  We present far-infrared (FIR) properties of an extremely luminous infrared
galaxy (ELIRG) at $z_{\rm spec}$ = 3.703, WISE J101326.25+611220.1
(WISE1013+6112). This ELIRG is selected as an IR-bright dust-obscured galaxy
(DOG) based on the photometry from the Sloan digital sky survey (SDSS) and
wide-field infrared survey explorer (WISE). In order to derive its accurate IR
luminosity, we perform follow-up observations at 89 and 154 $\mu$m using the
high-resolution airborne wideband camera-plus (HAWC+) on board the 2.7-m
stratospheric observatory for infrared astronomy (SOFIA) telescope. We conduct
spectral energy distribution (SED) fitting with CIGALE using 15 photometric
data (0.4-1300 $\mu$m). We successfully pin down FIR SED of WISE1013+6112 and
its IR luminosity is estimated to be $L_{\rm IR}$ = (1.62 $\pm$ 0.08) $\times
10^{14}$ $L_{\odot}$, making it one of the most luminous IR galaxies in the
universe. We determine the dust temperature of WISE1013+6112 is $T_{\rm dust}$
= 89 $\pm$ 3 K, which is significantly higher than that of other populations
such as SMGs and FIR-selected galaxies at similar IR luminosities. The
resultant dust mass is $M_{\rm dust}$ = (2.2 $\pm$ 0.1) $\times 10^{8}$
$M_{\odot}$. This indicates that WISE1013+6112 has a significant active
galactic nucleus (AGN) and star-forming activity behind a large amount of dust.


  We use the Horizon Run 4 cosmological N -body simulation to study the effects
of distant and close interactions on the alignments of the shapes, spins, and
orbits of targets haloes with their neighbours, and their dependence on the
local density environment and neighbour separation. Interacting targets have a
significantly lower spin and higher sphericity and oblateness than all targets.
Interacting pairs initially have anti-parallel spins, but the spins develop
parallel alignment as time goes on. Neighbours tend to evolve in the plane of
rotation of the target, and in the direction of the major axis of prolate
haloes. Moreover, interactions are preferentially radial, while pairs with
non-radial orbits are preferentially prograde. The alignment signals are
stronger at high-mass and for close separations, and independent on the
large-scale density. Positive alignment signals are found at redshifts up to 4,
and increase with decreasing redshifts. Moreover, the orbits tend to become
prograde at low redshift, while no alignment is found at high redshift (z = 4).


  Remote sensing shadow removal, which aims to recover contaminated surface
information, is tricky since shadows typically display overwhelmingly low
illumination intensities. In contrast, the infrared image is robust toward
significant light changes, providing visual clues complementary to the visible
image. Nevertheless, the existing methods ignore the collaboration between
heterogeneous modalities, leading to undesired quality degradation. To fill
this gap, we propose a weakly supervised shadow removal network with a
spherical feature space, dubbed S2-ShadowNet, to explore the best of both
worlds for visible and infrared modalities. Specifically, we employ a modal
translation (visible-to-infrared) model to learn the cross-domain mapping, thus
generating realistic infrared samples. Then, Swin Transformer is utilized to
extract strong representational visible/infrared features. Simultaneously, the
extracted features are mapped to the smooth spherical manifold, which
alleviates the domain shift through regularization. Well-designed similarity
loss and orthogonality loss are embedded into the spherical space, prompting
the separation of private visible/infrared features and the alignment of shared
visible/infrared features through constraints on both representation content
and orientation. Such a manner encourages implicit reciprocity between
modalities, thus providing a novel insight into shadow removal. Notably, ground
truth is not available in practice, thus S2-ShadowNet is trained by cropping
shadow and shadow-free patches from the shadow image itself, avoiding
stereotypical and strict pair data acquisition. More importantly, we contribute
a large-scale weakly supervised shadow removal benchmark, including 4000 shadow
images with corresponding shadow masks.


  The resistive or non-resistive nature of the extracellular space in the brain
is still debated, and is an important issue for correctly modeling
extracellular potentials. Here, we first show theoretically that if the medium
is resistive, the frequency scaling should be the same for electroencephalogram
(EEG) and magnetoencephalogram (MEG) signals at low frequencies (<10 Hz). To
test this prediction, we analyzed the spectrum of simultaneous EEG and MEG
measurements in four human subjects. The frequency scaling of EEG displays
coherent variations across the brain, in general between 1/f and 1/f^2, and
tends to be smaller in parietal/temporal regions. In a given region, although
the variability of the frequency scaling exponent was higher for MEG compared
to EEG, both signals consistently scale with a different exponent. In some
cases, the scaling was similar, but only when the signal-to-noise ratio of the
MEG was low. Several methods of noise correction for environmental and
instrumental noise were tested, and they all increased the difference between
EEG and MEG scaling. In conclusion, there is a significant difference in
frequency scaling between EEG and MEG, which can be explained if the
extracellular medium (including other layers such as dura matter and skull) is
globally non-resistive.


  Online resources today contain an abundant amount of code snippets for
documentation, collaboration, learning, and problem-solving purposes. Their
executability in a "plug and play" manner enables us to confirm their quality
and use them directly in projects. But, in practice that is often not the case
due to several requirements violations or incompleteness. However, it is a
difficult task to investigate the executability on a large scale due to
different possible errors during the execution. We have developed a scalable
framework to investigate this for SOTorrent Python snippets. We found that with
minor adjustments, 27.92% of snippets are executable. The executability has not
changed significantly over time. The code snippets referenced in GitHub are
more likely to be directly executable. But executability does not affect the
chances of the answer to be selected as the accepted answer significantly.
These properties help us understand and improve the interaction of users with
online resources that include code snippets.


