  Explainable Face Recognition is gaining growing attention as the use of the
technology is gaining ground in security-critical applications. Understanding
why two faces images are matched or not matched by a given face recognition
system is important to operators, users, anddevelopers to increase trust,
accountability, develop better systems, and highlight unfair behavior. In this
work, we propose xSSAB, an approach to back-propagate similarity score-based
arguments that support or oppose the face matching decision to visualize
spatial maps that indicate similar and dissimilar areas as interpreted by the
underlying FR model. Furthermore, we present Patch-LFW, a new explainable face
verification benchmark that enables along with a novel evaluation protocol, the
first quantitative evaluation of the validity of similarity and dissimilarity
maps in explainable face recognition approaches. We compare our efficient
approach to state-of-the-art approaches demonstrating a superior trade-off
between efficiency and performance. The code as well as the proposed Patch-LFW
is publicly available at: https://github.com/marcohuber/xSSAB.


  Despite the rapid growth of the home care industry, research on the
scheduling and routing of home care visits in the presence of uncertainty is
still limited. This paper investigates a dynamic version of this problem in
which the number of referrals and their required number of visits are
uncertain. We develop a Markov decision process (MDP) model for the
single-nurse problem to minimize the expected weighted sum of the rejection,
diversion, overtime, and travel time costs. Since optimally solving the MDP is
intractable, we employ an approximate linear program (ALP) to obtain a feasible
policy. The typical ALP approach can only solve very small-scale instances of
the problem. We derive an intuitively explainable closed-form solution for the
optimal ALP parameters in a special case of the problem. Inspired by this form,
we provide two heuristic reduction techniques for the ALP model in the general
problem to solve large-scale instances in an acceptable time. Numerical results
show that the ALP policy outperforms a myopic policy that reflects current
practice, and is better than a scenario-based policy in most instances
considered.


  We test the Coulomb exchange and correlation energy density functionals of
electron systems for atomic nuclei in the local density approximation (LDA) and
the generalized gradient approximation (GGA). For the exchange Coulomb
energies, it is found that the deviation between the LDA and GGA ranges from
around $ 11 \, \% $ in $ {}^{4} \mathrm{He} $ to around $ 2.2 \, \% $ in $
{}^{208} \mathrm{Pb} $, by taking the Perdew-Burke-Ernzerhof (PBE) functional
as an example of the<EMAIL_ADDRESS>For the correlation Coulomb energies, it is shown
that those functionals of electron systems are not suitable for atomic nuclei.


  A new approach to the quantization of constrained or otherwise reduced
classical mechanical systems is proposed. On the classical side, the
generalized symplectic reduction procedure of Mikami and Weinstein, as further
extended by Xu in connection with symplectic equivalence bimodules and Morita
equivalence of Poisson manifolds, is rewritten so as to avoid the use of
symplectic groupoids, whose quantum analogue is unknown. A theorem on
symplectic reduction in stages is given. This allows one to discern that the
`quantization' of the generalized moment map consists of an operator-valued
inner product on a (pre-) Hilbert space (that is, a structure similar to a
Hilbert $C^*$-module). Hence Rieffel's far-reaching operator-algebraic
generalization of the notion of an induced representation is seen to be the
exact quantum counterpart of the classical idea of symplectic reduction, with
imprimitivity bimodules and strong Morita equivalence of $C^*$-algebras falling
in the right place. Various examples involving groups as well as groupoids are
given, and known difficulties with both Dirac and BRST quantization are seen to
be absent in our approach.


  In this paper, we study the Tamagawa numbers of a crystalline representation
over a tower of cyclotomic extensions under certain technical conditions on the
representation. In particular, we show that we may improve the asymptotic
bounds given in the thesis of Arthur Laurent in certain cases.


  It is well-known that macroscopically-normalizable zero-energy wavefunctions
of spin-$\frac{1}{2}$ particles in a two-dimensional inhomogeneous magnetic
field are spin-polarized and exactly calculable with degeneracy equaling the
number of flux quanta linking the whole system. Extending this argument to
massless Dirac fermions subjected to magnetic fields that have \textit{zero}
net flux but are doubly periodic in real space, we show that there exist
\textit{only two} Bloch-normalizable zero-energy eigenstates, one for each spin
flavor. This result is immediately relevant to graphene multilayer systems
subjected to doubly-periodic strain fields, which at low energies, enter the
Hamiltonian as periodic pseudo-gauge vector potentials. Furthermore, we explore
various related settings including nonlinearly-dispersing band structure models
and systems with singly-periodic magnetic fields.


  The capture and subsequent in--spiral of compact stellar remnants by central
massive black holes, is one of the more interesting likely sources of
gravitational radiation detectable by LISA. The relevant stellar population
includes stellar mass black holes, and possibly intermediate mass black holes,
generally on initially eccentric orbits. Predicted detectable rates of capture
are highly uncertain, but may be high enough that source confusion is an issue.
Foreground events with relatively high signal-to-noise ratio may provide
important tests of general relativity. I review the rate estimates in the
literature, and the apparent discrepancy between different authors' estimates,
and discuss some of the relevant uncertainties and physical processes. The
white dwarf mergers rate are uncertain by a factor of few; the neutron star
merger rate is completely uncertain and likely to be small; the black hole
merger rate is likely to be dominant for detectable mergers and is uncertain by
at least two orders of magnitude, largely due to unknown physical conditions
and processes. The primary difference in rate estimates is due to different
initial conditions and less directly due to different estimates of key physical
processes, assumed in different model scenarios for in-spiral and capture.


  Although selected configuration interaction (SCI) algorithms can tackle much
larger Hilbert spaces than the conventional full CI (FCI) method, the scaling
of their computational cost with respect to the system size remains inherently
exponential. Additionally, inaccuracies in describing the correlation hole at
small interelectronic distances lead to the slow convergence of the electronic
energy relative to the size of the one-electron basis set. To alleviate these
effects, we show that the non-Hermitian, transcorrelated (TC) version of SCI
significantly compactifies the determinant space, allowing to reach a given
accuracy with a much smaller number of determinants. Furthermore, we note a
significant acceleration in the convergence of the TC-SCI energy as the basis
set size increases. The extent of this compression and the energy convergence
rate are closely linked to the accuracy of the correlation factor used for the
similarity transformation of the Coulombic Hamiltonian. Our systematic
investigation of small molecular systems in increasingly large basis sets
illustrates the magnitude of these effects.


  After the discovery of a Standard Model (SM) like Higgs boson, naturalness
strongly favors the next to the Minimal Supersymmetric SM (NMSSM). In this
letter, we point out that the most natural NMSSM predicts the following CP-even
Higgs $H_i$ sector: (A) $H_2$ is the SM-like Higgs boson with mass
pushed-upward by a lighter $H_1$ with mass overwhelmingly within
$[m_{H_2}/2,m_{H_2}]$; (B) $m_{H_3}\simeq 2\mu/\sin2\beta\gtrsim300$ GeV; (C)
$H_3$ has a significant coupling to top quarks and can decay to $H_1H_2$ with a
large branching ratio. Using jet substructure we show that all the three Higgs
bosons can be discovered via $gg\to H_3 \to H_1H_2\to b\bar b \ell\nu jj$ at
the 14 TeV LHC. Especially, the LEP-LHC scenario with $H_1\simeq98$ GeV has a
very good discovery potential.


  In the article the problem of the integrable classification of nonlinear
lattices depending on one discrete and two continuous variables is studied. By
integrability we mean the presence of reductions of a chain to a system of
hyperbolic equations of arbitrarily high order integrable in the Darboux sense.
Darboux integrablity admits a remarkable algebraic interpretation: the
Lie-Rinehart algebras related to both characteristic directions corresponding
to the reduced system of the hyperbolic equations have to be of finite
dimension. A classification algorithm based on the properties of the
characteristic algebra is discussed. Some classification results are presented.


  We consider dimension reduction of multiview data, which are emerging in
scientific studies. Formulating multiview data as multi-variate data with block
structures corresponding to the different views, or views of data, we estimate
top eigenvectors from multiview data that have two-fold sparsity, elementwise
sparsity and blockwise sparsity. We propose a Fantope-based optimization
criterion with multiple penalties to enforce the desired sparsity patterns and
a denoising step is employed to handle potential presence of heteroskedastic
noise across different data views. An alternating direction method of
multipliers (ADMM) algorithm is used for optimization. We derive the l2
convergence of the estimated top eigenvectors and establish their sparsity and
support recovery properties. Numerical studies are used to illustrate the
proposed method.


  We present an analysis of the spectral properties observed in X-rays from
active galactic nucleus BL Lacertae using RXTE, Suzaku, ASCA, BeppoSAX, and
Swift observations. The total time covered by these observations is
approximately 20 years. We show that this source undergoes X-ray spectral
transitions from the low hard state (LHS) through the intermediate state (IS)
to the high soft state (HSS) during these observations. During the RXTE
observations (1997 -- 2001, {180 ks, for a total 145 datasets}), the source was
approximately 75%, 20% and only 5 of the time in the IS, LHS, and HSS,
respectively. We also used Swift observations (470 datasets, for a total 800
ks), which occurred during 12 years (2005 -- 2016), the (0.3 -- 200 keV) data
of BeppoSAX (1997 -- 2000, 160 ks), and the (0.3 -- 10 keV) data of ASCA (1995
-- 1999, 160 ks). Two observations of Suzaku (2006, 2013; 50 ks) in
combinations with long-term RXTE and Swift data-sets allow us to describe all
spectral states of BL Lac. The spectra of BL Lac are well fitted by the bulk
motion Comptonization (BMC) model for all spectral states. We have established
the photon index saturation level, Gamma_{sat}=2.2+/-0.1, in the Gamma vs. mass
accretion rate (Mdot) correlation. This Gamma-Mdot correlation allows us to
estimate the black-hole (BH) mass in BL Lac to be M_{BH}~3x10^7 M_sol for a
distance of 300 Mpc. For the BH mass estimate, we use the scaling method taking
stellar-mass Galactic BHs 4U~1543--47 and GX~339--4 as reference sources. The
Gamma-Mdot correlation revealed in BL Lac is similar to those in a number of
stellar-mass Galactic BHs and two recently studied intermediate-mass
extragalactic BHs. It clearly shows the correlation along with the very
extended $\Gamma$ saturation at ~ 2.2. This is robust observational evidence
for the presence of a BH in BL Lac.


