  In this paper, we propose a novel method named Biomedical Confident Itemsets
Explanation (BioCIE), aiming at post-hoc explanation of black-box machine
learning models for biomedical text classification. Using sources of domain
knowledge and a confident itemset mining method, BioCIE discretizes the
decision space of a black-box into smaller subspaces and extracts semantic
relationships between the input text and class labels in different subspaces.
Confident itemsets discover how biomedical concepts are related to class labels
in the black-box's decision space. BioCIE uses the itemsets to approximate the
black-box's behavior for individual predictions. Optimizing fidelity,
interpretability, and coverage measures, BioCIE produces class-wise
explanations that represent decision boundaries of the black-box. Results of
evaluations on various biomedical text classification tasks and black-box
models demonstrated that BioCIE can outperform perturbation-based and decision
set methods in terms of producing concise, accurate, and interpretable
explanations. BioCIE improved the fidelity of instance-wise and class-wise
explanations by 11.6% and 7.5%, respectively. It also improved the
interpretability of explanations by 8%. BioCIE can be effectively used to
explain how a black-box biomedical text classification model semantically
relates input texts to class labels. The source code and supplementary material
are available at https://github.com/mmoradi-iut/BioCIE.


  The present article provides an overview of the recent progress in the direct
force measurements between individual pairs of colloidal particles in aqueous
salt solutions. Results obtained by two different techniques are being
highlighted, namely with the atomic force microscope (AFM) and optical
tweezers. One finds that the classical theory of Derjaguin, Landau, Verwey, and
Overbeek (DLVO) represents an accurate description of the force profiles even
in the presence of multivalent ions, typically down to distances of few
nanometers. However, the corresponding Hamaker constants and diffuse layer
potentials must be extracted from the force profiles. At low salt
concentrations, double layer forces remain repulsive and may become long
ranged. At short distances, additional short range non-DLVO interactions may
become important. Such an interaction is particularly relevant in the presence
of multivalent counterions.


  Active Brownian particles (ABPs) and Run-and-Tumble particles (RTPs) both
self-propel at fixed speed $v$ along a body-axis ${\bf u}$ that reorients
either through slow angular diffusion (ABPs) or sudden complete randomisation
(RTPs). We compare the physics of these two model systems both at microscopic
and macroscopic scales. Using exact results for their steady-state distribution
in the presence of external potentials, we show that they both admit the same
effective equilibrium regime perturbatively that breaks down for stronger
external potentials, in a model-dependent way. In the presence of collisional
repulsions such particles slow down at high density: their propulsive effort is
unchanged, but their average speed along ${\bf u}$ becomes $v(\rho) < v$. A
fruitful avenue is then to construct a mean-field description in which
particles are ghost-like and have no collisions, but swim at a variable speed
$v$ that is an explicit function or functional of the density $\rho$. We give
numerical evidence that the recently shown equivalence of the fluctuating
hydrodynamics of ABPs and RTPs in this case, which we detail here, extends to
microscopic models of ABPs and RTPs interacting with repulsive forces.


  We introduce a simplified model of the electron-beam/plasma system to model
the electrical breakdown caused by the inductive electric field created by a
rapidly rising electron beam current. The rigid-beam model is a reduction to
the problem geometry to cylindrical coordinated and simplifications to
Maxwell's equations that are driven by a prescribed electron beam current
density. The model is very convenient for comparing various reductions of the
plasma dynamics and air chemistry equation while maintaining a good
approximation to the overall magnitude of the beam-created electric field. The
usefulness of this model is demonstrated by comparing results for two different
fluid reductions of the plasma dynamics: one where the collision rates are
computed from the local reduced electric field (E/p) and another where the
collision rates are determined from the mean energy per particle. We find that
the two methods give similar results at higher pressures where the energy
relation rate is large but differs significantly at lower pressures where the
characteristic inelastic energy loss time scale is comparable to or greater
than the rise time of the electron beam current.


  We are interested in the ``smoothest'' averaging that can be achieved by
convolving functions $f \in \ell^2(\mathbb{Z})$ with an averaging function $u$.
More precisely, suppose $u:\{-n, \ldots, n\} \to \mathbb{R}$ is a symmetric
function normalized to $\sum_{k=-n}^{n}u(k) = 1$. We show that every
convolution operator is not-too-smooth, in the sense that $$\sup_{f \in
\ell^2(\mathbb{Z})} \frac{\| \nabla
(f*u)\|_{\ell^2(\mathbb{Z})}}{\|f\|_{\ell^2}}\geq \frac{2}{2n+1},$$ and we show
that equality holds if and only if $u$ is constant on the interval $\{-n,
\ldots, n\}$. In the setting where smoothness is measured by the $\ell^2$-norm
of the discrete second derivative and we further restrict our attention to
functions $u$ with nonnegative Fourier transform, we establish the inequality
$$\sup_{f \in \ell^2(\mathbb{Z})} \frac{\| \Delta
(f*u)\|_{\ell^2(\mathbb{Z})}}{\|f\|_{\ell^2(\mathbb{Z})}} \geq
\frac{4}{(n+1)^2},$$ with equality if and only if $u$ is the triangle function
$u(k)=(n+1-|k|)/(n+1)^2$. We also discuss a continuous analogue and several
open problems.


  Based on gauge invariance, we show that the new threshold phenomenon is
discovered in the \phi radiative decays \phi\to\gamma a0\to\gamma\pi0\eta and
\phi\to\gamma f0\to\gamma\pi0\pi0.
  This enables to conclude that production of the lightest scalar mesons
a0(980) and f0(980) in these decays is caused by the four-quark transitions,
resulting in strong restrictions on the large Nc expansions of the decay
amplitudes. The analysis shows that these constraints give new evidences in
favor of the four-quark nature of a0(980) and f0(980) mesons.


  We present a framework that resums threshold enhanced large logarithms to all
orders in perturbation theory for the production of a pair of leptons in
Drell-Yan process and of Higgs boson in gluon fusion as well as in bottom quark
annihilation. We restrict ourselves to contributions from diagonal partonic
channels. These logarithms include the distributions $((1-z)^{-1}
\ln^i(1-z))_+$ resulting from soft plus virtual (SV) and the logarithms
$\ln^i(1-z)$ from next-to-SV (NSV) contributions. We use collinear
factorisation and renormalisation group invariance to achieve this. The former
allows one to define a Soft-Collinear (SC) function which encapsulates soft and
collinear dynamics of the perturbative results to all orders in strong coupling
constant. The logarithmic structure of these results are governed by universal
infrared anomalous dimensions and process dependent functions of Sudakov
differential equation that the SC satisfies. The solution to the differential
equation is obtained by proposing an all-order ansatz in dimensional
regularisation, owing to several state-of-the-art perturbative results
available to third order. The $z$ space solutions thus obtained provide an
integral representation to sum up large logarithms originating from both soft
and collinear configurations, conveniently in Mellin $N$ space. We show that in
$N$ space, tower of logarithms $a_s^n/N^\alpha \ln^{2n-\alpha} (N),
a_s^n/N^\alpha \ln^{2n-1-\alpha}(N) \cdots $ etc for $\alpha =0,1$ are summed
to all orders in $a_s$.


  We obtain an upper bound on the number of extended irreducible q-ary Goppa
codes of degree $r$ and length $q^n+1$, where $q=p^t$ and $n$ and $r>2$ are
prime numbers.


  The quantum spin continuum and classical spin freezing, associated with a
glassy state, represent two opposite extremes of a correlated electronic
material. Here, we report the coexistence of a quantum spin continuum with a
weak spin glass order in Co-doped CaRuO$_{3}$ perovskite near the chemical
doping dependent metal-insulator transition boundary. Inelastic neutron
measurements on Ca(Co$_{0.15}$Ru$_{0.85}$)O$_{3}$ at low temperature, $T$ = 1.5
K, reveal a continuum spectrum in the $Q-E$ space due to uncorrelated spin
fluctuations. This persists across the glass transition at $T_G \simeq$23 K.
Furthermore, scaling of the dynamic susceptibility yields a very small scaling
coefficient $\alpha$ $\simeq$ 0.1, suggesting extreme locality of the dynamic
properties. The experimental results indicate the realization of a narrow
regime where the distinction between continuum dynamic behavior and glass-like
regimes is reduced.


  In countries such as Taiwan, with a high percentage of scooters,
scooter-related accidents are responsible for most injuries and deaths of all
traffic accidents. One viable approach to reduce the number of accidents is to
utilize short-range wireless communications between the scooter and other
vehicles. This would help neighboring vehicles to detect the scooter and
vice-versa, thus reducing the probability of a collision. In this paper, we
perform extensive measurements to characterize communication links between a
scooter and other vehicles. Our results suggest that, when the line-of-sight
propagation path is blocked by the body of the scooter driver (and possibly
also a passenger), shadowing of the human body results in significant signal
attenuation, ranging from 9 to 18 dB on average, presenting challenging channel
characteristics unique to scooters. In addition, we perform simulations, which
show that it is imperative to incorporate the body shadowing effect to obtain
realistic simulation results. We also develop a model to determine whether
human body shadowing is in effect, given the relative positions of the
transmitter and the receiver.


  The cross sections for the production of hadrons in quasi-real photon-photon
collisions in proton-proton and heavy ion interactions are compared with the
corresponding cross sections for central diffraction and for photon-pomeron
collisions. The signatures, heavy ions or protons with only slightly changed
momenta together with two large rapidity gaps and a cluster of produced hadrons
in the central region, are nearly identical in all three processes. Therefore,
it will be rather difficult to distinguish the reactions experimentally. It is
found, that central diffraction is the dominant process in collisions of
protons, light and medium-heavy ions. The photon-pomeron and photon-photon
processes have quite similar cross sections in collisions of heavy ions like
lead.


  For a given base space $M$ (spacetime), we consider the Guichardet space over
the Guichardet space over $M$. Here we develop a ''field calculus'' based on
the Guichardet integral. This is the natural setting in which to describe Green
function relations for Boson systems. Here we can follow the suggestion of
Schwinger and develop a differential (local field) approach rather than the
integral one pioneered by Feynman. This is helped by a DEFG
(Dyson-Einstein-Feynman-Guichardet) shorthand which greatly simplifies
expressions. This gives a convenient framework for the formal approach of
Schwinger and Tomonaga as opposed to Feynman diagrams. The Dyson-Schwinger is
recast in this language with the help of bosonic creation/annihilation
operators. We also give the combinatorial approach to tree-expansions.