  Given an infinite field $\mathbb{k}$ and a simplicial complex $\Delta$, a
common theme in studying the $f$- and $h$-vectors of $\Delta$ has been the
consideration of the Hilbert series of the Stanley--Reisner ring
$\mathbb{k}[\Delta]$ modulo a generic linear system of parameters $\Theta$.
Historically, these computations have been restricted to special classes of
complexes (most typically triangulations of spheres or manifolds). We provide a
compact topological expression of $h_{d-1}^\mathfrak{a}(\Delta)$, the dimension
over $\mathbb{k}$ in degree $d-1$ of $\mathbb{k}[\Delta]/(\Theta)$, for any
complex $\Delta$ of dimension $d-1$. In the process, we provide tools and
techniques for the possible extension to other coefficients in the Hilbert
series.


  We study some particular cases of Viterbo's conjecture relating volumes of
convex bodies and actions of closed characteristics on their boundaries,
focusing on the case of a Hamiltonian of classical mechanical type, splitting
into summands depending on the coordinates and the momentum separately. We
manage to establish the conjecture for sublevel sets of convex $2$-homogeneous
Hamiltonians of this kind in several particular cases. We also discuss open
cases of this conjecture.


  We give a quantitative version of Vainberg's method relating pole free
regions to propagation of singularities for black box scatterers. In
particular, we show that there is a logarithmic resonance free region near the
real axis of size $\tau$ with polynomial bounds on the resolvent if and only if
the wave propagator gains derivatives at rate $\tau$. Next we show that if
there exist singularities in the wave trace at times tending to infinity which
smooth at rate $\tau$, then there are resonances in logarithmic strips whose
width is given by $\tau$. As our main application of these results, we give
sharp bounds on the size of resonance free regions in scattering on
geometrically nontrapping manifolds with conic points. Moreover, these bounds
are generically optimal on exteriors of nontrapping polygonal domains.


  Given a compact K\"ahler manifold, we prove that all global isometries of the
space of K\"ahler metrics are induced by biholomorphisms and
anti-biholomorphisms of the manifold. In particular, there exist no global
symmetries for Mabuchi's metric. Moreover, we show that the Mabuchi completion
does not even admit local symmetries. Closely related to these findings, we
provide a large class of metric geodesic segments that can not be extended at
one end, pointing out the first such examples in the literature.


  We document the MATLAB code used in the following study: Numerical proof of
stability of roll waves in the small-amplitude limit for inclined thin film
flow.


  This paper investigates rate-splitting (RS) precoding for non-orthogonal
unicast and multicast (NOUM) transmissions using fully-digital and hybrid
precoders. We study the nonconvex weighted sum-rate (WSR) maximization problem
subject to a multicast requirement. We propose FALCON, an approach based on
sequential parametric optimization, to solve the aforementioned problem. We
show that FALCON converges to a local optimum without requiring judicious
selection of an initial feasible point. Besides, we show through simulations
that by leveraging RS, hybrid precoders can attain nearly the same performance
as their fully-digital counterparts under certain specific settings.


  A novel parallel algorithm for matrix multiplication is presented. The
hyper-systolic algorithm makes use of a one-dimensional processor abstraction.
The procedure can be implemented on all types of parallel systems. It can
handle matrix-vector multiplications as well as transposed matrix products.


  In this paper, a novel deep-learning based framework is proposed to infer 3D
human poses from a single image. Specifically, a two-phase approach is
developed. We firstly utilize a generator with two branches for the extraction
of explicit and implicit depth information respectively. During the training
process, an adversarial scheme is also employed to further improve the
performance. The implicit and explicit depth information with the estimated 2D
joints generated by a widely used estimator, in the second step, are together
fed into a deep 3D pose regressor for the final pose generation. Our method
achieves MPJPE of 58.68mm on the ECCV2018 3D Human Pose Estimation Challenge.


  We describe efficient algorithms to search for cases in which binomial
coefficients are equal or almost equal, give a conjecturally complete list of
all cases where two binomial coefficients differ by 1, and give some identities
for binomial coefficients that seem to be new.


  Building on ideas of Berthelot, we develop a crystalline cohomology formalism
over divided power rings $(A, I_0, \eta)$ for any ring $A$, allowing
$\mathbf{Z}$-flat $A$. For a smooth $A$-scheme $Y$ and a closed subscheme $X$
of $Y$ for which $\eta$ extends to $I_0 \mathscr{O}_X$, a (quasi-coherent)
crystal $\mathscr{F}$ on $(X/A)_{\rm{cris}}$ is equivalent to a specific type
of module with integrable $A$-linear connection over a certain completion
$D_{Y,\eta}(X)^{\wedge}$ (called "pd-adic") of the divided power envelope
$D_{Y,\eta}(X)$ of $Y$ along $X$ (with divided power structure $\delta$)
  Our main result, building on ideas of Bhatt and de Jong for
$\mathbf{Z}/(p^e)$-schemes (where pd-adic completion has no effect), is a
natural isomorphism between ${\rm{R}}\Gamma((X/A)_{\rm{cris}}, \mathscr{F})$
and the Zariski hypercohomology of the pd-adically completed de Rham complex
$\mathscr{F} \widehat{\otimes}
\widehat{\Omega}^*_{D_{Y,\eta}(X)^{\wedge}/A,\delta}$ arising from the module
with integrable connection over $D_{Y,\eta}(X)^{\wedge}$ associated to
$\mathscr{F}$. By a variant of the same methods, we obtain a representative of
the complex $\mathscr{F} \widehat{\otimes}
\widehat{\Omega}^*_{D_{Y,\eta}(X)^{\wedge}/A,\delta}$ in the derived category
of sheaves of $A$-modules on $X$ in terms of a \v{C}ech-Alexander construction.
  When $\mathscr{F}=\mathscr{O}_{X/A}$, our comparison theorem implies that in
the derived category of sheaves of $A$-modules on $X$, the pd-adic completion
of $\Omega^*_{D_{Y,\eta}(X)/A,\delta}$ functorially depends only on $X$. Over
$\mathbf{Q}$-algebras $A$, so pd-adic completion becomes ideal-adic completion,
this recovers a result of Hartshorne.