  The mid-infrared (MIR), far-infrared (FIR) to terahertz (THz) frequencies are
the least developed parts of the electromagnetic spectrum for applications.
Traditional semiconductor technologies like laser diodes and photodetectors are
successful in the visible light range, but are still confronted with great
challenges when extended into the MIR/FIR/THz range. In this paper, we
demonstrate that topological insulators (TIs), especially those with
Mexican-hat band structure (MHBS), provide a route to overcome these
challenges. The optical responses of MHBS TIs can be one to two orders of
magnitude larger than that of normal semiconductors at the optical-transition
edge. We explore the databases of topological materials and discover a number
of MHBS TIs whose bandgaps lie between $0.05\sim 0.5~\rm eV$ and possess giant
gains (absorption coefficients) on the order of $10^4 \sim 10^5~\rm cm^{-1}$ at
the transition edge. These findings may significantly boost potential
MIR/FIR/THz applications such as photon sources, detectors, ultrafast
electro-optical devices, and quantum information technologies.


  Magnetotransport experiments on Weyl semimetals are essential for
investigating the intriguing topological and low-energy properties of Weyl
nodes. If the transport direction is perpendicular to the applied magnetic
field, experiments have shown a large positive magnetoresistance. In this work,
we present a theoretical scattering matrix approach to transversal
magnetotransport in a Weyl node. Our numerical method confirms and goes beyond
the existing perturbative analytical approach by treating disorder exactly. It
is formulated in real space and is applicable to mesoscopic samples as well as
in the bulk limit. In particular, we study the case of clean and strongly
disordered samples.


  We present a fully memristive spiking neural network (MSNN) consisting of
physically-realizable memristive neurons and memristive synapses to implement
an unsupervised Spiking Time Dependent Plasticity (STDP) learning rule. The
system is fully memristive in that both neuronal and synaptic dynamics can be
realized by using memristors. The neuron is implemented using the SPICE-level
memristive integrate-and-fire (MIF) model, which consists of a minimal number
of circuit elements necessary to achieve distinct depolarization,
hyperpolarization, and repolarization voltage waveforms. The proposed MSNN
uniquely implements STDP learning by using cumulative weight changes in
memristive synapses from the voltage waveform changes across the synapses,
which arise from the presynaptic and postsynaptic spiking voltage signals
during the training process. Two types of MSNN architectures are investigated:
1) a biologically plausible memory retrieval system, and 2) a multi-class
classification system. Our circuit simulation results verify the MSNN's
unsupervised learning efficacy by replicating biological memory retrieval
mechanisms, and achieving 97.5% accuracy in a 4-pattern recognition problem in
a large scale discriminative MSNN.


  We calculate density profiles for self-gravitating clusters of an ideal
Bose-Einstein gas with nonrelativistic energy-momentum relation and macroscopic
mass at thermal equilibrium. Our study includes clusters with planar symmetry
in dimensions $\mathcal{D}=1,2,3$, clusters with cylindrical symmetry in
$\mathcal{D}=2,3$, and clusters with spherical symmetry in $\mathcal{D}=3$.
Wall confinement is imposed where needed to prevent escape. The length scale
and energy scale in use for the gaseous phase render density profiles for
gaseous macrostates independent of total mass. Density profiles for mixed-phase
macrostates have a condensed core surrounded by a gaseous halo. The spatial
extension of the core is negligibly small on the length scale tailored for the
halo. The mechanical stability conditions as evident in caloric curves permit
multiple macrostates to coexist. Their status regarding thermal equilibrium is
examined by a comparison of free energies. The onset of condensation takes
place at a nonzero temperature in all cases. The critical singularities and the
nature of the phase transition vary with the symmetry of the cluster and the
dimensionality of the space.


  For the Belle II experiment at KEK (Tsukuba, Japan) the KEKB accelerator was
upgraded to deliver a 40 times larger instantaneous luminosity than before,
which requires an increased radiation hardness of the detector components. As
the innermost part of the Belle II detector, the pixel detector (PXD), based on
DEPFET (DEpleted P-channel Field Effect Transistor) technology, is most exposed
to radiation from the accelerator. An irradiation campaign was performed to
verify that the PXD can cope with the expected amount of radiation. We present
the results of this measurement campaign in which an X-ray machine was used to
irradiate a single PXD half-ladder to a total dose of 266 kGy. The half-ladder
is from the same batch as the half-ladders used for Belle II. According to
simulations, the total accumulated dose corresponds to 7-10 years of Belle II
operation. While individual components have been irradiated before, this
campaign is the first full system irradiation. We discuss the effects on the
DEPFET sensors, as well as the performance of the front-end electronics. In
addition, we present efficiency studies of the half-ladder from beam tests
performed before and after the irradiation.


  The temperature and density profiles of protoplanetary discs depend crucially
on the mass fraction of micrometre-sized dust grains and on their chemical
composition. A larger abundance of micrometre-sized grains leads to an overall
heating of the disc, so that the water ice line moves further away from the
star. An increase in the water fraction inside the disc, maintaining a fixed
dust abundance, increases the temperature in the icy regions of the disc and
lowers the temperature in the inner regions. Discs with a larger silicate
fraction have the opposite effect. Here we explore the consequence of the dust
composition and abundance for the formation and migration of planets. We find
that discs with low water content can only sustain outwards migration for
planets up to 4 Earth masses, while outwards migration in discs with a larger
water content persists up to 8 Earth masses in the late stages of the disc
evolution. Icy planetary cores that do not reach run-away gas accretion can
thus migrate to orbits close to the host star if the water abundance is low.
Our results imply that hot and warm super-Earths found in exoplanet surveys
could have formed beyond the ice line and thus contain a significant fraction
in water. These water-rich super-Earths should orbit primarily around stars
with a low oxygen abundance, where a low oxygen abundance is caused by either a
low water-to-silicate ratio or by overall low metallicity.


  Competing density waves play an important role in the mystery of
high-temperature superconductors. In spite of the large amount of experimental
evidence, the fundamental question of whether these modulations represent
charge or pairing density waves (CDWs or PDWs) is still debated. Here we
present a method to answer this question using momentum and energy-resolved
resonant X-ray scattering maps. Starting from a minimal model of density waves
in superconductors, we identify distinctive signatures of incipient CDWs and
PDWs. The generality of our approach is confirmed by a self-consistent solution
of an extended Hubbard model with attractive interaction. By considering the
available experimental data, we claim that the spatial modulations in cuprates
have a predominant PDW character. Our work paves the way for using X-ray to
identify competing and intertwined orders in superconducting materials.


  Topological phases of matter exhibit phase transitions between distinct
topological classes. These phase transitions are exotic in that they do not
fall within the traditional Ginzburg-Landau paradigm but are instead associated
with changes in bulk topological invariants and associated topological surface
states. In the case of a Weyl semimetal this phase transition is particularly
unusual because it involves the creation of bulk chiral charges and the
nucleation of topological Fermi arcs. Here we image a topological phase
transition to a Weyl semimetal in Mo$_x$W$_{1-x}$Te$_2$ with changing
composition $x$. Using pump-probe ultrafast angle-resolved photoemission
spectroscopy (pump-probe ARPES), we directly observe the nucleation of a
topological Fermi arc at $x_c \sim 7\%$, showing the critical point of a
topological Weyl phase transition. For Mo dopings $x < x_c$, we observe no
Fermi arc, while for $x > x_c$, the Fermi arc gradually extends as the bulk
Weyl points separate. Our results demonstrate for the first time the creation
of magnetic monopoles in momentum space. Our work opens the way to manipulating
chiral charge and topological Fermi arcs in Weyl semimetals for transport
experiments and device applications.


  We present the Higgs boson pair production cross section at
next-to-next-to-leading order in QCD within the large top-mass approximation.
Numerical results for the LHC are provided, finding an increase of O(20%) with
respect to the previous order prediction and a substantial reduction in the
scale dependence. We normalize our results using the full top- and bottom-mass
dependence at leading order.


  We present a progress report on an extensive survey to find and characterize
all types of blue horizontal-branch stars in the nuclear bulge of the Galaxy.
We have obtained wide, shallow imaging in UBV of ~12 square degrees in the
bulge, with follow-up spectroscopy for radial velocities and metal abundance
determinations. We have discovered a number of metal-rich blue HB stars, whose
presence in the bulge is expected by the interpretation of the extragalactic
ultraviolet excess. Very deep images have been obtained in UBV and SDSS u along
the bulge minor axis, which reveal a significant number of EHB candidates
fainter than B = 19, i.e., with the same absolute magnitudes as EHB stars in
several globular clusters.


  Linear maps preserving pure states of a quantum system of any dimension are
characterized. This is then used to establish a structure theorem for linear
maps that preserve separable pure states in multipartite systems. As an
application, a characterization of separable pure state preserving affine maps
is obtained.


  The light-by-light contribution to the anomalous magnetic moment of muon
(g-2)_{\mu} from the hadronic exchanges in the neutral pseudoscalar meson
channel is calculated in the nonlocal chiral quark model. The full kinematic
dependence of the meson-two-photon vertices on the virtualities of the mesons
and photons is taken into account. The status of various phenomenological and
QCD short-distance constraints is discussed and the comparison with the
predictions of other models is performed. It is demonstrated that the effect of
the full kinematic dependence in the meson-photon vertices is to reduce the
contribution of pseudoscalar exchages to a_\mu ^{PS,LbL} by approximately
factor 1.5 in comparison with the most of previous estimates.


  We use the $H$-matrix technology to compute the approximate square root of a
covariance matrix in linear cost. This allows us to generate normal and
log-normal random fields on general point sets with optimal cost. We derive
rigorous error estimates which show convergence of the method. Our approach
requires only mild assumptions on the covariance function and on the point set.
Therefore, it might be also a nice alternative to the circulant embedding
approach which applies only to regular grids and stationary covariance
functions.


  A detector using liquid Xenon in the scintillation mode is studied for
Positron Emission Tomography (PET). The specific design aims at taking full
advantage of the liquid Xenon properties. It does feature a promising
insensitive to any parallax effect. This work reports on the performances of
the first LXe prototype module, equipped with a position sensitive PMT
operating in the VUV range (178 nm).


