  Dynamical properties of a long polymer ring in a melt of unknotted and
unconcatenated rings are calculated. We re-examine and generalize the well
known model of a ring confined to a lattice of topological obstacles in the
light of the recently developed Flory theory of unentangled rings which maps
every ring on an annealed branched polymer and establishes that the backbone
associated with each ring follows self-avoiding rather than Gaussian random
walk statistics. We find the scaling of ring relaxation time and diffusion
coefficient with ring length, as well as time dependence of stress relaxation
modulus, zero shear viscosity and mean square averaged displacements of both
individual monomers and ring's mass center. Our results agree within error bars
with all available experimental and simulations data of the ring melt, although
the quality of the data so far is insufficient to make a definitive judgment
for or against the annealed tree theory. In the end we review briefly the
relation between our findings and experimental data on chromatin dynamics.


  To access an unlicensed channel Wi-Fi follows Listen Before Talk (LBT)
mechanism whereas LTE-U adopts ON-OFF duty cycled mechanism to fairly share the
channel with Wi-Fi. These contrasting mechanisms result in quite different
performance for Wi-Fi and LTE-U based on their relative deployment and density
in the environment. In this work, we present an analytical model for
characterization of achievable throughputs of Wi-Fi and LTE-U networks in
spatially distributed high-density scenarios. The proposed model is used to
study how LTE-U and Wi-Fi coexist with each other in different deployment
scenarios. Our extensive simulation results prove it to be a reliable model for
estimating throughput of both Wi-Fi and LTE-U. We record a very good accuracy
in throughput estimation and the mean normalized error is less than 1% for
40-node scenario in which 50% of nodes belong to each of Wi-Fi and LTE-U.
Finally, we use the analytical model to conduct coexistence studies of LTE-U
and Wi-Fi.


  The complexity class $NP$ can be logically characterized both through
existential second order logic $SO\exists$, as proven by Fagin, and through
simulating a Turing machine via the satisfiability problem of propositional
logic SAT, as proven by Cook. Both theorems involve encoding a Turing machine
by a formula in the corresponding logic and stating that a model of this
formula exists if and only if the Turing machine halts, i.e. the formula is
satisfiable iff the Turing machine accepts its input. Trakhtenbrot's theorem
does the same in first order logic $FO$. Such different orders of encoding are
possible because the set of all possible configurations of any Turing machine
up to any given finite time instant can be defined by a finite set of
propositional variables, or is locally represented by a model of fixed finite
size. In the current paper, we first encode such time-limited computations of a
deterministic Turing machine (DTM) in first order logic. We then take a closer
look at DTMs that solve SAT. When the length of the input string to such a DTM
that contains effectively encoded instances of SAT is parameterized by the
natural number $M$, we proceed to show that the corresponding $FO$ theory
$SAT_M$ has a lower bound on the size of its models that grows almost
exponentially with $M$. This lower bound on model size also translates into a
lower bound on the deterministic time complexity of SAT.


  We consider a group of neighboring smartphone users who are roughly at the
same time interested in the same network content, called common interests.
However, ever-increasing data traffic challenges the limited capacity of
base-stations (BSs) in wireless networks. To better utilize the limited BSs'
resources under unreliable wireless networks, we propose local common-interests
sharing (enabled by D2D communications) by motivating the physically
neighboring users to form a social group. As users are selfish in practice, an
incentive mechanism is needed to motivate social grouping. We propose a novel
concept of equal-reciprocal incentive over broadcast communications, which
fairly ensures that each pair of the users in the social group share the same
amount of content with each other. As the equal-reciprocal incentive may
restrict the amount of content shared among the users, we analyze the optimal
equal-reciprocal scheme that maximizes local sharing content. While ensuring
fairness among users, we show that this optimized scheme also maximizes each
user's utility in the social group. Finally, we look at dynamic content
arrivals and extend our scheme successfully by proposing novel on-line
scheduling algorithms.


  In this study a single laser pulse spatially shaped into a ring is focused
into a thin water layer, creating an annular cavitation bubble and cylindrical
shock waves: an outer shock that diverges away from the excitation laser ring
and an inner shock that focuses towards the center. A few nanoseconds after the
converging shock reaches the focus and diverges away from the center, a single
bubble nucleates at the center. The inner diverging shock then reaches the
surface of the annular laser-induced bubble and reflects at the boundary,
initiating nucleation of a tertiary bubble cloud. In the present experiments,
we have performed time-resolved imaging of shock propagation and bubble wall
motion. Our experimental observations of single-bubble cavitation and collapse
and appearance of ring-shaped bubble clouds are consistent with our numerical
simulations that solve a one dimensional Euler equation in cylindrical
coordinates. The numerical results agree qualitatively with the experimental
observations of the appearance and growth of bubble clouds at the smallest
laser excitation rings. Our technique of shock-driven bubble cavitation opens
novel perspectives for the investigation of shock-induced single-bubble or
multi-bubble cavitation phenomena in thin liquids.


  Recently it has been shown that the evolution of open quantum systems may be
``unraveled'' into individual ``trajectories,'' providing powerful numerical
and conceptual tools. In this letter we use quantum trajectories to study
mesoscopic systems and their classical limit. We show that in this limit,
Quantum Jump (QJ) trajectories approach a diffusive limit very similar to the
Quantum State Diffusion (QSD) unraveling. The latter follows classical
trajectories in the classical limit. Hence, both unravelings show the rise of
classical orbits. This is true for both regular and chaotic systems (which
exhibit strange attractors).


  We consider two families of polynomials $\mathbb{P}=\polP$ and
$\mathbb{Q}=\polQ$\footnote{Here and below we consider only monic polynomials.}
orthogonal on the real line with respect to probability measures $\mu$ and
$\nu$ respectively. Let $\polQ$ and $\polP$ connected by the linear relations
$$ Q_n(x)=P_n(x)+a_1P_{n-1}(x)+...+a_kP_{n-k}(x).$$ Let us denote
$\mathfrak{A}_P$ and $\mathfrak{A}_Q$ generalized oscillator algebras
associated with the sequences $\mathbb{P}$ and $\mathbb{Q}$. In the case $k=2$
we describe all pairs ($\mathbb{P}$,$\mathbb{Q}$), for which the algebras
$\mathfrak{A}_P$ and $\mathfrak{A}_Q$ are equal. In addition, we construct
corresponding algebras of generalized oscillators for arbitrary $k\geq1$.


  In the network creation game with n vertices, every vertex (a player) buys a
set of adjacent edges, each at a fixed amount {\alpha} > 0. It has been
conjectured that for {\alpha} >= n, every Nash equilibrium is a tree, and has
been confirmed for every {\alpha} >= 273n. We improve upon this bound and show
that this is true for every {\alpha} >= 65n. To show this, we provide new and
improved results on the local structure of Nash equilibria. Technically, we
show that if there is a cycle in a Nash equilibrium, then {\alpha} < 65n.
Proving this, we only consider relatively simple strategy changes of the
players involved in the cycle. We further show that this simple approach cannot
be used to show the desired upper bound {\alpha} < n (for which a cycle may
exist), but conjecture that a slightly worse bound {\alpha} < 1.3n can be
achieved with this approach. Towards this conjecture, we show that if a Nash
equilibrium has a cycle of length at most 10, then indeed {\alpha} < 1.3n. We
further provide experimental evidence suggesting that when the girth of a Nash
equilibrium is increasing, the upper bound on {\alpha} obtained by the simple
strategy changes is not increasing. To the end, we investigate the approach for
a coalitional variant of Nash equilibrium, where coalitions of two players
cannot collectively improve, and show that if {\alpha} >= 41n, then every such
Nash equilibrium is a tree.


  In this article I chronicle a series of landmark events, with which I was
personally involved, that relate to the development of the theory of cosmic
life. The interpretation of events offered here might invite a sense of
incredulity on the part of the reader, but the facts themselves are
unimpeachable in regard to their authenticity. Of particular interest are
accounts of interactions between key players in an unfolding drama connected
with the origins of life. Attempts to censor evidence incompatible with the
cosmic life theory are beginning to look futile and a long-overdue paradigm
shift may have to be conceded.


  In this work, we explore the close relationship between an ideal map
structure S --> End(R) on a homomorphism of commutative k-algebras R --> S and
an ideal simplicial algebra structure on the associated bar construction Bar(S,
R).


  Equations of motion of large N quantum mechanics are solved for infinite N in
the case of unbroken global O(N) symmetry. It is shown that the only correction
to the action is a change in the potential. All characteristics of the motion
(frequencies of oscillations, times of rolling, a phase portrait etc.) can be
computed by means of a classical mechanics for the corrected potential. In
particular, a tunneling in the system is absent.


  Through HST imaging of the inner part of the main-ring of the Helix Nebula
together with CTIO 4-m images of the fainter outer parts, we have an
unprecedented-quality view of the nearest bright planetary nebula. These images
have allowed determination that the main-ring of the nebula is composed of an
inner-disk of about 499\arcsec diameter (0.52 pc) surrounded by an outer-ring
(in reality a torus) of 742\arcsec diameter (0.77 pc) whose plane is highly
inclined to the plane of the disk. This outer-ring is surrounded by an
outermost-ring of 1500\arcsec (1.76 pc) diameter which is flattened on the side
colliding with the ambient interstellar medium. The inner-disk has an extended
distribution of low density gas along its rotational axis of symmetry and the
disk is optically thick to ionizing radiation, as is the outer-ring.
  Published radial velocities of the knots provides support for the
two-component structure of the main-ring of the nebula and to the idea that the
knots found there are expanding along with the nebular material from which it
recently originated. There is a change in the morphology of the knots as a
function of the distance from the local ionization front. This supports a
scenario in which the knots are formed in or near the ionization front and are
then sculpted by the stellar radiation from the central star as the ionization
front advances beyond them.


  Fast and efficient motion planning algorithms are crucial for many
state-of-the-art robotics applications such as self-driving cars. Existing
motion planning methods become ineffective as their computational complexity
increases exponentially with the dimensionality of the motion planning problem.
To address this issue, we present Motion Planning Networks (MPNet), a neural
network-based novel planning algorithm. The proposed method encodes the given
workspaces directly from a point cloud measurement and generates the end-to-end
collision-free paths for the given start and goal configurations. We evaluate
MPNet on various 2D and 3D environments including the planning of a 7 DOF
Baxter robot manipulator. The results show that MPNet is not only consistently
computationally efficient in all environments but also generalizes to
completely unseen environments. The results also show that the computation time
of MPNet consistently remains less than 1 second in all presented experiments,
which is significantly lower than existing state-of-the-art motion planning
algorithms.