  A full fp calculation is performed for states which were degenerate in a
single-j-shell calculation in which isospin-zero two-body matrix elements were
set to zero energy. Most of the splitting in a complete shell calculation (but
not all) comes from the T=0 part of the interaction.


  Non-Hermitian models with real eigenenergies are highly desirable for their
stability. Yet, most of the currently known ones are constrained by symmetries
such as PT-symmetry, which is incompatible with realizing some of the most
exotic non-Hermitian phenomena. In this work, we investigate how the
non-Hermitian skin effect provides an alternative route towards enforcing real
spectra and system stability. We showcase, for different classes of energy
dispersions, various ansatz models that possess large parameter space regions
with real spectra, despite not having any obvious symmetry. These minimal local
models can be quickly implemented in non-reciprocal experimental setups such as
electrical circuits with operational amplifiers.


  Streaming graph processing involves performing updates and analytics on a
time-evolving graph. The underlying representation format largely determines
the throughputs of these updates and analytics phases. Existing formats usually
employ variations of hash tables or adjacency lists. However,
adjacency-list-based approaches perform poorly on heavy-tailed graphs, and the
hash-based approaches suffer on short-tailed graphs. We propose GraphTango, a
hybrid format that provides excellent update and analytics throughput
regardless of the graph's degree distribution. GraphTango switches among three
different formats based on a vertex's degree: i) Low-degree vertices store the
edges directly with the neighborhood metadata, confining accesses to a single
cache line, ii) Medium-degree vertices use adjacency lists, and iii)
High-degree vertices use hash tables as well as adjacency lists. In this case,
adjacency list provides fast traversal during the analytics phase, while the
hash table provides constant-time lookups during the update phase. We further
optimized the performance by designing an open-addressing-based hash table that
fully utilizes every fetched cache line. In addition, we developed a
thread-local lock-free memory pool that allows fast growing/shrinking of the
adjacency lists and hash tables in a multi-threaded environment. We evaluated
GraphTango with the help of the SAGA-Bench framework and compared it with four
other representation formats. On average, GraphTango provides 4.5x higher
insertion throughput, 3.2x higher deletion throughput, and 1.1x higher
analytics throughput over the next best format. Furthermore, we integrated
GraphTango with the state-of-the-art graph processing frameworks DZiG and
RisGraph. Compared to the vanilla DZiG and vanilla RisGraph, [GraphTango +
DZiG] and [GraphTango + RisGraph] reduces the average batch processing time by
2.3x and 1.5x, respectively.


  The purpose of this note is to attract attention to the following conjecture
(metastable $r$-fold Whitney trick) by clarifying its status as not having a
complete proof, in the sense described in the paper.
  Assume that $D=D_1\sqcup\ldots\sqcup D_r$ is disjoint union of $r$ disks of
dimension $s$, $f:D\to B^d$ a proper PL map such that $f\partial
D_1\cap\ldots\cap f\partial D_r=\emptyset$, $rd\ge (r+1)s+3$ and $d\ge s+3$. If
the map $$f^r:\partial(D_1\times\ldots\times D_r)\to
(B^d)^r-\{(x,x,\ldots,x)\in(B^d)^r\ |\ x\in B^d\}$$ extends to
$D_1\times\ldots\times D_r$, then there is a PL map $\overline f:D\to B^d$ such
that $$\overline f=f \quad\text{on}\quad D_r\cup\partial D\quad\text{and}\quad
\overline fD_1\cap\ldots\cap \overline fD_r=\emptyset.$$


  In the case of Krein's strings with spectral functions of polynomial growth a
necessary and sufficient condition for the Krein's correspondence to be
continuous is given.


  The properties of superdense matter in neutron star (NS) cores control NS
thermal states by affecting the efficiency of neutrino emission from NS
interiors. To probe these properties we confront the theory of thermal
evolution of NSs with observations of their thermal radiation. Our
observational basis includes cooling isolated NSs (INSs) and NSs in quiescent
states of soft X-ray transients (SXTs). We find that the data on SXTs support
the conclusions obtained from the analysis of INSs: strong proton superfluidity
with T_{cp,max} >= 10^9 K should be present, while mild neutron superfluidity
with T_{cn,max} =(2*10^8 -- 2*10^9) K is ruled out in the outer NS core. Here
T_{cn,max} and T_{cp,max} are the maximum values of the density dependent
critical temperatures of neutrons and protons. The data on SXTs suggest also
that: (i) cooling of massive NSs is enhanced by neutrino emission more powerful
than the emission due to Cooper pairing of neutrons; (ii) mild neutron
superfluidity, if available, might be present only in inner cores of massive
NSs. In the latter case SXTs would exhibit dichotomy, i.e. very similar SXTs
may evolve to very different thermal states.