  A 3D numerical model for the ablation of pellets and shattered pellet
injection (SPI) fragments in tokamaks in the plasma disruption mitigation and
fueling parameter space has been developed based on the Lagrangian particle
code [R. Samulyak, X. Wang, H.-S. Chen, Lagrangian Particle Method for
Compressible Fluid Dynamics, J. Comput. Phys., 362 (2018), 1-19]. The pellet
code implements the low magnetic Reynolds number MHD equations, kinetic models
for the electronic heating, a pellet surface ablation model, an equation of
state that supports multiple ionization states, radiation, and a model for
grad-B drift of the ablated material across the magnetic field. The Lagrangian
particle algorithm is highly adaptive, capable of simulating a large number of
fragments in 3D while eliminating numerical difficulties of dealing with the
tokamak background plasma. The code has achieved good agreement with theory for
spherically symmetric ablation flows. Axisymmetric simulations of neon and
deuterium pellets in magnetic fields ranging from 1 to 6 Tesla have been
compared with previous simulations using the FronTier code, and very good
agreement has also been obtained. The main physics contribution of the paper is
a detailed study of the influence of 3D effects, in particular grad-B drift, on
pellet ablation rates and properties of ablation clouds. Smaller reductions of
ablation rates in magnetic fields compared to axially symmetric simulations
have been demonstrated because the ablated material is not confined to
narrowing channels in the presence of grad-B drift. Contribution of various
factors in the grad-B drift model has also been quantified.


  Machine learning models that are developed with invariance to certain types
of data transformations have demonstrated superior generalization performance
in practice. However, the underlying mechanism that explains why invariance
leads to better generalization is not well-understood, limiting our ability to
select appropriate data transformations for a given dataset. This paper studies
the generalization benefit of model invariance by introducing the sample cover
induced by transformations, i.e., a representative subset of a dataset that can
approximately recover the whole dataset using transformations. Based on this
notion, we refine the generalization bound for invariant models and
characterize the suitability of a set of data transformations by the sample
covering number induced by transformations, i.e., the smallest size of its
induced sample covers. We show that the generalization bound can be tightened
for suitable transformations that have a small sample covering number.
Moreover, our proposed sample covering number can be empirically evaluated,
providing a practical guide for selecting transformations to develop model
invariance for better generalization. We evaluate the sample covering numbers
for commonly used transformations on multiple datasets and demonstrate that the
smaller sample covering number for a set of transformations indicates a smaller
gap between the test and training error for invariant models, thus validating
our propositions.


  Let $\mathcal F_1, \ldots, \mathcal F_\ell$ be families of subsets of $\{1,
\ldots, n\}$. Suppose that for distinct $k, k'$ and arbitrary $F_1 \in \mathcal
F_{k}, F_2 \in \mathcal F_{k'}$ we have $|F_1 \cap F_2|\le m.$ What is the
maximal value of $|\mathcal F_1|\ldots |\mathcal F_\ell|$? In this work we find
the asymptotic of this product as $n$ tends to infinity for constant $\ell$
and~$m$.
  This question is related to a conjecture of Bohn et al. that arose in the
2-level polytope theory and asked for the largest product of the number of
facets and vertices in a two-level polytope. This conjecture was recently
resolved by Weltge and the first author.
  The main result can be rephrased in terms of colorings. We give an asymptotic
answer to the following question. Given an edge coloring of a complete
$m$-uniform hypergraph into $\ell$ colors, what is the maximum of $\prod M_i$,
where $M_i$ is the number of monochromatic cliques in $i$-th color?


  Observables of topological Yang-Mills theory were defined by Witten as the
classes of an equivariant cohomology. We propose to define them alternatively
as the BRST cohomology classes of a superspace version of the theory, where
BRST invariance is associated to super Yang-Mills invariance. We provide and
discuss the general solution of this cohomology.


  Gravitational Waves (GWs) from coalescing binaries carry crucial information
about their component sources, like mass, spin and tidal effects. This implies
that the analysis of GW signals from binary neutron star mergers can offer
unique opportunities to extract information about the tidal properties of NSs,
thereby adding constraints to the NS equation of state. In this work, we use
Deep Learning (DL) techniques to overcome the computational challenges
confronted in conventional methods of matched-filtering and Bayesian analyses
for signal-detection and parameter-estimation. We devise a DL approach to
classify GW signals from binary black hole and binary neutron star mergers. We
further employ DL to analyze simulated GWs from binary neutron star merger
events for parameter estimation, in particular, the regression of mass and
tidal deformability of the component objects. The results presented in this
work demonstrate the promising potential of DL techniques in GW analysis,
paving the way for further advancement in this rapidly evolving field. The
proposed approach is an efficient alternative to explore the wealth of
information contained within GW signals of binary neutron star mergers, which
can further help constrain the NS EoS.


  Modified versions of two "standard" pulsar search techniques are presented
that allow large-scale searches for pulsations in long duration high-energy
data sets using relatively modest amounts of computer time. For small numbers
of photons (N_phot <~ 10^4), optimized brute-force epoch folding searches are
preferred. For larger numbers of photons, advanced Fourier domain acceleration
searches are used. Using these techniques, my collaborators and I have searched
Chandra observations of the CasA supernova remnant (SNR) point source and the
isolated neutron star RX J1856.5-3754 for pulsations, and confirmed the 65.6 ms
pulsar in the 3C 58 SNR during a blind search of archival RXTE data.


  Characterizing long-range electric fields and built-in potentials in
functional materials at nano- to micrometer scales is of supreme importance for
optimizing devices. E.g., the functionality of semiconductor heterostructures
or battery materials is determined by the electric fields established at
interfaces which can also vary spatially. In this study, we propose
momentum-resolved four-dimensional scanning transmission electron microscopy
(4D-STEM) for the quantification of these potentials and show the optimization
steps required to reach quantitative agreement with simulations for the GaAs /
AlAs hetero-junction model system. Using STEM the differences in the mean inner
potentials (DELTA MIP) of two materials forming an interface and resulting
dynamic diffraction effects have to be considered. We show that the measurement
quality is significantly improved by precession, energy filtering and a
non-zone-axis alignment of the specimen. Complementary simulations yielding a
DELTA MIP of 1.3 V confirm that the potential drop due to charge transfer at
the intrinsic interface is about 0.1 V, in agreement with experimental and
theoretical values found in literture. These results show the feasibility of
accurately measuring built-in potentials across hetero-interfaces of real
device structures and its promising application for more complex interfaces of
other polycrystalline materials on the nanometer scale.


  The surface critical behaviour of the interacting self-avoiding trail is
examined using transfer matrix methods coupled with finite-size scaling.
Particular attention is paid to the critical exponents at the ordinary and
special points along the collapse transition line. The phase diagram is also
presented.


  In this paper, we consider a monolithic approach to handle coupled
fluid-structure interaction problems with different hyperelastic models in an
all-at-once manner. We apply Newton's method in the outer iteration dealing
with nonlinearities of the coupled system. We discuss preconditioned Krylov
sub-space, algebraic multigrid and algebraic multilevel methods for solving the
linearized algebraic equations. Finally, we compare the results of the
monolithic approach with those of the corresponding partitioned approach that
we studied in our previous work.


  The ever-increasing number of threats and the existing diversity of
information sources pose challenges for Computer Emergency Response Teams
(CERTs). To respond to emerging threats, CERTs must gather information in a
timely and comprehensive manner. But the volume of sources and information
leads to information overload. This paper contributes to the question of how to
reduce information overload for CERTs. We propose clustering incoming
information as scanning this information is one of the most tiresome, but
necessary, manual steps. Based on current studies, we establish conditions for
such a framework. Different types of evaluation metrics are used and selected
in relation to the framework conditions. Furthermore, different document
embeddings and distance measures are evaluated and interpreted in combination
with clustering methods. We use three different corpora for the evaluation, a
novel ground truth corpus based on threat reports, one security bug report
(SBR) corpus, and one with news articles. Our work shows, it is possible to
reduce the information overload by up to 84.8% with homogeneous clusters. A
runtime analysis of the clustering methods strengthens the decision of selected
clustering methods. The source code and dataset will be made publicly available
after acceptance.


  The paper presents a novel method of finding a fragment in a long temporal
sequence similar to the set of shorter sequences. We are the first to propose
an algorithm for such a search that does not rely on computing the average
sequence from query examples. Instead, we use query examples as is, utilizing
all of them simultaneously. The introduced method based on the Dynamic Time
Warping (DTW) technique is suited explicitly for few-shot query-by-example
retrieval tasks. We evaluate it on two different few-shot problems from the
field of Natural Language Processing. The results show it either outperforms
baselines and previous approaches or achieves comparable results when a low
number of examples is available.


  We study a problem concerning parabolic induction in certain $p$-adic unitary
groups. More precisely, for $E/F$ a quadratic extension of $p$-adic fields the
associated unitary group $G=\mathrm{U}(n,n+1)$ contains a parabolic subgroup
$P$ with Levi component $L$ isomorphic to $\mathrm{GL}_n(E) \times
\mathrm{U}_1(E)$. Let $\pi$ be an irreducible supercuspidal representation of
$L$ of depth zero. We use Hecke algebra methods to determine when the
parabolically induced representation $\iota_P^G \pi$ is reducible.


  Fast and effective localization of gravitational wave (GW) events could play
a crucial role in identifying possible electromagnetic counterparts, and
thereby help usher in an era of GW multi-messenger astronomy. We discuss an
algorithm for accurate and very low latency ($<$ 1 second) localization of GW
sources using only the relative times of arrival, relative phases, and relative
signal-to-noise ratios for pairs of detectors. The algorithm is independent of
distances and masses to leading order, and can be generalized to all discrete
sources detected by ground-based detector networks. Our approach, while
developed independently, is similar to that of BAYESTAR with a few
modifications in the algorithm which result in increased computational
efficiency. For the LIGO two detector configuration (Hanford+Livingston)
expected in late 2015 we find a median 50\% (90\%) localization of 143 deg$^2$
(558 deg$^2$) for binary neutron stars (for network SNR threshold of 12,
corresponding to a horizon distance of $\sim 130$ Mpc), consistent with
previous findings. We explore the improvement in localization resulting from
high SNR events, finding that the loudest out of the first 4 (or 10) events
reduces the median sky localization area by a factor of 1.9 (3.0) for the case
of 2 GW detectors, and 2.2 (4.0) for 3 detectors. We consider the case of
multi-messenger joint detections in both the GW and the electromagnetic (EM)
spectra. We specifically explore the case of independent, and possibly highly
uncertain, localizations, showing that the joint localization area is
significantly reduced. We also show that a prior on the binary inclination,
potentially arising from GRB observations, has a negligible effect on GW
localization. Our algorithm is simple, fast, and accurate, and may be of
particular utility in the development of multi-messenger astronomy.


