  We study the existence and stability of phaselocked patterns and amplitude
death states in a closed chain of delay coupled identical limit cycle
oscillators that are near a supercritical Hopf bifurcation. The coupling is
limited to nearest neighbors and is linear. We analyze a model set of discrete
dynamical equations using the method of plane waves. The resultant dispersion
relation, which is valid for any arbitrary number of oscillators, displays
important differences from similar relations obtained from continuum models. We
discuss the general characteristics of the equilibrium states including their
dependencies on various system parameters. We next carry out a detailed linear
stability investigation of these states in order to delineate their actual
existence regions and to determine their parametric dependence on time delay.
Time delay is found to expand the range of possible phaselocked patterns and to
contribute favorably toward their stability. The amplitude death state is
studied in the parameter space of time delay and coupling strength. It is shown
that death island regions can exist for any number of oscillators N in the
presence of finite time delay. A particularly interesting result is that the
size of an island is independent of N when N is even but is a decreasing
function of N when N is odd.


  The refined analytic torsion associated to a flat vector bundle over a closed
odd-dimensional manifold canonically defines a quadratic form $\tau$ on the
determinant line of the cohomology. Both $\tau$ and the Burghelea-Haller
torsion are refinements of the Ray-Singer torsion. We show that whenever the
Burghelea-Haller torsion is defined it is equal to $\pm\tau$. As an application
we obtain new results about the Burghelea-Haller torsion. In particular, we
prove a weak version of the Burghelea-Haller conjecture relating their torsion
with the square of the Farber-Turaev combinatorial torsion.


  The Astrometric Gravitation Probe mission is a modern version of the 1919
Dyson-Eddington-Davidson experiment, based on a space-borne telescope with a
permanent built-in eclipse, provided by a coronagraphic system. The expected
improvement on experimental bounds to General Relativity and competing
gravitation theories is by at least two orders of magnitude. The measurement
principle is reviewed, in particular the principle of Fizeau-like combination
of a set of individual inverted coronagraphs simultaneously feeding a common
high resolution telescope. Also, the payload has a dual field of view property,
in order to support simultaneous observations of stellar fields either very
close, or far away, from the Sun, i.e. fields affected by either high or low
light bending. We discuss a set of solutions introduced in the optical design
to improve on technical feasibility and robustness of the optical performance
against perturbations, in particular induced by manufacturing and alignment
tolerances, and launch stresses.


  The spin-glass behavior in the ferromagnetic phase of $\mathrm{Tm_2Cu_2In}$
was investigated by dc-, ac-magnetization, and non-equilibrium dynamics
characterizations. Arc-melted polycrystalline compound of $\mathrm{Tm_2Cu_2In}$
crystallizes in $\mathrm{Mo_2FeB_2}$-type tetragonal structure (space group
$P_4/mbm$). The temperature variation dc-magnetization exhibits a ferromagnetic
behavior with Curie temperature $T_C$ = 32.5 K. The zero-field cooled (ZFC) and
field cooled (FC) magnetic curves show a thermomagnetic irreversible behavior
below $T_C$. The field dependence of irreversible temperature follows
Almeida-Thouless line. The frequency and ac-driven field-dependent anomalies in
ac-susceptibility results indicate the existence of spin-glass state in
$\mathrm{Tm_2Cu_2In}$. The time dependence of magnetization further supports
the spin-glass behavior observed in $\mathrm{Tm_2Cu_2In}$. The frequency
dependence of the freezing temperature in the real part of ac-susceptibility
has been analyzed on the basis power-law divergence and Vogel-Fulcher law. The
obtained results revealed that $\mathrm{Tm_2Cu_2In}$ belongs to a ferromagnetic
canonical spin-glass class of compounds.


  Recently, bidirectional recurrent neural network (BRNN) has been widely used
for question answering (QA) tasks with promising performance. However, most
existing BRNN models extract the information of questions and answers by
directly using a pooling operation to generate the representation for loss or
similarity calculation. Hence, these existing models don't put supervision
(loss or similarity calculation) at every time step, which will lose some
useful information. In this paper, we propose a novel BRNN model called
full-time supervision based BRNN (FTS-BRNN), which can put supervision at every
time step. Experiments on the factoid QA task show that our FTS-BRNN can
outperform other baselines to achieve the state-of-the-art accuracy.


  We implemented a rotationally-invariant Hubbard U extension to
density-functional theory in the Car-Parrinello molecular dynamics framework,
with the goal of bringing the accuracy of the DFT+U approach to
finite-temperature simulations, especially for liquids or solids containing
transition-metal ions. First, we studied the effects on the Hubbard U on the
static equilibrium structure of the hexa-aqua ferrous and ferric ions, and the
inner-sphere reorganization energy for the electron-transfer reaction between
aqueous ferrous and ferric ions. It is found that the reorganization energy is
increased, mostly as a result of the Fe-O distance elongation in the hexa-aqua
ferrous ion. Second, we performed a first-principles molecular dynamics study
of the solvation structure of the two aqueous ferrous and ferric ions. The
Hubbard term is found to change the Fe-O radial distribution function for the
ferrous ion, while having a negligible effect on the aqueous ferric ion.
Moreover, the frequencies of vibrations between Fe and oxygen atoms in the
first-solvation shell are shown to be unaffected by the Hubbard corrections for
both ferrous and ferric ions.


  Solving general Markov decision processes (MDPs) is a computationally hard
problem. Solving finite-horizon MDPs, on the other hand, is highly tractable
with well known polynomial-time algorithms. What drives this extreme disparity,
and do problems exist that lie between these diametrically opposed
complexities? In this paper we identify and analyse a sub-class of stochastic
shortest path problems (SSPs) for general state-action spaces whose dynamics
satisfy a particular drift condition. This construction generalises the
traditional, temporal notion of a horizon via decreasing reachability: a
property called reductivity. It is shown that optimal policies can be recovered
in polynomial-time for reductive SSPs -- via an extension of backwards
induction -- with an efficient analogue in reductive MDPs. The practical
considerations of the proposed approach are discussed, and numerical
verification provided on a canonical optimal liquidation problem.


  We study the spectrum and magnetic properties of double quantum dots in the
lowest Landau level for different values of the hopping and Zeeman parameters
by means of exact diagonalization techniques in systems of N=6 and N=7
electrons and filling factor close to 2. We compare our results with those
obtained in double quantum layers and single quantum dots. The Kohn theorem is
also discussed.


  Runtime verification is checking whether a system execution satisfies or
violates a given correctness property. A procedure that automatically, and
typically on the fly, verifies conformance of the system's behavior to the
specified property is called a monitor. Nowadays, a variety of formalisms are
used to express properties on observed behavior of computer systems, and a lot
of methods have been proposed to construct monitors. However, it is a frequent
situation when advanced formalisms and methods are not needed, because an
executable model of the system is available. The original purpose and structure
of the model are out of importance; rather what is required is that the system
and its model have similar sets of interfaces. In this case, monitoring is
carried out as follows. Two "black boxes", the system and its reference model,
are executed in parallel and stimulated with the same input sequences; the
monitor dynamically captures their output traces and tries to match them. The
main problem is that a model is usually more abstract than the real system,
both in terms of functionality and timing. Therefore, trace-to-trace matching
is not straightforward and allows the system to produce events in different
order or even miss some of them. The paper studies on-the-fly conformance
relations for timed systems (i.e., systems whose inputs and outputs are
distributed along the time axis). It also suggests a practice-oriented
methodology for creating and configuring monitors for timed systems based on
executable models. The methodology has been successfully applied to a number of
industrial projects of simulation-based hardware verification.


  In optical wireless scattering communication, received signal in each symbol
interval is captured by a photomultiplier tube (PMT) and then sampled through
very short but finite interval sampling. The resulting samples form a signal
vector for symbol detection. The upper and lower bounds on transmission rate of
such a processing system are studied. It is shown that the gap between two
bounds approaches zero as the thermal noise and shot noise variances approach
zero. The maximum a posteriori (MAP) signal detection is performed and a low
computational complexity receiver is derived under piecewise polynomial
approximation. Meanwhile, the threshold based signal detection is also studied,
where two threshold selection rules are proposed based on the detection error
probability and the Kullback-Leibler (KL) distance. For the latter, it is shown
that the KL distance is not sensitive to the threshold selection for small shot
and thermal noise variances, and thus the threshold can be selected among a
wide range without significant loss from the optimal KL distance. The
performances of the transmission rate bounds, the signal detection, and the
threshold selection approaches are evaluated by the numerical results.


  In this paper, a new class of Finsler metrics which are included
$(\alpha,\beta)$-metrics are introduced. They are defined by a Riemannian
metric and two 1-forms $\beta=b_i(x)y^i$ and $\gamma= \gamma_i(x)y^i$. This
class of metrics are a generalization of $(\alpha,\beta)$-metrics which are not
always $(\alpha,\beta)$-metric. We find a necessary and sufficient condition
for this metric to be locally projectively flat and then we prove the
conditions for this metric to be of Douglas type.


  Given two elements $A,B$ in a compact semisimple Lie algebra, we show that
there is a regular element $X$ and elements $Y,Z$ with $A=[X,Y]$ and $B=[X,Z]$.
In the course of the proof we show also that every linear subspace $V$ of
codimension at most 2 in the Lie algebra contains a CSA.


  The Internet provides access to communications, information and opportunity
to people. Growth of technology and its benefits should penetrate down to every
citizen of a country. Rural Areas without Internet access risk being left
behind in the information age. To be competitive, they must have broad and
sustainable Internet services. Internet connectivity is especially important in
developing countries like India where 67% of the population hail from rural
areas. It is clear that most developing countries have still basic problems
such as education, poverty, housing, and health, which are sure priorities for
any government, it is important to determine whether an advanced informational
infrastructure provides a tool for economic and social development. There are a
lot of challenges that need to be considered while providing connectivity to
rural areas such as high cost of installations, a dearth of skilled labor,
deficiency of reliable power sources, local cooperation and support, etc. In
this paper, we would like to discuss various dimensions and infrastructures in
order to establish connectivity in the rural parts of India. We would also
focus on the importance of maintenance of these wireless deployments and how it
can be achieved.