  We present spectrophotometric ISO imaging with the LWS and the CAM-CVF of the
Serpens molecular cloud core. The LWS map is centred on the far infrared and
submillimetre source SMM1 and its size is 8' x 8'. The fine structure line
emission in [OI] and [CII] is extended and can be successfully modelled to
originate in a PDR with G_0 = 15 and n(H2) about 10^4 - 10^5 cm^-3. Extended
emission is also observed in the rotational line emission of H2O and high-J CO.
However, lack of sufficient angular resolution prevents us from excluding the
possibility that the emssion regions of these lines are point like, which could
be linked to the embedded objects SMM9 and SMM4. Toward the Class0 source SMM1,
the LWS observations reveal, in addition to fine structure line emission, a
rich spectrum of molecular lines. The sub-thermally excited and optically thick
CO, H2O and OH lines are tracing an about 10^3 AU source with temperatures
higher than 300 K and densities above 10^6 cm^-3. We show that geometry is of
concern for the correct interpretation of the data and based on 2D-radiative
transfer modelling of the disk/torus around SMM1, which successfully reproduces
the entire observed SED and the observed line profiles of CO isotopomers, we
can exclude the disk to be the source of the LWS-molecular line emission. The
CAM-CVF permits us to see a region of rotational H2 emission. This H2 gas has a
temperature of 10^3 K, which suggests that the heating of the gas is achieved
through relatively slow shocks. Although we are not able to establish any firm
conclusion regarding the detailed nature of the shock waves, our observations
of the molecular line emission from SMM1 can be explainable in terms of an
admixture of J-shocks and of C-shocks.


  We derive the relativistic factor for splitting of the $g$-factors of a
fermion and its anti-fermion partner, which is important for placing
constraints on dimension-5, $CPT$-odd and Lorentz-invariance-violating
interactions from experiments performed in a cyclotron. From existing data, we
extract limits (1$\sigma$) on the coupling strengths of the temporal component,
$f^0$, of a background field (including the field amplitude), which is
responsible for such $g$-factor splitting, with an electron, proton, and muon:
$|f^0_e|< 2.3 \times 10^{-12} ~\mu_{\textrm{B}}$, $|f^0_p|< 4 \times 10^{-9}
~\mu_{\textrm{B}}$, and $|f^0_\mu|< 8 \times 10^{-11} ~\mu_{\textrm{B}}$,
respectively, in the laboratory frame. From existing data, we also extract
limits on the coupling strengths of the spatial components, $d^{\perp}$, of
related dimension-5 interactions of a background field with an electron,
proton, neutron, and muon: $| {d}_e^{\perp} | \lesssim 10^{-9}
~\mu_{\textrm{B}}$, $| {d}_p^{\perp} | \lesssim 10^{-9} ~\mu_{\textrm{B}}$, $|
{d}_n^{\perp} | \lesssim 10^{-10} ~\mu_{\textrm{B}}$, and $| {d}_\mu^{\perp} |
\lesssim 10^{-9} ~\mu_{\textrm{B}}$, respectively, in the laboratory frame.


  From magnetisation measurements we provide evidence that the ferromagnetic
superconductor RuSr$_{2}$GdCu$_{2}$O$_{8}$ with \QTR{it}{T}$_{c}$=45 K and
\QTR{it}{T}$_{M}$=137 K exhibits a sizeable diamagnetic signal at low
temperature (\QTR{it}{T}<\QTR{it}{T}$^{\QTR{it}{ms}}$=30 K) and low magnetic
field (\QTR{it}{H}$^{ext}$<30 Oe), corresponding to a bulk Meissner-phase. At
intermediate temperatures,
\QTR{it}{T}$^{\QTR{it}{ms}}$<\QTR{it}{T}<\QTR{it}{T}$_{c}$, a spontaneous
vortex phase forms which is characterized by unique thermal hysteresis effects.
We argue that a recent negative report [C.W. Chu et al., cond-mat/9910056]
regarding the Meissner-effect in Ru-1212 can be explained by impurity
scattering or grain size effects.


  Relation between the vacuum eigenvalues of CFT Q-operators and spectral
determinants of one-dimensional Schroedinger operator with homogeneous
potential, recently conjectured by Dorey and Tateo for special value of
Virasoro vacuum parameter p, is proven to hold, with suitable modification of
the Schroedinger operator, for all values of p.


  We give a complete characterization of the relationship between the shape of
a Euclidean polygon and the symbolic dynamics of its billiard flow. We prove
that the only pairs of tables that can have the same bounce spectrum are
right-angled tables that differ by an affine map. The main tool is a new
theorem that establishes that a flat cone metric is completely determined by
the support of its Liouville current.


  We investigate the 3D structure and drying dynamics of complex mixtures of
emulsion droplets and colloidal particles, using confocal microscopy. Air
invades and rapidly collapses large emulsion droplets, forcing their contents
into the surrounding porous particle pack at a rate proportional to the square
of the droplet radius. By contrast, small droplets do not collapse, but remain
intact and are merely deformed. A simple model coupling the Laplace pressure to
Darcy's law correctly estimates both the threshold radius separating these two
behaviors, and the rate of large-droplet evacuation. Finally, we use these
systems to make novel hierarchical structures.


  In this paper we construct and examine new supersymmetric solutions of
massive IIA supergravity that are obtained using non-Abelian T-duality applied
to the Baryonic Branch of the Klebanov-Strassler background. The geometries
display SU(2) structure which we show flows from static in the UV to dynamical
in the IR. Confinement and symmetry breaking are given a geometrical
interpretation by this change of structure. Various field theory observables
are studied, suggesting possible ways to break conformality and flow in $N=1$
$T_N$ and related field theories.


  We propose stochastic algorithms for solving large scale nonsmooth convex
composite minimization problems. They activate at each iteration blocks of
randomly selected proximity operators and achieve almost sure convergence of
the iterates to a solution without any regularity assumptions. Numerical
applications to data analysis problems are provided.


  Iris recognition is widely used in several fields such as mobile phones,
financial transactions, identification cards, airport security, international
border control, voter registration for living persons. However, the possibility
of identifying deceased individuals based on their iris patterns has emerged
recently as a supplementary or alternative method valuable in forensic
analysis. Simultaneously, it poses numerous new technological challenges and
one of the most challenging among them is the image segmentation stage as
conventional iris recognition approaches have struggled to reliably execute it.
This paper presents and compares Deep Learning (DL) models designed for
segmenting iris images collected from the deceased subjects, by training SegNet
and DeepLabV3+ semantic segmentation methods where using VGG19, ResNet18,
ResNet50, MobileNetv2, Xception, or InceptionResNetv2 as backbones. In this
study, our experiments demonstrate that our proposed method effectively learns
and identifies specific deformations inherent in post-mortem samples and
providing a significant improvement in accuracy. By employing our novel method
MobileNetv2 as the backbone of DeepLabV3+ and replacing the final layer with a
hybrid loss function combining Boundary and Dice loss, we achieve Mean
Intersection over Union of 95.54% on the Warsaw-BioBase-PostMortem-Iris-v1
dataset. To the best of our knowledge, this study provides the most extensive
evaluation of DL models for post-mortem iris segmentation.


  Recent statistical methods fitted on large-scale GPS data can provide
accurate estimations of the expected travel time between two points. However,
little is known about the distribution of travel time, which is key to
decision-making across a number of logistic problems. With sufficient data,
single road-segment travel time can be well approximated. The challenge lies in
understanding how to aggregate such information over a route to arrive at the
route-distribution of travel time. We develop a novel statistical approach to
this problem. We show that, under general conditions, without assuming a
distribution of speed, travel time {divided by route distance follows a
Gaussian distribution with route-invariant population mean and variance. We
develop efficient inference methods for such parameters and propose
asymptotically tight population prediction intervals for travel time. Using
traffic flow information, we further develop a trip-specific Gaussian-based
predictive distribution, resulting in tight prediction intervals for short and
long trips. Our methods, implemented in an R-package, are illustrated in a
real-world case study using mobile GPS data, showing that our trip-specific and
population intervals both achieve the 95\% theoretical coverage levels.
Compared to alternative approaches, our trip-specific predictive distribution
achieves (a) the theoretical coverage at every level of significance, (b)
tighter prediction intervals, (c) less predictive bias, and (d) more efficient
estimation and prediction procedures. This makes our approach promising for
low-latency, large-scale transportation applications.


  In this paper we consider regression problems subject to arbitrary noise in
the operator or design matrix. This characterization appropriately models many
physical phenomena with uncertainty in the regressors. Although the problem has
been studied extensively for ordinary/total least squares, and via models that
implicitly or explicitly assume Gaussianity, less attention has been paid to
improving estimation for regression problems under general uncertainty in the
design matrix. To address difficulties encountered when dealing with
distributions of sums of random variables, we rely on the saddle point method
to estimate densities and form an approximate log-likelihood to maximize. We
show that the proposed method performs favorably against other classical
methods.


  We study some new universal aspects of diffusion in chaotic systems,
especially such having very large Lyapunov coefficients on the chaotic
(indecomposable, topologically transitive) component. We do this by
discretizing the chaotic component on the Surface-of-Section in a (large)
number $N$ of simplectically equally big cells (in the sense of equal relative
invariant ergodic measure, normalized so that the total measure of the chaotic
component is unity). By iterating the transition of the chaotic orbit through
SOS, where $j$ counts the number of iteration (discrete time), and assuming
complete lack of correlations even between consecutive crossings (which can be
justified due to the very large Lyapunov exponents), we show the universal
approach of the relative measure of the occupied cells, denoted by $\rho(j)$,
to the asymptotic value of unity, in the following way: $\rho(j) = 1 -
(1-\frac{1}{N})^j$, so that in the limit of big $N$, $N\to \infty$, we have,
for $j/N$ fixed, the exponential law $\rho(j) \approx 1 - \exp (-j/N)$. This
analytic result is verified numerically in a variety of specific systems: For a
plane billiard (Robnik 1983, $\lambda=0.375$), for a 3-D billiard (Prosen 1997,
$a=-1/5, b=-12/5$), for ergodic logistic map (tent map), for standard map
($k=400$) and for hydrogen atom in strong magnetic field ($\epsilon=-0.05$) the
agreement is almost perfect (except, in the latter two systems, for some
long-time deviations on very small scale), but for H\'enon-Heiles system
($E=1/6$) and for the standard map ($k=3$) the deviations are noticed although
they are not very big (only about 1%). We have tested the random number
generators (Press et al 1986), and confirmed that some are almost perfect (ran0
and ran3), whilst two of them (ran1 and ran2) exhibit big deviations.