  The correlated metal UCoGe is a weak itinerant ferromagnet with a Curie
temperature T_C = 3 K and a superconductor with a transition temperature T_s =
0.6 K. We review its basic thermal, magnetic - on the macro and microscopic
scale - and transport properties, as well as the response to high pressure. The
data unambiguously show that superconductivity and ferromagnetism coexist below
T_s = 0.6 K and are carried by the same 5f electrons. We present evidence that
UCoGe is a p-wave superconductor and argue that superconductivity is mediated
by critical ferromagnetic spin fluctuations.


  We develop an intrinsic geometric approach to calculus of variations on
Wasserstein space. We show that the flows associated to the Schroedinger bridge
with general prior, to Optimal Mass Transport and to the Madelung fluid can all
be characterized as annihilating the first variation of a suitable action. We
then discuss the implications of this unified framework for stochastic
mechanics: It entails, in particular, a sort of fluid-dynamic reconciliation
between Bohm's and Nelson's stochastic mechanics.


  We report thermodynamic and neutron scattering measurements of the
triangular-lattice quantum Ising magnet TmMgGaO 4 in longitudinal magnetic
fields. Our experiments reveal a quasi-plateau state induced by quantum
fluctuations. This state exhibits an unconventional non-monotonic field and
temperature dependence of the magnetic order and excitation gap. In the high
field regime where the quantum fluctuations are largely suppressed, we observed
a disordered state with coherent magnon-like excitations despite the
suppression of the spin excitation intensity. Through detailed semi-classical
calculations, we are able to understand these behaviors quantitatively from the
subtle competition between quantum fluctuations and frustrated Ising
interactions.


  In the context of the Covid-19 pandemic, multiple studies rely on two-way
fixed effects (FE) models to assess the impact of mitigation policies on health
outcomes. Building on the SIRD model of disease transmission, I show that FE
models tend to be misspecified for three reasons. First, despite misleading
common trends in the pre-treatment period, the parallel trends assumption
generally does not hold. Second, heterogeneity in infection rates and infected
populations across regions cannot be accounted for by region-specific fixed
effects, nor by conditioning on observable time-varying confounders. Third,
epidemiological theory predicts heterogeneous treatment effects across regions
and over time. Via simulations, I find that the bias resulting from model
misspecification can be substantial, in magnitude and sometimes in sign.
Overall, my results caution against the use of FE models for mitigation policy
evaluation.


  In this paper, we extend original Neural Collapse Phenomenon by proving
Generalized Neural Collapse hypothesis. We obtain Grassmannian Frame structure
from the optimization and generalization of classification. This structure
maximally separates features of every two classes on a sphere and does not
require a larger feature dimension than the number of classes. Out of curiosity
about the symmetry of Grassmannian Frame, we conduct experiments to explore if
models with different Grassmannian Frames have different performance. As a
result, we discover the Symmetric Generalization phenomenon. We provide a
theorem to explain Symmetric Generalization of permutation. However, the
question of why different directions of features can lead to such different
generalization is still open for future investigation.


  We report measurements of cross sections of neutrinos of 0.7 GeV average
energy scattering off a carbon target cross sections with by the SciBooNE
experiment at Fermilab. These measurements are important inputs for current and
future accelerator-based neutrino oscillation experiments in the interpretation
of neutrino oscillation signals. Contribution to NUFACT 11, XIIIth
International Workshop on Neutrino Factories, Super beams and Beta beams, 1-6
August 2011, CERN and University of Geneva (Submitted to IOP conference series)


  A new fourth order compact formulation for the steady 2-D incompressible
Navier-Stokes equations is presented. The formulation is in the same form of
the Navier-Stokes equations such that any numerical method that solve the
Navier-Stokes equations can also be applied to this fourth order compact
formulation. In particular in this work the formulation is solved with an
efficient numerical method that requires the solution of tridiagonal systems
using a fine grid mesh of 601x601. Using this formulation, the steady 2-D
incompressible flow in a driven cavity is solved up to Reynolds number of
20,000 with fourth order spatial accuracy. Detailed solutions are presented.


  We propose a new approach to the solution of the wave propagation and full
waveform inversions (FWIs) based on a recent advance in deep learning called
Physics-Informed Neural Networks (PINNs). In this study, we present an
algorithm for PINNs applied to the 2D acoustic wave equation and test the model
with both forward wave propagation and FWIs case studies. These synthetic case
studies are designed to explore the ability of PINNs to handle varying degrees
of structural complexity using both teleseismic plane waves and seismic point
sources. PINNs meshless formalism allows for a flexible implementation of the
wave equation and different types of boundary conditions. For instance, our
models demonstrate that PINN automatically satisfies absorbing boundary
conditions, a serious computational challenge for common wave propagation
solvers. Furthermore, a priori knowledge of the subsurface structure can be
seamlessly encoded in PINNs formulation. We find that the current
state-of-the-art PINNs provide good results for the forward model, even though
spectral element or finite difference methods are more efficient and accurate.
More importantly, our results demonstrate that PINNs yield excellent results
for inversions on all cases considered and with limited computational
complexity. Using PINNs as a geophysical inversion solver offers exciting
perspectives, not only for the full waveform seismic inversions, but also when
dealing with other geophysical datasets (e.g., magnetotellurics, gravity) as
well as joint inversions because of its robust framework and simple
implementation.


  The method of location and spectral estimation of weak signals on a noise
background is being considered. The method is based on the optimized on order
and noise dispersion autoregressive model of a sought signal. A new approach of
model order determination is being offered. Available estimation of the noise
dispersion is close to the real one. The optimized model allows to define
function of empirical data spectral and dynamic features changes. The analysis
of the signal as dynamic invariant in respect of the linear shift
transformation yields the function of model consistency. Use of these both
functions enables to detect short-time and nonstationary wave packets at signal
to noise ratio as from -20 dB and above.


  Nonlinear Optical Spectroscopy is a well-developed field with theoretical and
experimental advances that have aided multiple fields including chemistry,
biology and physics. However, accurate quantum dynamical simulations based on
model Hamiltonians are need to interpret the corresponding multi-dimensional
spectral signals properly. In this article, we present the initial release of
our code, QuDPy (quantum dynamics in python) which addresses the need for a
robust numerical platform for performing quantum dynamics simulations based on
model systems, including open quantum systems. An important feature of our
approach is that one can specify various high-order optical response pathways
in the form of double-sided Feynman diagrams via a straightforward input syntax
that specifies the time-ordering of ket-sided or bra-sided optical interactions
acting upon the time-evolving density matrix of the system. We use the quantum
dynamics capabilities of QuTip for simulating the spectral response of complex
systems to compute essentially any n-th-order optical response of the model
system. We provide a series of example calculations to illustrate the utility
of our approach.


  In this study, we tackle the challenging fine-grained edge detection task,
which refers to predicting specific edges caused by reflectance, illumination,
normal, and depth changes, respectively. Prior methods exploit multi-scale
convolutional networks, which are limited in three aspects: (1) Convolutions
are local operators while identifying the cause of edge formation requires
looking at far away pixels. (2) Priors specific to edge cause are fixed in
prediction heads. (3) Using separate networks for generic and fine-grained edge
detection, and the constraint between them may be violated. To address these
three issues, we propose a two-stage transformer-based network sequentially
predicting generic edges and fine-grained edges, which has a global receptive
field thanks to the attention mechanism. The prior knowledge of edge causes is
formulated as four learnable cause tokens in a cause-aware decoder design.
Furthermore, to encourage the consistency between generic edges and
fine-grained edges, an edge aggregation and alignment loss is exploited. We
evaluate our method on the public benchmark BSDS-RIND and several newly derived
benchmarks, and achieve new state-of-the-art results. Our code, data, and
models are publicly available at https://github.com/Daniellli/ECT.git.


  We introduce a notion of compatibility for multiplicity matrices. This gives
rise to a necessary condition for the join of two (possibly disconnected)
graphs $G$ and $H$ to be the pattern of an orthogonal symmetric matrix, or
equivalently, for the minimum number of distinct eigenvalues $q$ of $G\vee H$
to be equal to two. Under additional hypotheses, we show that this necessary
condition is also sufficient. As an application, we prove that $q(G\vee H)$ is
either two or three when $G$ and $H$ are unions of complete graphs, and we
characterise when each case occurs.


  Expressing the energy content of food as the heat energy released by its
combustion is potentially misleading. Food is used to produce adenosine
triphosphate (ATP). The free energy of conversion of ATP into adenosine
diphosphate is used directly for processes such as muscular contraction,
without the need for intermediate heat production. The number of ATP molecules
produced depends on the task being performed and the fitness level of the
person performing the task, since both affect the extent to which aerobic and
anaerobic respiration are involved. The digestion and metabolism of soluble
carbohydrates, complex carbohydrates and fats requires production of different
enzymes. The information required to assess whether this affects the net ATP
production by these different types of food does not appear to be available.


  Reinforcement learning has been applied to many interesting problems such as
the famous TD-gammon and the inverted helicopter flight. However, little effort
has been put into developing methods to learn policies for complex persistent
tasks and tasks that are time-sensitive. In this paper, we take a step towards
solving this problem by using signal temporal logic (STL) as task
specification, and taking advantage of the temporal abstraction feature that
the options framework provide. We show via simulation that a relatively easy to
implement algorithm that combines STL and options can learn a satisfactory
policy with a small number of training cases


  We have obtained high-resolution spectra of Jupiter's Great Red Spot (GRS)
between 4.6 and 5.4 microns using telescopes on Mauna Kea in order to derive
gas abundances and to constrain its cloud structure between 0.5 and 5~bars. We
used line profiles of deuterated methane CH3D at 4.66 microns to infer the
presence of an opaque cloud at 5+/-1 bar. From thermochemical models this is
almost certainly a water cloud. We also used the strength of Fraunhofer lines
in the GRS to obtain the ratio of reflected sunlight to thermal emission. The
level of the reflecting layer was constrained to be at 570+/-30 mbar based on
fitting strong ammonia lines at 5.32 microns. We identify this layer as an
ammonia cloud based on the temperature where gaseous ammonia condenses. We
found evidence for a strongly absorbing, but not totally opaque, cloud layer at
pressures deeper than 1.3 bar by combining Cassini/CIRS spectra of the GRS at
7.18 microns with ground-based spectra at 5 microns. This is consistent with
the predicted level of an NH4SH cloud. We also constrained the vertical profile
of water and ammonia. The GRS spectrum is matched by a saturated water profile
above an opaque water cloud at 5~bars. The pressure of the water cloud
constrains Jupiter's O/H ratio to be at least 1.1 times solar. The ammonia mole
fraction is 200+/-50ppm for pressures between 0.7 and 5 bar. Its abundance is
40 ppm at the estimated pressure of the reflecting layer. We obtained 0.8+/-0.2
ppm for PH3, a factor of 2 higher than in the warm collar surrounding the GRS.
We detected all 5 naturally occurring isotopes of germanium in GeH4 in the
Great Red Spot. We obtained an average value of 0.35+/-0.05 ppb for GeH4.
Finally, we measured 0.8+/-0.2 ppb for CO in the deep atmosphere.