  A seismic wavefield reconstruction framework based on compressed sensing
using the data-driven reduced-order model (ROM) is proposed and its
characteristics are investigated through numerical experiments. The data-driven
ROM is generated from the dataset of the wavefield using the singular value
decomposition. The spatially continuous seismic wavefield is reconstructed from
the sparse and discrete observation and the data-driven ROM. The observation
sites used for reconstruction are effectively selected by the sensor
optimization method for linear inverse problems based on a greedy algorithm.
The proposed framework was applied to simulation data of theoretical waveform
with the subsurface structure of the horizontally-stratified three layers. The
validity of the proposed method was confirmed by the reconstruction based on
the noise-free observation. Since the ROM of the wavefield is used as prior
information, the reconstruction error is reduced to an approximately lower
error bound of the present framework, even though the number of sensors used
for reconstruction is limited and randomly selected. In addition, the
reconstruction error obtained by the proposed framework is much smaller than
that obtained by the Gaussian process regression. For the numerical experiment
with noise-contaminated observation, the reconstructed wavefield is degraded
due to the observation noise, but the reconstruction error obtained by the
present framework with all available observation sites is close to a lower
error bound, even though the reconstructed wavefield using the Gaussian process
regression is fully collapsed. Although the reconstruction error is larger than
that obtained using all observation sites, the number of observation sites used
for reconstruction can be reduced while minimizing the deterioration and
scatter of the reconstructed data by combining it with the sensor optimization
method.


  Recent experimental breakthrough in realizing spin-orbit (SO) coupling for
cold atoms has spurred considerable interest in the physics of 2D SO coupled
Fermi superfluids, especially topological Majorana fermions (MFs) which were
predicted to exist at zero temperature. However, it is well known that
long-range superfluid order is destroyed in 2D by the phase fluctuation at
finite temperature and the relevant physics is the
Berezinskii-Kosterlitz-Thouless (BKT) transition. In this Letter, we examine
finite temperature effects on SO coupled Fermi gases and show that finite
temperature is indeed necessary for the observation of MFs. MFs are
topologically protected by a quasiparticle energy gap which is found to be much
larger than the temperature. The restrictions to the parameter region for the
observation of MFs have been obtained.


  We present an analysis of the emission behaviour of PSR J1107-5907, a source
known to exhibit separate modes of emission, using observations obtained over
approximately 10 yr. We find that the object exhibits two distinct modes of
emission; a strong mode with a broad profile and a weak mode with a narrow
profile. During the strong mode of emission, the pulsar typically radiates very
energetic emission over sequences of ~200-6000 pulses (~60 s-24 min), with
apparent nulls over time-scales of up to a few pulses at a time. Emission
during the weak mode is observed outside of these strong-mode sequences and
manifests as occasional bursts of up to a few clearly detectable pulses at a
time, as well as low-level underlying emission which is only detected through
profile integration. This implies that the previously described null mode may
in fact be representative of the bottom-end of the pulse intensity distribution
for the source. This is supported by the dramatic pulse-to-pulse intensity
modulation and rarity of exceptionally bright pulses observed during both modes
of emission. Coupled with the fact that the source could be interpreted as a
rotating radio transient (RRAT)-like object for the vast majority of the time,
if placed at a further distance, we advance that this object likely represents
a bridge between RRATs and extreme moding pulsars. Further to these emission
properties, we also show that the source is consistent with being a
near-aligned rotator and that it does not exhibit any measurable spin-down rate
variation. These results suggest that nulls observed in other intermittent
objects may in fact be representative of very weak emission without the need
for complete cessation. As such, we argue that longer (> 1 h) observations of
pulsars are required to discern their true modulation properties.


  The focus of this paper is to show the absence of critical points for the
solutions to the Helmholtz equation in a bounded domain
$\Omega\subset\mathbb{R}^{3}$, given by \[ \left\{ \begin{array}{l}
-\rm{div}(a\,\nabla u_{\omega}^{g})-\omega qu_{\omega}^{g}=0\quad\text{in
$\Omega$,}\\ u_{\omega}^{g}=g\quad\text{on $\partial\Omega$.}
\end{array}\right. \] We prove that for an admissible $g$ there exists a finite
set of frequencies $K$ in a given interval and an open cover
$\overline{\Omega}=\cup_{\omega\in K}\Omega_{\omega}$ such that $|\nabla
u_{\omega}^{g}(x)|>0$ for every $\omega\in K$ and $x\in\Omega_{\omega}$. The
set $K$ is explicitly constructed. If the spectrum of the above problem is
simple, which is true for a generic domain $\Omega$, the admissibility
condition on $g$ is a generic property.


  Many years ago, G.-C.~Rota discovered a close connection between symmetric
functions and Rota-Baxter algebras, and proposed to study generalizations of
symmetric functions in the framework of Rota-Baxter algebras. Guided by this
proposal, quasisymmetric functions from weak composition (instead of just
compositions) were obtained from free Rota-Baxter algebras on one generator.
This paper aims to generalize this approach to free Rota-Baxter algebras on
multiple generators in order to obtain further generalizations of
quasisymmetric functions. For this purpose and also for its independent
interest, the space $\mathrm{MQSym}$ of quasisymmetric functions on multiple
sequences of variables is defined, generalizing quasisymmetric functions and
diagonally quasisymmetric functions of Aval, Bergeron and Bergeron. Linear
bases of such multi-quasisymmetric functions are given by monomial
multi-quasisymmetric functions and fundamental multi-quasisymmetric functions,
the latter recover the fundamental $G^m$-quasisymmetric functions of Aval and
Chapoton. Next introduced is the even more general notion of
multi-quasisymmetric functions $\mathrm{MQSym}^E$ with exponents in a semigroup
$E$, which also generalizes the quasisymmetric functions with semigroup
exponents in a recent work. Through this approach, a natural Hopf algebraic
structure is obtained on $\mathrm{MQSym}^E$. Finally, in support of Rota's
proposal, the free commutative unitary Rota-Baxter algebra on a finite set is
shown to be isomorphic to a scalar extension of $\mathrm{MQSym}^E$, a fact
which in turn equips the free Rota-Baxter algebra with a Hopf algebra
structure.


  The effects of dust on infrared emission vary among galaxies of different
morphological types. We investigated integrated spectral energy distributions
in infrared and submillimeter emissions from the Large Magellanic Cloud based
on the observations using the Herschel Space Observatory and near- to
mid-infrared observations from the Spitzer Space Telescope. We also used IRAS
and WMAP observations to constrain the SEDs. We explain the observations by
using dust models with different grain size distributions in the interstellar
medium of the LMC, noting that the LMC has undergone processes that are
different from those in the Milky Way. We determined a spectral index and a
normalization factor in the range of -3.5 to -3.45 with grain radii in the
range of 1 nm-300 nm for the silicate grain and 2 nm-1 um for the graphite
grain. The best fit to the observed SED was obtained with a spectral index of
-3.47. The grain size distribution is described using a power law but with a
break that is introduced below a_b, where a larger exponent is used. Changing
graphite grain size distribution significantly changed the SED pattern within
the observational uncertainties. Based on the SED fits to the observations from
submillimeter wavelengths to the infrared radiation in the LMC using GRASIL
(Silva et al. 1998), we obtained a reasonable set of parameter values in
chemical and geometric space together with the grain size distributions of a
modified MRN model with the LMC extinction curve.


  We explore the low energy implications of an F-theory inspired $E_6$ model
whose breaking yields, in addition to the MSSM gauge symmetry, a $Z'$ gauge
boson associated with a $U(1)$ symmetry broken at the TeV scale. The zero mode
spectrum of the effective low energy theory is derived from the decomposition
of the $27$ and $\overline{27}$ representations of $E_6$ and we parametrise
their multiplicities in terms of a minimum number of flux parameters. We
perform a two-loop renormalisation group analysis of the gauge and Yukawa
couplings of the effective theory model and estimate lower bounds on the new
vectorlike particles predicted in the model. We compute the third generation
Yukawa couplings in an F-theory context assuming an $E_8$ point of enhancement
and express our results in terms of the local flux densities associated with
the gauge symmetry breaking. We find that their values are compatible with the
ones computed by the renormalisation group equations, and we identify points in
the parameter space of the flux densities where the $t-b-\tau$ Yukawa couplings
unify.


  Previously we have presented evidence for stripe order of holes and spins in
La(1.6-x)Nd(0.4)Sr(x)CuO(4) with x=0.12. Here we show, via neutron diffraction
measurements of magnetic scattering, that similar order occurs in crystals with
x=0.15 and 0.20. Zero-field-cooled magnetization measurements show that all 3
compositions are also superconducting, with the superconducting transition
temperature increasing as the low-temperature staggered magnetization
decreases.


  We apply two new state-of-the-art methods that model the distribution of
observed tracers in projected phase space to lift the mass / velocity
anisotropy (VA) degeneracy and deduce constraints on the mass profiles of
galaxies, as well as their VA. We first show how a distribution function based
method applied to the satellite kinematics of otherwise isolated SDSS galaxies
shows convincing observational evidence of age matching: red galaxies have more
concentrated dark matter (DM) halos than blue galaxies of the same stellar or
halo mass. Then, applying the MAMPOSSt technique to M87 (traced by its red and
blue globular clusters) we find that very cuspy DM is favored, unless we
release priors on DM concentration or stellar mass (leading to unconstrained
slope). For the Fornax dwarf spheroidal (traced by its metal-rich and
metal-poor stars), the inner DM slope is unconstrained, with weak evidence for
a core if the stellar mass is fixed. This highlights how priors are crucial for
DM modeling. Finally, we find that blue GCs around M87 and metal-rich stars in
Fornax have tangential outer VA.


  In the setting of adjoint pairs of operators we consider the question: to
what extent does the Weyl M-function see the same singularities as the
resolvent of a certain restriction $A_B$ of the maximal operator? We obtain
results showing that it is possible to describe explicitly certain spaces $\Sc$
and $\tilde{\Sc}$ such that the resolvent bordered by projections onto these
subspaces is analytic everywhere that the M-function is analytic. We present
three examples -- one involving a Hain-L\"{u}st type operator, one involving a
perturbed Friedrichs operator and one involving a simple ordinary differential
operators on a half line -- which together indicate that the abstract results
are probably best possible.