  We report on the direct observation of an oscillating atomic current in a
one-dimensional array of Josephson junctions realized with an atomic
Bose-Einstein condensate. The array is created by a laser standing-wave, with
the condensates trapped in the valleys of the periodic potential and weakly
coupled by the inter-well barriers. The coherence of multiple tunneling between
adjacent wells is continuously probed by atomic interference. The square of the
small-amplitude oscillation frequency is proportional to the microscopic
tunneling rate of each condensate through the barriers, and provides a direct
measurement of the Josephson critical current as a function of the intermediate
barrier heights. Our superfluid array may allow investigation of phenomena so
far inaccessible to superconducting Josephson junctions and lays a bridge
between the condensate dynamics and the physics of discrete nonlinear media.


  Intense, ultrashort pulses of 800 nm laser light (12 fs, $\sim$4 optical
cycles) of peak intensity 5$\times$10$^{14}$ W cm$^{-2}$ have been used to
irradiate gas-phase Xe$_n$ clusters ($n$=500-25,000) so as to induce multiple
ionization and subsequent Coulomb explosion. Energy distributions of exploding
ions are measured in the few-cycle domain that does not allow sufficient time
for the cluster to undergo Coulomb-driven expansion. This results in overall
dynamics that appear to be significantly different to those in the many-cycle
regime. One manifestation is that the maximum ion energies are measured to be
much lower than those obtained when longer pulses of the same intensity are
used. Ion yields are cluster-size independent but polarization dependent in
that they are significantly larger when the polarization is perpendicular to
the detection axis than along it. This unexpected behavior is qualitatively
rationalized in terms of a spatially anisotropic shielding effect induced by
the electronic charge cloud within the cluster.


  Most deep-learning-based image classification methods assume that all samples
are generated under an independent and identically distributed (IID) setting.
However, out-of-distribution (OOD) generalization is more common in practice,
which means an agnostic context distribution shift between training and testing
environments. To address this problem, we present a novel Partial Feature
Decorrelation Learning (PFDL) algorithm, which jointly optimizes a feature
decomposition network and the target image classification model. The feature
decomposition network decomposes feature embeddings into the independent and
the correlated parts such that the correlations between features will be
highlighted. Then, the correlated features help learn a stable feature
representation by decorrelating the highlighted correlations while optimizing
the image classification model. We verify the correlation modeling ability of
the feature decomposition network on a synthetic dataset. The experiments on
real-world datasets demonstrate that our method can improve the backbone
model's accuracy on OOD image classification datasets.


  The Galactic Plane was searched for transient, monochromatic light at optical
and near-IR wavelengths to detect pulses shorter than 1 sec. An objective-prism
Schmidt telescope and CMOS camera were used to observe 973 square degrees along
the Galactic Plane within a strip 2.1 deg wide. The non-detections of laser
pulses from the Galactic Plane add to the non-detections from more than 5000
stars. The absence of extraterrestrial beacons reveals more of a SETI desert at
optical and radio wavelengths.


  Vortices in spin-$0$ superfluids generically carry magnetic fields inside
their cores, so that even neutral superfluid vortices may be thought of as
magnetic flux tubes. We give a systematic analysis of this `vortex magnetic
effect' using effective field theory, clarifying earlier literature on the
subject. Our analysis shows that in superfluid Helium-$4$ the vortex magnetic
effect may be large enough to be experimentally detectable.


  In this paper, we aim to improve abstractive dialogue summarization quality
and, at the same time, enable granularity control. Our model has two primary
components and stages: 1) a two-stage generation strategy that generates a
preliminary summary sketch serving as the basis for the final summary. This
summary sketch provides a weakly supervised signal in the form of
pseudo-labeled interrogative pronoun categories and key phrases extracted using
a constituency parser. 2) A simple strategy to control the granularity of the
final summary, in that our model can automatically determine or control the
number of generated summary sentences for a given dialogue by predicting and
highlighting different text spans from the source text. Our model achieves
state-of-the-art performance on the largest dialogue summarization corpus
SAMSum, with as high as 50.79 in ROUGE-L score. In addition, we conduct a case
study and show competitive human evaluation results and controllability to
human-annotated summaries.


  The Cepheid period-luminosity (PL) relation (or Leavitt law) has served as
the first rung of the most widely used extragalactic distance ladder and is
central to the determination of the local value of the Hubble constant ($H_0$).
We investigate the influence of metallicity on Cepheid brightness, a term that
significantly improves the overall fit of the distance ladder, to better define
its wavelength dependence. To this aim, we compare the PL relations obtained
for three Cepheid samples having distinct chemical composition (in the Milky
Way and Magellanic Clouds) and focusing on the use of improved and recent data
while covering a metallicity range of about 1 dex. We estimate the metallicity
effect (hereafter $\gamma$) in 15 filters from mid-IR to optical wavelengths,
including five Wesenheit indices, and we derive a significant metallicity term
in all filters, in agreement with recent empirical studies and models, in the
sense of metal-rich Cepheids being brighter than metal-poor ones. We describe
the contribution of various systematic effects in the determination of the
$\gamma$ term. We find no evidence of $\gamma$ changing over the wavelength
range $0.5-4.5 \, \rm \mu m$, indicating that the main influence of metallicity
on Cepheids is in their luminosity rather than color. Finally, we identify
factors that sharpen the empirical constraints on the metallicity term over
past studies, including corrections for the depth of the Magellanic Clouds,
better-calibrated Cepheid photometry, improved Milky Way extinction estimates,
and revised and expanded metallicity measurements in the LMC.


  Most no-hair theorems involve the assumption that the scalar field is
independent of time. Recently in [Phys. Rev. D90 (2014) 041501(R)] the
existence of time-dependent scalar hair outside a stationary black hole in
general relativity was ruled out. We generalize this work to modified gravities
and non-minimally coupled scalar field with an additional assumption that the
spacetime is axisymmetric. It is shown that in higher-order gravity such as
metric $f(R)$ gravity the time-dependent scalar hair doesn't exist. While in
Palatini $f(R)$ gravity and non-minimally coupled case the time-dependent
scalar hair may exist.