  Problem of global integration of geometric structures arising in the theory
of dynamical systems admitting the normal shift is considered. In the case when
such integration is possible the problem of globalization for shift maps is
studied.


  We have used neutron scattering to investigate the influence of concentration
on the conformation of a star polymer. By varying the contrast between the
solvent and isotopically labeled stars, we obtain the distributions of polymer
and solvent within a star polymer from analysis of scattering data. A
correlation between the local desolvation and the inward folding of star
branches is discovered. From the perspective of thermodynamics, we find an
analogy between the mechanism of polymer localization driven by solvent
depletion and that of the hydrophobic collapse of polymers in solutions.


  Small angle neutron scattering measurements have been performed to study the
thermodynamic stability of skyrmion-lattice phases in Cu$_2$OSeO$_3$. We found
that the two distinct skyrmion-lattice phases [SkX(1) and SkX(2) phases] can be
stabilized through different thermal histories; by cooling from the
paramagnetic phase under finite magnetic field, the SkX(2) phase is selected.
On the other hand, the 30$^{\circ}$-rotated SkX(1) phase becomes dominant by
heating the sample from the ordered conical phase under finite field. This
difference in stabilization is surprisingly similar to the irreversibility
observed in spin glasses. The zero-field cooling results in the co-existence of
the two phases. It is further found that once one of the skyrmion-lattice
phases is formed, it is hardly destabilized. This indicates unusual thermal
stability of the two skyrmion-lattice phases originating from an unexpectedly
large energy barrier between them.


  The inner regions of the most massive compact stellar objects might be
occupied by a phase of quarks. Since the observations of the massive pulsars
PSR J1614-2230 and of PSR J0348+0432 with about two solar masses, the equations
of state constructing relativistic stellar models have to be constrained
respecting these new limits. We discuss stable hybrid stars, i.e. compact
objects with an outer layer composed of nuclear matter and with a core
consisting of quark matter (QM). For the outer nuclear layer we utilize a
density dependent nuclear equation of state and we use a chiral SU(3)
Quark-Meson model with a vacuum energy pressure to describe the objects core.
The appearance of a disconnected mass-radius branch emerging from the hybrid
star branch implies the existence of a third family of compact stars, so called
twin stars. Twin stars did not emerge as the transition pressure has to be
relatively small with a large jump in energy density, which could not be
satisfied within our approach. This is, among other reasons, due to the fact
that the speed of sound in QM has to be relatively high, which can be
accomplished by an increase of the repulsive coupling. This increase on the
other hand yields too high transition pressures for twins stars to appear.


  We study and extend the semidefinite programming (SDP) hierarchies introduced
in [Phys. Rev. Lett. 115, 020501] for the characterization of the statistical
correlations arising from finite dimensional quantum systems. First, we
introduce the dimension-constrained noncommutative polynomial optimization
(NPO) paradigm, where a number of polynomial inequalities are defined and
optimization is conducted over all feasible operator representations of bounded
dimensionality. Important problems in device independent and semi-device
independent quantum information science can be formulated (or almost
formulated) in this framework. We present effective SDP hierarchies to attack
the general dimension-constrained NPO problem (and related ones) and prove
their asymptotic convergence. To illustrate the power of these relaxations, we
use them to derive new dimension witnesses for temporal and Bell-type
correlation scenarios, and also to bound the probability of success of quantum
random access codes.


  It is well known that if $\mathcal{P}_t$ denotes a set of power means then
the mapping $\mathbb{R} \ni t \mapsto \mathcal{P}_t(v) \in (\min v, \max v)$ is
both 1-1 and onto for any non-constant sequence $v = (v_1,\dots,\,v_n)$ of
positive numbers. Shortly: the family of power means is a scale.
  If $I$ is an interval and $f \colon I \rightarrow \mathbb{R}$ is a
continuous, strictly monotone function then $f^{-1}(\tfrac{1}{n} \sum f(v_i))$
is a natural generalization of power means, so called quasi-arithmetic mean
generated by $f$.
  A famous folk theorem says that the only homogeneous, quasi-a\-rith\-me\-tic
means are power means. We prove that, upon replacing the homogeneity
requirement by an invariant-type axiom, one gets a family of quasi-arithmetic
means building up a scale, too.


  Few circumstellar disks have been directly observed. Here we use sensitive
differential polarimetric techniques to overcome atmospheric speckle noise in
order to image the circumstellar material around HD 169142. The detected
envelope or disk is considerably smaller than expectations based on the
measured strength of the far-IR excess from this system.


  In search of extra dimensions in the ongoing LHC experiments, signatures of
Randall-Sundrum (RS) lightest KK graviton have been one of the main focus in
recent years. The recent data from the dilepton decay channel at the LHC has
determined the experimental lower bound on the mass of the RS lightest
Kaluza-Klein (KK) graviton for different choices of underlying parameters of
the theory. In this work we explore the effects of the backreaction of the bulk
scalar field, which is employed to stabilise the RS model, in modifying the
couplings of the lightest KK graviton with the standard model (SM) matter
fields located on the visible brane. In such a modified background geometry we
show that the coupling of the lightest KK graviton with the SM matter fields
gets a significant suppression due to the inclusion of the backreaction of the
bulk stabilising scalar field. This implies that the backreaction parameter
weakens the signals from RS scenario in collider experiments which in turn
explains the non-visibility of KK graviton in colliders. Thus we show that the
modulus stabilisation plays a crucial role in the search of warped extra
dimensions in collider experiments.


  The $\beta$-skeleton approach can be conveniently utilized to construct the
cosmic web based on the spatial geometry distribution of galaxies, particularly
in sparse samples. This method plays a key role in establishing the
three-dimensional structure of the Universe and serves as a tool for
quantitatively characterizing the nature of the cosmic web. This study is the
first application of $\beta$-skeleton information as weights in mark weighted
correlation functions (MCFs), presenting a novel statistical measure. We have
applied the $\beta$-skeleton approach to the CMASS NGC galaxy samples from SDSS
BOSS DR12 in the redshift interval $0.45 \leq z \leq 0.55$. Additionally, we
applied this approach to three COLA cosmological simulations with different
settings ($\Omega_m=0.25, \Omega_m=0.31, \Omega_m=0.4$) for comparison. We
measured three MCFs, each weighted by i) the number of neighboring galaxies
around each galaxy, ii) the average distance of each galaxy from its
surrounding neighbors, and iii) the reciprocal of the average distance of each
galaxy from its surrounding neighbors. By comparing measurements and
calculating corresponding $\chi^2$ statistics, we observe high sensitivity to
the cosmological parameter $\Omega_m$ through a joint analysis of the two-point
correlation and three MCFs.


  When one studies the structure (e.g. graded ideals, graded subspaces,
radicals, ...) or graded polynomial identities of graded algebras, the grading
group itself does not play an important role, but can be replaced by any other
group that realizes the same grading. Here we come to the notion of weak
equivalence of gradings: two gradings are weakly equivalent if there exists an
isomorphism between the graded algebras that maps each graded component onto a
graded component. Each group grading on an algebra can be weakly equivalent to
G-gradings for many different groups G, however it turns out that there is one
distinguished group among them called the universal group of the grading. In
this paper we study categories and functors related to the notion of weak
equivalence of gradings. In particular, we introduce an oplax 2-functor that
assigns to each grading its support and show that the universal grading group
functor has neither left nor right adjoint.


  K -> Pi Pi decays are investigated in the framework of the SU(2)left x U(1)
gauge theory of J=0 mesons proposed in ref.[1], to which is added an
interaction between mesons respecting the two symmetries generally attributed
to strong interactions, flavour symmetry and parity conservation. It is shown
that the damping of the K+ -> Pi+ Pi0 amplitude with respect to the ones of
Kshort -> Pi Pi results from a cancelation between the W and Z gauge bosons.
The ratio of the two types of amplitudes is sensitive to the spectrum of
electroweak (pseudoscalar and scalar) mass eigenstates, an in particular to the
mass of the Higgs boson. In the case where scalars and pseudoscalars are
degenerate, the amplitudes satisfy the lower bound |(Kshort -> Pi+ Pi-) / (K+
-> Pi+ Pi0)| > 1/tan(Weinberg angle)^2.


  Until now, systematic errors in strong gravitational lens modeling have been
acknowledged but never been fully quantified. Here, we launch an investigation
into the systematics induced by constraint selection. We model the simulated
cluster Ares 362 times using random selections of image systems with and
without spectroscopic redshifts and quantify the systematics using several
diagnostics: image predictability, accuracy of model-predicted redshifts,
enclosed mass, and magnification. We find that for models with $>15$ image
systems, the image plane rms does not decrease significantly when more systems
are added; however the rms values quoted in the literature may be misleading as
to the ability of a model to predict new multiple images. The mass is well
constrained near the Einstein radius in all cases, and systematic error drops
to $<2\%$ for models using $>10$ image systems. Magnification errors are
smallest along the straight portions of the critical curve, and the value of
the magnification is systematically lower near curved portions. For $>15$
systems, the systematic error on magnification is $\sim2\%$. We report no trend
in magnification error with fraction of spectroscopic image systems when
selecting constraints at random; however, when using the same selection of
constraints, increasing this fraction up to $\sim0.5$ will increase model
accuracy. The results suggest that the selection of constraints, rather than
quantity alone, determines the accuracy of the magnification. We note that
spectroscopic follow-up of at least a few image systems is crucial, as models
without any spectroscopic redshifts are inaccurate across all of our
diagnostics.


  We observe that a recent result by Gardiner and Sj\"odin, solving a problem
of Kr\'{a}l on subharmonic functions, can be easily generalized to yield a
somewhat stronger result. This can be combined with a viscosity technique of
ours, which we slightly improve, to obtain Rad\'{o}- type theorems for
plurisubharmonic functions. Finally, we study the Borel complexity of the
critical set and the set where the gradient does not exist finitely of
subharmonic functions and general real valued functions.