  We present two methods for efficient detection of chiral molecules based on
sequences of single pulses and Raman pulse pairs. The chiral molecules are
modelled by a closed-loop three-state system with different signs in one of the
couplings for the two enantiomers. One method uses a sequence of three
interaction steps: a single pulse, a Raman pulse, and another single pulse. The
other method uses a sequence of only two interaction steps: a Raman pulse, and
a single pulse. The second method is simpler and faster but requires a more
sophisticated Raman pulse than the first one. Both techniques allow for
straightforward generalizations by replacing the single and Raman pulses with
composite pulse sequences. The latter achieve very high signal contrast and far
greater robustness to experimental errors than by using single pulses. We
demonstrate that both constant-rotation (i.e., with phase compensation) and
variable-rotation (i.e., with phase distortion) composite pulses can be used,
the former being more accurate and the latter being simpler and faster.


  Distributed word representations have been demonstrated to be effective in
capturing semantic and syntactic regularities. Unsupervised representation
learning from large unlabeled corpora can learn similar representations for
those words that present similar co-occurrence statistics. Besides local
occurrence statistics, global topical information is also important knowledge
that may help discriminate a word from another. In this paper, we incorporate
category information of documents in the learning of word representations and
to learn the proposed models in a document-wise manner. Our models outperform
several state-of-the-art models in word analogy and word similarity tasks.
Moreover, we evaluate the learned word vectors on sentiment analysis and text
classification tasks, which shows the superiority of our learned word vectors.
We also learn high-quality category embeddings that reflect topical meanings.


  Deep neural network (DNN)-based speech enhancement algorithms in microphone
arrays have now proven to be efficient solutions to speech understanding and
speech recognition in noisy environments. However, in the context of ad-hoc
microphone arrays, many challenges remain and raise the need for distributed
processing. In this paper, we propose to extend a previously introduced
distributed DNN-based time-frequency mask estimation scheme that can
efficiently use spatial information in form of so-called compressed signals
which are pre-filtered target estimations. We study the performance of this
algorithm under realistic acoustic conditions and investigate practical aspects
of its optimal application. We show that the nodes in the microphone array
cooperate by taking profit of their spatial coverage in the room. We also
propose to use the compressed signals not only to convey the target estimation
but also the noise estimation in order to exploit the acoustic diversity
recorded throughout the microphone array.


  The CDF experiment at the Tevatron $p\bar{p}$ collider established that
extensive and detailed exploration of the $b$--quark dynamics is possible in
hadron collisions, with results competitive and supplementary to those from
$e^+e^-$ colliders. This provides a rich, and highly rewarding program that has
currently reached full maturity. In the following I report some recent results
on hadronic decays: the evidence for the charmless annihilation decay mode
$B^0_s \to pi^+\pi^-$, and the first reconstruction in hadron collisions of the
suppressed decays $B^- \to D(\to K^+\pi^-)K^-$ and $B^- \to D(\to
K^+\pi^-)\pi^-$.


  pyro is a Python-based simulation framework designed for ease of
implementation and exploration of hydrodynamics methods. It is built in a
object-oriented fashion, allowing for the reuse of the core components and fast
prototyping of new methods.


  COVID-19 pandemic has been a disruptive event from health, social, and
economic points of view. Besides that, changes in people's lifestyles,
especially during the 2020 lockdowns, also affected energy networks. COVID-19
pandemic has resulted in a significant decline in electricity demand. The
lockdown measures applied to handle the health crisis have caused the most
relevant energy impact of the last years. In this paper, the local experiences
of the distribution network of Milano, a city in northern Italy, are reported.
The analysis starts with a summary of the restrictions imposed during 2020 and
focuses on both active and reactive power flows, and faults. To this end, a
comparison with 2019 data has been performed, highlighting the main differences
with 2020. The outcome of the analysis is a valuable tool to predict urban
distribution networks behavior during times of disruption, helping distribution
system operators to prepare feasible short-term and long-term resilience plans.


  Edge localized modes (ELMs) are instabilities at the tokamak edge that can
have short outbursts of highly energetic particles and heat, which can severely
damage the walls of a plasma reactor. Resonant magnetic perturbations (RMPs)
are used to mitigate or eliminate ELMs from the plasma. One effect that can
reduce the intensity of the RMP is screening, which is caused by eddy currents
in conducting structures or a plasma that are induced by a time-varying
magnetic field. The eddy current code CARIDDI was recently coupled with the
magnetohydrodynamics (MHD) code JOREK, and is able to capture the behavior of
volumetric conducting structures that surround a plasma. The objective of this
study is to characterize screening behavior in the JOREK-CARIDDI coupling.
  The analysis is divided in three parts. First, CARIDDI results are
benchmarked against results from STARWALL, another JOREK extension that
captures interactions of (two-dimensional) conducting structures. It is found
that CARIDDI and STARWALL show good agreement, with slight variations. The
second part covers the screening of time-varying RMP fields by conducting
structures, oscillating at frequencies from 3 Hz to 10 kHz. The overall trends
in screening behavior align qualitatively with findings from a previous
analysis. However, an additional contribution to the screening at frequencies
above 1 kHz was not observed, because the simulation in this report does not
include screening by the RMP coil casing. Finally, a few tests were performed
using a plasma configuration, to study the effect of plasma screening,
including realistic plasma background flows. The results demonstrated the
induction of eddy currents at rational surfaces within the plasma. These
results contribute to an improved understanding of the screening of RMP fields,
which is relevant for several applications to the operation of fusion reactors.


  In this paper, modification of the classical PID controller and development
of open-loop control mechanisms to improve stability and robustness of a
differential wheeled robot are discussed. To deploy the algorithm, a test
platform has been constructed using low-cost and off-the-shelf components
including a microcontroller, reflectance sensor, and motor driver. This paper
describes the heuristic approach used in the identification of the system
specifications as well as the optimization of the controller. The PID
controller is analyzed in detail and the effect of each term is explained in
the context of stability. Lastly, the challenges encountered during the
development of controller and robot are discussed. Code is available at:
https://github.com/sametoguten/STM32-Line-Follower-with-PID.


  Recent results of Zlobin and Cresson-Fischler-Rivoal allow one to decompose
any suitable $p$-uple series of hypergeometric type into a linear combination
(over the rationals) of multiple zeta values of depth at most $p$; in some
cases, only the multiple zeta values with 2's and 3's are involved (as in
Hoffman's conjecture). In this text, we study the depth $p$ part of this linear
combination, namely the contribution of the multiple zeta values of depth
exactly $p$. We prove that it satisfies some symmetry property as soon as the
$p$-uple series does, and make some conjectures on the depth $p-1$ part of the
linear combination when $p=3$. Our result generalizes the property that (very)
well-poised univariate hypergeometric series involve only zeta values of a
given parity, which is crucial in the proof by Rivoal and Ball-Rivoal that
$\zeta(2n+1)$ is irrational for infinitely many $n \geq 1$.


  Low-cost, non-empirical corrections to semi-local density functional theory
are essential for accurately modeling transition metal chemistry. Here, we
demonstrate the judiciously-modified density functional theory (jmDFT) approach
with non-empirical U and J parameters obtained directly from frontier orbital
energetics on a series of transition metal complexes. We curate a set of nine
representative Ti(III) and V(IV) $d^1$ transition metal complexes and evaluate
their flat plane errors along the fractional spin and charge lines. We
demonstrate that while jmDFT improves upon both DFT+U and semi-local DFT with
the standard atomic orbital projectors (AOPs), it does so inefficiently. We
rationalize these inefficiencies by quantifying hybridization in the relevant
frontier orbitals for both the case of fractional spins and fractional charges.
To overcome these limitations, we introduce a procedure for computing a
molecular orbital projector (MOP) basis for use with jmDFT. We demonstrate this
single set of $d^1$ MOPs to be suitable for nearly eliminating all energetic
delocalization error and static correlation error. In all cases, the MOP jmDFT
outperforms AOP jmDFT, and it eliminates most flat plane errors at
non-empirical values. Unlike widely employed DFT+U or hybrid functionals, jmDFT
nearly eliminates energetic delocalization error and static correlation error
within a non-empirical framework.


  We analyze the level-rank duality of untwisted and epsilon-twisted D-branes
of the so(N)_K WZW model. Untwisted D-branes of so(N)_K are characterized by
integrable tensor and spinor representations of so(N)_K. Level-rank duality
maps untwisted so(N)_K D-branes corresponding to (equivalence classes of)
tensor representations onto those of so(K)_N. The epsilon-twisted D-branes of
so(2n)_2k are characterized by (a subset of) integrable tensor and spinor
representations of so(2n-1)_2k+1. Level-rank duality maps spinor
epsilon-twisted so(2n)_2k D-branes onto those of so(2k)_2n. For both untwisted
and epsilon-twisted D-branes, we prove that the spectrum of an open string
ending on these D-branes is isomorphic to the spectrum of an open string ending
on the level-rank-dual D-branes.


  The time evolution of primordial fluctuations conceals a wealth of insights
into the high-energy physics at play during the earliest moments of our
Universe, which is ultimately encoded in late-time spatial correlation
functions. However, the conventional procedure to compute them is technically
challenging, and a complete dictionary mapping the landscape of inflationary
theories and the corresponding observable signatures is not yet available. In
this paper, we develop a framework to compute tree-level cosmological
correlators based on following their time evolution from their origin as
quantum zero-point fluctuations to the end of inflation. From first principles,
the structure of the bulk time evolution imposes a set of universal
differential equations in time satisfied by equal-time correlators. We
automatise the process of systematically solving these equations. This allows
us to accurately capture all physical effects and obtain exact results in
theories formulated at the level of inflationary fluctuations that include any
number of degrees of freedom with arbitrary dispersion relations and masses,
coupled through any time-dependent interactions. We then illustrate the power
of this formalism by exploring the phenomenology of cosmological correlators
emerging from the interaction with a massive scalar field. We study both the
size and the shape dependence of non-Gaussianities in the entire parameter
space, including the strong mixing regime. We present novel characteristics of
cosmological collider signals in (would be) single-, double-, and
triple-exchange three-point correlators. In the presence of primordial
features, we show that soft limits of cosmological correlators offer a new
possibility to probe the inflationary landscape. Finally, we provide templates
to search for in future cosmological surveys.