  This paper presents the design and post-layout characteristics of a
differential capacitance based inertial accelerometer This includes a MEMS
based mechanical sensing element and a CMOS charge amplifier, which is the
first stage of a readout circuit. The mechanical sensor is designed according
to the SOIMUMPs fabrication process technology, and the readout circuit
targeted AMS 0.35um technology. Post layout simulations indicated a +/-5G
dynamic range, a maximum bandwidth of 1.58 kHz, non-linearity of 0.077% and a
resolution of 10.5 uG/Hz^0.5. The readout circuit charge amplifier is fully
differential and incorporated in a switched capacitor (SC) topology with CDS.


  The lower-critical dimension for the existence of the Ising spin-glass phase
is calculated, numerically exactly, as $d_L = 2.520$ for a family of
hierarchical lattices, from an essentially exact (correlation coefficent $R^2 =
0.999999$) near-linear fit to 23 different diminishing fractional dimensions.
To obtain this result, the phase transition temperature between the disordered
and spin-glass phases, the corresponding critical exponent $y_T$, and the
runaway exponent $y_R$ of the spin-glass phase are calculated for consecutive
hierarchical lattices as dimension is lowered.


  Gauge threshold corrections for intersecting D6-brane string models on
toroidal orbifold backgrounds are reconsidered. Both by dimensionally
regularising the appearing open string one-loop diagrams in tree-channel as
well as by zeta-function regularisation of the corresponding loop-channel
one-loop diagrams, we arrive at a result which takes into account the infrared
divergence from the contribution of the massless states in the running of the
gauge coupling constant as well as the contribution of states, which become
light in certain regions of the moduli space.


  The resonant structure of the four pion final state in the decay $\tau \to
3\pi\pi^0\nu_\tau$ is analyzed using 4.27 million $\tau^+\tau^-$ pairs
collected by the CLEO II experiment. We search for second class currents in the
decay $\tau \to \omega\pi\nu_\tau$ using spin-parity analysis and establish an
upper limit on the non-vector current contribution. The mass and width of the
$\rho'$ resonance are extracted from a fit to the $\tau \to \omega\pi\nu_\tau$
spectral function. A partial wave analysis of the resonant structure of the
$\tau \to 3\pi\pi^0\nu_\tau$ decay is performed; the spectral decomposition of
the four pion system is dominated by the $\omega\pi$ and $a_1 \pi$ final
states.


  We perform an independent search for annual modulation caused by dark
matter-induced scatterings in the recently released COSINE-100 data. We test
the hypothesis that the data contains a sinusoidal modulation against the null
hypothesis that the data consists of only background. We compare the
significance using frequentist, information theoretic techniques (such as AIC
and BIC), and also using the Bayesian model comparison technique. The
information theory-based tests mildly prefer a constant background over a
sinusoidal signal with the same period as that found by the DAMA collaboration.
The Bayesian test however strongly prefers a background model. This is the
first proof of principles demonstration of application of Bayesian and
information theory based techniques to COSINE-100 data to assess the
significance of annual modulation.


  Numerous valuable efforts have been devoted to achieving arbitrary style
transfer since the seminal work of Gatys et al. However, existing
state-of-the-art approaches often generate insufficiently stylized results
under challenging cases. We believe a fundamental reason is that these
approaches try to generate the stylized result in a single shot and hence fail
to fully satisfy the constraints on semantic structures in the content images
and style patterns in the style images. Inspired by the works on
error-correction, instead, we propose a self-correcting model to predict what
is wrong with the current stylization and refine it accordingly in an iterative
manner. For each refinement, we transit the error features across both the
spatial and scale domain and invert the processed features into a residual
image, with a network we call Error Transition Network (ETNet). The proposed
model improves over the state-of-the-art methods with better semantic
structures and more adaptive style pattern details. Various qualitative and
quantitative experiments show that the key concept of both progressive strategy
and error-correction leads to better results. Code and models are available at
https://github.com/zhijieW94/ETNet.


  In this paper we propose a modified cross correlation method to align images
from the same class in single-particle electron microscopy of highly
non-spherical structures. In this new method, First we coarsely align
projection images, and then re-align the resulting images using the cross
correlation (CC) method. The coarse alignment is obtained by matching the
centers of mass and the principal axes of the images. The distribution of
misalignment in this coarse alignment can be quantified based on the
statistical properties of the additive background noise. As a consequence, the
search space for re-alignment in the cross correlation method can be reduced to
achieve better alignment. In order to overcome problems associated with false
peaks in the cross correlations function, we use artificially blurred images
for the early stage of the iterative cross correlation method and segment the
intermediate class average from every iteration step. These two additional
manipulations combined with the reduced search space size in the cross
correlation method yield better alignments for low signal-to-noise ratio images
than both classical cross correlation and maximum likelihood(ML) methods.


  We investigate the physics reach and potential for the study of various
decays involving a $b \to s \nu \bar{\nu}$ transition at the Future Circular
Collider running electron-positron collisions at the $Z$-pole (FCC-ee). Signal
and background candidates, which involve inclusive $Z$ contributions from
$b\bar{b}$, $c\bar{c}$ and $uds$ final states, are simulated for a proposed
multi-purpose detector. Signal candidates are selected using two Boosted
Decision Tree algorithms. We determine expected relative sensitivities of
$0.53\%$, $1.20\%$, $3.37\%$ and $9.86\%$ for the branching fractions of the
$B^{0} \to K^{*0} \nu \bar{\nu}$, $B^{0}_{s} \to \phi \nu \bar{\nu}$, $B^{0}
\to K^{0}_{S} \nu \bar{\nu}$ and $\Lambda_{b}^{0} \to \Lambda^{0} \nu
\bar{\nu}$ decays, respectively. In addition, we investigate the impact of
detector design choices related to particle-identification and vertex
resolution. The phenomenological impact of such measurements on the extraction
of Standard Model and new physics parameters is also studied.