  We study $m$-linear homogeneous rough singular integral operators
$\mathcal{L}_{\Omega}$ associated with integrable functions $\Omega$ on
$\mathbb{S}^{mn-1}$ with mean value zero. We prove boundedness for
$\mathcal{L}_{\Omega}$ from $L^{p_1}\times \cdots \times L^{p_m}$ to $L^p$ when
$1<p_1,\dots, p_m<\infty$ and $1/p=1/p_1+\cdots +1/p_m$ in the largest possible
open set of exponents when $\Omega \in L^q(\mathbb S^{mn-1})$ and $q\ge 2$.
This set can be described by a convex polyhedron in $\mathbb R^m$.


  By coupling on chip a carbon nanotube to a quantum noise detector, a
superconductor-insulator-superconductor junction, via a resonant circuit, we
measure the emission noise of a carbon nanotube quantum dot in the Kondo
regime. The signature of the Kondo effect in the current noise is measured for
different ratios of the Kondo temperature over the measured frequency and for
different asymmetries of the coupling to the contacts, and compared to finite
frequency quantum noise calculations. Our results point towards the existence
of a high frequency cut-off of the electronic emission noise associated with
the Kondo resonance. This cut-off frequency is of the order of a few times the
Kondo temperature when the electronic system is close to equilibrium, which is
the case for a strongly asymmetric coupling. On the other hand, this cut-off is
shifted to lower frequency in a symmetric coupling situation, where the bias
voltage drives the Kondo state out-of-equilibrium. We then attribute the low
frequency cut-off to voltage induced spin relaxation.


  Three types of numerical data are provided for simple Lie groups of any type
and rank. This data is indispensable for Fourier-like expansions of
multidimensional digital data into finite series of $C-$ or $S-$functions on
the fundamental domain $F$ of the underlying Lie group $G$. Firstly, we
consider the number $|F_M|$ of points in $F$ from the lattice $P^{\vee}_M$,
which is the refinement of the dual weight lattice $P^{\vee}$ of $G$ by a
positive integer $M$. Secondly, we find the lowest set $\Lambda_M$ of dominant
weights, specifying the maximal set of $C-$ and $S-$functions that are pairwise
orthogonal on the point set $F_M$. Finally, we describe an efficient algorithm
for finding, on the maximal torus of $G$, the number of conjugate points to
every point of $F_M$. Discrete $C-$ and $S-$transforms, together with their
continuous interpolations, are presented in full generality.


  We discuss $L_p$-estimates for finite difference schemes approximating
parabolic, possibly degenerate, SPDEs, with initial conditions from $W^m_p$ and
free terms taking values in $W^m_p.$ Consequences of these estimates include an
asymptotic expansion of the error, allowing the acceleration of the
approximation by Richardson's method.


  In the paper published in Duke Math. J. 1993, Y. Wen studied a second-order
parabolic equation for inextensible elastic \emph{closed} curves in
$\mathbb{R}^{2}$ toward inextensible elasticae. In this article, we extend
Wen's result to the case of open inextensible planar curves with hinged ends.
We obtain the long time existence of smooth solutions when the initial curves
fulfill certain regular conditions.


  Nowadays, people can retrieve and share digital information in an
increasingly easy and fast fashion through the well-known digital platforms,
including sensitive data, inappropriate or illegal content, and, in general,
information that might serve as probative evidence in court. Consequently, to
assess forensics issues, we need to figure out how to trace back to the posting
chain of a digital evidence (e.g., a picture, an audio) throughout the involved
platforms -- this is what Digital (also Forensics) Ballistics basically deals
with. With the entry of Machine Learning as a tool of the trade in many
research areas, the need for vast amounts of data has been dramatically
increasing over the last few years. However, collecting or simply find the
"right" datasets that properly enables data-driven research studies can turn
out to be not trivial in some cases, if not extremely challenging, especially
when it comes with highly specialized tasks, such as creating datasets analyzed
to detect the source media platform of a given digital media. In this paper we
propose an automated approach by means of a digital tool that we created on
purpose. The tool is capable of automatically uploading an entire image dataset
to the desired digital platform and then downloading all the uploaded pictures,
thus shortening the overall time required to output the final dataset to be
analyzed.


  Behavioral theories posit that investor sentiment exhibits predictive power
for stock returns, whereas there is little study have investigated the
relationship between the time horizon of the predictive effect of investor
sentiment and the firm characteristics. To this end, by using a Granger
causality analysis in the frequency domain proposed by Lemmens et al. (2008),
this paper examine whether the time horizon of the predictive effect of
investor sentiment on the U.S. returns of stocks vary with different firm
characteristics (e.g., firm size (Size), book-to-market equity (B/M) rate,
operating profitability (OP) and investment (Inv)). The empirical results
indicate that investor sentiment has a long-term (more than 12 months) or
short-term (less than 12 months) predictive effect on stock returns with
different firm characteristics. Specifically, the investor sentiment has strong
predictability in the stock returns for smaller Size stocks, lower B/M stocks
and lower OP stocks, both in the short term and long term, but only has a
short-term predictability for higher quantile ones. The investor sentiment
merely has predictability for the returns of smaller Inv stocks in the short
term, but has a strong short-term and long-term predictability for larger Inv
stocks. These results have important implications for the investors for the
planning of the short and the long run stock investment strategy.


  We investigate the effective potential of the Polyakov loop, which is the
order parameter for the deconfinement phase transition in finite temperature
QCD. Our work is based on the Hamiltonian approach in Coulomb gauge where
finite temperature $T$ is introduced by compactifying one space direction. We
briefly review this approach and extend earlier work in the Yang-Mills sector
by including dynamical quarks. In a first approximation, we follow the usual
functional approach and include only one-loop contributions to the energy, with
the finite temperature propagators replaced by their $T=0$ counter parts. It is
found that this gives a poor description of the phase transition, in particular
for the case of full QCD with $N_f = 3$ light flavours. The physical reasons
for this unexpected result are discussed, and pinned down to a relative
weakness of gluon confinement compared to the deconfining tendency of the
quarks. We attempt to overcome this issue by including the relevant gluon
contributions from the two-loop terms to the energy. We find that the two-loop
corrections have indeed a tendency to strengthen the gluon confinement and
weaken the unphysical effects in the confining phase, while slightly increasing
the (pseudo-)critical temperature $T^\ast$ at the same time. To fully suppress
artifacts in the confining phase, we must tune the parameters to rather large
values, increasing the critical temperature to $T^\ast \approx
340\,\mathrm{MeV}$ for $G=SU(2)$.


  Unsupervised and semi-supervised ML methods such as variational autoencoders
(VAE) have become widely adopted across multiple areas of physics, chemistry,
and materials sciences due to their capability in disentangling representations
and ability to find latent manifolds for classification and regression of
complex experimental data. Like other ML problems, VAEs require hyperparameter
tuning, e.g., balancing the Kullback Leibler (KL) and reconstruction terms.
However, the training process and resulting manifold topology and connectivity
depend not only on hyperparameters, but also their evolution during training.
Because of the inefficiency of exhaustive search in a high-dimensional
hyperparameter space for the expensive to train models, here we explored a
latent Bayesian optimization (zBO) approach for the hyperparameter trajectory
optimization for the unsupervised and semi-supervised ML and demonstrate for
joint-VAE with rotational invariances. We demonstrate an application of this
method for finding joint discrete and continuous rotationally invariant
representations for MNIST and experimental data of a plasmonic nanoparticles
material system. The performance of the proposed approach has been discussed
extensively, where it allows for any high dimensional hyperparameter tuning or
trajectory optimization of other ML models.


  We present hinglishNorm -- a human annotated corpus of Hindi-English
code-mixed sentences for text normalization task. Each sentence in the corpus
is aligned to its corresponding human annotated normalized form. To the best of
our knowledge, there is no corpus of Hindi-English code-mixed sentences for
text normalization task that is publicly available. Our work is the first
attempt in this direction. The corpus contains 13494 parallel segments.
Further, we present baseline normalization results on this corpus. We obtain a
Word Error Rate (WER) of 15.55, BiLingual Evaluation Understudy (BLEU) score of
71.2, and Metric for Evaluation of Translation with Explicit ORdering (METEOR)
score of 0.50.


  We report on the search for gamma-ray emission from 20 magnetars using 6
years of Fermi, Large Area Telescope (LAT) observations. No significant
evidence for gamma-ray emission from any of the currently-known magnetars is
found. We derived the most stringent upper limits to date on the 0.1--10 GeV
emission of Galactic magnetars, which are estimated between
$\sim10^{-12}-10^{-11}$ erg s$^{-1}$ cm$^{-2}$. Gamma-ray pulsations were
searched for the four magnetars having reliable ephemerides over the observing
period, but none were detected. On the other hand, we also studied the
gamma-ray morphology and spectra of seven Supernova Remnants associated or
adjacent to the magnetars.


  Symbolic Regression is the study of algorithms that automate the search for
analytic expressions that fit data. While recent advances in deep learning have
generated renewed interest in such approaches, the development of symbolic
regression methods has not been focused on physics, where we have important
additional constraints due to the units associated with our data. Here we
present $\Phi$-SO, a Physical Symbolic Optimization framework for recovering
analytical symbolic expressions from physics data using deep reinforcement
learning techniques by learning units constraints. Our system is built, from
the ground up, to propose solutions where the physical units are consistent by
construction. This is useful not only in eliminating physically impossible
solutions, but because the "grammatical" rules of dimensional analysis restrict
enormously the freedom of the equation generator, thus vastly improving
performance. The algorithm can be used to fit noiseless data, which can be
useful for instance when attempting to derive an analytical property of a
physical model, and it can also be used to obtain analytical approximations to
noisy data. We test our machinery on a standard benchmark of equations from the
Feynman Lectures on Physics and other physics textbooks, achieving
state-of-the-art performance in the presence of noise (exceeding 0.1%) and show
that it is robust even in the presence of substantial (10%) noise. We showcase
its abilities on a panel of examples from astrophysics.