  The Heath-Jarrow-Morton (HJM) formulation of treasury bonds in terms of
forward rates is recast as a problem in path integration. The HJM-model is
generalized to the case where all the forward rates are allowed to fluctuate
independently. The resulting theory is shown to be a two-dimensional Gaussian
quantum field theory. The no arbitrage condition is obtained and a functional
integral derivation is given for the price of a futures and an options
contract.


  We introduce a ``Statistical Query Sampling'' model, in which the goal of an
algorithm is to produce an element in a hidden set $Ssubseteqbit^n$ with
reasonable probability. The algorithm gains information about $S$ through
oracle calls (statistical queries), where the algorithm submits a query
function $g(cdot)$ and receives an approximation to $Pr_{x in S}[g(x)=1]$. We
show how this model is related to NMR quantum computing, in which only
statistical properties of an ensemble of quantum systems can be measured, and
in particular to the question of whether one can translate standard quantum
algorithms to the NMR setting without putting all of their classical
post-processing into the quantum system. Using Fourier analysis techniques
developed in the related context of {em statistical query learning}, we prove a
number of lower bounds (both information-theoretic and cryptographic) on the
ability of algorithms to produces an $xin S$, even when the set $S$ is fairly
simple. These lower bounds point out a difficulty in efficiently applying NMR
quantum computing to algorithms such as Shor's and Simon's algorithm that
involve significant classical post-processing. We also explicitly relate the
notion of statistical query sampling to that of statistical query learning.
  An extended abstract appeared in the 18th Aunnual IEEE Conference of
Computational Complexity (CCC 2003), 2003.
  Keywords: statistical query, NMR quantum computing, lower bound


  A convex polygon Q is circumscribed about a convex polygon P if every vertex
of P lies on at least one side of Q. We present an algorithm for finding a
maximum area convex polygon circumscribed about any given convex n-gon in
O(n^3) time. As an application, we disprove a conjecture of Farris. Moreover,
for the special case of regular n-gons we find an explicit solution.


  With the Corona Virus Disease 2019 (COVID-19) pandemic spreading across the
world, protective measures for containing the virus are essential, especially
as long as no vaccine or effective treatment is available. One important
measure is the so-called physical distancing or social distancing. In this
paper, we propose an agent-based numerical simulation of pedestrian dynamics in
order to assess behaviour of pedestrians in public places in the context of
contact-transmission of infectious diseases like COVID-19, and to gather
insights about exposure times and the overall effectiveness of distancing
measures. To abide the minimum distance of $1.5m$ stipulated by the German
government at an infection rate of 2%, our simulation results suggest that a
density of one person per $16m^2$ or below is sufficient. The results of this
study give insight about how physical distancing as a protective measure can be
carried out more efficiently to help reduce the spread of COVID-19.


  Holographic mutual and tripartite information have been studied in a
non-conformal background. We have investigated how these observables behave as
the energy scale and number of degrees of freedom vary. We have found out that
the effect of degrees of freedom and energy scale is opposite. Moreover, it has
been observed that the disentangling transition occurs at large distance
between sub-systems in non-conformal field theory independent of l. The mutual
information in a non-conformal background remains also monogamous.


  Asymptotic efficiency of targeted maximum likelihood estimators (TMLE) of
target features of the data distribution relies on a a second order remainder
being asymptotically negligible. In previous work we proposed a nonparametric
MLE termed Highly Adaptive Lasso (HAL) which parametrizes the relevant
functional of the data distribution in terms of a multivariate real valued
cadlag function that is assumed to have finite variation norm. We showed that
the HAL-MLE converges in Kullback-Leibler dissimilarity at a rate n-1/3 up till
logn factors. Therefore, by using HAL as initial density estimator in the TMLE,
the resulting HAL-TMLE is an asymptotically efficient estimator only assuming
that the relevant nuisance functions of the data density are cadlag and have
finite variation norm. However, in finite samples, the second order remainder
can dominate the sampling distribution so that inference based on asymptotic
normality would be anti-conservative.
  In this article we propose a new higher order TMLE, generalizing the regular
first order TMLE. We prove that it satisfies an exact linear expansion, in
terms of efficient influence functions of sequentially defined higher order
fluctuations of the target parameter, with a remainder that is a k+1th order
remainder. As a consequence, this k-th order TMLE allows statistical inference
only relying on the k+1th order remainder being negligible. We also provide a
rationale for the higher order TMLE that it will be superior to the first order
TMLE by (iteratively) locally minimizing the exact finite sample remainder of
the first order TMLE. The second order TMLE is demonstrated for nonparametric
estimation of the integrated squared density and for the treatment specific
mean outcome. We also provide an initial simulation study for the second order
TMLE of the treatment specific mean confirming the theoretical analysis.


  Trapped ultracold neutrons (UCN) have for many years been the mainstay of
experiments to search for the electric dipole moment (EDM) of the neutron, a
critical parameter in constraining scenarios of new physics beyond the Standard
Model. Because their energies are so low, UCN preferentially populate the lower
region of their physical enclosure, and do not sample uniformly the ambient
magnetic field throughout the storage volume. This leads to a substantial
increase in the rate of depolarization, as well as to shifts in the measured
frequency of the stored neutrons. Consequences for EDM measurements are
discussed.


  Pervasive cross-section dependence is increasingly recognized as a
characteristic of economic data and the approximate factor model provides a
useful framework for analysis. Assuming a strong factor structure where
$\Lop\Lo/N^\alpha$ is positive definite in the limit when $\alpha=1$, early
work established convergence of the principal component estimates of the
factors and loadings up to a rotation matrix. This paper shows that the
estimates are still consistent and asymptotically normal when $\alpha\in(0,1]$
albeit at slower rates and under additional assumptions on the sample size. The
results hold whether $\alpha$ is constant or varies across factor loadings. The
framework developed for heterogeneous loadings and the simplified proofs that
can be also used in strong factor analysis are of independent interest.


  Experimental cross sections are presented for the
159Tb(p,xn)153,155,157,159Dy, 152,153,155,156m2,m1,g,158Tb and 153,151Gd
nuclear reactions up to 65 MeV. The experimental results are compared with the
recently reported experimental data and with the results of the nuclear
reaction codes ALICE-IPPE, EMPIRE and TALYS as reported in the TENDL-2015
on-line library. Integral thick-target yields are also derived for the reaction
products used in practical applications and production routes are discussed.


  The results of a search for sub-km Kuiper Belt Objects (KBOs) with the method
of serendipitous stellar occultations are reported. Photometric time series
were obtained on the 1.8m telescope at the Dominion Astrophysical Observatory
(DAO) in Victoria, BC, and were analyzed for the presence of occultation
events. Observations were performed at 40 Hz and included a total of 5.0
star-hours for target stars in the ecliptic open cluster M35 (beta=0.9deg), and
2.1 star-hours for control stars in the off-ecliptic open cluster M34
(beta=25.7deg). To evaluate the recovery fraction of the analysis method, and
thereby determine the limiting detectable size, artificial occultation events
were added to simulated time series (1/f scintillation-like power-spectra), and
to the real data. No viable candidate occultation events were detected. This
limits the cumulative surface density of KBOs to 3.5e10 deg^{-2} (95%
confidence) for KBOs brighter than m_R=35.3 (larger than ~860m in diameter,
assuming a geometric albedo of 0.04 and a distance of 40 AU). An evaluation of
TNO occultations reported in the literature suggests that they are unlikely to
be genuine, and an overall 95%-confidence upper limit on the surface density of
2.8e9 deg^{-2} is obtained for KBOs brighter than m_R=35 (larger than ~1 km in
diameter, assuming a geometric albedo of 0.04 and a distance of 40 AU) when all
existing surveys are combined.


  An approximation method is presented for probabilistic inference with
continuous random variables. These problems can arise in many practical
problems, in particular where there are "second order" probabilities. The
approximation, based on the Gaussian influence diagram, iterates over linear
approximations to the inference problem.


  The development of models for multiple heterogeneous network data is of
critical importance both in statistical network theory and across multiple
application domains. Although single-graph inference is well-studied, multiple
graph inference is largely unexplored, in part because of the challenges
inherent in appropriately modeling graph differences and yet retaining
sufficient model simplicity to render estimation feasible. This paper addresses
exactly this gap, by introducing a new model, the common subspace
independent-edge (COSIE) multiple random graph model, which describes a
heterogeneous collection of networks with a shared latent structure on the
vertices but potentially different connectivity patterns for each graph. The
COSIE model encompasses many popular network representations, including the
stochastic blockmodel. The model is both flexible enough to meaningfully
account for important graph differences and tractable enough to allow for
accurate inference in multiple networks. In particular, a joint spectral
embedding of adjacency matrices - the multiple adjacency spectral embedding
(MASE) - leads, in a COSIE model, to simultaneous consistent estimation of
underlying parameters for each graph. Under mild additional assumptions, MASE
estimates satisfy asymptotic normality and yield improvements for graph
eigenvalue estimation and hypothesis testing. In both simulated and real data,
the COSIE model and the MASE embedding can be deployed for a number of
subsequent network inference tasks, including dimensionality reduction,
classification, hypothesis testing and community detection. Specifically, when
MASE is applied to a dataset of connectomes constructed through diffusion
magnetic resonance imaging, the result is an accurate classification of brain
scans by patient and a meaningful determination of heterogeneity across scans
of different subjects.


  A "minimum model" for debris disks consists of a narrow ring of parent
bodies, secularly forced by a single planet on a possibly eccentric orbit,
colliding to produce dust grains that are perturbed by stellar radiation
pressure. We demonstrate how this minimum model can reproduce a wide variety of
disk morphologies imaged in scattered starlight. Five broad categories of disk
shape can be captured: "rings," "needles," "ships-and-wakes," "bars," and
"moths (a.k.a. fans)," depending on the viewing geometry. Moths can also sport
"double wings." We explain the origin of morphological features from first
principles, exploring the dependence on planet eccentricity, disk inclination
dispersion, and the parent body orbital phases at which dust grains are born. A
key determinant in disk appearance is the degree to which dust grain orbits are
apsidally aligned. Our study of a simple steady-state (secularly relaxed) disk
should serve as a reference for more detailed models tailored to individual
systems. We use the intuition gained from our guidebook of disk morphologies to
interpret, informally, the images of a number of real-world debris disks. These
interpretations suggest that the farthest reaches of planetary systems are
perturbed by eccentric planets, possibly just a few Earth masses each.