  We present InvVis, a new approach for invertible visualization, which is
reconstructing or further modifying a visualization from an image. InvVis
allows the embedding of a significant amount of data, such as chart data, chart
information, source code, etc., into visualization images. The encoded image is
perceptually indistinguishable from the original one. We propose a new method
to efficiently express chart data in the form of images, enabling
large-capacity data embedding. We also outline a model based on the invertible
neural network to achieve high-quality data concealing and revealing. We
explore and implement a variety of application scenarios of InvVis.
Additionally, we conduct a series of evaluation experiments to assess our
method from multiple perspectives, including data embedding quality, data
restoration accuracy, data encoding capacity, etc. The result of our
experiments demonstrates the great potential of InvVis in invertible
visualization.


  Some techniques for the study of intermittency by means of wavelet
transforms, are presented on an example of synthetic turbulent signal. Several
features of the turbulent field, that cannot be probed looking at standard
structure function scaling, become accessible in this way. The concept of a
directly measurable intermittency scale, distinct from the scale of the
fluctuations, is introduced. A method for optimizing the analyzing wavelets,
which exploits this concept, and allows to minimize non-local contributions in
scale to wavelet correlations, is described. The transition from a wavelet to a
Fourier transform based description of an intermittent random field, and the
possibility of using Fourier correlations to measure intermittency are
discussed. Important limitations in the ability of structure functions to give
a local in scale description of intermittent random fields, are observed.


  A relativistic statistical field theory is constructed for a fluctuating
complex-valued scalar field on a discretized Minkowski lattice. A Hilbert space
of observables is then constructed from functionals of the fluctuating
complex-valued scalar field with an inner product defined in terms of
expectation values of the functionals. A bosonic Fock space is then constructed
from the Hilbert space and creation and annihilation operators that act on the
Fock space are defined. The creation and annihilation operators are used to
define field operators. These field operators have some interesting
quantum-like properties. For example, the field operators do not commute in
general and, in the particular case of the free field theory, can be shown to
satisfy the microcausality condition.


  The identification of abnormal electrographic activity is important in a wide
range of neurological disorders, including epilepsy for localising
epileptogenic tissue. However, this identification may be challenging during
non-seizure (interictal) periods, especially if abnormalities are subtle
compared to the repertoire of possible healthy brain dynamics. Here, we
investigate if such interictal abnormalities become more salient by
quantitatively accounting for the range of healthy brain dynamics in a
location-specific manner. To this end, we constructed a normative map of brain
dynamics, in terms of relative band power, from interictal intracranial
recordings from 234 subjects (21,598 electrode contacts). We then compared
interictal recordings from 62 patients with epilepsy to the normative map to
identify abnormal regions. We hypothesised that if the most abnormal regions
were spared by surgery, then patients would be more likely to experience
continued seizures post-operatively. We first confirmed that the spatial
variations of band power in the normative map across brain regions were
consistent with healthy variations reported in the literature. Second, when
accounting for the normative variations, regions which were spared by surgery
were more abnormal than those resected only in patients with persistent
post-operative seizures (t=-3.6, p=0.0003), confirming our hypothesis. Third,
we found that this effect discriminated patient outcomes (AUC=0.75 p=0.0003).
Normative mapping is a well-established practice in neuroscientific research.
Our study suggests that this approach is feasible to detect interictal
abnormalities in intracranial EEG, and of potential clinical value to identify
pathological tissue in epilepsy. Finally, we make our normative intracranial
map publicly available to facilitate future investigations in epilepsy and
beyond.


  Confined hydration and conformational flexibility are some of the challenges
encountered for the rational design of selective antagonists of G-protein
coupled receptors. We present a set of C3-substituted (-)-stepholidine
derivatives as potent binders of the dopamine D3 receptor. The compounds are
characterized biochemically, as well as by computer modeling using a novel
molecular dynamics-based alchemical binding free energy approach which
incorporates the effect of the displacement of enclosed water molecules from
the binding site. The free energy of displacement of specific hydration sites
is obtained using the Hydration Site Analysis method with explicit solvation.
This work underscores the critical role of confined hydration and
conformational reorganization in the molecular recognition mechanism of
dopamine receptors and illustrates the potential of binding free energy models
to represent these key phenomena.


  BGP route leaks frequently precipitate serious disruptions to interdomain
routing. These incidents have plagued the Internet for decades while deployment
and usability issues cripple efforts to mitigate the problem. Peerlock,
introduced in 2016, addresses route leaks with a new approach. Peerlock enables
filtering agreements between transit providers to protect their own networks
without the need for broad cooperation or a trust infrastructure. We outline
the Peerlock system and one variant, Peerlock-lite, and conduct live Internet
experiments to measure their deployment on the control plane. Our measurements
find evidence for significant Peerlock protection between Tier 1 networks in
the peering clique, where 48% of potential Peerlock filters are deployed, and
reveal that many other networks also deploy filters against Tier 1 leaks. To
guide further deployment, we also quantify Peerlock's impact on route leaks
both at currently observed levels and under hypothetical future deployment
scenarios via BGP simulation. These experiments reveal present Peerlock
deployment restricts Tier 1 leak export to 10% or fewer networks for 40% of
simulated leaks. Strategic additional Peerlock-lite deployment at all large
ISPs (fewer than 1% of all networks), in tandem with Peerlock within the
peering clique as deployed, completely mitigates 80% of simulated Tier 1 route
leaks.


  The multipole expansion method (MEM) is a spatial discretization technique
that is widely used in applications that feature scattering of waves from
circular cylinders. Moreover, it also serves as a key component in several
other numerical methods in which scattering computations involving arbitrarily
shaped objects are accelerated by enclosing the objects in artificial
cylinders. A fundamental question is that of how fast the approximation error
of the MEM converges to zero as the truncation number goes to infinity. Despite
the fact that the MEM was introduced in 1913, and has been in widespread usage
as a numerical technique since as far back as 1955, to the best of the authors'
knowledge, a precise characterization of the asymptotic rate of convergence of
the MEM has not been obtained. In this work, we provide a resolution to this
issue. While our focus in this paper is on the Dirichlet scattering problem,
this is merely for convenience and our results actually establish convergence
rates that hold for all MEM formulations irrespective of the specific boundary
conditions or boundary integral equation solution representation chosen.


  Using knowledge of the explicit $n$ dependence of the RG functions and
expressions of critical exponents in the framework of large $N$ expansion in
the Gross Neveu model we derive RG functions in 4- and 5-loop approximation.


  We study lifelong reinforcement learning (RL) in a regret minimization
setting of linear contextual Markov decision process (MDP), where the agent
needs to learn a multi-task policy while solving a streaming sequence of tasks.
We propose an algorithm, called UCB Lifelong Value Distillation (UCBlvd), that
provably achieves sublinear regret for any sequence of tasks, which may be
adaptively chosen based on the agent's past behaviors. Remarkably, our
algorithm uses only sublinear number of planning calls, which means that the
agent eventually learns a policy that is near optimal for multiple tasks (seen
or unseen) without the need of deliberate planning. A key to this property is a
new structural assumption that enables computation sharing across tasks during
exploration. Specifically, for $K$ task episodes of horizon $H$, our algorithm
has a regret bound $\tilde{\mathcal{O}}(\sqrt{(d^3+d^\prime d)H^4K})$ based on
$\mathcal{O}(dH\log(K))$ number of planning calls, where $d$ and $d^\prime$ are
the feature dimensions of the dynamics and rewards, respectively. This
theoretical guarantee implies that our algorithm can enable a lifelong learning
agent to accumulate experiences and learn to rapidly solve new tasks.


  We study M-theory solutions involving compact hyperbolic spaces. The
combination of a gap `a la Randall-Sundrum and the topology of an internal
Riemann surface allows a geometrical solution to the hierarchy problem that
does not require light Kaluza--Klein modes. We comment on the consequences of
such a compactification for LHC physics.


  We present new photometric and spectroscopic observations of an unusual
luminous blue variable (LBV) in NGC 3432, covering three major outbursts in
October 2008, April 2009 and November 2009. Previously, this star experienced
an outburst also in 2000 (known as SN 2000ch). During outbursts the star
reached an absolute magnitude between -12.1 and -12.8. Its spectrum showed H,
He I and Fe II lines with P-Cygni profiles during and soon after the eruptive
phases, while only intermediate-width lines in pure emission (including He II
4686A were visible during quiescence. The fast-evolving light curve soon after
the outbursts, the quasi-modulated light curve, the peak magnitude and the
overall spectral properties are consistent with multiple episodes of
variability of an extremely active LBV. However, the widths of the spectral
lines indicate unusually high wind velocities (1500-2800 km/s), similar to
those observed in Wolf-Rayet stars. Although modulated light curves are typical
of LBVs during the S-Dor variability phase, the luminous maxima and the high
frequency of outbursts are unexpected in S-Dor variables. Such extreme
variability may be associated with repeated ejection episodes during a giant
eruption of an LBV. Alternatively, it may be indicative of a high level of
instability shortly preceding the core-collapse or due to interaction with a
massive, binary companion. In this context, the variable in NGC 3432 shares
some similarities with the famous stellar system HD 5980 in the Small
Magellanic Cloud, which includes an erupting LBV and an early Wolf-Rayet star.


  The heterogeneous cloud radio access network (HCRAN) is a promising paradigm
which integrates the advantages of cloud radio access network (C-RAN) and
heterogeneous network (HetNet). In this paper, we study the joint congestion
control and resource optimization to explore the energy efficiency
(EE)-guaranteed tradeoff between throughput utility and delay performance in a
downlink slotted H-CRAN. We formulate the considered problem as a stochastic
optimization problem, which maximizes the utility of average throughput and
maintains the network stability subject to required EE constraint and transmit
power consumption constraints by traffic admission control, user association,
resource block allocation and power allocation. Leveraging on the Lyapunov
optimization technique, the stochastic optimization problem can be transformed
and decomposed into three separate subproblems which can be solved concurrently
at each slot. The third mixed-integer nonconvex subproblem is efficiently
solved utilizing the continuity relaxation of binary variables and the Lagrange
dual decomposition method. Theoretical analysis shows that the proposal can
quantitatively control the throughput-delay performance tradeoff with required
EE performance. Simulation results consolidate the theoretical analysis and
demonstrate the advantages of the proposal from the prospective of queue
stability and power consumption.


