishes the pulsations as resonances between
the dynamic tides at periastron and the free oscillation modes of one of the
stars. KOI-54 is only the 4th star to show such a phenomenon, and is by far the
richest in terms of excited modes.


  Microbiome research is now moving beyond the compositional analysis of
microbial taxa in a sample. Increasing evidence from large human microbiome
studies suggests that functional consequences of changes in the intestinal
microbiome may provide more power for studying their impact on inflammation and
immune responses. Although 16S rRNA analysis is one of the most popular and a
cost-effective method to profile the microbial compositions, marker-gene
sequencing cannot provide direct information about the functional genes that
are present in the genomes of community members. Bioinformatic tools have been
developed to predict microbiome function with 16S rRNA gene data. Among them,
PICRUSt2 has become one of the most popular functional profile prediction
tools, which generates community-wide pathway abundances. However, no
state-of-art inference tools are available to test the differences in pathway
abundances between comparison groups. We have developed ggpicrust2, an R
package, to do extensive differential abundance(DA) analyses and provide
publishable visualization to highlight the signals.


  Scene text detection, which is one of the most popular topics in both
academia and industry, can achieve remarkable performance with sufficient
training data. However, the annotation costs of scene text detection are huge
with traditional labeling methods due to the various shapes of texts. Thus, it
is practical and insightful to study simpler labeling methods without harming
the detection performance. In this paper, we propose to annotate the texts by
scribble lines instead of polygons for text detection. It is a general labeling
method for texts with various shapes and requires low labeling costs.
Furthermore, a weakly-supervised scene text detection framework is proposed to
use the scribble lines for text detection. The experiments on several
benchmarks show that the proposed method bridges the performance gap between
the weakly labeling method and the original polygon-based labeling methods,
with even better performance. We will release the weak annotations of the
benchmarks in our experiments and hope it will benefit the field of scene text
detection to achieve better performance with simpler annotations.


  We calculate the current and the spin-torque in small symmetric double tunnel
barrier ferromagnet - superconductor - ferromagnet (F-S-F) systems.
Spin-accumulation on the superconductor governs the transport properties when
the spin-flip relaxation time is longer than the transport dwell time. In the
elastic transport regime, it is demonstrated that the relative change in the
current (spin-torque) for F-S-F systems equals the relative change in the
current (spin-torque) for F-N-F systems upon changing the relative
magnetization direction of the two ferromagnets. This differs from the results
in the inelastic transport regime where spin-accumulation suppresses the
superconducting gap and dramatically changes the magnetoresistance [S.
Takahashi, H. Imamura, and S. Maekawa, Phys. Rev. Lett. 82, 3911 (1999)]. The
experimental relevance of the elastic and inelastic transport regimes,
respectively, as well as the reasons for the change in the transport properties
are discussed.


  The application of tunable diode laser absorption spectroscopy (TDLAS) to
flames with non-homogeneous temperature and concentration fields is an area
where only few studies exist. Experimental work explores the performance of
tomographic reconstructions of concentration and temperature profiles from
wavelength-modulated TDLAS measurements within the plume of an axisymmetric
McKenna burner. Water vapor transitions at 1391.67 nm and 1442.67 nm are probed
using calibration free wavelength modulation spectroscopy with second harmonic
detection (WMS-2f). A single collimated laser beam is swept parallel to the
burner surface, where scans yield pairs of line-of-sight (LOS) data at multiple
radial locations. Radial profiles of absorption data are reconstructed using
Tikhonov regularized Abel inversion, which suppresses the amplification of
experimental noise that is typically observed for reconstructions with high
spatial resolution. Based on spectral data, temperatures and concentrations are
calculated point-by-point. Here, a least-squares approach addresses
difficulties due to modulation depths that cannot be universally optimized due
to a non-uniform domain. Experimental results show successful reconstructions
of temperature and concentration profiles based on two-transition,
non-optimally modulated WMS-2f and Tikhonov regularized Abel inversion, and
thus validate the technique as a viable diagnostic tool for flame measurements.