  We examine the effect of nuclear response functions, as laid out in
[Fitzpatrick et al, arXiv:1203.3542], on dark matter (DM) direct detection in
the context of well-motivated UV completions, including electric and magnetic
dipoles, anapole, spin-orbit, and pseudoscalar-mediated DM. Together, these
encompass five of the six nuclear responses extracted from the non-relativistic
effective theory of [Fitzpatrick et al, arXiv:1203.3542] (with the sixth
difficult to UV complete), with two of the six combinations corresponding to
standard spin-independent and -dependent responses. For constraints from
existing direct detection experiments, we find that only the COUPP constraint,
due to its heavy iodine target with large angular momentum and an unpaired
spin, and its large energy range sensitivity, is substantially modified by the
new responses compared to what would be inferred using the standard form
factors to model the energy dependence of the response. For heavy targets such
as xenon and germanium, the behavior of the new nuclear responses as recoil
energy increases can be substantially different than that of the standard
responses, but this has almost no impact on the constraints derived from
experiments such as LUX, XENON100 and CDMS since the maximum nuclear recoil
energy detected in these experiments is relatively low. We simulate mock data
for 80 and 250 GeV DM candidates utilizing the new nuclear responses to
highlight how they might affect a putative signal, and find the new responses
are most important for momentum-suppressed interactions such as the magnetic
dipole or pseudoscalar-mediated interaction when the target is relatively heavy
(such as xenon and iodine).


  In this article, a $q$-series examined by Kluyver and Uchimura is
generalized. This allows us to find generalization of the identities in the
random acyclic digraph studied by Simon, Crippa, and Collenberg in 1993. As one
of the corollaries of our main theorem, we get results of Dilcher and Andrews,
Crippa, and Simon. This main theorem involves a surprising new generalization
of the divisor function $\sigma_s(n)$, which we denote by $\sigma_{s,z}(n)$.
Analytic properties of $\sigma_{s,z}(n)$ are also studied. As a special case of
one of our theorem we obtain result from a recent paper of Bringmann and
Jennings-Shaffer.


  This study compares Term Frequency-Inverse Document Frequency (TF-IDF)
features with Sentence Transformers for detecting writers' stances--favorable,
opposing, or neutral--towards three significant topics: COVID-19 vaccine,
digital transformation, and women empowerment. Through empirical evaluation, we
demonstrate that Sentence Transformers outperform TF-IDF features across
various experimental setups. Our team, dzStance, participated in a stance
detection competition, achieving the 13th position (74.91%) among 15 teams in
Women Empowerment, 10th (73.43%) in COVID Vaccine, and 12th (66.97%) in Digital
Transformation. Overall, our team's performance ranked 13th (71.77%) among all
participants. Notably, our approach achieved promising F1-scores, highlighting
its effectiveness in identifying writers' stances on diverse topics. These
results underscore the potential of Sentence Transformers to enhance stance
detection models for addressing critical societal issues.


  We prove three sharp estimates for the generalized Zalcman coefficient
functional: one for the Hurwitz class, another for the Noshiro-Warschawski
class, and yet another for the functions in the closed convex hull of convex
univalent functions. In each case the extremal functions are identified. We
also observe that an asymptotic version of the Zalcman conjecture is true.


  Using the simple current method we study a class of $(0,2)$ SCFTs which we
conjecture to be equivalent to (0,2) sigma models constructed in the framework
of gauged linear sigma models.


  It is expected that gravitational waves, similar to electromagnetic waves,
can be gravitationally lensed by intervening matters, producing multiple
instances of the same signal arriving at different times from different
apparent luminosity distances with different phase shifts compared to the
un-lensed signal due to lensing. If unaccounted for, these lensed signals will
masquerade as separate systems with higher mass and lower redshift. Here we
present a Bayesian statistical framework for identifying strongly-lensed
gravitational-wave signals that incorporates astrophysical information and
accounts for selection effects. We also propose a two-step hierarchical
analysis for more efficient computations of the probabilities and inferences of
source parameters free from bias introduced by lensing. We show with examples
on how changing the astrophysical models could shift one's interpretation on
the origin of the observed gravitational waves, and possibly lead to
indisputable evidence of strong lensing of the observed waves. In addition, we
demonstrate the improvement in the sky localization of the source of the lensed
signals, and in some cases the identification of the Morse indices of the
lensed signals. If confirmed, lensed gravitational waves will allow us to probe
the Universe at higher redshift, and to constrain the polarization contents of
the waves with fewer detectors.


  Current decoder-based pre-trained language models (PLMs) successfully
demonstrate multilingual capabilities. However, it is unclear how these models
handle multilingualism. We analyze the neuron-level internal behavior of
multilingual decoder-based PLMs, Specifically examining the existence of
neurons that fire ``uniquely for each language'' within decoder-only
multilingual PLMs. We analyze six languages: English, German, French, Spanish,
Chinese, and Japanese, and show that language-specific neurons are unique, with
a slight overlap (< 5%) between languages. These neurons are mainly distributed
in the models' first and last few layers. This trend remains consistent across
languages and models. Additionally, we tamper with less than 1% of the total
neurons in each model during inference and demonstrate that tampering with a
few language-specific neurons drastically changes the probability of target
language occurrence in text generation.


  We study the Moore complex of a simplicial cocommutative Hopf algebra through
Hopf kernels. The most striking result to emerge from this construction is the
coherent definition of 2-crossed modules of cocommutative Hopf algebras. This
unifies the 2-crossed module theory of groups and of Lie algebras when we take
the group-like and primitive functors into consideration.


  Detections of planets in eccentric, close (separations of ~20 AU) binary
systems such as \alpha Cen or \gamma Cep provide an important test of planet
formation theories. Gravitational perturbations from the companion are expected
to excite high planetesimal eccentricities resulting in destruction, rather
than growth, of objects with sizes of up to several hundred km in collisions of
similar-size bodies. It was recently suggested that gravity of a massive
axisymmetric gaseous disk in which planetesimals are embedded drives rapid
precession of their orbits, suppressing eccentricity excitation. However, disks
in binaries are themselves expected to be eccentric, leading to additional
planetesimal excitation. Here we develop secular theory of eccentricity
evolution for planetesimals perturbed by the gravity of an elliptical
protoplanetary disk (neglecting gas drag) and the companion. For the first time
we derive an expression for the disturbing function due to an eccentric disk,
which can be used for a variety of other astrophysical problems. We obtain
explicit analytical solutions for planetesimal eccentricity evolution and
delineate four different regimes of dynamical excitation. We show that in
systems with massive (>10^{-2}M_Sun) disks, planetesimal eccentricity is
usually determined by the gravity of the eccentric disk alone, and is
comparable to the disk eccentricity. As a result, the latter imposes a lower
limit on collisional velocities of solids, making their growth problematic.
This fragmentation barrier can be removed if the gaseous disk rapidly precesses
or if its own self-gravity is efficient at lowering disk eccentricity.


  From a topological viewpoint, a rational curve in the real projective plane
is generically a smoothly immersed circle and a finite collection of isolated
points. We give an isotopy classification of generic rational quintics in
$\mathbb{RP}^2$ in the spirit of Hilbert's 16th problem.


  Improving the efficiency of current neural networks and modeling them in
biological neural systems have become popular research directions in recent
years. Pulse-coupled neural network (PCNN) is a well applicated model for
imitating the computation characteristics of the human brain in computer vision
and neural network fields. However, differences between the PCNN and biological
neural systems remain: limited neural connection, high computational cost, and
lack of stochastic property. In this study, random-coupled neural network
(RCNN) is proposed. It overcomes these difficulties in PCNN's neuromorphic
computing via a random inactivation process. This process randomly closes some
neural connections in the RCNN model, realized by the random inactivation
weight matrix of link input. This releases the computational burden of PCNN,
making it affordable to achieve vast neural connections. Furthermore, the image
and video processing mechanisms of RCNN are researched. It encodes constant
stimuli as periodic spike trains and periodic stimuli as chaotic spike trains,
the same as biological neural information encoding characteristics. Finally,
the RCNN is applicated to image segmentation, fusion, and pulse shape
discrimination subtasks. It is demonstrated to be robust, efficient, and highly
anti-noised, with outstanding performance in all applications mentioned above.


  We demonstrate that nonlinear decay of obliquely propagating Langmuir waves
into Langmuir and Alfven waves (L --> L'+A) is possible in a one-dimensional,
highly relativistic, streaming, pair plasma. Such a plasma may be in the
magnetospheres of pulsars. It is shown that the characteristic frequency of
generated Alfven waves is much less than the frequency of Langmuir waves and
may be consistent with the observational data on the radio emission of pulsars.


  The finite basis set method is commonly used to calculate atomic spectra,
including QED contributions such as bound-electron self-energy. Still, it
remains problematic and underexplored for vacuum-polarization calculations. We
fill this gap by trying this approach in its application to the calculation of
the vacuum-polarization charge density and the Wichmann-Kroll correction to the
electron binding energy in a hydrogen-like ion. We study the convergence of the
method with different types and sizes of basis sets. We cross-check our results
for the Wichmann-Kroll correction by direct integration of the Green's
function. As a relevant example, we consider several heavy hydrogen-like ions
and evaluate the vacuum polarization correction for $S$ and $P$ electron
orbitals.


  Complexiton solutions (or complexitons for short) are exact solutions newly
introduced to integrable equations. Starting with the solution classification
for a linear differential equation, the Korteweg-de Vries equation and the Toda
lattice equation are considered as examples to exhibit complexiton structures
of nonlinear integrable equations. The crucial step in the solution process is
to apply the Wronskian and Casoratian techniques for Hirota's bilinear
equations. Correspondence between complexitons of the Korteweg-de Vries
equation and complexitons of the Toda lattice equation is provided.