  It is argued that Goedel's incompleteness theorem should be seen as
self-evident, rather than unexpected or surprising.


  We study numerically the effect of on-site Hubbard interaction U between two
electrons in the quasiperiodic Harper's equation. In the periodic chain limit
by mapping the problem to that of one electron in two dimensions with a
diagonal line of impurities of strength U we demonstrate a band of resonance
two particle pairing states starting from E=U. In the ballistic (metallic)
regime we show explicitly interaction-assisted extended pairing states and
multifractal pairing states in the diffusive (critical) regime. We also obtain
localized pairing states in the gaps and the created subband due to U, whose
number increases when going to the localized regime, which are responsible for
reducing the velocity and the diffusion coefficient in the qualitatively
similar to the non-interacting case ballistic and diffusive dynamics. In the
localized regime we find propagation enhancement for small U and stronger
localization for larger U, as in disordered systems.


  Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic
tool for cardiac diseases. However, a limitation of CMR is its slow imaging
speed, which causes patient discomfort and introduces artifacts in the images.
There has been growing interest in deep learning-based CMR imaging algorithms
that can reconstruct high-quality images from highly under-sampled k-space
data. However, the development of deep learning methods requires large training
datasets, which have not been publicly available for CMR. To address this gap,
we released a dataset that includes multi-contrast, multi-view, multi-slice and
multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac
cine and mapping sequences. Manual segmentations of the myocardium and chambers
of all the subjects are also provided within the dataset. Scripts of
state-of-the-art reconstruction algorithms were also provided as a point of
reference. Our aim is to facilitate the advancement of state-of-the-art CMR
image reconstruction by introducing standardized evaluation criteria and making
the dataset freely accessible to the research community. Researchers can access
the dataset at https://www.synapse.org/#!Synapse:syn51471091/wiki/.


  Codimension 2 contact submanifolds are the natural generalization of
transverse knots to contact manifolds of arbitrary dimension. In this paper, we
construct new invariants of codimension 2 contact submanifolds. Our main
invariant can be viewed as a deformation of the contact homology algebra of the
ambient manifold. We describe various applications of these invariants to
contact topology. In particular, we exhibit examples of codimension 2 contact
embeddings into overtwisted and tight contact manifolds which are formally
isotopic but fail to be isotopic through contact embeddings. We also give new
obstructions to certain relative symplectic and Lagrangian cobordisms.


  In turbulent dynamos the production of large-scale magnetic fields is
accompanied by a separation of magnetic helicity in scale. The large- and
small-scale parts increase in magnitude. The small-scale part can eventually
work against the dynamo and quench it, especially at high magnetic Reynolds
numbers. A one-dimensional mean-field model of a dynamo is presented where
diffusive magnetic helicity fluxes within the domain are important. It turns
out that this effect helps to alleviate the quenching. Here we show that
internal magnetic helicity fluxes, even within one hemisphere, can be important
for alleviating catastrophic quenching.


  To provide a live, active and high-quality virtual touring streaming
experience, we propose an unmanned drone stereoscopic streaming paradigm using
a control and streaming infrastructure of a 2.4GHz Wi-Fi grid. Our system
allows users to actively control the streaming captured by a drone, receive and
watch the streaming using a head mount display (HMD); a Wi-Fi grid is deployed
across the remote scene with multi-channel support to enable high-bitrate
stream- ing broadcast from the drones. The system adopt a joint view adaptation
and drone control scheme to enable fast viewer movement including both head
rotation and touring. We implement the prototype on Dji M100 quadcopter and HTC
Vive in a demo scene.


  We report on the formation of critical states in disordered graphene, at the
origin of variable and unconventional transport properties in the quantum Hall
regime, such as a zero-energy Hall conductance plateau in the absence of an
energy bandgap and Landau level degeneracy breaking. By using efficient
real-space transport methodologies, we compute both the dissipative and Hall
conductivities of large size graphene sheets with random distribution of model
single and double vacancies. By analyzing the scaling of transport coefficients
with defect density, system size and magnetic length, we elucidate the origin
of anomalous quantum Hall features as magnetic-field dependent impurity states,
which percolate at some critical energies. These findings shed light on
unidentified states and quantum transport anomalies reported experimentally.


  We consider simply-connected $4$-manifolds admitting Lefschetz fibrations
over the $2$-sphere. We explicitly construct nonhyperelliptic and hyperelliptic
Lefschetz fibrations of genus $4$ on simply-connected $4$-manifolds which are
exotic symplectic $4$-manifolds in the homeomorphism classes of
$\mathbb{C}P^{2}\#8\overline{\mathbb{C} P^{2}}$ and $\mathbb{C}
P^{2}\#9\overline{\mathbb{C} P^{2}}$, respectively. From these, we provide
upper bounds for the minimal number of singular fibers of such fibrations. In
addition, we prove that this number is equal to $18$ for $g=3$ when such
fibrations are hyperelliptic. Moreover, we discuss these numbers for higher
genera.


  There appears to be a fundamental problem facing Active Galactic Nuclei jet
models that require highly relativistic ejection speeds and small jet viewing
angles to explain the large apparent superluminal motions seen in so many of
the radio-loud quasars with high redshift. When the data are looked at closely
it is found that only a small percentage of the observed radio frequency flux
density from these sources can be Doppler boosted. Without a highly directed,
Doppler boosted component that dominates the observed flux, radio sources found
in low-frequency finding surveys cannot be preferentially selected with small
jet viewing angles. The distribution of jet orientations will then follow the
sin$i$ curve associated with a random distribution, where only a very few
sources (~1%) will have the small viewing angles (<8 deg) required to explain
apparent superluminal motions v{app > 10c, and this makes it difficult to
explain how around 33% of the radio-loud AGNs with high redshift can exhibit
such highly superluminal motions. When the boosted component is the dominant
one it can be argued that in a flux limited sample only those members with
small viewing angles would be picked up while those with larger viewing angles
(the un-boosted ones) would be missed. However, this is not the case when the
boosted component is small and a new model to explain the high apparent
superluminal motions may be needed if the redshifts of high-redshift quasars
are to remain entirely cosmological.


  Optical microcavities confine light to wavelength-scale volumes and are a key
component for manipulating and enhancing the interaction of light, vacuum
states, and matter. Current microcavities are constrained to a small number of
spatial mode profiles. Imaging cavities can accommodate complicated modes but
require an externally pre-shaped input. Here, we experimentally demonstrate a
visible-wavelength, metasurface-based, holographic microcavity that overcomes
these limitations. The micron-scale metasurface cavity fulfills the round-trip
condition for a designed mode with a complex-shaped intensity profile and thus
selectively enhances light that couples to this mode, achieving a spectral
bandwidth of 0.8 nm. By imaging the intracavity mode, we show that the
holographic mode changes quickly with the cavity length, and the cavity
displays the desired spatial mode profile only close to the design cavity
length. When placing a metasurface on a distributed Bragg reflector and
realizing steep phase gradients, the correct choice of the reflector's top
layer material can boost metasurface performance considerably. The applied
forward-design method is readily transferable to other spectral regimes and
mode profiles.


  We consider the hungry Volterra hierarchy from the view point of the multi
boson KP hierarchy. We construct the hungry Volterra equation as the
B\"{a}cklund transformations (BT) which are not the ordinary ones. We call them
``fractional '' BT. We also study the relations between the (discrete time)
hungry Volterra equation and two matrix models. From this point of view we
study the reduction from (discrete time) 2d Toda lattice to the (discrete time)
hungry Volterra equation.


  We present several new results on one of the most extensively studied topics
in computational geometry, orthogonal range searching. All our results are in
the standard word RAM model for points in rank space:
  ** We present two data structures for 2-d orthogonal range emptiness. The
first achieves O(n lglg n) space and O(lglg n) query time. This improves the
previous results by Alstrup, Brodal, and Rauhe(FOCS'00), with O(n lg^eps n)
space and O(lglg n) query time, or with O(nlglg n) space and O(lg^2 lg n) query
time. Our second data structure uses O(n) space and answers queries in O(lg^eps
n) time. The best previous O(n)-space data structure, due to Nekrich (WADS'07),
answers queries in O(lg n/lglg n) time.
  ** For 3-d orthogonal range reporting, we obtain space O(n lg^{1+eps} n) and
query time O(lglg n + k), for any constant eps>0. This improves previous
results by Afshani (ESA'08), Karpinski and Nekrich (COCOON'09), and Chan
(SODA'11), with O(n lg^3 n) space and O(lglg n + k) query time, or with O(n
lg^{1+eps} n) space and O(lg^2 lg n + k) query time. This implies improved
bounds for orthogonal range reporting in all constant dimensions above 3.
  ** We give a randomized algorithm for 4-d offline dominance range
reporting/emptiness with running time O(n lg n + k). This resolves two open
problems from Preparata and Shamos' seminal book:
  **** given n axis-aligned rectangles in the plane, we can report all k
enclosure pairs in O(n lg n + k) expected time. The best known result was an
O([n lg n + k] lglg n) algorithm from SoCG'95 by Gupta, Janardan, Smid, and
Dasgupta.
  **** given n points in 4-d, we can find all maximal points in O(n lg n)
expected time. The best previous result was an O(n lg n lglg n) algorithm due
to Gabow, Bentley, and Tarjan (STOC'84). This implies record time bounds for
the maxima problem in all constant dimensions above 4.


  We present a complete spectral analysis of an XMM-Newton and Chandra campaign
of the obscured AGN in NGC 4507, consisting of six observations spanning a
period of six months, ranging from June 2010 to December 2010. We detect strong
absorption variability on time scales between 1.5 and 4 months, suggesting that
the obscuring material consists of gas clouds at parsec-scale distance. The
lack of significant variability on shorter time scales suggests that this event
is not due to absorption by broad line region clouds, which was instead found
in other studies of similar sources. This shows that a single, universal
structure of the absorber (either BLR clouds, or the parsec-scale torus) is not
enough to reproduce the observed complexity of the X-ray absorption features of
this AGN.


  Hadron-hadron collisions at high energies are investigated in the
Ultra-relativistic-Quantum-Molecular-Dynamics approach (UrQMD). This
microscopic transport model is designed to study pp, pA and A+A collisions. It
describes the phenomenology of hadronic interactions at low and intermediate
energies ($\sqrt s <5$ GeV) in terms of interactions between known hadrons and
their resonances. At high energies, $\sqrt s >5$ GeV, the excitation of color
strings and their subsequent fragmentation into hadrons dominates the multiple
production of particles in the UrQMD model. The model shows a fair overall
agreement with a large body of experimental h-h data over a wide range of h-h
center-of-mass energies. Hadronic reaction data with higher precision would be
useful to support the use of the UrQMD model for relativistic heavy ion
collisions.