  Gauge theories, through the local symmetry which is in their core, exhibit
many local constraints, that must be taken care of and addressed in any
calculation. In the Hamiltonian picture this is phrased through the Gauss laws,
local constraints that restrict the physical Hilbert space and relate the
matter and gauge degrees of freedom. In this work, we present a way that uses
all the Gauss laws in lattice gauge theories with staggered fermions for
completely removing the matter degrees of freedom, at the cost of locally
extending the interaction range, breaking the symmetry and introducing new
local constraints, due to the finiteness of the original local matter spaces.


  The ability to generate highly realistic 2D images from mere text prompts has
recently made huge progress in terms of speed and quality, thanks to the advent
of image diffusion models. Naturally, the question arises if this can be also
achieved in the generation of 3D content from such text prompts. To this end, a
new line of methods recently emerged trying to harness diffusion models,
trained on 2D images, for supervision of 3D model generation using view
dependent prompts. While achieving impressive results, these methods, however,
have two major drawbacks. First, rather than commonly used 3D meshes, they
instead generate neural radiance fields (NeRFs), making them impractical for
most real applications. Second, these approaches tend to produce over-saturated
models, giving the output a cartoonish looking effect. Therefore, in this work
we propose a novel method for generation of highly realistic-looking 3D meshes.
To this end, we extend NeRF to employ an SDF backbone, leading to improved 3D
mesh extraction. In addition, we propose a novel way to finetune the mesh
texture, removing the effect of high saturation and improving the details of
the output 3D mesh.


  The generation of electron vortex states in ionization by intense and short
laser pulses is analyzed under the scope of the lowest-order Born
approximation. For near infrared laser fields and nonrelativistic intensities
of the order of $10^{16}$~W/cm$^2$, we show that one has to modify the
nonrelativistic treatment of ionization by accounting for recoil and mass
relativistic corrections. By using the corrected quasi-relativistic theory, the
requirements for the observation of electron vortex states with non-negligible
probability and large topological charge are determined.


  MR Fingerprinting is a novel quantitative MR technique that could
simultaneously provide multiple tissue property maps. When optimizing MRF
scans, modeling undersampling errors and field imperfections in cost functions
will make the optimization results more practical and robust. However, this
process is computationally expensive and impractical for sequence optimization
algorithms when MRF signal evolutions need to be generated for each
optimization iteration. Here, we introduce a fast MRF simulator to simulate
aliased images from actual scan scenarios including undersampling and system
imperfections, which substantially reduces computational time and allows for
direct error estimation and efficient sequence optimization. By constraining
the total number of tissues present in a brain phantom, MRF signals from highly
undersampled scans can be simulated as the product of the spatial response
functions based on sampling patterns and sequence-dependent temporal functions.
During optimization, the spatial response function is independent of sequence
design and does not need to be recalculated. We evaluate the performance and
computational speed of the proposed approach by simulations and in vivo
experiments. We also demonstrate the power of applying the simulator in MRF
sequence optimization. The simulation results from the proposed method closely
approximate the signals and MRF maps from in vivo scans, with 158 times shorter
processing time than the conventional simulation method using Non-uniform
Fourier transform. Incorporating the proposed simulator in the MRF optimization
framework makes direct estimation of undersampling errors during the
optimization process feasible, and provide optimized MRF sequences that are
robust against undersampling factors and system inhomogeneity.


  We face the problem of characterizing the periodic cases in parametric
families of (real or complex) rational diffeomorphisms having a fixed point.
Our approach relies on the Normal Form Theory, to obtain necessary conditions
for the existence of a formal linearization of the map, and on the introduction
of a suitable rational parametrization of the parameters of the family. Using
these tools we can find a finite set of values p for which the map can be
p-periodic, reducing the problem of finding the parameters for which the
periodic cases appear to simple computations. We apply our results to several
two and three dimensional classes of polynomial or rational maps. In particular
we find the global periodic cases for several Lyness type recurrences.


  We study some qualitative properties (including removable singularities and
superharmonicity) of non-negative solutions to $$ (-\Delta)^\gamma
u=fu^p\quad\text{in }\mathbb R^n\setminus\Sigma $$ which are singular at
$\Sigma$. Here $\gamma \in (0, \frac{n}{2})$. Among other things, we first
prove that if $\Sigma$ is a compact set in $\mathbb R^n$ with Assouad dimension
$\bf d$ (not necessarily an integer), ${\bf d}<n-2\gamma$, and $ u\in
L_\gamma(\mathbb R^n)\cap L^p_{loc}({\mathbb R^n\setminus\Sigma})$ is a
non-negative solution for some $$p>\frac{n-\bf d}{n-{\bf d}-2\gamma},$$ then
$u\in L^p_{loc}(\mathbb R^n)$ and $u$ is a distributional solution in $\mathbb
R^n$. Then we prove that $ (-\Delta)^\sigma u >0$ for all $ \sigma \in (0,
\gamma)$, if $\Sigma=\phi$.


  We consider a broker who has to place a large order which consumes a sizable
part of average daily trading volume. The broker's aim is thus to minimize
execution costs he incurs from the adverse impact of his trades on market
prices. By contrast to the previous literature, see, e.g., Obizhaeva and Wang
(2005), Predoiu, Shaikhet, and Shreve (2011), we allow the liquidity parameters
of market depth and resilience to vary deterministically over the course of the
trading period. The resulting singular optimal control problem is shown to be
tractable by methods from convex analysis and, under minimal assumptions, we
construct an explicit solution to the scheduling problem in terms of some
concave envelope of the resilience adjusted market depth.


  The thermal transport properties of oriented polymers are of fundamental and
practical interest. High thermal conductivities ($\gtrsim 50$
Wm$^{-1}$K$^{-1}$) have recently been reported in disentangled ultra-high
molecular weight polyethylene (UHMWPE) films, considerably exceeding prior
reported values for oriented films. However, conflicting explanations have been
proposed for the microscopic origin of the high thermal conductivity. Here, we
report a characterization of the thermal conductivity and mean free path
accumulation function of disentangled UHMWPE films (draw ratio $\sim 200$)
using cryogenic steady-state thermal conductivity measurements and transient
grating spectroscopy. We observe a marked dependence of the thermal
conductivity on grating period over temperatures from 30 - 300 K. Considering
this observation, cryogenic bulk thermal conductivity measurements, and
analysis using an anisotropic Debye model, we conclude that longitudinal atomic
vibrations with mean free paths around 400 nanometers are the primary heat
carriers and that the high thermal conductivity for draw ratio $\gtrsim 150$
arises from the enlargement of extended crystals with drawing. The mean free
paths appear to remain limited by the extended crystal dimensions, suggesting
that the upper limit of thermal conductivity of disentangled UHMWPE films has
not yet been realized.


  Following up on a proposal to use four-wave mixing in an underdense plasma at
mildly relativistic laser intensities to produce vastly more energetic x-ray
pulses [V. M. Malkin and N. J. Fisch, Phys. Rev. E, 101, 023211 (2020)], we
perform the first numerical simulations in one dimension to demonstrate
amplification of a short high frequency seed through four-wave mixing. We find
that parasitic processes including phase modulation and spatial pulse slippage
limit the amplification efficiency. We numerically explore the previously
proposed "dual seed" configuration as a countermeasure against phase
modulation. We show how this approach tends to be thwarted by longitudinal
slippage. In the examples we considered, the best performance was in fact
achieved through optimization of signal and pump parameters in a "single seed"
configuration.


  Temporal Relationalism (TR) is that there is no time for the universe as a
whole at the primary level. Time emerges rather at a secondary level; one
compelling idea for this is Mach's `time is to be abstracted from change'. TR
leads to, and better explains, the well-known Frozen Formalism Problem
encountered in GR at the quantum level. Indeed, abstraction from change is a
type of emergent time resolution of this.Moreover, the Frozen Formalism Problem
is but one of the many Problem of Time facets, which are furthermore
notoriously interconnected. The other local and already classically present
facets are as follows. 2) The GR Thin Sandwich involves a subcase of Best
Matching, which is one particular implementation of Configurational
Relationalism. 3) The Constraint Closure Problem, 4) the Problem of Observables
or Beables, 5) Spacetime Relationalism, 6) the Spacetime Construction Problem,
and 7) the Foliation Dependence Problem as resolved in classical GR by
Refoliation Invariance. In this Article, I bring together the individual
classical resolutions of these, and how these can be rendered compatible with
TR. Having covered that in detail for 2) to 6) elsewhere, the rest of the
current Article is dedicated to the detailed form that 7) and its TR compatible
modification takes. I.e. I consider TR implementing foliations, the TR versions
of Refoliation Invariance and the associated TR version of hypersurface
kinematics and hypersurface deformations. These require a TR counterpart of the
Arnowitt--Deser--Misner split.


  In this paper we investigate necessary conditions of optimality for
infinite-horizon optimal control problems with overtaking optimality as an
optimality criterion. For the case of local Lipschitz continuity of the payoff
function, we construct a boundary condition on the co-state arc that is
necessary for the optimality. We also show that, under additional assumptions
on the payoff function's asymptotic behavior, the Pontryagin Maximum Principle
with this condition becomes a complete system of relations, and this boundary
condition points out the unique co-state arc through a Cauchy-type formula. An
example is given to clarify the application of this formula as an explicit
expression of the co-state arc. The cornerstone of this paper is the theorem on
convergence of subdifferentials.


  We generalise theorems of Cochran-Lickorish and Owens-Strle to the case of
links with more than one component. This enables the use of linking forms on
double branched covers, Heegaard Floer correction terms, and Donaldson's
diagonalisation theorem to complete the table of unlinking numbers for nonsplit
prime links with crossing number nine or less.


  We study the Harvey-Lawson spark characters of level p on complex manifolds.
Presenting Deligne cohomology classes by sparks of level $p$, we give an
explicit analytic product formula for Deligne cohomology. We also define
refined Chern classes in Deligne cohomology for holomorphic vector bundles over
complex manifolds. Applications to algebraic cycles are given. A Bott-type
vanishing theorem in Deligne cohomology for holomorphic foliations is
established. A general construction of Nadel-type invariants is given together
with a new proof of Nadel's conjecture.


  In the last year, new models and methods for pretraining and transfer
learning have driven striking performance improvements across a range of
language understanding tasks. The GLUE benchmark, introduced a little over one
year ago, offers a single-number metric that summarizes progress on a diverse
set of such tasks, but performance on the benchmark has recently surpassed the
level of non-expert humans, suggesting limited headroom for further research.
In this paper we present SuperGLUE, a new benchmark styled after GLUE with a
new set of more difficult language understanding tasks, a software toolkit, and
a public leaderboard. SuperGLUE is available at super.gluebenchmark.com.