  A new class of modified theories of gravity, consisting of the superposition
of the metric Einstein-Hilbert Lagrangian with an $f(\cal R)$ term constructed
\`{a} la Palatini was proposed recently. The dynamically equivalent
scalar-tensor representation of the model was also formulated, and it was shown
that even if the scalar field is very light, the theory passes the Solar System
observational constraints. Therefore the model predicts the existence of a
long-range scalar field, modifying the cosmological and galactic dynamics. An
explicit model that passes the local tests and leads to cosmic acceleration was
also obtained. In the present work, it is shown that the theory can be also
formulated in terms of the quantity $X\equiv \kappa^2 T+R$, where T and R are
the traces of the stress-energy and Ricci tensors, respectively. The variable X
represents the deviation with respect to the field equation trace of general
relativity. The cosmological applications of this hybrid metric-Palatini
gravitational theory are also explored, and cosmological solutions coming from
the scalar-tensor representation of f(X)-gravity are presented. Criteria to
obtain cosmic acceleration are discussed and the field equations are analyzed
as a dynamical system. Several classes of dynamical cosmological solutions,
depending on the functional form of the effective scalar field potential,
describing both accelerating and decelerating Universes are explicitly
obtained. Furthermore, the cosmological perturbation equations are derived and
applied to uncover the nature of the propagating scalar degree of freedom and
the signatures these models predict in the large-scale structure.


  We perform a Bohmian-trajectory analysis of high-order harmonic generation
(HHG), focusing on the fact that typical HHG spectra are best reproduced by the
Bohmian trajectory starting at the innermost part of the core [Phys. Rev. A
\textbf{88}, 023415 (2013)]. Using ensemble averages around this central
trajectory, we show that, for the high-plateau and cutoff harmonics, small
ensembles of Bohmian trajectories are sufficient for a quantitative agreement
with the numerical solution of the time-dependent Schr\"odinger equation
(TDSE), while larger ensembles are necessary in the low-plateau region.
Furthermore, we relate the Bohmian trajectories to the "short" and "long"
trajectories encountered in the Strong-Field Approximation (SFA), and show that
the time-frequency maps from the central Bohmian trajectory overestimate the
contributions of the long SFA trajectory, in comparison to the outcome of the
TDSE computations. We also discuss how the time-frequency profile of the
central trajectory may be influenced nonlocally by degrading the wave-packet
propagation far from the core.


  We consider the random $\beta$-transformation $K_{\beta}$, defined on
$\{0,1\}^{\mathbb N}\times[0, \frac{\lfloor\beta\rfloor}{\beta-1}]$, that
generates all possible expansions of the form
$x=\sum_{i=0}^{\infty}\frac{a_i}{\beta^i}$, where $a_i\in
\{0,1,\cdots,\lfloor\beta\rfloor\}$. This transformation was first introduced
by Dajani and Kraaikamp, and later studied by Dajani and de Vries, where two
natural invariant ergodic measures were found. The first is the unique measure
of maximal entropy, and the second is a measure of the form $m_p\times
\mu_{\beta}$, with $m_p$ the Bernoulli $(p,1-p)$ product measure and
$\mu_{\beta}$ is a measure equivalent to Lebesgue measure. In this paper, we
give an uncountable family of $K_{\beta}$-invariant exact $g$-measures for a
certain collection of algebraic $\beta$'s.


  We outline transient quasi-magnetostatic phenomena associated with
Weibel-type instabilities, mainly, the formation and decay of the current
sheets and filaments. We consider a collisionless anisotropic plasma cloud with
hot electrons expanding into an inhomogeneous background plasma taking into
account an external magnetic field in different geometries entailing (i) a
hot-electron spot of a circular or cylindrical form within an initial-value
problem or a finite-time injection of electrons from a target surface, (ii)
inhomogeneous layers of cold background plasma of different densities and
spatial scales, (iii) an external magnetic field with three orientations:
perpendicular to the target or along it, directed either across or parallel to
a long axis of the hot-electron spot. We heed typical laser-plasma experiments.
We outline development of the principle current structures linked with distinct
forms of the anisotropic electron velocity distribution using particle-in-cell
modeling of the instability process for diverse sets of the attributes
(i)--(iii). Applications to the analysis of laboratory and space plasma
problems involving an explosive development of the small-scale filamentation of
the electric current and self-consistent magnetic turbulence are discussed.


  We use techniques of proof mining to obtain a computable and uniform rate of
metastability (in the sense of Tao) for the mean ergodic theorem for a finite
number of commuting linear contractive operators on a uniformly convex Banach
space.


  In this paper we are concerned with a family of $N$-urn branching processes,
where some particles are put into $N$ urns initially and then each particle
gives birth to several new particles in some urn when dies. This model includes
the $N$-urn Ehrenfest model and the $N$-urn branching random walk as special
cases. We show that the scaling limit of the process is driven by a
$C(\mathbb{T})$-valued linear ordinary differential equation and the
fluctuation of the process is driven by a generalized Ornstein-Uhlenbeck
process in the dual of $C^\infty(\mathbb{T})$, where $\mathbb{T}=(0, 1]$ is the
one-dimensional torus. A crucial step for proofs of above main results is to
show that numbers of particles in different urns are approximately independent.
As applications of our main results, limit theorems of hitting times of the
process are also discussed.


  In this paper we study geometric aspects of codes in the sum-rank metric. We
establish the geometric description of generalised weights, and analyse the
Delsarte and geometric dual operations. We establish a correspondence between
maximum sum-rank distance codes and h-designs, extending the well-known
correspondence between MDS codes and arcs in projective spaces and between MRD
codes and h-scatttered subspaces. We use the geometric setting to construct new
h-designs and new MSRD codes via new families of pairwise disjoint maximum
scattered linear sets.


  We study the variability of predictions made by bagged learners and random
forests, and show how to estimate standard errors for these methods. Our work
builds on variance estimates for bagging proposed by Efron (1992, 2012) that
are based on the jackknife and the infinitesimal jackknife (IJ). In practice,
bagged predictors are computed using a finite number B of bootstrap replicates,
and working with a large B can be computationally expensive. Direct
applications of jackknife and IJ estimators to bagging require B on the order
of n^{1.5} bootstrap replicates to converge, where n is the size of the
training set. We propose improved versions that only require B on the order of
n replicates. Moreover, we show that the IJ estimator requires 1.7 times less
bootstrap replicates than the jackknife to achieve a given accuracy. Finally,
we study the sampling distributions of the jackknife and IJ variance estimates
themselves. We illustrate our findings with multiple experiments and simulation
studies.


  Average magnetic field measurements are presented for 62 M-dwarf members of
the Pleiades open cluster, derived from Zeeman-enhanced Fe I lines in the
H-band. An MCMC methodology was employed to model magnetic filling factors
using SDSS-IV APOGEE high-resolution spectra, along with the radiative transfer
code SYNMAST, MARCS stellar atmosphere models, and the APOGEE DR17 spectral
line list. There is a positive correlation between mean magnetic fields and
stellar rotation, with slow-rotator stars (Rossby number, Ro$>$0.13) exhibiting
a steeper slope than rapid-rotators (Ro$<$0.13). However, the latter sample
still shows a positive trend between Ro and magnetic fields, which is given by
$<$B$>$ = 1604 $\times$ Ro$^{-0.20}$. The derived stellar radii, when compared
with physical isochrones, show that on average, our sample shows radius
inflation, with median enhanced radii ranging from +3.0$\%$ to +7.0$\%$,
depending on the model. There is a positive correlation between magnetic field
strength and radius inflation, as well as with stellar spot coverage,
correlations that together indicate that stellar spot-filling factors generated
by strong magnetic fields might be the mechanism that drives radius inflation
in these stars. We also compare our derived magnetic fields with chromospheric
emission lines (H$\alpha$, H$\beta$ and Ca II K), as well as with X-ray and
H$\alpha$ to bolometric luminosity ratios, and find that stars with higher
chromospheric and coronal activity tend to be more magnetic.


  What does the age of participation look like from the perspective of a museum
visitor? Arguably, the concept of participative experiences is already so
deeply ingrained in our culture that we may not even think about it as
participation. Museum visitors engage in a number of activities, of which
observing the exhibits is only one part. Since most visitors come to the museum
together with someone else, they spend time and attention on the people they
came with, and often the needs of the group are given priority over individual
preferences. How can museums tap into these activities - and make themselves
relevant to visitors? In this chapter we will try to approach this
constructively, as a design opportunity. Could it be productive for the museum
to consider itself not only as a disseminator of knowledge, but also as the
facilitator of participative activities between visitors?
  In what follows, we will outline a range of practical design projects that
serve as examples of this approach. These projects were part of the European
Union funded Horizon2020 project GIFT, a cross-disciplinary collaboration
between researchers, artists, designers and many international museums and
heritage organisations, exploring the concept of interpersonal museum
experiences (see https://gifting.digital/). What the projects have in common is
that they build on visitors co-creating and sharing their own narratives in the
museum context. We suggest that these projects demonstrate a spectrum of
possibilities: From experiences that take place almost without any museum
involvement, to those that give museums a role in curating these narratives.


  Traffic assignment methods are some of the key approaches used to model flow
patterns that arise in transportation networks. Since static traffic assignment
does not have a notion of time, it is not designed to represent temporal
dynamics that arise as vehicles flow through the network and demand varies
through the day. Dynamic traffic assignment methods attempt to resolve these
issues, but require significant computational resources if modeling urban-scale
regions (on the order of millions of links and vehicles) and often take days of
compute time to complete. The focus of this work is two-fold: 1) to introduce a
new traffic assignment approach - a quasi-dynamic traffic assignment (QDTA)
model and 2) to describe how we parallelized the QDTA algorithms to leverage
High-Performance Computing (HPC) and scale to large metropolitan areas while
dramatically reducing compute time. We examine and compare different scenarios,
including a baseline static traffic assignment (STA) and a quasi-dynamic
scenario inspired by the user-equilibrium (UET). Results are presented for the
San Francisco Bay Area which accounts for 19M trips/day and an urban road
network of 1M links. We utilize an iterative gradient descent method, where the
step size is selected using a Quasi-Newton method with parallelized cost
function evaluations and compare it to using pre-defined step sizes (MSA).
Using the parallelized line search provides a 16 percent reduction in total
execution time due to a reduction in the number of gradient descent iterations
required for convergence. The full day QDTA comprising 96 optimization steps
over 15 minute intervals runs in about 4 minutes on 1,024 cores of the NERSC
Cori computer, which represents a speedup of over 36x versus serial execution.
To our knowledge, this compute time is significantly lower than other traffic
assignment solutions for a problem of this scale.