  Based on generalization and extension of previous work [Phys. Rev. Lett. {\bf
112}, 067201 (2014)] to multiple independent markovian baths we will compute
the charge and spin current statistics of the open Hubbard model with weak
system-bath coupling up to next-to-leading order in the coupling parameter. The
physical results are related to those for the $XXZ$ model in the analogous
setup implying a certain universality which potentially holds in this class of
nonequilibrium models.


  Cancer is one of the most widespread diseases around the world with millions
of new patients each year. Bladder cancer is one of the most prevalent types of
cancer affecting all individuals alike with no obvious prototypical patient.
The current standard treatment for BC follows a routine weekly Bacillus
Calmette-Guerin (BCG) immunotherapy-based therapy protocol which is applied to
all patients alike. The clinical outcomes associated with BCG treatment vary
significantly among patients due to the biological and clinical complexity of
the interaction between the immune system, treatments, and cancer cells. In
this study, we take advantage of the patient's socio-demographics to offer a
personalized mathematical model that describes the clinical dynamics associated
with BCG-based treatment. To this end, we adopt a well-established BCG
treatment model and integrate a machine learning component to temporally adjust
and reconfigure key parameters within the model thus promoting its
personalization. Using real clinical data, we show that our personalized model
favorably compares with the original one in predicting the number of cancer
cells at the end of the treatment, with 14.8% improvement, on average.


  We show that quantum fields confined to Lorentzian histories of freely
falling networks in Minkowski spacetime probe entanglement properties of vacuum
fluctuations that extend unrestricted across spacetime regions. Albeit
instantaneous field configurations are localized on one-dimensional edges,
angular momentum emerges on these network histories and establish the
celebrated area scaling of entanglement entropy.


  The bulk magnetic properties of the lanthanide metaborates, $Ln$(BO$_2$)$_3$,
$Ln$ = Pr, Nd, Gd, Tb are studied using magnetic susceptibility, heat capacity
and isothermal magnetisation measurements. They crystallise in a monoclinic
structure containing chains of magnetic $Ln^{3+}$ and could therefore exhibit
features of low-dimensional magnetism and frustration. Pr(BO$_2$)$_3$ is found
to have a non-magnetic singlet ground state. No magnetic ordering is observed
down to 0.4 K for Nd(BO$_2$)$_3$. Gd(BO$_2$)$_3$ exhibits a sharp magnetic
transition at 1.1 K, corresponding to three-dimensional magnetic ordering.
Tb(BO$_2$)$_3$ shows two magnetic ordering features at 1.05 K and 1.95 K. A
magnetisation plateau at a third of the saturation magnetisation is seen at 2 K
for both Nd(BO$_2$)$_3$ and Tb(BO$_2$)$_3$ which persists in an applied field
of 14 T. This is proposed to be a signature of quasi one-dimensional behaviour
in Nd(BO$_2$)$_3$ and Tb(BO$_2$)$_3$.


  We show that the description of laser-matter interaction in length gauge at
short short and in velocity gauge at longer distances allows for compact
physical modeling in terms of field free states, rapidly convergent numerical
approximation, and efficient absorption of outgoing flux. The mathematical and
numerical framework for using mixed gauge in practice is introduced. We
calculate photoelectron spectra generated by a laser field at wavelengths of
400$\sim$800 nm from single-electron systems and from the helium atom and
hydrogen molecule. We assess the accuracy of coupled channels calculations by
comparison to full two-electron solutions of the time-dependent Schrodinger
equation and find substantial advantages of mixed over velocity and length
gauges.


  We study theoretically the deterministic dynamics of single-domain
ferromagnetic nanoparticles in dilute ferrofluids, which is induced by a
time-varying gradient magnetic field. Using the force and torque balance
equations, we derive a set of the first-order differential equations describing
the translational and rotational motions of such particles characterized by
small Reynolds numbers. Since the gradient magnetic field generates both the
translations and rotations of particles, these motions are coupled. Based on
the derived set of equations, we demonstrate this fact explicitly by expressing
the particle position through the particle orientation angle, and vice versa.
The obtained expressions are used to show that the solution of the basic set of
equations is periodic in time and to determine the intervals, where the
particle coordinate and orientation angle oscillate. In addition, this set of
equations is solved approximately for the case of small characteristic
frequency of the particle oscillations. With this condition, we find that all
particles perform small translational oscillations near their initial
positions. In contrast, the orientation angle oscillates near the initial angle
only if particles are located in the vicinity of zero point of the gradient
magnetic field. The possible applications of the obtained results in
biomedicine and separation processes are also discussed.


  Active--sterile neutrino oscillations provide a mechanism by which large
differences in the neutrino and antineutrino number densities can be created in
the early universe. The quantum kinetic equations are employed in the study of
these asymmetries, which, when solved analytically in the adiabatic limit,
generate physically transparent evolution equations that are very useful for
the understanding of the nature of the asymmetry growth.


  Let $A=(a^i_j)$ be an orthogonal matrix with no entries zero. Let $B=(b^i_j)$
be the matrix defined by $b^i_j=\frac 1{a^i_j}$. M. Kontsevich conjectured that
the rank of $B$ is never equal to three. We interpret this conjecture
geometrically and prove it. The geometric statment can be understood as a
generalization of the Castelnouvo lemma and Brianchon's theorem.


  Diabatic description of rotational bands provides a clear-cut picture for
understanding the back-bending phenomena, where the internal structure of the
yrast band changes dramatically as a function of angular momentum. A
microscopic framework to obtain the diabatic bands within the mean-field
approximation is presented by making use of the selfconsistent collective
coordinate method. Applying the framework, both the ground state rotational
bands and the Stockholm bands are studied systematically for the rare-earth
deformed nuclei. An overall agreement has been achieved between the calculated
and observed rotational spectra. It is also shown that the inclusion of the
double-stretched quadrupole-pairing interaction is crucial to obtain an overall
agreement for the even-odd mass differences and the rotational spectra
simultaneously.


  The arrival of Sora marks a new era for text-to-video diffusion models,
bringing significant advancements in video generation and potential
applications. However, Sora, along with other text-to-video diffusion models,
is highly reliant on prompts, and there is no publicly available dataset that
features a study of text-to-video prompts. In this paper, we introduce VidProM,
the first large-scale dataset comprising 1.67 Million unique text-to-Video
Prompts from real users. Additionally, this dataset includes 6.69 million
videos generated by four state-of-the-art diffusion models, alongside some
related data. We initially discuss the curation of this large-scale dataset, a
process that is both time-consuming and costly. Subsequently, we underscore the
need for a new prompt dataset specifically designed for text-to-video
generation by illustrating how VidProM differs from DiffusionDB, a large-scale
prompt-gallery dataset for image generation. Our extensive and diverse dataset
also opens up many exciting new research areas. For instance, we suggest
exploring text-to-video prompt engineering, efficient video generation, and
video copy detection for diffusion models to develop better, more efficient,
and safer models. The project (including the collected dataset VidProM and
related code) is publicly available at https://vidprom.github.io under the
CC-BY-NC 4.0 License.


  It is known that any symmetric matrix $M$ with entries in $\R[x]$ and which
is positive semi-definite for any substitution of $x\in\R$, has a Smith normal
form whose diagonal coefficients are constant sign polynomials in $\R[x]$. We
generalize this result by considering a symmetric matrix $M$ with entries in a
formally real principal domain $A$, we assume that $M$ is positive
semi-definite for any ordering on $A$ and, under one additionnal hypothesis
concerning non-real primes, we show that the Smith normal of $M$ is positive,
up to association. Counterexamples are given when this last hypothesis is not
satisfied. We give also a partial extension of our results to the case of
Dedekind domains.


  Topological signals defined on nodes, links and higher dimensional simplices
define the dynamical state of a network or of a simplicial complex. As such,
topological signals are attracting increasing attention in network theory,
dynamical systems, signal processing and machine learning. Topological signals
defined on the nodes are typically studied in network dynamics, while
topological signals defined on links are much less explored. Here we
investigate Dirac synchronization, describing locally coupled topological
signals defined on the nodes and on the links of a network, and treated using
the topological Dirac operator. The dynamics of signals defined on the nodes is
affected by a phase lag depending on the dynamical state of nearby links and
vice versa. We show that Dirac synchronization on a fully connected network is
explosive with a hysteresis loop characterized by a discontinuous forward
transition and a continuous backward transition. The analytical investigation
of the phase diagram provides a theoretical understanding of this topological
explosive synchronization. The model also displays an exotic coherent
synchronized phase, also called rhythmic phase, characterized by non-stationary
order parameters which can shed light on topological mechanisms for the
emergence of brain rhythms.


  The macroscopic current density responsible for the mean magnetization
$\mathbf{M}$ of a uniformly magnetized bounded sample is localized near its
surface. In order to evaluate $\mathbf{M}$ one needs the current distribution
in the whole sample: bulk and boundary. In recent years it has been shown that
the boundary has no effect on $\mathbf{M}$ in insulators: therein, $\mathbf{M}$
admits an alternative expression, not based on currents. $\mathbf{M}$ can be
expressed in terms of the bulk electron distribution only, which is
"nearsighted" (exponentially localized); this virtue is not shared by metals,
having a qualitatively different electron distribution. We show, by means of
simulations on paradigmatic model systems, that even in metals the $\mathbf{M}$
value can be retrieved in terms of the bulk electron distribution only.


  5G millimeter wave (mmWave) signals can enable accurate positioning in
vehicular networks when the base station and vehicles are equipped with large
antenna arrays. However, radio-based positioning suffers from multipath signals
generated by different types of objects in the physical environment. Multipath
can be turned into a benefit, by building up a radio map (comprising the number
of objects, object type, and object state) and using this map to exploit all
available signal paths for positioning. We propose a new method for cooperative
vehicle positioning and mapping of the radio environment, comprising a
multiple-model probability hypothesis density filter and a map fusion routine,
which is able to consider different types of objects and different fields of
views. Simulation results demonstrate the performance of the proposed method.