  In this paper we prove Liouville type result for the stationary solutions to
the compressible Navier-Stokes-Poisson equations(NSP) and the compressible
Navier-Stokes equations(NS) in $\Bbb R^N$, $N\geq 2$. Assuming suitable
integrability and the uniform boundedness conditions for the solutions we are
led to the conclusion that $v=0$. In the case of (NS) we deduce that the
similar integrability conditions imply $v=0$ and $\rho=$constant on $\Bbb R^N$.
This shows that if we impose the the non-vacuum boundary condition at spatial
infinity for (NS), $v\to 0$ and $\rho\to \rho_\infty >0$, then $v=0$,
$\rho=\rho_\infty$ are the solutions.


  The quantum anomalous Hall (QAH) effect has been demonstrated in
two-dimensional topological insulator systems incorporated with ferromagnetism.
However, a comprehensive understanding of mesoscopic transport in sub-micron
QAH devices has yet been established. Here we fabricated miniaturized QAH
devices with channel widths down to 600 nm, where the QAH features are still
preserved. A back-scattering channel is formed in narrow QAH devices through
percolative hopping between 2D compressible puddles. Large resistance
fluctuations are observed in narrow devices near the coercive field, which is
associated with collective interference between intersecting paths along domain
walls when the device geometry is smaller than the phase coherence length
$L_\phi$. Through measurement of size-dependent breakdown current, we confirmed
that the chiral edge states are confined at the physical boundary with its
width on the order of Fermi wavelength.


  The 105 K second-order displacive phase transition of SrTiO3 has been studied
with the help of Electron Paramagnetic Resonance. The photochromic non-cubic
Fe5+ centre is used as local probe. Critical phenomena, characterized by an
exponent {\beta} = 1/3, are presented. Line broadening effects are interpreted
as stemming from time-dependent fluctuations near the phase transition point.
The data are consistent with those reported earlier on the Fe3+-VO pair centre,
indicating cooperative effects in the crystal. Also a model for the non-cubic
Fe5+ is proposed, i.e., the ion is substitutional for Ti4+ with an empty
adjacent expanded octahedron. Other Fe5+ centres in SrTiO3 and BaTiO3 are
reviewed and reinterpreted.


  Anticipation for 6G's arrival comes with growing concerns about increased
energy consumption in computing and networking. The expected surge in connected
devices and resource-demanding applications presents unprecedented challenges
for energy resources. While sustainable resource allocation strategies have
been discussed in the past, these efforts have primarily focused on
single-domain orchestration or ignored the unique requirements posed by 6G. To
address this gap, we investigate the joint problem of service instance
placement and assignment, path selection, and request prioritization, dubbed
PIRA. The objective function is to maximize the system's overall profit as a
function of the number of concurrently supported requests while simultaneously
minimizing energy consumption over an extended period of time. In addition,
end-to-end latency requirements and resource capacity constraints are
considered for computing and networking resources, where queuing theory is
utilized to estimate the Age of Information (AoI) for requests. After
formulating the problem in a non-linear fashion, we prove its NP-hardness and
propose a method, denoted ORIENT. This method is based on the Double Dueling
Deep Q-Learning (D3QL) mechanism and leverages Graph Neural Networks (GNNs) for
state encoding. Extensive numerical simulations demonstrate that ORIENT yields
near-optimal solutions for varying system sizes and request counts.


  In this short note we observe that a recent result of C.-W. Chen meshes well
with earlier work of H.-D. Cao and D.-T. Zhou, O. Munteanu, J. Carrillo and L.
Ni, and S.-J. Zhang to give a necessary and sufficient condition for complete
noncompact shrinking gradient Ricci solitons to have positive asymptotic volume
ratio.


  The purpose of this paper is threefold: a) Check if the rather poor Standard
Model (SM) fit to all electroweak data can be improved in its minimal
supersymmetric extension (MSSM); b) Check what constraints present electroweak
data has on the parameter space of the supergravity inspired Constrained MSSM
(CMSSM); c) Check if with the present high precision of the gauge coupling
constants exact gauge coupling unification is still possible. The value of
tan\beta in the CMSSM is constrained to be above 6.5, while the value of the
gaugino masses at the GUT scale has to be above 220 GeV, which corresponds to a
lower limit on the lightest neutralino (chargino) of ca. 95 (175) GeV. It is
shown that the larger values of \alpha_s from R_l and \sinw from A_FB are
consistent with perfect gauge unification. The low \alpha_s value from the
hadronic cross section is correlated with the fact that the number of neutrino
flavours is 2\sigma below the known value of 3 in the SM, which is shown to
correspond to a 3sigma deviation in the hadronic cross section. Lowering
sigma_{had} by 3sigma brings alpha_s up to 0.12 and N_nu up to 3.


  We study the conservation laws associated with the asymptotic Poincare
symmetry of spacetime in the general teleparallel theory of gravity. Demanding
that the canonical Poincare generators have well defined functional derivatives
in a properly defined phase space, we obtain the improved form of the
generators, containing certain surface terms. These terms are shown to
represent the values of the related conserved charges: energy-momentum and
angular momentum.