  This study aims to investigate the effects of violations of the sphericity
assumption on Type I error rates for different methodical approaches of
repeated measures analysis using a simulation approach. In contrast to previous
simulation studies on this topic, up to nine measurement occasions were
considered. Therefore, two populations representing the conditions of a
violation vs. a non-violation of the sphericity assumption without any
between-group effect or within-subject effect were created and 5,000 random
samples of each population were drawn. Finally, the mean Type I error rates for
Multilevel linear models (MLM) with an unstructured covariance matrix (MLM-UN),
MLM with compound-symmetry (MLM-CS) and for repeated measures analysis of
variance (rANOVA) models (without correction, with
Greenhouse-Geisser-correction, and Huynh-Feldt-correction) were computed. To
examine the effect of both the sample size and the number of measurement
occasions, sample sizes of n = 20, 40, 60, 80, and 100 were considered as well
as measurement occasions of m = 3, 6 and 9. For MLM-UN, the results illustrate
a massive progressive bias for small sample sizes (n =20) and m = 6 or more
measurement occasions. This effect could not be found in previous simulation
studies with a smaller number of measurement occasions. The mean Type I error
rates for rANOVA with Greenhouse-Geisser-correction demonstrate a small
conservative bias if sphericity was not violated, sample sizes were small (n =
20), and m = 6 or more measurement occasions were conducted. The results plead
for a use of rANOVA with Huynh-Feldt-correction, especially when the sphericity
assumption is violated, the sample size is rather small and the number of
measurement occasions is large. MLM-UN may be used when the sphericity
assumption is violated and when sample sizes are large.


  We report new near-IR integral field spectroscopy of the central starburst
region of the barred spiral galaxy M83 obtained with CIRPASS on Gemini-S, which
we analyse in conjunction with GHaFaS Fabry-Perot data, an AAT IRIS2 Ks-band
image, and near- and mid-IR imaging from the Hubble and Spitzer space
telescopes. The bulk of the current star formation activity is hidden from
optical view by dust extinction, but is seen in the near- and mid-IR to the
north of the nucleus. This region is being fed by inflow of gas through the bar
of M83, traced by the prominent dust lane entering into the circumnuclear
region from the north. An analysis of stellar ages confirms that the youngest
stars are indeed in the northwest. A gradual age gradient, with older stars
further to the south, characterises the well-known star-forming arc in the
central region of M83. Detailed analyses of the Pa beta ionised gas kinematics
and near-IR imaging confirm that the kinematic centre coincides with the
photometric centre of M83, and that these are offset significantly, by about 3
arcsec or 60 pc, from the visible nucleus of the galaxy. We discuss two
possible options, the first of which postulates that the kinematic and
photometric centre traces a galaxy nucleus hidden by a substantial amount of
dust extinction, in the range A_V=3-10 mag. By combining this information with
kinematic results and using arguments from the literature, we conclude that
such a scenario is, however, unlikely, as is the existence of other "hidden"
nuclei in M83. We thus concur with recent authors and favour a second option,
in which the nucleus of the galaxy is offset from its kinematic and photometric
centre. This is presumably a result of some past interaction, possibly related
to the event which lies at the origin of the disturbance of the outer disk of
the galaxy. (Abridged)


  Analog-to-digital conversion (ADC) is a key bottleneck in scaling DSP-centric
receiver architectures to multiGigabit/s speeds. Recent information-theoretic
results, obtained under ideal channel conditions (perfect synchronization, no
dispersion), indicate that low-precision ADC (1-4 bits) could be a suitable
choice for designing such high speed systems. In this work, we study the impact
of employing low-precision ADC in a {\it carrier asynchronous} system.
Specifically, we consider transmission over the block noncoherent Additive
White Gaussian Noise (AWGN) channel, and investigate the achievable performance
under low-precision output quantization. We focus attention on an architecture
in which the receiver quantizes {\it only the phase} of the received signal:
this has the advantage of being implementable without automatic gain control,
using multiple 1-bit ADCs preceded by analog multipliers. For standard uniform
Phase Shift Keying (PSK) modulation, we study the structure of the transition
density of the resulting phase-quantized block noncoherent channel. Several
results, based on the symmetry inherent in the channel model, are provided to
characterize this transition density. Low-complexity procedures for computing
the channel capacity, and for block demodulation, are obtained using these
results. Numerical computations are performed to compare the performance of
quantized and unquantized systems, for different quantization precisions, and
different block lengths. It is observed, for example, that with QPSK
modulation, 8-bin phase quantization of the received signal recovers about
80-85% of the capacity attained with unquantized observations, while 12-bin
phase quantization recovers more than 90% of the unquantized capacity.
Dithering the constellation is shown to improve the performance in the face of
drastic quantization.


  Although Type Ia supernovae (SNe Ia) are a major tool in cosmology and play a
key role in the chemical evolution of galaxies, the nature of their progenitor
systems (apart from the fact that they must contain at least one white dwarf,
that explodes) remains largely unknown. In the last decade, considerable
efforts have been made, both observationally and theoretically, to solve this
problem. Observations have, however, revealed a previously unsuspected variety
of events, ranging from very underluminous outbursts to clearly overluminous
ones, and spanning a range well outside the peak luminosity--decline rate of
the light curve relationship, used to make calibrated candles of the SNe Ia. On
the theoretical side, new explosion scenarios, such as violent mergings of
pairs of white dwarfs, have been explored. We review those recent developments,
emphasizing the new observational findings, but also trying to tie them to the
different scenarios and explosion mechanisms proposed thus far.


  We consider the wave equation with a boundary condition of memory type. Under
natural conditions on the acoustic impedance $\hat{k}$ of the boundary one can
define a corresponding semigroup of contractions (Desch, Fasangova, Milota,
Probst 2010). With the help of Tauberian theorems we establish energy decay
rates via resolvent estimates on the generator $-\mathcal{A}$ of the semigroup.
We reduce the problem of estimating the resolvent of $-\mathcal{A}$ to the
problem of estimating the resolvent of the corresponding stationary problem.
Under not too strict additional assumptions on $\hat{k}$ we establish an upper
bound on the resolvent. For the wave equation on the interval or the disk we
prove our estimates to be sharp.


  We classify all possible one-particle (scalar and fermion) extensions of the
Standard Model that can contribute to the anomalous magnetic moment of leptons.
We review the cases already discussed in the literature and complete the
picture by performing the calculation for a fermionic doublet with hypercharge
-3/2. We conclude that, out of the listed possibilities, only two scalar
leptoquarks and the pseudoscalar of a peculiar two-Higgs-doublet model could be
the responsibles for the muon anomalous magnetic moment discrepancy. Were this
the case, this particles could be seen in the next LHC run. To this aim,
especially to test the leptoquark hypothesis, we suggest to look for final
states with tops and muons.


  We describe an approach for incorporating prior knowledge into machine
learning algorithms. We aim at applications in physics and signal processing in
which we know that certain operations must be embedded into the algorithm. Any
operation that allows computation of a gradient or sub-gradient towards its
inputs is suited for our framework. We derive a maximal error bound for deep
nets that demonstrates that inclusion of prior knowledge results in its
reduction. Furthermore, we also show experimentally that known operators reduce
the number of free parameters. We apply this approach to various tasks ranging
from CT image reconstruction over vessel segmentation to the derivation of
previously unknown imaging algorithms. As such the concept is widely applicable
for many researchers in physics, imaging, and signal processing. We assume that
our analysis will support further investigation of known operators in other
fields of physics, imaging, and signal processing.


  This paper presents an efficient sensor management approach for multi-target
tracking in passive sensor networks. Compared with active sensor networks,
passive sensor networks have larger uncertainty due to the nature of passive
sensing. Multi-target tracking in passive sensor networks is challenging
because the multi-sensor multi-target fusion problem is difficult and sensor
management is necessary to achieve good trade-offs between tracking accuracy
and energy consumption or other costs. To address this problem, we present an
efficient information-theoretic approach to manage the sensors for better
tracking of the unknown and time-varying number of targets. This is
accomplished with two main technical innovations. The first is a tractable
information-based multi-sensor selection solution via a partially observed
Markov decision process framework. The Cauchy-Schwarz divergence is used as the
criterion to select informative sensors sequentially from the candidates. The
second is a novel dual-stage fusion strategy based on the iterated-corrector
multi-sensor generalized labeled multi-Bernoulli filter. Since the performance
of the iterated-corrector scheme is greatly influenced by the order of sensor
updates, the selected sensors are first ranked in order of their abilities to
detect targets according to the Cauchy-Schwarz divergence, followed the
iterated-corrector update. The computation costs of ranking the sensors are
negligible, since the Cauchy-Schwarz divergence has been computed in the
multi-sensor selection procedure. Simulation results validate the effectiveness
and efficiency of the proposed approach.


  Big data has become the norm in astronomy, making it an ideal domain for
computer science research. Astronomers typically classify galaxies based on
their morphologies, a practice that dates back to Hubble (1936). With small
datasets, classification could be performed by individuals or small teams, but
the exponential growth of data from modern telescopes necessitates automated
classification methods.
  In December 2013, Winton Capital, Galaxy Zoo, and the Kaggle team created the
Galaxy Challenge, which tasked participants with developing models to classify
galaxies. The Kaggle Galaxy Zoo dataset has since been widely used by
researchers. This study investigates the impact of colour space transformation
on classification accuracy and explores the effect of CNN architecture on this
relationship. Multiple colour spaces (RGB, XYZ, LAB, etc.) and CNN
architectures (VGG, ResNet, DenseNet, Xception, etc.) are considered, utilizing
pre-trained models and weights. However, as most pre-trained models are
designed for natural RGB images, we examine their performance with transformed,
non-natural astronomical images.
  We test our hypothesis by evaluating individual networks with RGB and
transformed colour spaces and examining various ensemble configurations. A
minimal hyperparameter search ensures optimal results. Our findings indicate
that using transformed colour spaces in individual networks yields higher
validation accuracy, and ensembles of networks and colour spaces further
improve accuracy.
  This research aims to validate the utility of colour space transformation for
astronomical image classification and serve as a benchmark for future studies.