  Results of experimental investigations of a new assumed interaction in nature
with the aid of high-current magnets, torsion and piezoresonance balances,
high-precision gravimeter, fluctuations in intensity of betta-decay of
radioactive elements, plasma devices and manifestations in astrophysics are
presented. A possible explanation of the results obtained based on a hypothesis
of global anisotropy of physical space caused by the existence of a
cosmological vectorial potential A_g, is given. It is shown that the vector A_g
has the following coordinates in the second equatorial coordinate system: right
ascension alpha = 293 +- 10, declination delta = 36 +- 10.


  In this review paper, we delve into the realm of Large Language Models
(LLMs), covering their foundational principles, diverse applications, and
nuanced training processes. The article sheds light on the mechanics of
in-context learning and a spectrum of fine-tuning approaches, with a special
focus on methods that optimize efficiency in parameter usage. Additionally, it
explores how LLMs can be more closely aligned with human preferences through
innovative reinforcement learning frameworks and other novel methods that
incorporate human feedback. The article also examines the emerging technique of
retrieval augmented generation, integrating external knowledge into LLMs. The
ethical dimensions of LLM deployment are discussed, underscoring the need for
mindful and responsible application. Concluding with a perspective on future
research trajectories, this review offers a succinct yet comprehensive overview
of the current state and emerging trends in the evolving landscape of LLMs,
serving as an insightful guide for both researchers and practitioners in
artificial intelligence.


  Due to rapid data growth, statistical analysis of massive datasets often has
to be carried out in a distributed fashion, either because several datasets
stored in separate physical locations are all relevant to a given problem, or
simply to achieve faster (parallel) computation through a divide-and-conquer
scheme. In both cases, the challenge is to obtain valid inference that does not
require processing all data at a single central computing node. We show that
for a very widely used class of spatial low-rank models, which can be written
as a linear combination of spatial basis functions plus a fine-scale-variation
component, parallel spatial inference and prediction for massive distributed
data can be carried out exactly, meaning that the results are the same as for a
traditional, non-distributed analysis. The communication cost of our
distributed algorithms does not depend on the number of data points. After
extending our results to the spatio-temporal case, we illustrate our
methodology by carrying out distributed spatio-temporal particle filtering
inference on total precipitable water measured by three different satellite
sensor systems.


  Pretrained language models based on the transformer architecture have shown
great success in NLP. Textual training data often comes from the web and is
thus tagged with time-specific information, but most language models ignore
this information. They are trained on the textual data alone, limiting their
ability to generalize temporally. In this work, we extend the key component of
the transformer architecture, i.e., the self-attention mechanism, and propose
temporal attention - a time-aware self-attention mechanism. Temporal attention
can be applied to any transformer model and requires the input texts to be
accompanied with their relevant time points. It allows the transformer to
capture this temporal information and create time-specific contextualized word
representations. We leverage these representations for the task of semantic
change detection; we apply our proposed mechanism to BERT and experiment on
three datasets in different languages (English, German, and Latin) that also
vary in time, size, and genre. Our proposed model achieves state-of-the-art
results on all the datasets.


  In this paper, it is shown that every orientable closed 3-manifold maps with
nonzero degree onto at most finitely many homeomorphically distinct irreducible
non-geometric orientable closed 3-manifolds. Moreover, given any nonzero
integer, as a mapping degree up to sign, every orientable closed 3-manifold
maps with that degree onto only finitely many homeomorphically distinct
orientable closed 3-manifolds.


  Data augmentation has been demonstrated as an effective strategy for
improving model generalization and data efficiency. However, due to the
discrete nature of natural language, designing label-preserving transformations
for text data tends to be more challenging. In this paper, we propose a novel
data augmentation framework dubbed CoDA, which synthesizes diverse and
informative augmented examples by integrating multiple transformations
organically. Moreover, a contrastive regularization objective is introduced to
capture the global relationship among all the data samples. A momentum encoder
along with a memory bank is further leveraged to better estimate the
contrastive loss. To verify the effectiveness of the proposed framework, we
apply CoDA to Transformer-based models on a wide range of natural language
understanding tasks. On the GLUE benchmark, CoDA gives rise to an average
improvement of 2.2% while applied to the RoBERTa-large model. More importantly,
it consistently exhibits stronger results relative to several competitive data
augmentation and adversarial training base-lines (including the low-resource
settings). Extensive experiments show that the proposed contrastive objective
can be flexibly combined with various data augmentation approaches to further
boost their performance, highlighting the wide applicability of the CoDA
framework.


  A rigorous and comprehensive testing plays a key role in training
self-driving cars to handle variety of situations that they are expected to see
on public roads.
  The physical testing on public roads is unsafe, costly, and not always
reproducible. This is where testing in simulation helps fill the gap, however,
the problem with simulation testing is that it is only as good as the simulator
used for testing and how representative the simulated scenarios are of the real
environment. In this paper, we identify key requirements that a good simulator
must have. Further, we provide a comparison of commonly used simulators. Our
analysis shows that CARLA and LGSVL simulators are the current state-of-the-art
simulators for end to end testing of self-driving cars for the reasons
mentioned in this paper. Finally, we also present current challenges that
simulation testing continues to face as we march towards building fully
autonomous cars.


  The recognition and understanding of traffic incidents, particularly traffic
accidents, is a topic of paramount importance in the realm of intelligent
transportation systems and intelligent vehicles. This area has continually
captured the extensive focus of both the academic and industrial sectors.
Identifying and comprehending complex traffic events is highly challenging,
primarily due to the intricate nature of traffic environments, diverse
observational perspectives, and the multifaceted causes of accidents. These
factors have persistently impeded the development of effective solutions. The
advent of large vision-language models (VLMs) such as GPT-4V, has introduced
innovative approaches to addressing this issue. In this paper, we explore the
ability of GPT-4V with a set of representative traffic incident videos and
delve into the model's capacity of understanding these complex traffic
situations. We observe that GPT-4V demonstrates remarkable cognitive,
reasoning, and decision-making ability in certain classic traffic events.
Concurrently, we also identify certain limitations of GPT-4V, which constrain
its understanding in more intricate scenarios. These limitations merit further
exploration and resolution.


  Let $G$ be a simply connected semisimple algebraic group over $\mathbb{C}$
and let $\rho :G\rightarrow GL(V_\lambda)$ be an irreducible representation of
highest weight $\lambda$. Suppose that $\rho$ has finite kernel. Springer
defined adjoint-invariant regular map with Zariski dense image from the group
to its Lie algebra, $\theta_\lambda:G\rightarrow\mathfrak{g}$, which depends on
$\lambda$ [Kumar]. By a lemma in Kumar's recent paper, $\theta_\lambda$ takes
the maximal torus to its Lie algebra $\mathfrak{t}$. Thus, for a given simple
group $G$ and an irreducible representation $V_\lambda$, one may write
$\theta_\lambda (t)=\sum\limits_{i=1}^n c_i(t)\check{\alpha_i}$, where the
simple co-roots $\{\check{\alpha_i}\}$ are a basis for $\mathfrak{t}$. We give
a complete determination of these coefficients $c_i(t)$ for any simple group
$G$ as a sum over the weights of the torus action on $V_\lambda$.


  In this work, we are dedicated to text-guided image generation and propose a
novel framework, i.e., CLIP2GAN, by leveraging CLIP model and StyleGAN. The key
idea of our CLIP2GAN is to bridge the output feature embedding space of CLIP
and the input latent space of StyleGAN, which is realized by introducing a
mapping network. In the training stage, we encode an image with CLIP and map
the output feature to a latent code, which is further used to reconstruct the
image. In this way, the mapping network is optimized in a self-supervised
learning way. In the inference stage, since CLIP can embed both image and text
into a shared feature embedding space, we replace CLIP image encoder in the
training architecture with CLIP text encoder, while keeping the following
mapping network as well as StyleGAN model. As a result, we can flexibly input a
text description to generate an image. Moreover, by simply adding mapped text
features of an attribute to a mapped CLIP image feature, we can effectively
edit the attribute to the image. Extensive experiments demonstrate the superior
performance of our proposed CLIP2GAN compared to previous methods.


  Using a set of oscillator strengths and excited-state dipole moments of near
full configuration interaction (FCI) quality determined for small compounds, we
benchmark the performances of several single-reference wave function methods
(CC2, CCSD, CC3, CCSDT, ADC(2), and ADC(3/2)) and time-dependent
density-functional theory (TD-DFT) with various functionals (B3LYP, PBE0,
M06-2X, CAM-B3LYP, and $\omega$B97X-D). We consider the impact of various
gauges (length, velocity, and mixed) and formalisms: equation of motion (EOM)
\emph{vs} linear response (LR), relaxed \emph{vs} unrelaxed orbitals, etc.
Beyond the expected accuracy improvements and a neat decrease of formalism
sensitivy when using higher-order wave function methods, the present
contribution shows that, for both ADC(2) and CC2, the choice of gauge impacts
more significantly the magnitude of the oscillator strengths than the choice of
formalism, and that CCSD yields a notable improvement on this transition
property as compared to CC2. For the excited-state dipole moments, switching on
orbital relaxation appreciably improves the accuracy of both ADC(2) and CC2,
but has a rather small effect at the CCSD level. Going from ground to excited
states, the typical errors on dipole moments for a given method tend to roughly
triple. Interestingly, the ADC(3/2) oscillator strengths and dipoles are
significantly more accurate than their ADC(2) counterparts, whereas the two
models do deliver rather similar absolute errors for transition energies.
Concerning TD-DFT, one finds: i) a rather negligible impact of the gauge on
oscillator strengths for all tested functionals (except for M06-2X); ii)
deviations of ca.~0.10 D on ground-state dipoles for all functionals; iii) the
better overall performance of CAM-B3LYP for the two considered excited-state
properties.


  If the Pioneer Anomaly (PA) was a genuine dynamical effect of gravitational
origin, it should also affect the orbital motions of the solar system's bodies
moving in the space regions in which the PA manifested itself in its presently
known form, i.e. as a constant and uniform acceleration approximately directed
towards the Sun with a non-zero magnitude APio = (8.74 +/- 1.33) x 10^-10 m
s^-2 after 20 au from the Sun. In this paper we preliminarily investigate its
effects on the orbital motions of the Neptunian satellites Triton, Nereid and
Proteus, located at about 30 au from the Sun, both analytically and
numerically. Extensive observational records covering sev- eral orbital
revolutions have recently been analyzed for them, notably improving the
knowledge of their orbits. Both analytical and numerical calculations, limited
to the direct, Neptune-satellite interaction, show that the peak-to-peak
amplitudes of the PA-induced radial, transverse and out-of-plane perturbations
over one century are up to 300 km, 600 km, 8 m for Triton, 17,500 km, 35,000
km, 800 km for Nereid, and 60 km, 120 km, 30 m for Proteus. The corresponding
orbital uncertainties ob- tained from a recent analysis of all the data
available for the satellites considered are, in general, smaller by one-two
orders of magnitude, although obtained without modeling a Pioneer-like
extra-force. Further investigations based on a re-processing of the satellites'
real or simulated data with modified equations of motions including an
additional Pioneer-type force as well are worth being implemented and may shed
further light on this important issue.