  Exploring the capabilities of Neural Radiance Fields (NeRF) and
Gaussian-based methods in the context of 3D scene reconstruction, this study
contrasts these modern approaches with traditional Simultaneous Localization
and Mapping (SLAM) systems. Utilizing datasets such as Replica and ScanNet, we
assess performance based on tracking accuracy, mapping fidelity, and view
synthesis. Findings reveal that NeRF excels in view synthesis, offering unique
capabilities in generating new perspectives from existing data, albeit at
slower processing speeds. Conversely, Gaussian-based methods provide rapid
processing and significant expressiveness but lack comprehensive scene
completion. Enhanced by global optimization and loop closure techniques, newer
methods like NICE-SLAM and SplaTAM not only surpass older frameworks such as
ORB-SLAM2 in terms of robustness but also demonstrate superior performance in
dynamic and complex environments. This comparative analysis bridges theoretical
research with practical implications, shedding light on future developments in
robust 3D scene reconstruction across various real-world applications.


  Visual Simultaneous Localization and Mapping (vSLAM) has achieved great
progress in the computer vision and robotics communities, and has been
successfully used in many fields such as autonomous robot navigation and AR/VR.
However, vSLAM cannot achieve good localization in dynamic and complex
environments. Numerous publications have reported that, by combining with the
semantic information with vSLAM, the semantic vSLAM systems have the capability
of solving the above problems in recent years. Nevertheless, there is no
comprehensive survey about semantic vSLAM. To fill the gap, this paper first
reviews the development of semantic vSLAM, explicitly focusing on its strengths
and differences. Secondly, we explore three main issues of semantic vSLAM: the
extraction and association of semantic information, the application of semantic
information, and the advantages of semantic vSLAM. Then, we collect and analyze
the current state-of-the-art SLAM datasets which have been widely used in
semantic vSLAM systems. Finally, we discuss future directions that will provide
a blueprint for the future development of semantic vSLAM.


  This document contains a description of physics entirely based on a geometric
presentation: all of the theory is described giving only a pseudo-riemannian
manifold (M, g) of dimension n > 5 for which the g tensor is, in studied
domains, almost everywhere of signature (-, -, +, ..., +). No object is added
to this space-time, no general principle is supposed. The properties we impose
to some domains of (M, g) are only simple geometric constraints, essentially
based on the concept of "curvature". These geometric properties allow to
define, depending on considered cases, some objects (frequently depicted by
tensors) that are similar to the classical physics ones, they are however built
here only from the g tensor. The links between these objects, coming from their
natural definitions, give, applying standard theorems from the
pseudo-riemannian geometry, all equations governing physical phenomena usually
described by classical theories, including general relativity and quantum
physics. The purely geometric approach introduced hear on quantum phenomena is
profoundly different from the standard one. Neither Lagrangian nor Hamiltonian
are used. This document ends with a quick presentation of our approach of
complex quantum phenomena usually studied by quantum field theory.


  We construct a finitely presented group $G$ with non-quadratic Dehn function
$f$ majorizable by a quadratic function on arbitrary long intervals.


  A SIC is a maximal equiangular tight frame in a finite dimensional Hilbert
space. Given a SIC in dimension $d$, there is good evidence that there always
exists an aligned SIC in dimension $d(d-2)$, having predictable symmetries and
smaller equiangular tight frames embedded in them. We provide a recipe for how
to calculate sets of vectors in dimension $d(d-2)$ that share these properties.
They consist of maximally entangled vectors in certain subspaces defined by the
numbers entering the $d$ dimensional SIC. However, the construction contains
free parameters and we have not proven that they can always be chosen so that
one of these sets of vectors is a SIC. We give some worked examples that, we
hope, may suggest to the reader how our construction can be improved. For
simplicity we restrict ourselves to the case of odd dimensions.


  The global outbreak of COVID-19 has led to focus on efforts to manage and
mitigate the continued spread of the disease. One of these efforts include the
use of contact tracing to identify people who are at-risk of developing the
disease through exposure to an infected person. Historically, contact tracing
has been primarily manual but given the exponential spread of the virus that
causes COVID-19, there has been significant interest in the development and use
of digital contact tracing solutions to supplement the work of human contact
tracers. The collection and use of sensitive personal details by these
applications has led to a number of concerns by the stakeholder groups with a
vested interest in these solutions. We explore digital contact tracing
solutions in detail and propose the use of a transparent reporting mechanism,
FactSheets, to provide transparency of and support trust in these applications.
We also provide an example FactSheet template with questions that are specific
to the contact tracing application domain.


  We propose a new parameterization to measure the neutrino mass hierarchy,
namely $\Delta=(m_3-m_1)/(m_1+m_3)$ which is dimensionless and varies in the
range $[-1,1]$. Taking into account the results of neutrino oscillation
experiments, $\Delta$ is the unique parameter for determining all the masses of
neutrinos, and a positive (negative) sign of $\Delta$ denotes the normal
(inverted) mass hierarchy. Adopting the currently available cosmic
observations, we find that the normal mass hierarchy is slightly favored, and
the mass of lightest neutrino is less than $0.030$ eV for the normal mass
hierarchy and $0.024$ eV for the inverted mass hierarchy at $95\%$ confidence
level.


  Fast radio bursts (FRBs) are brief, intense flashes of radio waves from
unidentified extragalactic sources. Polarized FRBs originate in highly
magnetized environments. We report observations of the repeating FRB 20190520B
spanning seventeen months , which show its amount of Faraday rotation is highly
variable and twice changes its sign. The FRB also depolarizes below radio
frequencies around 1 to 3 GHz. We interpret these properties as due to change
in the parallel component of the integrated magnetic field along the
line-of-sight, including reversals. This could result from propagation through
a turbulent, magnetized screen of plasma located between $10^{-5}$ to 100
parsecs of the FRB source. This is consistent with the bursts passing through
the stellar wind of a binary companion of the FRB source.


  Spins associated to optically accessible solid-state defects have emerged as
a versatile platform for exploring quantum simulation, quantum sensing and
quantum communication. Pioneering experiments have shown the sensing, imaging,
and control of multiple nuclear spins surrounding a single electron-spin
defect. However, the accessible size of these spin networks has been
constrained by the spectral resolution of current methods. Here, we map a
network of 50 coupled spins through high-resolution correlated sensing schemes,
using a single nitrogen-vacancy center in diamond. We develop concatenated
double-resonance sequences that identify spin-chains through the network. These
chains reveal the characteristic spin frequencies and their interconnections
with high spectral resolution, and can be fused together to map out the
network. Our results provide new opportunities for quantum simulations by
increasing the number of available spin qubits. Additionally, our methods might
find applications in nano-scale imaging of complex spin systems external to the
host crystal.


  We describe theoretical bounds and a practical algorithm for teaching a model
by demonstration in a sequential decision making environment. Unlike previous
efforts that have optimized learners that watch a teacher demonstrate a static
policy, we focus on the teacher as a decision maker who can dynamically choose
different policies to teach different parts of the environment. We develop
several teaching frameworks based on previously defined supervised protocols,
such as Teaching Dimension, extending them to handle noise and sequences of
inputs encountered in an MDP.We provide theoretical bounds on the learnability
of several important model classes in this setting and suggest a practical
algorithm for dynamic teaching.


  The task of image generation started to receive some attention from artists
and designers to inspire them in new creations. However, exploiting the results
of deep generative models such as Generative Adversarial Networks can be long
and tedious given the lack of existing tools. In this work, we propose a simple
strategy to inspire creators with new generations learned from a dataset of
their choice, while providing some control on them. We design a simple
optimization method to find the optimal latent parameters corresponding to the
closest generation to any input inspirational image. Specifically, we allow the
generation given an inspirational image of the user choice by performing
several optimization steps to recover optimal parameters from the model's
latent space. We tested several exploration methods starting with classic
gradient descents to gradient-free optimizers. Many gradient-free optimizers
just need comparisons (better/worse than another image), so that they can even
be used without numerical criterion, without inspirational image, but with only
with human preference. Thus, by iterating on one's preferences we could make
robust Facial Composite or Fashion Generation algorithms. High resolution of
the produced design generations are obtained using progressive growing of GANs.
Our results on four datasets of faces, fashion images, and textures show that
satisfactory images are effectively retrieved in most cases.


  We present a causal inference framework to improve Weakly-Supervised Semantic
Segmentation (WSSS). Specifically, we aim to generate better pixel-level
pseudo-masks by using only image-level labels -- the most crucial step in WSSS.
We attribute the cause of the ambiguous boundaries of pseudo-masks to the
confounding context, e.g., the correct image-level classification of "horse"
and "person" may be not only due to the recognition of each instance, but also
their co-occurrence context, making the model inspection (e.g., CAM) hard to
distinguish between the boundaries. Inspired by this, we propose a structural
causal model to analyze the causalities among images, contexts, and class
labels. Based on it, we develop a new method: Context Adjustment (CONTA), to
remove the confounding bias in image-level classification and thus provide
better pseudo-masks as ground-truth for the subsequent segmentation model. On
PASCAL VOC 2012 and MS-COCO, we show that CONTA boosts various popular WSSS
methods to new state-of-the-arts.


  An invariant of a model of genus one curve is a polynomial in the
coefficients of the model that is stable under certain linear transformations.
The classical example of an invariant is the discriminant, which characterizes
the singularity of models. The ring of invariants of genus one models over a
field is generated by two elements. Fisher normalized these invariants for
models of degree n=2,3,4 in such a way that these invariants are moreover
defined over the integers. We provide an alternative way to express these
normalized invariants using modular forms. This method relies on a direct
computation for the discriminants based on their own geometric properties.


  Electronic edge states in topological insulators have become a major paradigm
in physics. The oldest and primary example is that of quantum Hall (QH) edge
channels that propagate along the periphery of two-dimensional electron gases
(2DEGs) under perpendicular magnetic field. Yet, despite 40 years of intensive
studies using a variety of transport and scanning probe techniques, imaging the
real-space structure of QH edge channels has proven difficult, mainly due to
the buried nature of most 2DEGs in semiconductors. Here, we show that QH edge
states in graphene are confined to a few magnetic lengths at the crystal edges
by performing scanning tunneling spectroscopy up to the edge of a graphene
flake on hexagonal boron nitride. These findings indicate that QH edge states
are defined by boundary conditions of vanishing electronic wavefunctions at the
crystal edges, resulting in ideal one-dimensional chiral channels, free of
electrostatic reconstruction. We further evidence a uniform charge carrier
density at the edges, contrasting with conjectures on the existence of
non-topological upstream modes. The absence of electrostatic reconstruction of
quantum Hall edge states has profound implications for the universality of
electron and heat transport experiments in graphene-based systems and other 2D
crystalline materials.