  Given a plane graph $G$ (i.e., a planar graph with a fixed planar embedding)
and a simple cycle $C$ in $G$ whose vertices are mapped to a convex polygon, we
consider the question whether this drawing can be extended to a planar
straight-line drawing of $G$. We characterize when this is possible in terms of
simple necessary conditions, which we prove to be sufficient. This also leads
to a linear-time testing algorithm. If a drawing extension exists, it can be
computed in the same running time.


  Generating robot motion for multiple tasks in dynamic environments is
challenging, requiring an algorithm to respond reactively while accounting for
complex nonlinear relationships between tasks. In this paper, we develop a
novel policy synthesis algorithm, RMPflow, based on geometrically consistent
transformations of Riemannian Motion Policies (RMPs). RMPs are a class of
reactive motion policies that parameterize non-Euclidean behaviors as dynamical
systems in intrinsically nonlinear task spaces. Given a set of RMPs designed
for individual tasks, RMPflow can combine these policies to generate an
expressive global policy, while simultaneously exploiting sparse structure for
computational efficiency. We study the geometric properties of RMPflow and
provide sufficient conditions for stability. Finally, we experimentally
demonstrate that accounting for the natural Riemannian geometry of task
policies can simplify classically difficult problems, such as planning through
clutter on high-DOF manipulation systems.


  We develop a clearance and settlement model for Peer-to-Peer (P2P) energy
trading in low-voltage networks. The model enables direct transactions between
parties within an open and distributed system and integrates unused capacity
while respecting network constraints. We evaluate the model through simulations
of different scenarios (normal operating conditions and extreme conditions) for
24-hour time blocks. Our simulations highlight the benefits of our model in a
decentralized energy system, notably its ability to deal with high-trade
volumes.


  This paper presents a novel framework for speech-driven gesture production,
applicable to virtual agents to enhance human-computer interaction.
Specifically, we extend recent deep-learning-based, data-driven methods for
speech-driven gesture generation by incorporating representation learning. Our
model takes speech as input and produces gestures as output, in the form of a
sequence of 3D coordinates. We provide an analysis of different representations
for the input (speech) and the output (motion) of the network by both objective
and subjective evaluations. We also analyse the importance of smoothing of the
produced motion. Our results indicated that the proposed method improved on our
baseline in terms of objective measures. For example, it better captured the
motion dynamics and better matched the motion-speed distribution. Moreover, we
performed user studies on two different datasets. The studies confirmed that
our proposed method is perceived as more natural than the baseline, although
the difference in the studies was eliminated by appropriate post-processing:
hip-centering and smoothing. We conclude that it is important to take both
motion representation and post-processing into account when designing an
automatic gesture-production method.


  We study weighted $H^\infty$ spaces of analytic functions on the open unit
disc in the case of non-doubling weights, which decrease rapidly with respect
to the boundary distance. We characterize the solid hulls of such spaces and
give quite explicit representations of them in the case of the most natural
exponentially decreasing weights.


  While there have been considerable advancements in machine learning driven by
extensive datasets, a significant disparity still persists in the availability
of data across various sources and populations. This inequality across domains
poses challenges in modeling for those with limited data, which can lead to
profound practical and ethical concerns. In this paper, we address a
representative case of data inequality problem across domains termed
Semi-Supervised Domain Generalization (SSDG), in which only one domain is
labeled while the rest are unlabeled. We propose a novel algorithm, ProUD,
which can effectively learn domain-invariant features via domain-aware
prototypes along with progressive generalization via uncertainty-adaptive
mixing of labeled and unlabeled domains. Our experiments on three different
benchmark datasets demonstrate the effectiveness of ProUD, outperforming all
baseline models including single domain generalization and semi-supervised
learning. Source code will be released upon acceptance of the paper.


  Data augmentation is a key component of CNN based image recognition tasks
like object detection. However, it is relatively less explored for 3D object
detection. Many standard 2D object detection data augmentation techniques do
not extend to 3D box. Extension of these data augmentations for 3D object
detection requires adaptation of the 3D geometry of the input scene and
synthesis of new viewpoints. This requires accurate depth information of the
scene which may not be always available. In this paper, we evaluate existing 2D
data augmentations and propose two novel augmentations for monocular 3D
detection without a requirement for novel view synthesis. We evaluate these
augmentations on the RTM3D detection model firstly due to the shorter training
times . We obtain a consistent improvement by 4% in the 3D AP (@IoU=0.7) for
cars, ~1.8% scores 3D AP (@IoU=0.25) for pedestrians & cyclists, over the
baseline on KITTI car detection dataset. We also demonstrate a rigorous
evaluation of the mAP scores by re-weighting them to take into account the
class imbalance in the KITTI validation dataset.


  Commercial data analytics products such as Microsoft Azure SQL Data Warehouse
or Amazon Redshift provide ready-to-use scale-out database solutions for
OLAP-style workloads in the cloud. While the provisioning of a database cluster
is usually fully automated by cloud providers, customers typically still have
to make important design decisions which were traditionally made by the
database administrator such as selecting the partitioning schemes.
  In this paper we introduce a learned partitioning advisor for analytical
OLAP-style workloads based on Deep Reinforcement Learning (DRL). The main idea
is that a DRL agent learns its decisions based on experience by monitoring the
rewards for different workloads and partitioning schemes. We evaluate our
learned partitioning advisor in an experimental evaluation with different
databases schemata and workloads of varying complexity. In the evaluation, we
show that our advisor is not only able to find partitionings that outperform
existing approaches for automated partitioning design but that it also can
easily adjust to different deployments. This is especially important in cloud
setups where customers can easily migrate their cluster to a new set of
(virtual) machines.


  Rossby waves (r-modes) have been suggested as a means to regulate the spin
periods of young or accreting neutron stars, and also to produce observable
gravitational wave radiation. R-modes involve primarily transverse,
incompressive motions of the star's fluid core. However, neutron stars gain
crusts early in their lives: therefore, r-modes also imply shear in the fluid
beneath the crust. We examine the criterion for this shear layer to become
turbulent, and derive the rate of dissipation in the turbulent regime. Unlike
dissipation from a viscous boundary layer, turbulent energy loss is nonlinear
in mode energy and can therefore cause the mode to saturate at amplitudes
typically much less than unity. This energy loss also reappears as heat below
the crust. We study the possibility of crust melting as well as its
implications for the spin evolution of low-mass X-ray binaries. Lastly, we
identify some universal features of the spin evolution that may have
observational consequences.


  We present the overall conductance as well as the circulating currents in
individual loops of a Sierpinski gasket (SPG) as we apply bias voltage via the
side attached electrodes. SPG being a self-similar structure, its manifestation
on loop currents and magnetic fields are examined in various generations of
this fractal and it has been observed that for a given configuration of the
electrodes, the physical quantities exhibit certain regularity as we go from
one generation to another. Also a notable feature is the introduction of
anisotropy in hopping causes an increase in magnitude of overall transport
current. These features are a subject of interest in this article.


  Cyber-physical systems, such as self-driving cars or autonomous aircraft,
must defend against attacks that target sensor hardware. Analyzing system
design can help engineers understand how a compromised sensor could impact the
system's behavior; however, designing security analyses for cyber-physical
systems is difficult due to their combination of discrete dynamics, continuous
dynamics, and nondeterminism.
  This paper contributes a framework for modeling and analyzing sensor attacks
on cyber-physical systems, using the formalism of hybrid programs. We formalize
and analyze two relational properties of a system's robustness. These
relational properties respectively express (1) whether a system's safety
property can be influenced by sensor attacks, and (2) whether a system's
high-integrity state can be affected by sensor attacks. We characterize these
relational properties by defining an equivalence relation between a system
under attack and the original unattacked system. That is, the system satisfies
the robustness properties if executions of the attacked system are
appropriately related to executions of the unattacked system.
  We present two techniques for reasoning about the equivalence relation and
thus proving the relational properties for a system. One proof technique
decomposes large proof obligations to smaller proof obligations. The other
proof technique adapts the self-composition technique from the literature on
secure information-flow, allowing us to reduce reasoning about the equivalence
of two systems to reasoning about properties of a single system. This technique
allows us to reuse existing tools for reasoning about properties of hybrid
programs, but is challenging due to the combination of discrete dynamics,
continuous dynamics, and nondeterminism.
  To evaluate, we present three case studies motivated by real design flaws in
existing cyber-physical systems.


  In a power grid, the electricity supply and demand must be balanced at all
times to maintain the system's frequency. In practice, the grid operator
achieves this balance by procuring frequency reserves in an ahead-of-time
market setting. During runtime, these reserves are then dispatched whenever
there is an imbalance in the grid. Recently, there has been an increasing
interest in engaging electricity consumers, such as plug-in electric vehicles
or buildings, to offer such frequency reserves by exploiting their flexibility
in power consumption. In this work, we focus on an aggregation of buildings
that places a joint bid on a reserve market. The resulting shared decision is
modeled as a large-scale optimization problem. Our main contribution is to show
that the aggregation can make its decision in a computationally efficient and
conceptually meaningful way, using the alternating direction method of
multipliers (ADMM). The proposed approach exhibits several attractive features
that include (i) the computational burden is distributed between the buildings;
(ii) the setup naturally provides privacy and flexibility; (iii) the iterative
algorithm can be stopped at any time, providing a feasible (though suboptimal)
solution; and (iv) the algorithm provides the foundation for a reward
distribution scheme that strengthens the group.


  This paper describes adaptations for EaFi, a parser for easy-first parsing of
discontinuous constituents, to adapt it to multiple languages as well as make
use of the unlabeled data that was provided as part of the SPMRL shared task
2014.