  The Bootstrap approach to calculating cosmological correlators relies on a
well motivated ansatz. It is typical in the literature to assume that
correlators are rational functions as this greatly increases our constraining
power. However, this has only previously been demonstrated for some specific
theories. In this paper we find a set of assumptions which we prove are
sufficient to ensure that the wavefunction coefficients are rational. As a
corollary of this we generalise the manifestly local test to higher dimensions.
This result greatly reduces the allowed space of functions that wavefunction
coefficients can take in both the Effective Field Theory of Inflation and Pure
Gravity models and is thus a key ingredient in the Cosmological Bootstrap
program.


  We study the relation between supersymmetry and geometric flows driven by the
Bianchi identity for the three-form flux $H$ in heterotic supergravity. We
describe how the flow equations can be derived from a functional that appears
in a rewriting of the bosonic action in terms of squares of supersymmetry
operators. On a complex threefold, the resulting equations match what is known
in the mathematics literature as "anomaly flow". We generalise this to seven-
and eight-manifolds with G$_2$ or Spin(7) structures and discuss examples where
the manifold is a torus fibration over a K3 surface. In the latter cases, the
flow simplifies to a single scalar equation, with the existence of the
supergravity solution implied by the long-time existence and convergence of the
flow. We also comment on the $\alpha'$ expansion and highlight the importance
of using the proper connection in the Bianchi identity to ensure that the
flow's fixed points satisfy the supergravity equations of motion.


  We study a modification of Poisson geometry by a closed 3-form. Just as for
ordinary Poisson structures, these "twisted" Poisson structures are
conveniently described as Dirac structures in suitable Courant algebroids. The
additive group of 2-forms acts on twisted Poisson structures and permits them
to be seen as glued from ordinary Poisson structures defined on local patches.
We conclude with remarks on deformation quantization and twisted symplectic
groupoids.


  The confluence of major theoretical, experimental, and observational advances
are providing a unique perspective on the equation of state of dense
neutron-rich matter -- particularly its symmetry energy -- and its imprint on
the mass-radius relation for neutron stars. In this contribution we organize
these developments in an equation of state density ladder. Of particular
relevance to this discussion is the impact of the various rungs on the equation
of state and the identification of possible discrepancies among the various
methods. A preliminary analysis identifies a possible tension between
laboratory measurements and gravitational-wave detections that could indicate
the emergence of a phase transition in the stellar core.


  I perform an unprecedented template-based search for stimulated emission of
Hawking radiation (or Boltzmann echoes) by combining the gravitational wave
data from 65 binary black hole merger events observed by the LIGO/Virgo
collaboration. With a careful Bayesian inference approach, I found no
statistically significant evidence for this signal in either of the 3
Gravitational Wave Transient Catalogs GWTC-1, GWTC-2 and GWTC-3. However, the
data cannot yet conclusively rule out the presence of Boltzmann echoes either,
with the Bayesian evidence ranging within 0.3-1.6 for most events, and a common
(non-vanishing) echo amplitude for all mergers being disfavoured at only 2:5
odds. The only exception is GW190521, the most massive and confidently detected
event ever observed, which shows a positive evidence of 9.2 for stimulated
Hawking radiation. An optimal combination of posteriors yields an upper limit
of $A < 0.42$ (at $90\%$ confidence level) for a universal echo amplitude,
whereas $A \sim 1$ was predicted in the canonical model. The next generation of
gravitational wave detectors such as LISA, Einstein Telescope, and Cosmic
Explorer can draw a definitive conclusion on the quantum nature of black hole
horizons.


  Voting mechanisms are widely accepted and used methods for decentralized
decision-making. Ensuring the acceptance of the voting mechanism's outcome is a
crucial characteristic of robust voting systems. Consider this scenario: A
group of individuals wants to choose an option from a set of alternatives
without requiring an identification or proof-of-personhood system. Moreover,
they want to implement utilitarianism as their selection criteria. In such a
case, players could submit votes multiple times using dummy accounts, commonly
known as a Sybil attack (SA), which presents a challenge for decentralized
organizations. Is there a voting mechanism that always prevents players from
benefiting by casting votes multiple times (SA-proof) while also selecting the
alternative that maximizes the added valuations of all players (efficient)?
One-person-one-vote is neither SA-proof nor efficient. Coin voting is SA-proof
but not efficient. Quadratic voting is efficient but not SA-proof. This study
uses Bayesian mechanism design to propose a solution. The mechanism's structure
is as follows: Players make wealth deposits to indicate the strength of their
preference for each alternative. Each player then receives an amount based on
their deposit and the voting outcome. The proposed mechanism relies on two main
concepts: 1) Transfers are influenced by the outcome in a way that each
player's optimal action depends only on individual preferences and the number
of alternatives; 2) A player who votes through multiple accounts slightly
reduces the expected utility of all players more than the individual benefit
gained. This study demonstrates that if players are risk-neutral and each
player has private information about their preferences and beliefs, then the
mechanism is SA-proof and efficient. This research provides new insights into
the design of more robust decentralized decision-making mechanisms.


  Dataset distillation compresses large datasets into smaller synthetic
coresets which retain performance with the aim of reducing the storage and
computational burden of processing the entire dataset. Today's best-performing
algorithm, \textit{Kernel Inducing Points} (KIP), which makes use of the
correspondence between infinite-width neural networks and kernel-ridge
regression, is prohibitively slow due to the exact computation of the neural
tangent kernel matrix, scaling $O(|S|^2)$, with $|S|$ being the coreset size.
To improve this, we propose a novel algorithm that uses a random feature
approximation (RFA) of the Neural Network Gaussian Process (NNGP) kernel, which
reduces the kernel matrix computation to $O(|S|)$. Our algorithm provides at
least a 100-fold speedup over KIP and can run on a single GPU. Our new method,
termed an RFA Distillation (RFAD), performs competitively with KIP and other
dataset condensation algorithms in accuracy over a range of large-scale
datasets, both in kernel regression and finite-width network training. We
demonstrate the effectiveness of our approach on tasks involving model
interpretability and privacy preservation.