  Employing the functional renormalization group approach at next-to-leading
order of the derivative expansion, we refine our earlier findings for the
location of the Yang-Lee edge singularity in classic O(N) universality classes.
For the universality classes of interest to QCD, in three dimensions, we found
$|z_c|/R_\chi^{1/\gamma} = 1.612(9),\ 1.597(3)$ for $N=2$, $4$ correspondingly.
We also established $|z_c| = 2.04(8),\ 1.69(3)$ for $N=2$, $4$ albeit with
greater systematic error.


  Cardiac Magnetic Resonance (CMR) images are widely used for cardiac diagnosis
and ventricular assessment. Extracting specific landmarks like the right
ventricular insertion points is of importance for spatial alignment and 3D
modeling. The automatic detection of such landmarks has been tackled by
multiple groups using Deep Learning, but relatively little attention has been
paid to the failure cases of evaluation metrics in this field. In this work, we
extended the public ACDC dataset with additional labels of the right
ventricular insertion points and compare different variants of a heatmap-based
landmark detection pipeline. In this comparison, we demonstrate very likely
pitfalls of apparently simple detection and localisation metrics which
highlights the importance of a clear detection strategy and the definition of
an upper limit for localisation-based metrics. Our preliminary results indicate
that a combination of different metrics is necessary, as they yield different
winners for method comparison. Additionally, they highlight the need of a
comprehensive metric description and evaluation standardisation, especially for
the error cases where no metrics could be computed or where no lower/upper
boundary of a metric exists. Code and labels:
https://github.com/Cardio-AI/rvip_landmark_detection


  Commonly, SLAM algorithms are focused on a static environment, however, there
are several scenes where dynamic objects are present. This work presents the
STDyn-SLAM an image feature-based SLAM system working on dynamic environments
using a series of sub-systems, like optic flow, orb features extraction, visual
odometry, and convolutional neural networks to discern moving objects in the
scene. The neural network is used to support object detection and segmentation
to avoid erroneous maps and wrong system localization. The STDyn-SLAM employs a
stereo pair and is developed for outdoor environments. Moreover, the processing
time of the proposed system is fast enough to run in real-time as it was
demonstrated through the experiments given in real dynamic outdoor
environments. Further, we compare our SLAM with state-of-the-art methods
achieving promising results.


  In this paper, we consider Einstein-Hilbert gravity in the presence of
cosmological constant with cylindrical symmetry to introduce the black hole
solution of this model. Here, we solve the Einstein's vacuum field equation,
and then we calculate the appropriate metric for this problem.


  We describe a compression method for floating-point astronomical images that
gives compression ratios of 6 -- 10 while still preserving the scientifically
important information in the image. The pixel values are first preprocessed by
quantizing them into scaled integer intensity levels, which removes some of the
uncompressible noise in the image. The integers are then losslessly compressed
using the fast and efficient Rice algorithm and stored in a portable FITS
format file. Quantizing an image more coarsely gives greater image compression,
but it also increases the noise and degrades the precision of the photometric
and astrometric measurements in the quantized image. Dithering the pixel values
during the quantization process can greatly improve the precision of
measurements in the images. This is especially important if the analysis
algorithm relies on the mode or the median which would be similarly quantized
if the pixel values are not dithered. We perform a series of experiments on
both synthetic and real astronomical CCD images to quantitatively demonstrate
that the magnitudes and positions of stars in the quantized images can be
measured with the predicted amount of precision. In order to encourage wider
use of these image compression methods, we have made available a pair of
general-purpose image compression programs, called fpack and funpack, which can
be used to compress any FITS format image.


  We study multidimensional backward stochastic differential equations (BSDEs)
which cover the logarithmic nonlinearity u log u. More precisely, we establish
the existence and uniqueness as well as the stability of p-integrable solutions
(p > 1) to multidimensional BSDEs with a p-integrable terminal condition and a
super-linear growth generator in the both variables y and z. This is done with
a generator f(y, z) which can be neither locally monotone in the variable y nor
locally Lipschitz in the variable z. Moreover, it is not uniformly continuous.
As application, we establish the existence and uniqueness of Sobolev solutions
to possibly degenerate systems of semilinear parabolic PDEs with super-linear
growth generator and an p-integrable terminal data. Our result cover, for
instance, certain (systems of) PDEs arising in physics.


  In this paper, a novel far-field plasmonic resonance enhanced
nanoparticle-seeded Particle Image Velocimetry (nPIV) has been demonstrated to
measure the velocity profile in a micro channel. Chemically synthesized silver
nanoparticles have been used to seed the flow in the micro channel. By using
Discrete Dipole Approximation (DDA), plasmonic resonance enhanced light
scattering has been calculated for spherical silver nanoparticles with
diameters ranging from 15nm to 200nm. Optimum scattering wavelength is
specified for the nanoparticles in two media: water and air. The
diffraction-limited plasmonic resonance enhanced images of silver nanoparticles
at different diameters have been recorded and analyzed. By using standard PIV
techniques, the velocity profile within the micro channel has been determined
from the images.


  The aim of this article is to give a pedagogical introduction to the exact
equilibrium and nonequilibrium properties of free fermionic quantum spin
chains. In a first part we present in full details the canonical
diagonalisation procedure and review quickly the equilibrium dynamical
properties. The phase diagram is analysed and possible phase transitions are
discussed. The two next chapters are concerned with the effect of aperiodicity
and quenched disorder on the critical properties of the quantum chain. The
remaining part is devoted to the nonequilibrium dynamical behaviour of such
quantum chains relaxing from a nonequilibrium pure initial state. In
particular, a special attention is made on the relaxation of transverse
magnetization. Two-time linear response functions and correlation functions are
also considered, giving insights on the nature of the final nonequilibrium
stationnary state. The possibility of aging is also discussed.


  This paper proves a long standing conjecture in formal language theory. It
shows that all regular languages are Church-Rosser congruential. The class of
Church-Rosser congruential languages was introduced by McNaughton, Narendran,
and Otto in 1988. A language L is Church-Rosser congruential, if there exists a
finite confluent, and length-reducing semi-Thue system S such that L is a
finite union of congruence classes modulo S. It was known that there are
deterministic linear context-free languages which are not Church-Rosser
congruential, but on the other hand it was strongly believed that all regular
language are of this form. Actually, this paper proves a more general result.


  We discuss how to consistently perform effective Lagrangian computations in
quantum gravity with branes in compact extra dimensions. A reparametrization
invariant and infrared finite result is obtained in a non trivial way. It is
crucial to properly account for brane fluctuations and to correctly identify
physical observables. Our results correct some confusing claims in the
literature. We discuss the implications of graviton loops on electroweak
precision observables and on the muon g-2 in models with large extra
dimensions. We model the leading effects, not controlled by effective field
theory, by introducing a hard momentum cut-off.


  In contrast to recent description [Phys. Rev. Lett. 106 (2011)116803], we
show that pseudo spin in graphene is not completely a real angular momentum.
The pseudo spin only in the direction perpendicular to graphene sheet is real
angular momentum, while the pseudo spin parallel to graphene plane is still not
real angular momentum. Interestingly, it is also shown that the Newtonian-like
force and pseudo spin torque of massive Dirac electrons in graphene under
strain field mimic gravitomagnetic force and gravitomagnetic spin torque,
respectively. This is due to the equivalence of pseudo spin and velocity
operators of 2+1 dimensional massive electrons in graphene, different from that
in real 3+1 dimensional Dirac fields. This work reveals new physical property
of graphene as a pseudo gravitomagnetic material.


  We give axioms in the language of rings augmented by a 1-ary predicate symbol
$Fin(x)$ with intended interpretation in the Boolean algebra of idempotents as
the ideal of finite elements, i.e. finite unions of atoms. We prove that any
commutative unital ring satisfying these axioms is elementarily equivalent to a
restricted product of connected rings. This is an extension of the results in
\cite{elem-prod} for products. While the results in \cite{elem-prod} give a
converse to the Feferman-Vaught theorem for products, our results prove the
same for restricted products. We give a complete set of axioms in the language
of rings for the ring of adeles of a number field, uniformly in the number
field.


  We propose a general framework for obtaining probabilistic solutions to
PDE-based inverse problems. Bayesian methods are attractive for uncertainty
quantification but assume knowledge of the likelihood model or data generation
process. This assumption is difficult to justify in many inverse problems,
where the specification of the data generation process is not obvious. We adopt
a Gibbs posterior framework that directly posits a regularized variational
problem on the space of probability distributions of the parameter. We propose
a novel model comparison framework that evaluates the optimality of a given
loss based on its "predictive performance". We provide cross-validation
procedures to calibrate the regularization parameter of the variational
objective and compare multiple loss functions. Some novel theoretical
properties of Gibbs posteriors are also presented. We illustrate the utility of
our framework via a simulated example, motivated by dispersion-based wave
models used to characterize arterial vessels in ultrasound vibrometry.


  A mass relation of rho and a_[1} mesons has been obtained from Weinberg's
first sum rule and KSFR sum rule.


  The multipath radio channel is considered to have a non-bandlimited channel
impulse response. Therefore, it is challenging to achieve high resolution
time-delay (TD) estimation of multipath components (MPCs) from bandlimited
observations of communication signals. It this paper, we consider the problem
of multiband channel sampling and TD estimation of MPCs. We assume that the
nonideal multibranch receiver is used for multiband sampling, where the noise
is nonuniform across the receiver branches. The resulting data model of Hankel
matrices formed from acquired samples has multiple shift-invariance structures,
and we propose an algorithm for TD estimation using weighted subspace fitting.
The subspace fitting is formulated as a separable nonlinear least squares (NLS)
problem, and it is solved using a variable projection method. The proposed
algorithm supports high resolution TD estimation from an arbitrary number of
bands, and it allows for nonuniform noise across the bands. Numerical
simulations show that the algorithm almost attains the Cramer Rao Lower Bound,
and it outperforms previously proposed methods such as multiresolution TOA,
MI-MUSIC, and ESPRIT.