  Knowledge Graph (KG) is a flexible structure that is able to describe the
complex relationship between data entities. Currently, most KG embedding models
are trained based on negative sampling, i.e., the model aims to maximize some
similarity of the connected entities in the KG, while minimizing the similarity
of the sampled disconnected entities. Negative sampling helps to reduce the
time complexity of model learning by only considering a subset of negative
instances, which may fail to deliver stable model performance due to the
uncertainty in the sampling procedure. To avoid such deficiency, we propose a
new framework for KG embedding -- Efficient Non-Sampling Knowledge Graph
Embedding (NS-KGE). The basic idea is to consider all of the negative instances
in the KG for model learning, and thus to avoid negative sampling. The
framework can be applied to square-loss based knowledge graph embedding models
or models whose loss can be converted to a square loss. A natural side-effect
of this non-sampling strategy is the increased computational complexity of
model learning. To solve the problem, we leverage mathematical derivations to
reduce the complexity of non-sampling loss function, which eventually provides
us both better efficiency and better accuracy in KG embedding compared with
existing models. Experiments on benchmark datasets show that our NS-KGE
framework can achieve a better performance on efficiency and accuracy over
traditional negative sampling based models, and that the framework is
applicable to a large class of knowledge graph embedding models.


  Let $K/\mathbb{Q}$ be a number field. Let $\pi$ and $\pi^\prime$ be cuspidal
automorphic representations of $\mathrm{GL}_d(\mathbb{A}_K)$ and
$\mathrm{GL}_{d^\prime}(\mathbb{A}_K)$, and suppose that either both $d$ and
$d'$ are at most 2 or at least one of $\pi$ and $\pi^\prime$ is self-dual. When
$d=d^\prime=2$, we prove an unconditional and effective log-free zero density
estimate for the Rankin-Selberg $L$-function $L(s,\pi\otimes\pi^\prime,K)$. For
other choices of $d$ and $d^\prime$, we obtain similar results assuming that
either $\pi$ or $\pi^\prime$ satisfies the generalized Ramanujan conjecture.
With these density estimates, we make effective the Hoheisel phenomenon of
Moreno regarding primes in short intervals and extend it to the context of the
Sato-Tate conjecture; additionally, we bound the least prime in the Sato-Tate
conjecture in analogy with Linnik's theorem on the least prime in an arithmetic
progression. When $K=\mathbb{Q}$, we also prove an effective log-free density
estimate for $L(s,\pi\otimes\pi^\prime,\mathbb{Q})$ averaged over twists by
Dirichlet characters. With this second density estimate, we prove an averaged
form of the prime number theorem in short intervals for
$L(s,\pi\otimes\tilde{\pi},\mathbb{Q})$ when $\pi$ is a cuspidal automorphic
representation of $\mathrm{GL}_2(\mathbb{A}_{\mathbb{Q}})$.


  AST3-3 is the third robotic facility of the Antarctic Survey Telescopes
(AST3) for transient surveys to be deployed at Dome A, Antarctica. Due to the
current pandemic, the telescope has been currently deployed at the Yaoan
Observation Station in China, starting the commissioning observation and a
transient survey. This paper presents a fully automatic data processing system
for AST3-3 observations. The transient detection pipeline uses state-of-the-art
image subtraction techniques optimised for GPU devices. Image reduction and
transient photometry are accelerated by concurrent task methods. Our
Python-based system allows for transient detection from wide-field data in a
real-time and accurate way. A ResNet-based rotational-invariant neural network
was employed to classify the transient candidates. As a result, the system
enables auto-generation of transients and their light curves.


  Continuum mechanics describes compressive failure as a standard bifurcation
in the response of a material to an increasing load: damage, which initially
grows uniformly in the material, localizes within a thin band at failure. Yet,
experiments recording the acoustic activity preceding localization evidence
power-law distributed failure precursors of increasing size, suggesting that
compressive failure is a critical phenomenon. We examine here this apparent
contradiction by probing the spatial organization of the damage activity and
its evolution until localization during compression experiments of 2D cellular
solids. The intermittent damage evolution measured in our experiments is
adequately described by a non-stationary depinning equation derived from damage
mechanics and reminiscent of critical phenomena. In this description,
precursors are damage cascades emerging from the interplay between the
material's disorder and the long-range stress redistributions following
individual damage events. Yet, the divergence of their characteristic size
close to failure, which we observe in our experiments, is not the signature of
a transition towards criticality. Instead, the system remains at a fixed
distance to the critical point at all stages of the damage evolution. The
divergence results from the progressive loss of stability of the material as it
is driven towards localization. Thus, our study shows that compressive failure
is a standard bifurcation for which the material disorder plays a marginal
role. It also shows that precursory activity constitute by-products of the
evolution towards localization and can serve to build a predictive method to
assess the residual lifetime of structures.


  In this paper we discuss some connections between groupoids and Frobenius
algebras specialized in the case of Poisson sigma models with boundary. We
prove a correspondence between groupoids in the category Set and relative
Frobenius algebras in the category Rel, as well as an adjunction between a
special type of semigroupoids and relative H*-algebras. The connection between
groupoids and Frobenius algebras is made explicit by introducing what we called
weak monoids and relational symplectic groupoids, in the context of Poisson
sigma models with boundary and in particular, describing such structures in the
ex- tended symplectic category and the category of Hilbert spaces. This is part
of a joint work with Alberto Cattaneo and Chris Heunen.


  Standard bias and variance (B&V) terminologies were originally defined for
the regression setting and their extensions to classification have led to
several different models / definitions in the literature. In this paper, we aim
to provide the link between the commonly used frameworks of Tumer & Ghosh (T&G)
and James. By unifying the two approaches, we relate the B&V defined for the
0/1 loss to the standard B&V of the boundary distributions given for the
squared error loss. The closed form relationships provide a deeper
understanding of classification performance, and their use is demonstrated in
two case studies.


  We use a unified method to give an isomorphism between direct sums of
cyclotomic affine (and degenerate affine) Hecke algebras and cyclotomic
BK-subalgebras which are some KLR-type algebras.


  There is renewed interest in performing a long baseline neutrino oscillation
experiment using accelerator neutrinos because of a discrepancy between the
measured and the predicted values of the ratio of electron to muon neutrinos
produced in the upper atmosphere by cosmic rays.The approximate range in
oscillation parameter space indicated by the Kamiokande atmospheric neutrino
results and confirmed by IMB is bounded by $ 10^{-3}~ eV^2 < \dmsq < 10^{-1}~
eV^2 $ and $\s2t > 0.4~$. To reach such small $\dmsq$, using an accelerator as
the source of neutrinos where the energy is typically 1 GeV or greater,
requires baselines in the range of 10~km to 1000~km. In this talk I will give
an overview of the most likely possibilities for such a long baseline
accelerator neutrino oscillation experiment.


  Quantum computers, if fully realized, promise to be a revolutionary
technology. As a result, quantum computing has become one of the hottest areas
of research in the last few years. Much effort is being applied at all levels
of the system stack, from the creation of quantum algorithms to the development
of hardware devices. The quantum age appears to be arriving sooner rather than
later as commercially useful small-to-medium sized machines have already been
built. However, full-scale quantum computers, and the full-scale algorithms
they would perform, remain out of reach for now. It is currently uncertain how
the first such computer will be built. Many different technologies are
competing to be the first scalable quantum computer.


  Defects, and in particular topological defects, are architectural motifs that
play a crucial role in natural materials. Here we provide a systematic strategy
to introduce such defects in mechanical metamaterials. We first present
metamaterials that are a mechanical analogue of spin systems with tunable
ferromagnetic and antiferromagnetic interactions, then design an exponential
number of frustration-free metamaterials, and finally introduce topological
defects by rotating a string of building blocks in these metamaterials. We
uncover the distinct mechanical signature of topological defects by experiments
and simulations, and leverage this to design complex metamaterials in which we
can steer deformations and stresses towards parts of the system. Our work
presents a new avenue to systematically include spatial complexity,
frustration, and topology in mechanical metamaterials.


  High temporal stability and spin dynamics of individual nitrogen-vacancy (NV)
centers in diamond crystals make them one of the most promising quantum
emitters operating at room temperature. We demonstrate a chip-integrated
cavity-coupled emission into propagating surface plasmon polariton (SPP) modes
narrowing NV center's broad emission bandwidth with enhanced coupling
efficiency. The cavity resonator consists of two distributed Bragg mirrors that
are built at opposite sides of the coupled NV emitter and are integrated with a
dielectric-loaded SPP waveguide (DLSPPW), using electron-beam lithography of
hydrogen silsesquioxane resist deposited on silver-coated silicon substrates. A
quality factor of ~ 70 for the cavity (full width at half maximum ~ 10 nm) with
full tunability of the resonance wavelength is demonstrated. An up to 42-fold
decay rate enhancement of the spontaneous emission at the cavity resonance is
achieved, indicating high DLSPPW mode confinement.


  By this short preface we show the main idea and we will bring some
definitions and concepts in each section.


  Context: The detection and identification of oscillation modes (in terms of
their $\ell$, $m$ and successive $n$) is a great challenge for present and
future asteroseismic space missions. The "peak tagging" is an important step in
the analysis of these data to provide estimations of stellar oscillation mode
parameters, i.e., frequencies, rotation rates, and further studies on the
stellar structure.
  Aims: To increase the signal-to-noise ratio of the asteroseismic spectra
computed from time series representative of MOST and CoRoT observations (30-
and 150-day observations).
  Methods: We apply the curvelet transform -- a recent image processing
technique which looks for curved patterns -- to echelle diagrams built using
asteroseismic power spectra. In this diagram the eigenfrequencies appear as
smooth continuous ridges. To test the method we use Monte Carlo simulations of
several sun-like stars with different combinations of rotation rates,
rotation-axis inclination and signal-to-noise ratios.
  Results: The filtered diagrams enhance the contrast between the ridges of the
modes and the background allowing a better tagging of the modes and a better
extraction of some stellar parameters. Monte Carlo simulations have also shown
that the region where modes can be detected is enlarged at lower and higher
frequencies compared to the raw spectra. Even more, the extraction of the mean
rotational splitting from modes at low frequency can be done more easily than
using the raw spectrum.