  When considering particles produced in reactive plasmas, their basic
properties, such as refractive index and grain size often need to be known.
They can be constrained both ex-situ, e.g., by microscopy, and in-situ by
polarimetry, i.e., analyzing the polarization state of scattered light.
Polarimetry has the advantage of temporal resolution and real-time measurement,
but the analysis is often limited by the assumption of single scattering and
thus optically thin dust clouds. This limits the investigation of the growth
process typically to grain sizes smaller than about 200 nm. Using 3D polarized
radiative transfer simulations, however, it is possible to consider multiple
scattering and to analyze the properties of dense particle clouds.
  We study the impact of various properties of dust clouds on the scattering
polarization, namely the optical depth of the cloud, the spatial density
distribution of the particles, their refractive index as well as the particle
size dispersion. We find that ambiguities can occur regarding optical depth and
spatial density distribution as well as regarding refractive index and particle
size dispersion. Determining the refractive index correctly is especially
important as it has a strong impact on the derived particle sizes. With this
knowledge, we are able to design an in-situ diagnostics strategy for the
investigation of the particle growth process based on radiative transfer
simulations which are used to model the polarization over the whole growth
process. The application of this strategy allows us for the first time to
analyze the polarization measured during a growth experiment in a reactive
argon-acetylene plasma for particle radii up to 280 nm.


  Finite-size scaling analysis is a well-accepted method for identification and
characterization of quantum phase transitions (QPTs) in superconducting,
magnetic and insulating systems. We formally apply this analysis in the form
suitable for QPTs in 2-dimensional superconducting films to magnetic-field
driven superconductor-metal transition in 1-dimensional MoGe nanowires. Despite
being obviously inapplicable to nanowires, the 2d scaling equation leads to a
high-quality scaling collapse of the nanowire resistance in the temperature and
resistance ranges comparable or better to what is accepted in the analysis of
the films. Our results suggest that the appearance and the quality of the
scaling collapse by itself is not a reliable indicator of a QPT. We have also
observed a sign-change of the zero-bias anomaly (ZBA) in the non-linear
resistance, occurring exactly at the critical field of the accidental QPT. This
behavior is often taken as an additional confirmation of the transition. We
argue that in nanowires, the non-linearity is caused by electron heating and
has no relation to the critical fluctuations. Our observation suggests that
similar to the scaling collapse, the sign-change of ZBA can be a misleading
indicator of QPT.


  Elastic collisions of solitons generally have a finite phase shift. When the
phase shift has a finitely large value, the two vertices of the
(2+1)-dimensional 2-soliton are significantly separated due to the phase shift,
accompanied by the formation of a local structure connecting the two V-shaped
solitons. We define this local structure as the stem structure. This study
systematically investigates the localized stem structures between two solitons
in the (2+1)-dimensional asymmetric Nizhnik-Novikov-Veselov system. These stem
structures, arising from quasi-resonant collisions between the solitons,
exhibit distinct features of spatial locality and temporal invariance. We
explore two scenarios: one characterized by weakly quasi-resonant collisions
(i.e. $a_{12}\approx 0$), and the other by strongly quasi-resonant collisions
(i.e. $a_{12}\approx +\infty$). Through mathematical analysis, we extract
comprehensive insights into the trajectories, amplitudes, and velocities of the
soliton arms. Furthermore, we discuss the characteristics of the stem
structures, including their length and extreme points. Our findings shed new
light on the interaction between solitons in the (2+1)-dimensional asymmetric
Nizhnik-Novikov-Veselov system.


  We study time-reversal symmetry breaking in non-Hermitian fluctuating field
theories with conserved dynamics, comprising the mesoscopic descriptions of a
wide range of nonequilibrium phenomena. They exhibit continuous parity-time
($\mathcal{PT}$) symmetry breaking phase transitions to dynamical phases. For
two concrete transition scenarios, exclusive to non-Hermitian dynamics, namely
oscillatory instabilities and critical exceptional points, a low-noise
expansion exposes a pre-transitional surge of the mesoscale (informatic)
entropy production rate, inside the static phases. Its scaling in the
susceptibility contrasts conventional critical points (such as second-order
phase transitions), where the susceptibility also diverges, but the entropy
production generally remains finite. The difference can be attributed to active
fluctuations in the wavelengths that become unstable. For critical exceptional
points, we identify the coupling of eigenmodes as the entropy-generating
mechanism, causing a drastic noise amplification in the Goldstone mode.


  We investigate numerically the critical behaviour of a one-dimensional
non-attractive lattice gas model that is the continuous-time version of the
Domany-Kinzel cellular automaton in one of its parameter subspaces. The model
shows a phase transition in the directed percolation universality class of
critical behaviour, as expected.