  We test if the cosmological zoom-in simulations of isolated galaxies from the
FIRE project reproduce the properties of ultra diffuse galaxies. We show that
stellar feedback-generated outflows that dynamically heat galactic stars,
together with a passively aging stellar population after imposed quenching
(from e.g. infall into a galaxy cluster), naturally reproduce the observed
population of red UDGs, without the need for high spin halos or dynamical
influence from their host cluster. We reproduce the range of surface
brightness, radius and absolute magnitude of the observed z=0 red UDGs by
quenching simulated galaxies at a range of different times. They represent a
mostly uniform population of dark matter-dominated galaxies with M_star ~1e8
Msun, low metallicity and a broad range of ages. The most massive simulated
UDGs require earliest quenching and are therefore the oldest. Our simulations
provide a good match to the central enclosed masses and the velocity
dispersions of the observed UDGs (20-50 km/s). The enclosed masses of the
simulated UDGs remain largely fixed across a broad range of quenching times
because the central regions of their dark matter halos complete their growth
early. A typical UDG forms in a dwarf halo mass range of Mh~4e10-1e11 Msun. The
most massive red UDG in our sample requires quenching at z~3 when its halo
reached Mh ~ 1e11 Msun. If it, instead, continues growing in the field, by z=0
its halo mass reaches > 5e11 Msun, comparable to the halo of an L* galaxy. If
our simulated dwarfs are not quenched, they evolve into bluer low-surface
brightness galaxies with mass-to-light ratios similar to observed field dwarfs.
While our simulation sample covers a limited range of formation histories and
halo masses, we predict that UDG is a common, and perhaps even dominant, galaxy
type around Ms~1e8 Msun, both in the field and in clusters.


  We have measured an extensive set of UBVRIJHK colors for M31 globular
clusters [Barmby et al. 2000]. We compare the predicted simple stellar
population colors of three population synthesis models to the intrinsic colors
of Galactic and M31 globular clusters. The best-fitting models fit the cluster
colors very well -- the weighted mean color offsets are all < 0.05 mag. The
most significant offsets between model and data are in the U and B passbands;
these are not unexpected and are likely due to problems with the spectral
libraries used by the models. The metal-rich clusters ([Fe/H] > -0.8) are best
fit by young (8 Gyr) models, while the metal-poor clusters are best fit by
older (12--16 Gyr) models. If this range of globular cluster ages is correct,
it implies that conditions for cluster formation must have existed for a
substantial fraction of the galaxies' lifetimes.


  We present a Ginzburg-Landau theory of ion-induced nucleation in a gas phase
of polar one-component fluids, where a liquid droplet grows with an ion at its
center. By calculating the density profile around an ion, we show that the
solvation free energy is larger in gas than in liquid at the same temperature
on the coexistence curve. This difference much reduces the nucleation barrier
in a metastable gas.


  An $(\alpha,\beta)$-manifold $(M,F)$ is a Finsler manifold with the Finsler
metric $F$ being defined by a Riemannian metric $\alpha$ and $1$-form $\beta$
on the manifold $M$. In this paper, we classify $n$-dimensional
$(\alpha,\beta)$-manifolds (non-Randers type) which are positively complete and
locally projectively flat. We show that the non-trivial class is that $M$ is
homeomorphic to the $n$-sphere $S^n$ and $(S^n,F)$ is projectively related to a
standard spherical Riemannian manifold, and then we obtain some special
geometric properties on the geodesics and scalar flag curvature of $F$ on
$S^n$, especially when $F$ is a metric of general square type.


  People are often entities of interest in tasks such as search and information
extraction. In these tasks, the goal is to find as much information as possible
about people specified by their name. However in text, some of the references
to people are by pronouns (she, his) or generic descriptions (the professor,
the German chancellor). It is therefore important that coreference resolution
systems are able to link these different types of mentions to the correct
person name. Here, we evaluate two state of the art coreference resolution
systems on the subtask of Named Person Coreference, in which we are interested
in identifying a person mentioned by name, along with all other mentions of the
person, by pronoun or generic noun phrase. Our analysis reveals that standard
coreference metrics do not reflect adequately the requirements in this task:
they do not penalize systems for not identifying any mentions by name and they
reward systems even if systems find correctly mentions to the same entity but
fail to link these to a proper name (she--the student---no name). We introduce
new metrics for evaluating named person coreference that address these
discrepancies. We present a simple rule-based named entity recognition driven
system, which outperforms the current state-of-the-art systems on these
task-specific metrics and performs on par with them on traditional coreference
evaluations. Finally, we present similar evaluation for coreference resolution
of other named entities and show that the rule-based approach is effective only
for person named coreference, not other named entity types.


  We present high spatial resolution optical imaging and polarization
observations of the PSR B0540-69.3 and its highly dynamical pulsar wind nebula
(PWN) performed with HST, and compare them with X-ray data obtained with the
Chandra X-ray Observatory. We have studied the bright region southwest of the
pulsar where a bright "blob" is seen in 1999. We show that it may be a result
of local energy deposition around 1999, and that the emission from this then
faded away. Polarization data from 2007 show that the polarization properties
show dramatic spatial variations at the 1999 blob position arguing for a local
process. Several other positions along the pulsar-"blob" orientation show
similar changes in polarization, indicating previous recent local energy
depositions. In X-rays, the spectrum steepens away from the "blob" position,
faster orthogonal to the pulsar-"blob" direction than along this axis of
orientation. This could indicate that the pulsar-"blob" orientation is an axis
along where energy in the PWN is mainly injected, and that this is then
mediated to the filaments in the PWN by shocks. We highlight this by
constructing an [S II]-to-[O III]-ratio map. We argue, through modeling, that
the high [S II]/[O III] ratio is not due to time-dependent photoionization
caused by possible rapid Xray emission variations in the "blob" region. We have
also created a multiwavelength energy spectrum for the "blob" position showing
that one can, to within 2sigma, connect the optical and X-ray emission by a
single power law. We obtain best power-law fits for the X-ray spectrum if we
include "extra" oxygen, in addition to the oxygen column density in the
interstellar gas of the Large Magellanic Cloud and the Milky Way. This oxygen
is most naturally explained by the oxygen-rich ejecta of the supernova remnant.
The oxygen needed likely places the progenitor mass in the 20 - 25 Msun range.


  Electronic states at the ends of a narrow armchair nanoribbon give rise to a
pair of non-locally entangled spins. We propose two experiments to probe these
magnetic states, based on magnetometry and tunneling spectroscopy, in which
correlation effects lead to a striking, nonlinear response to external magnetic
fields. On the basis of low-energy theories that we derive here, it is
remarkably simple to assess these nonlinear signatures for magnetic edge
states. The effective theories are especially suitable in parameter regimes
where other methods such as quantum Monte-Carlo simulations are exceedingly
difficult due to exponentially small energy scales. The armchair ribbon setup
discussed here provides a promisingly well-controlled (both experimentally and
theoretically) environment for studying the principles behind edge magnetism in
graphene-based nano-structures.


  Many modern spatio-temporal data sets, in sociology, epidemiology or
seismology, for example, exhibit self-exciting characteristics, triggering and
clustering behaviors both at the same time, that a suitable Hawkes space-time
process can accurately capture. This paper aims to develop a fast and flexible
parametric inference technique to recover the parameters of the kernel
functions involved in the intensity function of a space-time Hawkes process
based on such data. Our statistical approach combines three key ingredients: 1)
kernels with finite support are considered, 2) the space-time domain is
appropriately discretized, and 3) (approximate) precomputations are used. The
inference technique we propose then consists of a $\ell_2$ gradient-based
solver that is fast and statistically accurate. In addition to describing the
algorithmic aspects, numerical experiments have been carried out on synthetic
and real spatio-temporal data, providing solid empirical evidence of the
relevance of the proposed methodology.


  For the first time, we have a plausible, complete accounting of matter and
energy in the Universe. Expressed a fraction of the critical density it goes
like this: neutrinos, between 0.3% and 15%; stars, 0.5%; baryons (total), 5%;
matter (total), 40%; smooth, dark energy, 60%; adding up to the critical
density. This accounting is consistent with the inflationary prediction of a
flat Universe and defines three dark-matter problems: Where are the dark
baryons? What is the nonbaryonic dark matter? What is the nature of the dark
energy? The leading candidate for the (optically) dark baryons is diffuse hot
gas; the leading candidates for the nonbaryonic dark matter are slowly moving
elementary particles left over from the earliest moments (cold dark matter),
such as axions or neutralinos; the leading candidates for the dark energy
involve fundamental physics and include a cosmological constant (vacuum
energy), a rolling scalar field (quintessence), and light, frustrated
topological defects.


  Dust particles play a major role in the formation, evolution and chemistry of
interstellar clouds, stars and planetary systems. Commonly identified forms
include amorphous and crystalline carbon-rich particles and silicates. Also
present in many astrophysical environments are polycyclic aromatic hydrocarbons
(PAHs), detected through their infrared emission, and which are essentially
small flakes of graphene. Astronomical observations over the past four decades
have revealed a widespread unassigned Extended Red Emission (ERE) feature which
is attributed to luminescence of dust grains. Numerous potential carriers for
ERE have been proposed but none has gained general acceptance. In this Letter
it is shown that there is a strong similarity between laboratory optical
emission spectra of graphene oxide and ERE, leading to this proposal that
emission from graphene oxide nanoparticles is the origin of ERE and that these
are a significant component of interstellar dust. The proposal is supported by
infrared emission features detected by the Infrared Space Observatory (ISO) and
the Spitzer Space Telescope.


  Wave-particle duality is one of the most striking and counter-intuitive
features of quantum mechanics, illustrating that two incompatible observables
cannot be measured simultaneously with arbitrary precision. In this work, we
experimentally demonstrate the equivalence of wave-particle duality and
entropic uncertainty relations using orbital angular momentum (OAM) states of
light. Our experiment utilizes an innovative and reconfigurable platform
composed of few-mode optical fibers and photonic lanterns, showcasing the
versatility of this technology for quantum information processing. Our results
provide fundamental insights into the complementarity principle from an
informational perspective, with implications for the broader field of quantum
technologies.