  Improved understanding of charge-transport in single molecules is essential
for harnessing the potential of molecules e.g. as circuit components at the
ultimate size limit. However, interpretation and analysis of the large,
stochastic datasets produced by most quantum transport experiments remains an
ongoing challenge to discovering much-needed structure-property relationships.
Here, we introduce Segment Clustering, a novel unsupervised hypothesis
generation tool for investigating single molecule break junction
distance-conductance traces. In contrast to previous machine learning
approaches for single molecule data, Segment Clustering identifies groupings of
similar pieces of traces instead of entire traces. This offers a new and
advantageous perspective into dataset structure because it facilitates the
identification of meaningful local trace behaviors that may otherwise be
obscured by random fluctuations over longer distance scales. We illustrate the
power and broad applicability of this approach with two case studies that
address common challenges encountered in single molecule studies: First,
Segment Clustering is used to extract primary molecular features from a varying
background to increase the precision and robustness of conductance
measurements, enabling small changes in conductance in response to molecular
design to be identified with confidence. Second, Segment Clustering is applied
to a known data mixture to qualitatively separate distinct molecular features
in a rigorous and unbiased manner. These examples demonstrate two powerful ways
in which Segment Clustering can aid in the development of structure-property
relationships in molecular quantum transport, an outstanding challenge in the
field of molecular electronics.


  Facial expression recognition is a challenging task, arguably because of
large intra-class variations and high inter-class similarities. The core
drawback of the existing approaches is the lack of ability to discriminate the
changes in appearance caused by emotions and identities. In this paper, we
present a novel identity-enhanced network (IDEnNet) to eliminate the negative
impact of identity factor and focus on recognizing facial expressions. Spatial
fusion combined with self-constrained multi-task learning are adopted to
jointly learn the expression representations and identity-related information.
We evaluate our approach on three popular datasets, namely Oulu-CASIA, CK+ and
MMI. IDEnNet improves the baseline consistently, and achieves the best or
comparable state-of-the-art on all three datasets.


  Optical properties of thin layers of rhenium diselenide (ReSe$_2$) with
thickness ranging from mono- (1 ML) to nona-layer (9 MLs) are demonstrated. The
photoluminescence (PL) and Raman scattering were measured at low ($T$=5 K) and
room ($T$=300 K) temperature, respectively. The PL spectra of ReSe$_2$ layers
display two well-resolved emission lines, which blueshift by about 120 meV when
the layer thickness decreases from 9 MLs to a monolayer. A rich structure of
the observed low-energy Raman scattering modes can be explained within a linear
chain model. The two phonon modes of intralayer vibrations, observed in Raman
scattering spectra at about 120 cm$^{-1}$, exhibit very sensitive and opposite
evolution as a function of layer thickness. It is shown that their energy
difference can serve as a convenient and reliable tool to determine the
thickness of ReSe$_2$ flakes in the few-layer limit.


  We discuss the importance of accretion in calculating disk models for young
stellar objects. In particular, we show that a disk inner rim, irradiated by
both the star and the accretion shocks at the stellar surface, can naturally
explain recent observations of DG Tau with the Keck interferometer. We present
models for two objects, with mass accretion rates differing by almost two
orders of magnitude, to illustrate the effects of accretion on the overall disk
structure and emission.


  We report on INTEGRAL observations performed during the 2004 outburst of the
bright black hole transient GX 339-4. We analysed IBIS and JEM-X public data
starting on 9th August and lasting about one month. During this period GX 339-4
showed spectral state transitions. In order to seek for variability patterns, a
principal component analysis (PCA) has been used.


  We consider the urn setting with two different objects, ``good'' and ``bad'',
and analyze the number of draws without replacement until a good object is
picked. Although the expected number of draws for this setting is a standard
textbook exercise, we compute the variance, and show that this distribution
converges to the geometric distribution.


  The goal of this paper is to show that a wide class of Harish-Chandra
$(\mathfrak{g},K)$-modules including all irreducible ones come with a certain
canonical filtration.


  We study the intermediate extension of the character sheaves on an adjoint
group to the semi-stable locus of its wonderful compactification. We show that
the intermediate extension can be described by a direct image construction. As
a consequence, we show that the ``ordinary'' restriction of a character sheaf
on the compactification to a boundary piece inside the semi-stable locus is a
shift of semisimple perverse sheaf and is closely related to Lusztig's
restriction functor (from a character sheaf on a reductive group to a direct
sum of character sheaves on a Levi subgroup). We also provide a (conjectural)
formula for the boundary values inside the semi-stable locus of an irreducible
character of a finite group of Lie type, which gives a partial answer to a
question of Springer \cite{Sp2}. This formula holds for Steinberg character and
characters coming from generic character sheaves. In the end, we verify
Lusztig's conjecture \cite[12.6]{L3} inside the semi-stable locus of the
wonderful compactification.


  Comparative effectiveness research frequently employs the instrumental
variable design since randomized trials can be infeasible for many reasons. In
this study, we investigate and compare treatments for emergency cholecystitis
-- inflammation of the gallbladder. A standard treatment for cholecystitis is
surgical removal of the gallbladder, while alternative non-surgical treatments
include managed care and pharmaceutical options. As randomized trials are
judged to violate the principle of equipoise, we consider an instrument for
operative care: the surgeon's tendency to operate. Standard instrumental
variable estimation methods, however, often rely on parametric models that are
prone to bias from model misspecification. We outline instrumental variable
estimation methods based on the doubly robust machine learning framework. These
methods enable us to employ various machine learning techniques for nuisance
parameter estimation and deliver consistent estimates and fast rates of
convergence for valid inference. We use these methods to estimate the primary
target causal estimand in an IV design. Additionally, we expand these methods
to develop estimators for heterogeneous causal effects, profiling principal
strata, and a sensitivity analyses for a key instrumental variable assumption.
We conduct a simulation study to demonstrate scenarios where more flexible
estimation methods outperform standard methods. Our findings indicate that
operative care is generally more effective for cholecystitis patients, although
the benefits of surgery can be less pronounced for key patient subgroups.


  This paper proposes a method to extract the position and pose of vehicles in
the 3D world from a single traffic camera. Most previous monocular 3D vehicle
detection algorithms focused on cameras on vehicles from the perspective of a
driver, and assumed known intrinsic and extrinsic calibration. On the contrary,
this paper focuses on the same task using uncalibrated monocular traffic
cameras. We observe that the homography between the road plane and the image
plane is essential to 3D vehicle detection and the data synthesis for this
task, and the homography can be estimated without the camera intrinsics and
extrinsics. We conduct 3D vehicle detection by estimating the rotated bounding
boxes (r-boxes) in the bird's eye view (BEV) images generated from inverse
perspective mapping. We propose a new regression target called tailed r-box and
a dual-view network architecture which boosts the detection accuracy on warped
BEV images. Experiments show that the proposed method can generalize to new
camera and environment setups despite not seeing imaged from them during
training.


  We study a recently proposed kinetic exchange opinion model (Lallouache et.
al., Phys. Rev E 82:056112, 2010) in the limit of a single parameter map.
Although it does not include the essentially complex behavior of the multiagent
version, it provides us with the insight regarding the choice of order
parameter for the system as well as some of its other dynamical properties. We
also study the generalized two- parameter version of the model, and provide the
exact phase diagram. The universal behavior along this phase boundary in terms
of the suitably defined order parameter is seen.


  In this manuscript, we propose the generation of complex vector beams with
high quality and stability based on a novel approach that relies on the
combination of two techniques that seem incompatible at first glance. The first
is Complex Amplitude Modulation (CAM), which produces scalar structured light
fields in phase and amplitude with high accuracy. The second is on-axis
modulation for the generation of vector beams, a method that requires
phase-only holograms, therefore yielding beams of reduced quality. More
precisely, the idea behind our technique is to send the shaped light produced
by CAM co-axially to the zeroth order, rather than to the first order, as
commonly done. We describe our technique, explaining the generation of the
hologram and experimental setup to isolate the desired vector mode, and then
present experimental results that corroborate our approach. We first address
the quality of the generated beams using Stokes polarimetry to reconstruct
their transverse polarisation distribution, and then compare their stability
against the same mode produced using a popular interferometric method. Our
vector beams are of good quality and remarkably stable, two qualities that we
expect will appeal to the community working with vector modes.


  In this paper we consider the trace regression model. Assume that we observe
a small set of entries or linear combinations of entries of an unknown matrix
$A_0$ corrupted by noise. We propose a new rank penalized estimator of $A_0$.
For this estimator we establish general oracle inequality for the prediction
error both in probability and in expectation. We also prove upper bounds for
the rank of our estimator. Then, we apply our general results to the problems
of matrix completion and matrix regression. In these cases our estimator has a
particularly simple form: it is obtained by hard thresholding of the singular
values of a matrix constructed from the observations.


  This paper presents a novel algorithm for building an automatic speech
recognition (ASR) model with imperfect training data. Imperfectly transcribed
speech is a prevalent issue in human-annotated speech corpora, which degrades
the performance of ASR models. To address this problem, we propose Bypass
Temporal Classification (BTC) as an expansion of the Connectionist Temporal
Classification (CTC) criterion. BTC explicitly encodes the uncertainties
associated with transcripts during training. This is accomplished by enhancing
the flexibility of the training graph, which is implemented as a weighted
finite-state transducer (WFST) composition. The proposed algorithm improves the
robustness and accuracy of ASR systems, particularly when working with
imprecisely transcribed speech corpora. Our implementation will be
open-sourced.


  We present a sample of 2700 Ap stars in LAMOST DR9. The candidates are first
selected to be in a temperature range typical of Ap stars by using the
$BP$-$RP$ color index from Gaia DR3. Then the 5200\,\AA\ flux depression
features characteristic of Ap stars are visually checked in LAMOST DR9 spectra.
The detailed spectral features are given by applying a modified spectral
classification program, MKCLASS. Stellar parameters of these Ap stars such as
$T_{\rm eff}$, $\log g$, [Fe/H], [Si/H], and $v{\sin}i$ are either extracted
from a hot star catalog or derived through empirical relations and then a
statistical analysis is carried out. The evolutionary stages are also
discussed. Finally, we discuss the rotation and pulsation features of those who
have TESS or Kepler light curves. Among these Ap stars we find 7 new rotation
variables, 1 new roAp star, and new $\delta$ Scuti pulsation of a previously
known roAp star.


  Within the international studies of a high energy linear electron positron
collider, several groups are developing and testing prototypes for a Linear
Collider TPC. This detector is planned to be used as a central part in the
tracking system of a detector at such a machine. In this note we describe a DAQ
system, which has been developed for the use in tests of TPC prototypes. It is
based on electronics used at the ALEPH experiment at CERN.