  Accurate and precise radius estimates of transiting exoplanets are critical
for understanding their compositions and formation mechanisms. To know the
planet, we must know the host star in as much detail as possible. We present
complete results for planet-candidate hosts from the K2-HERMES survey, which
uses the HERMES multi-object spectrograph on the Anglo-Australian Telescope to
obtain R$\sim$28,000 spectra for more than 30,000 K2 stars. We present complete
host-star parameters and planet-candidate radii for 224 K2 candidate planets
from C1-C13. Our results cast severe doubt on 30 K2 candidates, as we derive
unphysically large radii, larger than 2 $R_{Jup}$. This work highlights the
importance of obtaining accurate, precise, and self-consistent stellar
parameters for ongoing large planet search programs - something that will only
become more important in the coming years, as TESS begins to deliver its own
harvest of exoplanets.


  We consider Schr\"odinger operators with periodic potentials on periodic
discrete graphs. The spectrum of the Schr\"odinger operator consists of an
absolutely continuous part (a union of a finite number of non-degenerated
bands) plus a finite number of flat bands, i.e., eigenvalues of infinite
multiplicity. We obtain estimates of the Lebesgue measure of the spectrum in
terms of geometric parameters of the graph and show that they become identities
for some class of graphs. Moreover, we obtain stability estimates and show the
existence and positions of large number of flat bands for specific graphs. The
proof is based on the Floquet theory and the precise representation of fiber
Schr\"odinger operators, constructed in the paper.


  We modify the reweighting factor of the projector used in diffusion Monte
Carlo to reduce the time-step error of the total energy. Further, we present a
reweighting scheme that has the desirable feature that it is exactly
size-consistent, i.e, the energy of a system containing widely separated
fragments is the same as the sum of the energies of the individual fragments.
The practical utility of the latter improvement is that it reduces the
time-step error of the binding energies of some weakly interacting systems.


  We examine the heating of the intra-cluster medium (ICM) of cooling flow
clusters of galaxies by jet-inflated bubbles and conclude that mixing of hot
bubble gas with the ICM is more important than turbulent heating and shock
heating. We use the PLUTO hydrodynamical code in full 3D to properly account
for the inflation of the bubbles and to the multiple vortices induced by the
jets and bubbles. The vortices mix some hot shocked jet gas with the ICM. For
the parameters used by us the mixing process accounts for about four times as
much heating as that by the kinetic energy in the ICM, namely, turbulence and
sound waves. We conclude that turbulent heating plays a smaller role than
mixing. Heating by shocks is even less efficient.


  An adversarial machine learning approach is introduced to launch jamming
attacks on wireless communications and a defense strategy is presented. A
cognitive transmitter uses a pre-trained classifier to predict the current
channel status based on recent sensing results and decides whether to transmit
or not, whereas a jammer collects channel status and ACKs to build a deep
learning classifier that reliably predicts the next successful transmissions
and effectively jams them. This jamming approach is shown to reduce the
transmitter's performance much more severely compared with random or
sensing-based jamming. The deep learning classification scores are used by the
jammer for power control subject to an average power constraint. Next, a
generative adversarial network (GAN) is developed for the jammer to reduce the
time to collect the training dataset by augmenting it with synthetic samples.
As a defense scheme, the transmitter deliberately takes a small number of wrong
actions in spectrum access (in form of a causative attack against the jammer)
and therefore prevents the jammer from building a reliable classifier. The
transmitter systematically selects when to take wrong actions and adapts the
level of defense to mislead the jammer into making prediction errors and
consequently increase its throughput.


  It is hard for us humans to recognize things in nature until we have invented
them ourselves. For image-forming optics, nature has made virtually every kind
of lens humans have devised. But what about lensless "imaging"? Recently, we
showed that a bare array of sensors on a curved substrate could achieve
resolution not limited by diffraction- without any lens at all provided that
the objects imaged conform to our a priori assumptions. Is it possible that
somewhere in nature we will find this kind of vision system? We think so and
provide examples that seem to make no sense whatever unless they are using
something like our lensless imaging work.


  Existing corpora for intrinsic evaluation are not targeted towards tasks in
informal domains such as Twitter or news comment forums. We want to test
whether a representation of informal words fulfills the promise of eliding
explicit text normalization as a preprocessing step. One possible evaluation
metric for such domains is the proximity of spelling variants. We propose how
such a metric might be computed and how a spelling variant dataset can be
collected using UrbanDictionary.


  We present a preliminary measurement of the CP asymmetry in singly
Cabibbo-suppressed D+ -> K-K+\pi+ decays and in the resonant decays D+ -> \phi
\pi+ and D+ -> K*0 K+. We use a data sample of 79.9 fb^{-1} recorded by the
BABAR detector. The Cabibbo-favored Ds+ -> K-K+\pi+ branching fraction is used
as normalization in the measurements to reduce systematic uncertainties.
Preliminary results of the CP asymmetries obtained are A_{CP}(K+K-\pi \pm) =
(1.4 \pm 1.0(stat.) \pm 1.1(syst.))\times 10^{-2}, A_{CP}(\phi\pi \pm) = (0.2
\pm 1.5(stat.) \pm 0.8(syst.))\times 10^{-2}, and A_{CP}(K^{\pm} K*0) = (0.9
\pm 1.7(stat.) \pm 0.8(syst.))\times 10^{-2}. A preliminary determination of
the branching ratio is \frac{\Gamma (D+ -> K-K+\pi+)}{\Gamma(D+ -> K-\pi+\pi+)}
=(10.7 \pm 0.1(stat.) \pm 0.2(syst.))\times 10^{-2}.


  On the basis of previous work on chiral gauged fermions on a lattice, we
discuss the lattice-regularization of the standard model by introducing two
Weyl fields interacting with quarks and leptons. These interactions form
massive bound states to gauge-invariantly decouple doublers at high energies
and these bound states dissolve into their constituents at low energies. No any
hard spontaneous symmetry breakings occur at the lattice scale \pi/a. As a
consequence, the gauge symmetries of the standard model are realized by both
massive vectorlike spectra at high energies and massless chiral spectra at low
energies. Such a scenario is consistent with the gauge-anomaly cancelation,
flavor-singlet anomaly and Witten's anomaly. These studies predict that the
parity symmetry must be restored at high energies.


  We have created a new semantic tool called AstroConcepts, providing
definitions of astronomical concepts present on Web pages. This tool is a
Google Chrome plug-in that interrogates the Etymological Dictionary of
Astronomy and Astrophysics, developed at Paris Observatory. Thanks to this
tool, if one selects an astronomical concept on a web page, a pop-up window
will display the definition of the available English or French terms. Another
expected use of this facility could be its implementation in Virtual
Observatory services.


  Building systems with capability of natural language understanding (NLU) has
been one of the oldest areas of AI. An essential component of NLU is to detect
logical succession of events contained in a text. The task of sentence ordering
is proposed to learn succession of events with applications in AI tasks. The
performance of previous works employing statistical methods is poor, while the
neural networks-based approaches are in serious need of large corpora for model
learning. In this paper, we propose a method for sentence ordering which does
not need a training phase and consequently a large corpus for learning. To this
end, we generate sentence embedding using BERT pre-trained model and measure
sentence similarity using cosine similarity score. We suggest this score as an
indicator of sequential events' level of coherence. We finally sort the
sentences through brute-force search to maximize overall similarities of the
sequenced sentences. Our proposed method outperformed other baselines on
ROCStories, a corpus of 5-sentence human-made stories. The method is
specifically more efficient than neural network-based methods when no huge
corpus is available. Among other advantages of this method are its
interpretability and needlessness to linguistic knowledge.


  We consider the convex optimization problem P: min {f(x): x in K} where "f"
is convex continuously differentiable, and K is a compact convex set in Rn with
representation {x: g_j(x) >=0, j=1,;;,m} for some continuously differentiable
functions (g_j). We discuss the case where the g_j's are not all concave (in
contrast with convex programming where they all are). In particular, even if
the g_j's are not concave, we consider the log-barrier function phi_\mu with
parameter \mu, associated with P, usually defined for concave functions (g_j).
We then show that any limit point of any sequence (x_\mu) of stationary points
of phi_\mu, \mu ->0, is a Karush-Kuhn-Tucker point of problem P and a global
minimizer of f on K.


  Social interactions and personal tastes shape our consumption behavior of
cultural products. In this study, we present a computational model of a
cultural market and we aim to analyze the behavior of the consumer population
as an emergent phenomena. Our results suggest that the final market shares of
cultural products dramatically depend on consumer heterogeneity and social
interaction pressure. Furthermore, the relation between the resulting market
shares and social interaction is robust with respect to a wide range of
variation in the parameter values and the type of topology.


  A general scheme is developed to deal with 1D lattice systems that could be
topologically complicated. It is aimed to give a complete study of two coupled
normal metal rings. Our method starts with an investigation of the local
expressions of the eigenfunctions. By connecting different parts of the system,
all the eigenvalues and eigenfunctions can be obtained. It is found that there
is a possibility for the existence of localized states, which is beyond
previous expectations.


  We report on the preparation of a one-dimensional ultracold medium in a
hollow-core photonic crystal fiber, reaching an effective optical depth of
1000(150). We achieved this extreme optical depth by transferring atoms from a
magneto-optical trap into a far-detuned optical dipole trap inside the
hollow-core fiber, yielding up to 2.5(3)$\times$10$^5$ atoms inside the core
with a loading efficiency of $2.5(6)~\%$. The preparation of an ultracold
medium of such huge optical depth paves the way towards new applications in
quantum optics and nonlinear optics.


  Many problems in Computer Vision can be reduced to either working around a
known transform, or given a model for the transform computing the inverse
problem of the transform itself. We will look at two ways of working with the
matrix $A$ and see how transforms are at the root of image processing and
vision problems.


  Time-reversal-invariant topological superconductor (TRITOPS) wires host
Majorana Kramers pairs that have been predicted to mediate a fractional
Josephson effect with $4\pi$ periodicity in the superconducting phase
difference. We explore the TRITOPS fractional Josephson effect in the presence
of time-dependent `local mixing' perturbations that instantaneously preserve
time-reversal symmetry. Specifically, we show that just as such couplings
render braiding of Majorana Kramers pairs non-universal, the Josephson current
becomes either aperiodic or $2\pi$-periodic (depending on conditions that we
quantify) unless the phase difference is swept sufficiently quickly. We further
analyze topological superconductors with $\mathcal{T}^2 = +1$ time-reversal
symmetry and reveal a rich interplay between interactions and local mixing that
can be experimentally probed in nanowire arrays.