  We address the interaction of graphene with 3d transition metal adatoms and
the formation of localized magnetic moments by means of first-principles
calculations. By comparing calculations within the generalized gradient
approximation (GGA) to GGA+U we find that the electronic configuration and the
adsorption geometries can be very sensitive to effects of local Coulomb
interactions U in the transition metal d-orbitals. We find high-spin
configurations being favorable for Cr and Mn adatoms independent of the
functional. For Fe, Co and Ni different electronic configurations are realized
depending on the value of the local Coulomb interaction strength U. Chemical
control over the spin of the adatoms by hydrogenation is demonstrated: NiH and
CoH adsorbed to graphene exhibit spin S=1/2 and S=1, respectively.


  A perspective is taken on the intangible complexity of economic and social
systems by investigating the underlying dynamical processes that produce, store
and transmit information in financial time series in terms of the
\textit{moving average cluster entropy}. An extensive analysis has evidenced
market and horizon dependence of the \textit{moving average cluster entropy} in
real world financial assets. The origin of the behavior is scrutinized by
applying the \textit{moving average cluster entropy} approach to long-range
correlated stochastic processes as the Autoregressive Fractionally Integrated
Moving Average (ARFIMA) and Fractional Brownian motion (FBM). To that end, an
extensive set of series is generated with a broad range of values of the Hurst
exponent $H$ and of the autoregressive, differencing and moving average
parameters $p,d,q$. A systematic relation between \textit{moving average
cluster entropy}, \textit{Market Dynamic Index} and long-range correlation
parameters $H$, $d$ is observed. This study shows that the characteristic
behaviour exhibited by the horizon dependence of the cluster entropy is related
to long-range positive correlation in financial markets. Specifically, long
range positively correlated ARFIMA processes with differencing parameter $
d\simeq 0.05$, $d\simeq 0.15$ and $ d\simeq 0.25$ are consistent with
\textit{moving average cluster entropy} results obtained in time series of
DJIA, S\&P500 and NASDAQ.


  We analyze the forced incompressible stationary Navier-Stokes flow in
$\mathbb{R}^n_+$, $n>2$. Existence of a unique solution satisfying a global
integrabilty property measured in a scale of tent spaces is established for
small data in homogenous Sobolev space with $s=-\frac{1}{2}$ degree of
smoothness. Moreover, the velocity field is shown to be locally H\"{o}lder
continuous while the pressure belongs to $L^p_{loc}$ for any $p\in (1,\infty)$.
Our approach is based on the analysis of the inhomogeneous Stokes system for
which we derive a new solvability result involving Dirichlet data in
Triebel-Lizorkin classes with negative amount of smoothness and is of
independent interest.


  Hybrid sp-sp2 structures can be efficiently obtained on metal substrates via
on-surface synthesis. The choice of both the precursor and of the substrate
impacts on the effectiveness of the process and the stability of the formed
structures. Here we demonstrate that using anthracene-based molecules as
precursor, the formation on Au(111) of polymers hosting sp carbon chains is
affected by the steric hindrance between aromatic groups. In particular, by
scanning tunneling microscopy and density functional theory calculations we
show that the de-metalation of organometallic structures induces a lateral
separation of adjacent polymers preventing the formation of ordered domains.


  Text-to-image (T2I) generative models have attracted significant attention
and found extensive applications within and beyond academic research. For
example, the Civitai community, a platform for T2I innovation, currently hosts
an impressive array of 74,492 distinct models. However, this diversity presents
a formidable challenge in selecting the most appropriate model and parameters,
a process that typically requires numerous trials. Drawing inspiration from the
tool usage research of large language models (LLMs), we introduce DiffAgent, an
LLM agent designed to screen the accurate selection in seconds via API calls.
DiffAgent leverages a novel two-stage training framework, SFTA, enabling it to
accurately align T2I API responses with user input in accordance with human
preferences. To train and evaluate DiffAgent's capabilities, we present
DABench, a comprehensive dataset encompassing an extensive range of T2I APIs
from the community. Our evaluations reveal that DiffAgent not only excels in
identifying the appropriate T2I API but also underscores the effectiveness of
the SFTA training framework. Codes are available at
https://github.com/OpenGVLab/DiffAgent.