  Adelic quantum mechanics is form invariant under an interchange of real and
p-adic number fields as well as rings of p-adic integers. We also show that in
adelic quantum mechanics Feynman's path integrals for quadratic actions with
rational coefficients are invariant under changes of their entries within
nonzero rational numbers.


  A crystallographic and magnetic phase diagram of SmFeAsO1-xFx is determined
as a function of x in terms of temperature based on electrical transport and
magnetization, synchrotron powder x-ray diffraction, 57Fe Mossbauer spectra
(MS), and 149Sm nuclear resonant forward scattering (NRFS) measurements. MS
revealed that the magnetic moments of Fe were aligned antiferromagnetically at
~144 K (TN(Fe)). The magnetic moment of Fe (MFe) is estimated to be 0.34
myuB/Fe at 4.2 K for undoped SmFeAsO; MFe is quenched in superconducting
F-doped SmFeAsO. 149Sm NRFS spectra revealed that the magnetic moments of Sm
start to order antiferromagnetically at 5.6 K (undoped) and 4.4 K (TN(Sm)) (x =
0.069). Results clearly indicate that the antiferromagnetic Sm sublattice
coexists with the superconducting phase in SmFeAsO1-xFx below TN(Sm), while
antiferromagnetic Fe sublattice does not coexist with the superconducting
phase.


  John Horton Conway's Cosmological Theorem, about Audioactive sequences, for
which no extant proof existed, is given a computer-generated proof, hopefully
for good.


  We study QCD in 1+1 dimensions in the large Nc limit using light-front
Hamiltonian perturbation theory in the 1/Nc expansion. We use this formalism to
exactly compute hadronic transition matrix elements for arbitrary currents at
leading order in 1/Nc, which we use to write the semileptonic differential
decay rate of a heavy meson and its moments. We then compare with the results
obtained using an effective field theory approach based on perturbative
factorization, with the intention of better understanding quark-hadron duality.
A very good numerical agreement is obtained between the exact result and the
result using effective theories.


  We introduce a concept of causality in the framework of generalized
pseudo-Riemannian Geometry in the sense of J.F. Colombeau and establish the
inverse Cauchy-Schwarz inequality in this context. As an application, we prove
a dominant energy condition for some energy tensors as put forward in Hawking
and Ellis's book "The large scale structure of space-time". Our work is based
on a new characterization of free elements in finite dimensional modules over
the ring of generalized numbers.


  Horava gravity breaks Lorentz symmetry by introducing a dynamical timelike
scalar field (the khronon), which can be used as a preferred time coordinate
(thus selecting a preferred space-time foliation). Adopting the khronon as the
time coordinate, the theory is invariant only under time reparametrizations and
spatial diffeomorphisms. In the infrared limit, this theory is sometimes
referred to as khronometric theory. Here, we explicitly construct a
generalization of khronometric theory, which avoids the propagation of
Ostrogradski modes as a result of a suitable degeneracy condition (although
stability of the latter under radiative corrections remains an open question).
While this new theory does not have a general-relativistic limit and does not
yield a Friedmann-Robertson-Walker-like cosmology on large scales, it still
passes, for suitable choices of its coupling constants, local tests on Earth
and in the solar system, as well as gravitational-wave tests. We also comment
on the possible usefulness of this theory as a toy model of quantum gravity, as
it could be completed in the ultraviolet into a 'degenerate Horava gravity'
theory that could be perturbatively renormalizable without imposing any
projectability condition.


  We introduce Rota-Baxter categories and construct examples of such
structures.


  Three-nucleon (3N) forces are an indispensable ingredient for accurate
few-body and many-body nuclear structure and reaction theory calculations.
While the direct implementation of chiral 3N forces can be technically very
challenging, a simpler approach is given by employing instead a
medium-dependent NN interaction V_med that reflects the physics of three-body
forces at the two-body normal-ordered approximation. We review the derivation
and construction of V_med from the chiral 3N interaction at
next-to-next-to-leading order (N2LO), consisting of a long-range
$2\pi$-exchange term, a mid-range $1\pi$-exchange component and a short-range
contact term. Several applications of V_med to the equation of state of cold
nuclear and neutron matter, the nucleon single-particle potential in nuclear
matter, and the nuclear quasiparticle interaction are discussed. We also
explore differences in using local vs. nonlocal regulating functions on 3N
forces and make direct comparisons to exact results at low order in
perturbation theory expansions for the equation of state and single-particle
potential. We end with a discussion and numerical calculation of the in-medium
NN potential V_med from the next-to-next-to-next-to-leading order (N3LO) chiral
3N force, which consists of a series of long-range and short-range terms.


  We study several controllability properties for some semilinear parabolic PDE
with a quadratic gradient term. For internal distributed controls, it is shown
that the system is approximately and null controllable. The proof relies on the
Cole-Hopf transformation. The same approach is used to deal with initial
controls.


  We propose an analysis of heart rate marathon runners implemented by
computing a multifractal spectrum based on p-exponents. We draw physiological
conclusions about their performance. Finally, we link this analysis with the
disturbances of the heart rate autoregulation during the marathon, which had
been put in evidence up to now only by scales of feeling of marathon runners
during the race.


  Observations of binary systems obtained recently with the High Energy
Stereoscopic System (H.E.S.S) of Cherenkov telescopes are reported. The
outcomes of a detailed observation campaign on PSR B1259-63 during its
periastron passage in 2014 will be presented. This system was observed for the
first time with H.E.S.S. II, providing spectra and light curves down to 200
GeV, which will be compared with observations conducted during previous
periastron passages and with results from an analysis of contemporaneously
taken Fermi-LAT data. Also long-term observations of LS 5039 with H.E.S.S in
phase I and phase II are reported. This source was monitored at very high
energies (VHEs) in a period of time spanning more than ten years. Its spectral
energy distribution measured with H.E.S.S. II extends down to 120 GeV. Spectral
results from the Fermi-LAT observations are shown as well, and the
compatibility with H.E.S.S. results in the overlapping energy range is
discussed. The identification of the new gamma-ray binary candidate HESS
J1832-093 will also be presented. Furthermore, the search for VHE emission from
the microquasars GRS 1915+105, Circinus X-1 and V4641 Sgr based on data from
H.E.S.S. observations conducted contemporaneously with the RXTE satellite
experiment will be reported. These data provide constraints on the integral
gamma-ray flux at different X-ray states of the three sources.


  By considering the renormalization group flow between $N$ coupled Ising
models in the UV and the cubic fixed point in the IR, we study the large $N$
behavior of the cubic fixed points in three dimensions. We derive a
diagrammatic expansion for the $1/N$ corrections to correlation functions.
Leading large $N$ corrections to conformal dimensions at the cubic fixed point
are then evaluated using numeric conformal bootstrap data for the 3d Ising
model.


  After a short review of baryogenesis mechanisms, we focus on the charge
transport mechanism at the electroweak scale, effective at strong electroweak
phase transitions. Starting from the one-loop Schwinger-Dyson equations for
fermions coupled to bosons, we present a derivation of the relevant kinetic
equations in the on-shell and gradient approximations, relevant for the thick
wall baryogenesis regime. We then discuss the CP-violating source from the
semiclassical force in the flow term, and compare it with the source arising in
the collision term of the kinetic equation. Finally, we summarize the results
concerning the chargino mediated baryogenesis in the Minimal Supersymmetric
Standard Model.


  In this paper, we consider numerical approximations of a hydrodynamically
coupled phase field diblock copolymer model, in which the free energy contains
a kinetic potential, a gradient entropy, a Ginzburg-Landau double well
potential, and a long range nonlocal type potential. We develop a set of second
order time marching schemes for this system using the "Invariant Energy
Quadratization" approach for the double well potential, the projection method
for the Navier-Stokes equation, and a subtle implicit-explicit treatment for
the stress and convective term. The resulting schemes are linear and lead to
symmetric positive definite systems at each time step, thus they can be
efficiently solved. We further prove that these schemes are unconditionally
energy stable. Various numerical experiments are performed to validate the
accuracy and energy stability of the proposed schemes.


  A covariant spin-foam formulation of quantum gravity has been recently
developed, characterized by a kinematics which appears to match well the one of
canonical loop quantum gravity. In particular, the geometrical observable
giving the area of a surface has been shown to be the same as the one in loop
quantum gravity. Here we discuss the volume observable. We derive the volume
operator in the covariant theory, and show that it matches the one of loop
quantum gravity, as does the area. We also reconsider the implementation of the
constraints that defines the model: we derive in a simple way the boundary
Hilbert space of the theory from a suitable form of the classical constraints,
and show directly that all constraints vanish weakly on this space.


  A series of MgB$_2$ thin films systematically disordered by neutron
irradiation have been studied by Scanning Tunneling Spectroscopy. The c-axis
orientation of the films allowed a reliable determination of local density of
state of the $\pi$ band. With increasing disorder, the conductance peak moves
towards higher voltages and becomes lower and broader, indicating a monotonic
increase of the $\pi$ gap and of the broadening parameter. These results are
discussed in the frame of two-band superconductivity.


  In my review of jet phenomena in quasars, I focus on the following questions:
How powerful are jets in radio-loud quasars? What is their composition? How are
they launched? And why, in most quasars, are they so weak? I demonstrate the
exceptional role that blazar studies can play in exploring the physics and
structure of the innermost parts of quasar jets.


  We consider a trading marketplace that is populated by traders with diverse
trading strategies and objectives. The marketplace allows the suppliers to list
their goods and facilitates matching between buyers and sellers. In return,
such a marketplace typically charges fees for facilitating trade. The goal of
this work is to design a dynamic fee schedule for the marketplace that is
equitable and profitable to all traders while being profitable to the
marketplace at the same time (from charging fees). Since the traders adapt
their strategies to the fee schedule, we present a reinforcement learning
framework for simultaneously learning a marketplace fee schedule and trading
strategies that adapt to this fee schedule using a weighted optimization
objective of profits and equitability. We illustrate the use of the proposed
approach in detail on a simulated stock exchange with different types of
investors, specifically market makers and consumer investors. As we vary the
equitability weights across different investor classes, we see that the learnt
exchange fee schedule starts favoring the class of investors with the highest
weight. We further discuss the observed insights from the simulated stock
exchange in light of the general framework of equitable marketplace mechanism
design.