  Nowadays, working from home (WFH) has become a popular work arrangement due
to its many potential benefits for both companies and employees (e.g.,
increasing job satisfaction and retention of employees). Many previous studies
have investigated the impact of working from home on the productivity of
employees. However, most of these studies usually use a qualitative analysis
method such as survey and interview, and the studied participants do not work
from home for a long continuing time. Due to the outbreak of coronavirus
disease 2019 (COVID-19), a large number of companies asked their employees to
work from home, which provides us an opportunity to investigate whether working
from home affects their productivity.
  In this study, to investigate the difference of developer productivity
between working from home and working onsite, we conduct a quantitative
analysis based on a dataset of developers' daily activities from Baidu Inc, one
of the largest IT companies in China. In total, we collected approximately four
thousand records of 139 developers' activities of 138 working days. Out of
these records, 1,103 records are submitted when developers work from home due
to COVID-19 pandemic. We find that WFH has both positive and negative impacts
on developer productivity in terms of different metrics, e.g., the number of
builds/commits/code reviews. We also notice that working from home has
different impacts on projects with different characteristics including
programming language, project type/age/size. For example, working from home has
a negative impact on developer productivity for large projects. Additionally,
we find that productivity varies for different developers. Based on these
findings, we get some feedbacks from developers of Baidu and understand some
reasons why WFH has different impacts on developer productivity.


  The superconducting infinite-layer nickelate family has risen as a promising
platform for revealing the mechanism of high-temperature superconductivity.
However, its challenging material synthesis has obscured effort in
understanding the nature of its ground state and low-lying excitations, which
is a prerequisite for identifying the origin of the Cooper pairing in
high-temperature superconductors. In particular, the superconducting gap
symmetry of nickelates has hardly been investigated and remains controversial.
Here, we report the pairing symmetry of the infinite-layer nickelates
determined by London penetration depth measurements in neodymium-based
(Nd,Sr)NiO$_2$ and lanthanide-based (La,Ca)NiO$_2$ thin films of high
crystallinity. A rare-earth-specific order parameter is observed. While the
lanthanide nickelates follow dirty line-node behaviour, the
neodymium-counterpart exhibits nodeless order parameters such as the $(d+is)$
wave. In contrast to the cuprates, our results suggest that the superconducting
order parameter in nickelates is beyond a single $d_(x^2-y^2 )$-wave gap.
Furthermore, the superfluid density shows a long tail near the superconducting
transition temperature which is consistent with the emergence of a
two-dimensional to three-dimensional crossover in the superconducting state.
These observations challenge the early theoretical framework and propel further
experimental and theoretical interests in the pairing nature of the
infinite-layer nickelate family.


  An \textit{ab initio} electronic structure calculation based on the
generalized gradient approximation in the density functional theory is carried
out to study the basic electronic states of hollandite vanadate
K$_2$V$_8$O$_{16}$. We find that the states near the Fermi energy consist
predominantly of the three $t_{2g}$-orbital components and the hybridization
with oxygen $2p$ orbitals is small. The $d_{yz}$ and $d_{zx}$ orbitals are
exactly degenerate and are lifted from the $d_{xy}$ orbital. The calculated
band dispersion and Fermi surface indicate that the system is not purely
one-dimensional but the coupling between the VO double chains is important.
Comparison with available experimental data suggests the importance of electron
correlations in this system.


  File-sharing networks are distributed systems used to disseminate files among
nodes of a communication network. The general simple principle of these systems
is that once a node has retrieved a file, it may become a server for this file.
In this paper, the capacity of these networks is analyzed with a stochastic
model when there is a constant flow of incoming requests for a given file. It
is shown that the problem can be solved by analyzing the asymptotic behavior of
a class of interacting branching processes. Several results of independent
interest concerning these branching processes are derived and then used to
study the file-sharing systems.


  The temperature at the chemical freeze-out and at zero baryochemical
potential has been extracted in a global analysis of $e^+e^-$, $p+p$, $p+
\overline{p}$ and $A+A$ collisions at $\sqrt{s}$=2-1800 GeV per N+N pair. We
demonstrate that the temperature at $\mu_B$=0, rises with the initial energy
density $\epsilon_i$, and saturates above $\epsilon_i$ $\sim$ 1 GeV/fm$^3$.
This behaviour is interpreted as mapping out the QCD phase transition
universally in particle and nuclear collisions. The critical energy density is
therefore identified to be $\epsilon_{crit}$ $\sim$ 1 $\pm$ 0.3 GeV/fm$^3$. We
show that strange particles at $\mu_B$=0, are not significantly enhanced in A+A
collisions as compared to $p+ \overline{p}$. The so called 'strangeness
suppression factor' ($\lambda_s = \frac{(2 \overline{s})} {(\overline{u} +
\overline{d})}$) as a function of $\epsilon_i$ is following the temperature,
rising and saturating universally above $\epsilon_{crit}$. This leads to a
reinterpretation of strangeness enhancement as QGP signature. Within this
interpretation the experimental puzzles with respect to strangeness production
can be naturally explained: e.g. the recent measured maximum of $K^+/\pi^+$ in
Pb+Pb collisions at 40 A GeV, is explained as due to $\mu_B$. We discuss under
which conditions 'strangeness enhancement' and '$J/\Psi$ suppresion' both set
in at $\epsilon_{crit}$ $\sim$ 1 GeV/fm$^3$.


  We perform a comprehensive redshift-space distortion analysis based on cosmic
voids in the large-scale distribution of galaxies observed with the Sloan
Digital Sky Survey. To this end, we measure multipoles of the void-galaxy
cross-correlation function and compare them with standard model predictions in
cosmology. Merely considering linear-order theory allows us to accurately
describe the data on the entire available range of scales and to probe
void-centric distances down to about $2h^{-1}{\rm Mpc}$. Common systematics,
such as the Fingers-of-God effect, scale-dependent galaxy bias, and nonlinear
clustering do not seem to play a significant role in our analysis. We constrain
the growth rate of structure via the redshift-space distortion parameter
$\beta$ at two median redshifts, $\beta(\bar{z}=0.32)=0.599^{+0.134}_{-0.124}$
and $\beta(\bar{z}=0.54)=0.457^{+0.056}_{-0.054}$, with a precision that is
competitive with state-of-the-art galaxy-clustering results. While the
high-redshift constraint perfectly agrees with model expectations, we observe a
mild $2\sigma$ deviation at $\bar{z}=0.32$, which increases to $3\sigma$ when
the data is restricted to the lowest available redshift range of $0.15<z<0.33$.


  We construct an independent increments Gaussian process associated to a class
of multicolor urn models. The construction uses random variables from the urn
model which are different from the random variables for which central limit
theorems are available in the two color case.


  Purpose: The study aims to analyze the synergy of Artificial Intelligence
(AI), with scientometrics, webometrics, and bibliometrics to unlock and to
emphasize the potential of the applications and benefits of AI algorithms in
these fields.
  Design/methodology/approach: By conducting a systematic literature review,
our aim is to explore the potential of AI in revolutionizing the methods used
to measure and analyze scholarly communication, identify emerging research
trends, and evaluate the impact of scientific publications. To achieve this, we
implemented a comprehensive search strategy across reputable databases such as
ProQuest, IEEE Explore, EBSCO, Web of Science, and Scopus. Our search
encompassed articles published from January 1, 2000, to September 2022,
resulting in a thorough review of 61 relevant articles.
  Findings: (i) Regarding scientometrics, the application of AI yields various
distinct advantages, such as conducting analyses of publications, citations,
research impact prediction, collaboration, research trend analysis, and
knowledge mapping, in a more objective and reliable framework. (ii) In terms of
webometrics, AI algorithms are able to enhance web crawling and data
collection, web link analysis, web content analysis, social media analysis, web
impact analysis, and recommender systems. (iii) Moreover, automation of data
collection, analysis of citations, disambiguation of authors, analysis of
co-authorship networks, assessment of research impact, text mining, and
recommender systems are considered as the potential of AI integration in the
field of bibliometrics.
  Originality/value: This study covers the particularly new benefits and
potential of AI-enhanced scientometrics, webometrics, and bibliometrics to
highlight the significant prospects of the synergy of this integration through
AI.


  Binary spatter code (BSC)-based hyperdimensional computing (HDC) is a highly
error-resilient approximate computational paradigm suited for error-prone,
emerging hardware platforms. In BSC HDC, the basic datatype is a hypervector, a
typically large binary vector, where the size of the hypervector has a
significant impact on the fidelity and resource usage of the computation.
Typically, the hypervector size is dynamically tuned to deliver the desired
accuracy; this process is time-consuming and often produces hypervector sizes
that lack accuracy guarantees and produce poor results when reused for very
similar workloads. We present Heim, a hardware-aware static analysis and
optimization framework for BSC HD computations. Heim analytically derives the
minimum hypervector size that minimizes resource usage and meets the target
accuracy requirement. Heim guarantees the optimized computation converges to
the user-provided accuracy target on expectation, even in the presence of
hardware error. Heim deploys a novel static analysis procedure that unifies
theoretical results from the neuroscience community to systematically optimize
HD computations.
  We evaluate Heim against dynamic tuning-based optimization on 25 benchmark
data structures. Given a 99% accuracy requirement, Heim-optimized computations
achieve a 99.2%-100.0% median accuracy, up to 49.5% higher than dynamic
tuning-based optimization, while achieving 1.15x-7.14x reductions in
hypervector size compared to HD computations that achieve comparable query
accuracy and finding parametrizations 30.0x-100167.4x faster than dynamic
tuning-based approaches. We also use Heim to systematically evaluate the
performance benefits of using analog CAMs and multiple-bit-per-cell ReRAM over
conventional hardware, while maintaining iso-accuracy -- for both emerging
technologies, we find usages where the emerging hardware imparts significant
benefits.