  The wave-particle duality, as one of the expressions of Bohr complementarity,
is usually quantified by path predictability and the visibility of interference
fringes. With the development of quantum resource theory, the quantitative
analysis of wave-particle duality is increasing, most of which are expressed in
the form of specific functions. In this paper, we obtain the path information
measure for pure states by converting the coherence measure for pure state into
a symmetric concave function. Then we prove the function as a path information
measure is also valid for mixed states. Furthermore, we also establish a
generalized wave-particle-mixedness traility. Although the mixedness proposed
in the text is not a complete mixedness measure, it also satisfies some
conditions of mixdness measure. From the perspective of resource theory, the
path information we establish can be used as the measure of the resource of
predictability, and the triaility relationship we establish reveals the
relationship among coherence, predictability, purity and mixdness degree to a
certain extent. According to our method, given either coherence measure or path
information, a particular form of wave-particle-mixedness traility can be
established. This will play an important role in establishing connections
between wave, particle and other physical quantifiers.


  Vision-based activity recognition is essential for security, monitoring and
surveillance applications. Further, real-time analysis having low-quality video
and contain less information about surrounding due to poor illumination, and
occlusions. Therefore, it needs a more robust and integrated model for low
quality and night security operations. In this context, we proposed a hybrid
model for illumination invariant human activity recognition based on sub-image
histogram equalization enhancement and k-key pose human silhouettes. This
feature vector gives good average recognition accuracy on three low exposure
video sequences subset of original actions video datasets. Finally, the
performance of the proposed approach is tested over three manually downgraded
low qualities Weizmann action, KTH, and Ballet Movement dataset. This model
outperformed on low exposure videos over existing technique and achieved
comparable classification accuracy to similar state-of-the-art methods.


  This paper addresses the problem of self-supervised general-purpose audio
representation learning. We explore the use of Joint-Embedding Predictive
Architectures (JEPA) for this task, which consists of splitting an input
mel-spectrogram into two parts (context and target), computing neural
representations for each, and training the neural network to predict the target
representations from the context representations. We investigate several design
choices within this framework and study their influence through extensive
experiments by evaluating our models on various audio classification
benchmarks, including environmental sounds, speech and music downstream tasks.
We focus notably on which part of the input data is used as context or target
and show experimentally that it significantly impacts the model's quality. In
particular, we notice that some effective design choices in the image domain
lead to poor performance on audio, thus highlighting major differences between
these two modalities.


  This paper documents an exploratory pilot study to define the term Autonomous
Entity, and any characteristics that are required to identify or classify an
Autonomous Entity. Our solution builds on previous work with regard to
philosophical and scientific classification methods but focuses on a novel
Design Science Research Methodology (DSRM) and model to help identify those
characteristics which make any autonomous entity similar or different from
others. We have solved the problem of not having an existing term to define our
lens by creating a new combinatorial term: "Riskyishness". We present a DSRM
and instrument for initial investigation, as well as observational and
statistical descriptions of their use in the real world to solicit domain
expertise and statistical evidence. Further, we demonstrate a specific
application of the methodology by creating a second artifact - a tool to score
existing and future technologies based on Riskyishness. The first artifact also
provides a technique to disentangle miscellaneous existing technologies or add
dimensions to the tools to capture future additions and paradigm shifts.


  We propose a method for learning expressive energy-based policies for
continuous states and actions, which has been feasible only in tabular domains
before. We apply our method to learning maximum entropy policies, resulting
into a new algorithm, called soft Q-learning, that expresses the optimal policy
via a Boltzmann distribution. We use the recently proposed amortized Stein
variational gradient descent to learn a stochastic sampling network that
approximates samples from this distribution. The benefits of the proposed
algorithm include improved exploration and compositionality that allows
transferring skills between tasks, which we confirm in simulated experiments
with swimming and walking robots. We also draw a connection to actor-critic
methods, which can be viewed performing approximate inference on the
corresponding energy-based model.


  We describe a simple machinery which translates results on algebraic sums of
sets of reals into the corresponding results on their cartesian product. Some
consequences are:
  1. The product of a meager/null-additive set and a strong measure
zero/strongly meager set in the Cantor space has strong measure zero/is
strongly meager, respectively.
  2. Using Scheepers' notation for selection principles:
Sfin(Omega,Omega^gp)\cap S1(O,O)=S1(Omega,Omega^gp), and Borel's Conjecture for
S1(Omega,Omega) (or just S1(Omega,Omega^gp)) implies Borel's Conjecture.
  These results extend results of Scheepers and Miller, respectively.