  A new partial gauge fixing condition for the abelian projection is
introduced. It is based on the lowest-lying eigenvector of a covariant
Laplacian operator. This gauge is smooth and free of lattice Gribov copies.
These properties are important for an unambiguous computation of the abelian
projected gauge field configuration.


  In this paper, we formalise order-robust optimisation as an instance of
online learning minimising simple regret, and propose Vroom, a zero'th order
optimisation algorithm capable of achieving vanishing regret in non-stationary
environments, while recovering favorable rates under stochastic
reward-generating processes. Our results are the first to target simple regret
definitions in adversarial scenarios unveiling a challenge that has been rarely
considered in prior work.


  We construct divide knots with arbitrary smooth four-genus but topological
four-genus equal to one. In particular, for strongly quasipositive fibred
knots, the ratio between the topological and the smooth four-genus can be
arbitrarily close to zero.


  We present a programmatic approach to incorporating ethics into an
undergraduate major in statistical and data sciences. We discuss
departmental-level initiatives designed to meet the National Academy of
Sciences recommendation for integrating ethics into the curriculum from
top-to-bottom as our majors progress from our introductory courses to our
senior capstone course, as well as from side-to-side through co-curricular
programming. We also provide six examples of data science ethics modules used
in five different courses at our liberal arts college, each focusing on a
different ethical consideration. The modules are designed to be portable such
that they can be flexibly incorporated into existing courses at different
levels of instruction with minimal disruption to syllabi. We connect our
efforts to a growing body of literature on the teaching of data science ethics,
present assessments of our effectiveness, and conclude with next steps and
final thoughts.


  Secure group communication in heterogeneous environment is gaining popularity
due to the advent of wireless and ubiquitous computing. Although a number of
protocols for group key agreement have been proposed, most of them are not
applicable in heterogeneous environment where a number of computationally
limited nodes coexist with one or more computationally efficient nodes. Among
the few existing protocols, where some fail to satisfy the key agreement
properties, some are unable to handle the agreement for dynamic group. In this
work, we propose a constant round group key agreement protocol for
heterogeneous environment using polynomial interpolation. The protocol ensures
both communication and computation efficiency by shifting the major computation
load on powerful users, achieves true contributory key agreement property and
dynamic handling of user join and leave. The security of the protocol has been
analyzed under formal model. The comparison result shows considerable
improvement in protocol efficiency compared to the existing ones.


  The resonance strength of the two-step process of nuclear excitation by
electron capture followed by $\gamma$ decay of the nucleus can be significantly
increased in highly charged ions if the resonant capture proceeds via an
excited electronic state with subsequent fast x-ray emission. For fully ionized
$^{238}_{92}\mathrm{U}$ and $^{232}_{90}\mathrm{Th}$, the {x-ray} decay
stabilizes the system against internal conversion of the captured electron,
with an increase of both nuclear lifetimes and resonance strengths of up to two
orders of magnitude compared with the case when occupied atomic orbitals
prevent the x-ray de-excitation. Applications of this effect to the measurement
of the not yet experimentally observed nuclear excitation by electron capture
and to dense astrophysical plasmas are discussed.


  Local graph clustering is an important machine learning task that aims to
find a well-connected cluster near a set of seed nodes. Recent results have
revealed that incorporating higher order information significantly enhances the
results of graph clustering techniques. The majority of existing research in
this area focuses on spectral graph theory-based techniques. However, an
alternative perspective on local graph clustering arises from using max-flow
and min-cut on the objectives, which offer distinctly different guarantees. For
instance, a new method called capacity releasing diffusion (CRD) was recently
proposed and shown to preserve local structure around the seeds better than
spectral methods. The method was also the first local clustering technique that
is not subject to the quadratic Cheeger inequality by assuming a good cluster
near the seed nodes. In this paper, we propose a local hypergraph clustering
technique called hypergraph CRD (HG-CRD) by extending the CRD process to
cluster based on higher order patterns, encoded as hyperedges of a hypergraph.
Moreover, we theoretically show that HG-CRD gives results about a quantity
called motif conductance, rather than a biased version used in previous
experiments. Experimental results on synthetic datasets and real world graphs
show that HG-CRD enhances the clustering quality.


  Let $U\subseteq\mathbb{R}^d$ be open and convex. We prove that every (not
necessarily Lipschitz or strongly) convex function $f:U\to\mathbb{R}$ can be
approximated by real analytic convex functions, uniformly on all of $U$. We
also show that $C^0$-fine approximation of convex functions by smooth (or real
analytic) convex functions on $\mathbb{R}^d$ is possible in general if and only
if $d=1$. Nevertheless, for $d\geq 2$ we give a characterization of the class
of convex functions on $\mathbb{R}^d$ which can be approximated by real
analytic (or just smoother) convex functions in the $C^0$-fine topology. It
turns out that the possibility of performing this kind of approximation is not
determined by the degree of local convexity or smoothness of the given
function, but by its global geometrical behaviour. We also show that every
$C^{1}$ convex and proper function on $U$ can be approximated by $C^{\infty}$
convex functions in the $C^{1}$-fine topology, and we provide some applications
of these results, concerning prescription of (sub-)differential boundary data
to convex real analytic functions, and smooth surgery of convex bodies.


  We show that non-classical intensity correlations and quadrature entanglement
can be generated by frequency doubling in a resonator with two output ports. We
predict twin-beam intensity correlations 6 dB below the coherent state limit,
and that the product of the inference variances of the quadrature fluctuations
gives an Einstein-Podolsky-Rosen (EPR) correlation coefficient of V_EPR = 0.6 <
1. Comparison with an entanglement source based on combining two frequency
doublers with a beam splitter shows that the dual ported resonator provides
stronger entanglement at lower levels of individual beam squeezing.
Calculations are performed using a self-consistent propagation method that does
not invoke a mean field approximation. Results are given for physically
realistic parameters that account for the Gaussian shape of the intracavity
beams, as well as intracavity losses.


  Social media platforms have emerged as prominent information sharing
ecosystems in the context of a variety of recent crises, ranging from mass
emergencies, to wars and political conflicts. We study affective responses in
social media and how they might indicate desensitization to violence
experienced in communities embroiled in an armed conflict. Specifically, we
examine three established affect measures: negative affect, activation, and
dominance as observed on Twitter in relation to a number of statistics on
protracted violence in four major cities afflicted by the Mexican Drug War.
During a two year period (Aug 2010-Dec 2012), while violence was on the rise in
these regions, our findings show a decline in negative emotional expression as
well as a rise in emotional arousal and dominance in Twitter posts: aspects
known to be psychological markers of desensitization. We discuss the
implications of our work for behavioral health, facilitating rehabilitation
efforts in communities enmeshed in an acute and persistent urban warfare, and
the impact on civic engagement.


  In a practical molecular communication scenario such as monitoring air
pollutants released from an unknown source, it is essential to estimate the
location of the molecular transmitter (TX). This paper presents a novel Sensor
Network-based Localization Algorithm (SNCLA) for passive transmission by using
a novel experimental platform which mainly comprises a clustered sensor network
(SN) with $24$ sensor nodes and evaporating ethanol molecules as the passive
TX. In SNCLA, a Gaussian plume model is employed to derive the location
estimator. The parameters such as transmitted mass, wind velocity, detection
time, and actual concentration are calculated or estimated from the measured
signals via the SN to be employed as the input for the location estimator. The
numerical results show that the performance of SNCLA is better for stronger
winds in the medium. Our findings show that evaporated molecules do not
propagate homogeneously through the SN due to the presence of the wind. In
addition, our statistical analysis based on the measured experimental data
shows that the sensed signals by the SN have a log-normal distribution, while
the additive noise follows a Student's t-distribution in contrast to the
Gaussian assumption in the literature.


  We present estimates of Single Spin Asymmetry(SSA) in the electroproduction
of J/{\psi} taking into account the transverse momentum dependent(TMD)
evolution of the Sivers function. We estimate SSA for JLab, HERMES, COMPASS and
eRHIC energies using color evaporation model of J/{\psi}. We have calculated
the asymmetry using recent parameters extracted by Echevarria et al. using the
CSS approach to TMD evolution. These recent TMD evolution fits are based on the
evolution kernel in which the perturbative part is resummed up to NLL accuracy.
We have also estimated the asymmetry by using parameters which had been
obtained by a fit by Anselmino et al., using both an exact numerical and an
approximate analytical solution of the TMD evolution equations. We find that
the variation among the different estimates obtained using TMD evolution is
much smaller than between these on one hand and the estimates obtained using
DGLAP evolution on the other. Even though the use of TMD evolution causes an
overall reduction in asymmetries compared to the ones obtained without it, they
remain sizable. Overall, upon use of TMD evolution, predictions for asymmetries
stabilize.


  We report measurements of the Sunyaev-Zel'dovich (SZ) effect in three
high-redshift (0.89 < z < 1.03), X-ray selected galaxy clusters. The
observations were obtained at 30 GHz during the commissioning period of a new,
eight-element interferometer -- the Sunyaev-Zel'dovich Array (SZA) -- built for
dedicated SZ effect observations. The SZA observations are sensitive to angular
scales larger than those subtended by the virial radii of the clusters.
Assuming isothermality and hydrostatic equilibrium for the intracluster medium,
and gas-mass fractions consistent with those for clusters at moderate redshift,
we calculate electron temperatures, gas masses, and total cluster masses from
the SZ data. The SZ-derived masses, integrated approximately to the virial
radii, are 1.9 ^{+0.5}_{-0.4}x10^{14} solar masses for CLl1415.1+3612,
3.4^{+0.6}_{-0.5}x10^{14} solar masses for CL1429.0+4241, and
7.2^{+1.3}_{-0.9}x10^{14} solar masses for CL1226.9+3332. The SZ-derived
quantities are in good agreement with the cluster properties derived from X-ray
measurements.


  We consider a gauge-invariant Hamiltonian analysis for Yang-Mills theories in
three spatial dimensions. The gauge potentials are parametrized in terms of a
matrix variable which facilitates the elimination of the gauge degrees of
freedom. We develop an approximate calculation of the volume element on the
gauge-invariant configuration space. We also make a rough estimate of the ratio
of $0^{++}$ glueball mass and the square root of string tension by comparison
with $(2+1)$-dimensional Yang-Mills theory.