  Scene text spotting is essential in various computer vision applications,
enabling extracting and interpreting textual information from images. However,
existing methods often neglect the spatial semantics of word images, leading to
suboptimal detection recall rates for long and short words within long-tailed
word length distributions that exist prominently in dense scenes. In this
paper, we present WordLenSpotter, a novel word length-aware spotter for scene
text image detection and recognition, improving the spotting capabilities for
long and short words, particularly in the tail data of dense text images. We
first design an image encoder equipped with a dilated convolutional fusion
module to integrate multiscale text image features effectively. Then,
leveraging the Transformer framework, we synergistically optimize text
detection and recognition accuracy after iteratively refining text region image
features using the word length prior. Specially, we design a Spatial Length
Predictor module (SLP) using character count prior tailored to different word
lengths to constrain the regions of interest effectively. Furthermore, we
introduce a specialized word Length-aware Segmentation (LenSeg) proposal head,
enhancing the network's capacity to capture the distinctive features of long
and short terms within categories characterized by long-tailed distributions.
Comprehensive experiments on public datasets and our dense text spotting
dataset DSTD1500 demonstrate the superiority of our proposed methods,
particularly in dense text image detection and recognition tasks involving
long-tailed word length distributions encompassing a range of long and short
words.


  We use gauge-gravity duality to compute spectral functions of fermionic
operators in a strongly-coupled defect field theory in p-wave superfluid
states. The field theory is (3+1)-dimensional N=4 supersymmetric SU(Nc)
Yang-Mills theory, in the 't Hooft limit and with large coupling, coupled to
two massless flavors of (2+1)-dimensional N=4 supersymmetric matter. We show
that a sufficiently large chemical potential for a U(1) subgroup of the global
SU(2) isospin symmetry triggers a phase transition to a p-wave superfluid
state, and in that state we compute spectral functions for the fermionic
superpartners of mesons valued in the adjoint of SU(2) isospin. In the spectral
functions we see the breaking of rotational symmetry and the emergence of a
Fermi surface comprised of isolated points as we cool the system through the
superfluid phase transition. The dual gravitational description is two
coincident probe D5-branes in AdS5 x S5 with non-trivial worldvolume SU(2)
gauge fields. We extract spectral functions from solutions of the linearized
equations of motion for the D5-branes' worldvolume fermions, which couple to
one another through the worldvolume gauge field. We develop an efficient method
to compute retarded Green's functions from a system of coupled bulk fermions.
We also perform the holographic renormalization of free bulk fermions in any
asymptotically Euclidean AdS space.


  The constant increase in energy consumption has created the necessity of
extending the energy transmission and distribution network. Placement of
powerlines represent a risk for bird population. Hence, better understanding of
deaths induced by powerlines, and the factors behind them are of paramount
importance to reduce the impact of powerlines. To address this concern,
professional surveys and citizen science data are available. While the former
data type is observed in small portions of the space by experts through
expensive standardized sampling protocols, the latter is opportunistically
collected by citizen scientists.
  We set up full Bayesian spatial models that 1) fusion both professional
surveys and citizen science data and 2) explicitly account for preferential
sampling that affects professional surveys data and for factors that affect the
quality of citizen science data. The proposed models are part of the family of
latent Gaussian models as both data types are interpreted as thinned spatial
point patterns and modeled as log-Gaussian Cox processes. The specification of
these models assume the existence of a common latent spatial process underlying
the observation of both data types.
  The proposed models are used both on simulated data and on real-data of
powerline-induced death of birds in the Trondelag in Norway. The simulation
studies clearly show increased accuracy in parameter estimates when both data
types are fusioned and factors that bias their collection processes are
properly accounted for. The study of powerline-induced deaths shows a clear
association between the density of the powerline network and the risk that
powerlines represent for bird populations. The choice of model is relevant for
the conclusions from this case study as different models estimated the
association between risk of powerline-induced deaths and the amount of exposed
birds differently.


  The avalanche statistics in a stochastic sandpile model where toppling takes
place with a probability p is investigated. The limiting case p=1 corresponds
to the Bak-Tang-Wiesenfeld (BTW) model with deterministic toppling rule. Based
on the moment analysis of the distribution of avalanche sizes we conclude that
for 0<p<p_c the model belongs to the DP universality class while for p_c<p<1 it
belongs to the BTW universality class, where p_c is identified with the
critical probability for directed percolation in the corresponding lattice.


  Nontrivial topology in bulk matter has been linked with the existence of
topologically protected interfacial states. We show that a gaseous plasmon
polariton (GPP), an electromagnetic surface wave existing at the boundary of
magnetized plasma and vacuum, has a topological origin that arises from the
nontrivial topology of magnetized plasma. Because a gaseous plasma cannot
sustain a sharp interface with discontinuous density, one must consider a
gradual density falloff with scale length comparable or longer than the
wavelength of the wave. We show that the GPP may be found within a gapped
spectrum in present-day laboratory devices, suggesting that platforms are
currently available for experimental investigation of topological wave physics
in plasmas.


  Single photon emitters with internal spin are leading contenders for
developing quantum repeater networks, enabling long-range entanglement
distribution for transformational technologies in communications and sensing.
However, scaling beyond current few-node networks will require radical
improvements to quantum link efficiencies and fidelities. Solid-state emitters
are particularly promising due to their crystalline environment, enabling
nanophotonic integration and providing spins for memory and processing.
However, inherent spatial and temporal variations in host crystals give rise to
static shifts and dynamic fluctuations in optical transition frequencies,
posing formidable challenges in establishing large-scale, multipartite
entanglement. Here, we introduce a scalable approach to quantum networking that
utilizes frequency erasing photon detection in conjunction with adaptive,
real-time quantum control. This enables frequency multiplexed entanglement
distribution that is also insensitive to deleterious optical frequency
fluctuations. Single rare-earth ions are an ideal platform for implementing
this protocol due to their long spin coherence, narrow optical inhomogeneous
distributions, and long photon lifetimes. Using two 171Yb:YVO4 ions in remote
nanophotonic cavities we herald bipartite entanglement and probabilistically
teleport quantum states. Then, we extend this protocol to include a third ion
and prepare a tripartite W state: a useful input for advanced quantum
networking applications. Our results provide a practical route to overcoming
universal limitations imposed by non-uniformity and instability in solid-state
emitters, whilst also showcasing single rare-earth ions as a scalable platform
for the future quantum internet.


  Recently integral representations for the eigenfunctions of quadratic open
Toda chain Hamiltonians for classical groups was proposed. This representation
generalizes Givental representation for A_n. In this note we verify that the
wave functions defined by these integral representations are common
eigenfunctions for the complete set of open Toda chain Hamiltonians. We
consider the zero eigenvalue wave functions for classical groups C_n and D_n
thus completing the generalization of the Givental construction in these cases.
The construction is based on a recursive procedure and uses the formalism of
Baxter Q-operators. We also verify that the integral Q-operators for C_n, D_n
and twisted affine algebra A_{2n-1}^{(2)} proposed previously intertwine
complete sets of Hamiltonian operators. Finally we provide integral
representations of the eigenfunctions of the quadratic $D_n$ Toda chain
Hamiltonians for generic nonzero eigenvalues.


  Understanding diffusion in microstructures plays a crucial role in many
scientific fields, including neuroscience, cancer or energy research. While
magnetic resonance (MR) methods are the gold standard for diffusion
measurements, spatial encoding in MR imaging has limitations. Here, we
introduce nitrogen-vacancy (NV) center based nuclear magnetic resonance (NMR)
spectroscopy as a powerful tool to probe diffusion with an optical readouts. We
have developed an experimental scheme combining pulsed gradient spin echo
(PGSE) with optically detected NV-NMR spectroscopy, which allows for the local
quantification of molecular diffusion and flow within microscopic sample
volumes. We demonstrate correlated optical imaging with spatially resolved PGSE
NV-NMR experiments probing anisotropic water diffusion within a model
microstructure. Our optically detected PGSE NV-NMR technique opens up prospects
for extending the current capabilities of investigating diffusion processes
with the future potential of probing single cells, tissue microstructures, or
ion mobility in thin film materials for battery applications.


  Digital health, including telemedicine, has increased access to abortion
care. The convenience, flexibility of appointment times, and ensured privacy to
abortion users may make abortion services via telemedicine preferable. This
scoping review systematically mapped studies conducted on abortion services via
telemedicine, including their effectiveness and acceptability for abortion
users and providers. All published papers included abortion services via
telemedicine in the United States were considered. Articles were searched in
PubMed, CINAHL, and Google Scholar databases in September 2022. The findings
were synthesized narratively, and the PRISMA-ScR guidelines were used to report
this study. Out of 757 retrieved articles, 33 articles were selected based on
the inclusion criteria. These studies were published between 2011 and 2022,
with 24 published in the last 3 years. The study found that telemedicine
increased access to abortion care in the United States, especially for people
in remote areas or those worried about stigma from in-person visits. The
effectiveness of abortion services via telemedicine was comparable to in-clinic
visits, with 6% or fewer abortions requiring surgical intervention. Both care
providers and abortion seekers expressed positive perceptions of
telemedicine-based abortion services. However, abortion users reported mixed
emotions, with some preferring in-person visits. The most common reasons for
choosing telemedicine included the distance to the abortion clinic,
convenience, privacy, cost, flexibility of appointment times, and state laws
imposing waiting periods or restrictive policies. Telemedicine offered a
preferable option for abortion seekers and providers. The feasibility of
accessing abortion services via telemedicine in low-resource settings needs
further investigation.


  Exploration is a significant challenge in practical reinforcement learning
(RL), and uncertainty-aware exploration that incorporates the quantification of
epistemic and aleatory uncertainty has been recognized as an effective
exploration strategy. However, capturing the combined effect of aleatory and
epistemic uncertainty for decision-making is difficult. Existing works estimate
aleatory and epistemic uncertainty separately and consider the composite
uncertainty as an additive combination of the two. Nevertheless, the additive
formulation leads to excessive risk-taking behavior, causing instability. In
this paper, we propose an algorithm that clarifies the theoretical connection
between aleatory and epistemic uncertainty, unifies aleatory and epistemic
uncertainty estimation, and quantifies the combined effect of both
uncertainties for a risk-sensitive exploration. Our method builds on a novel
extension of distributional RL that estimates a parameterized return
distribution whose parameters are random variables encoding epistemic
uncertainty. Experimental results on tasks with exploration and risk challenges
show that our method outperforms alternative approaches.