  In this paper, we investigate the problem of beam alignment in millimeter
wave (mmWave) systems, and design an optimal algorithm to reduce the overhead.
Specifically, due to directional communications, the transmitter and receiver
beams need to be aligned, which incurs high delay overhead since without a
priori knowledge of the transmitter/receiver location, the search space spans
the entire angular domain. This is further exacerbated under dynamic conditions
(e.g., moving vehicles) where the access to the base station (access point) is
highly dynamic with intermittent on-off periods, requiring more frequent beam
alignment and signal training. To mitigate this issue, we consider an online
stochastic optimization formulation where the goal is to maximize the
directivity gain (i.e., received energy) of the beam alignment policy within a
time period. We exploit the inherent correlation and unimodality properties of
the model, and demonstrate that contextual information improves the
performance. To this end, we propose an equivalent structured Multi-Armed
Bandit model to optimally exploit the exploration-exploitation tradeoff. In
contrast to the classical MAB models, the contextual information makes the
lower bound on regret (i.e., performance loss compared with an oracle policy)
independent of the number of beams. This is a crucial property since the number
of all combinations of beam patterns can be large in transceiver antenna
arrays, especially in massive MIMO systems. We further provide an
asymptotically optimal beam alignment algorithm, and investigate its
performance via simulations.


  Plasmonic chirality exhibits great potential for novel nanooptical devices
due to the generation of a strong chiroptical response. Previous reports on
plasmonic chirality explanations are mainly based on phase retardation and
coupling. We propose a quantitative model similar to the chiral molecules for
explaining the mechanism of the intrinsic plasmonic chirality quantitatively
based on the interplay and mixing of electric and magnetic dipole modes, which
forms a mixed electric and magnetic polarizability. The analysis method is also
suitable for small chiral object down to quasi-static limit without phase delay
and expected to be a universal rule.


  The congruence subgroup property is established for the modular
representations associated to any modular tensor category. This result is used
to prove that the kernel of the representation of the modular group on the
conformal blocks of any rational, C_2-cofinite vertex operator algebra is a
congruence subgroup. In particular, the q-character of each irreducible module
is a modular function on the same congruence subgroup. The Galois symmetry of
the modular representations is obtained and the order of the anomaly for those
modular categories satisfying some integrality conditions is determined.


  Despite much theoretical and observational progress, there is no known firm
upper limit to the masses of stars. Our understanding of the interplay between
the immense radiation pressure produced by massive stars in formation and the
opacity of infalling material is subject to theoretical uncertainties, and many
observational claims of ``the most massive star'' have failed the singularity
test. LBV 1806-20 is a particularly luminous object, L~10^6 Lsun, for which
some have claimed very high mass estimates (M_initial>200 Msun), based, in
part, on its similarity to the Pistol Star. We present high-resolution
near-infrared spectroscopy of LBV 1806-20, showing that it is possibly a binary
system with components separated in velocity by ~70 kms. If correct, then this
system is not the most massive star known, yet it is a massive binary system.
We argue that a binary, or merged, system is more consistent with the ages of
nearby stars in the LBV 1806-20 cluster. In addition, we find that the velocity
of V_LSR=36 kms is consistent with a distance of 11.8 kpc, a luminosity of
10^6.3 Lsun, and a system mass of ~130 Msun.


  The modular (or entanglement) Hamiltonian correspondent to the
half-space-bipartition of a quantum state uniquely characterizes its
entanglement properties. However, in the context of lattice models, its
explicit form is analytically known only for the Ising chain and certain free
theories in one-dimension. In this work, we provide a throughout investigation
of entanglement Hamiltonians in lattice models obtained via the
Bisognano-Wichmann theorem, which provides an explicit functional form for the
entanglement Hamiltonian itself in quantum field theory. Our study encompasses
a variety of one- and two-dimensional models, supporting diverse quantum phases
and critical points, and, most importantly, scanning several universality
classes, including Ising, Potts, and Luttinger liquids. We carry out extensive
numerical simulations based on the density-matrix-renormalization-group method,
exact diagonalization, and quantum Monte Carlo. In particular, we compare the
exact entanglement properties and correlation functions to those obtained
applying the Bisognano-Wichmann theorem on the lattice. We carry out this
comparison on both the eigenvalues and eigenvectors of the entanglement
Hamiltonian, and expectation values of correlation functions and order
parameters. Our results evidence that, as long as the low-energy description of
the lattice model is well-captured by a Lorentz-invariant quantum field theory,
the Bisognano-Wichmann theorem provides a qualitatively and quantitatively
accurate description of the lattice entanglement Hamiltonian. The resulting
framework paves the way to direct studies of entanglement properties utilizing
well-established statistical mechanics methods and experiments.


  We study a revised version of Witten's 2d black hole, in which the matter and
(b,c) ghosts are mixed. The level of the coset model is still 9/4. We show that
this model is equivalent to that of Mukhi and Vafa, in which the level of the
coset model is taken as 3, and the stress tensor is improved. We argue that the
exact metric in such a model is just the semi-classical one, quite different
from the exact metric in Witten's black hole, being studied by Dijkgraaf,
Verlinde and Verlinde. In addition, there appear ghost-related terms as a part
of the background in the world sheet action.


  Cubic hafnia (HfO$_2$) is of great interest for a number of applications in
electronics because of its high dielectric constant. However, common defects in
such applications degrade the properties of hafina. We have investigated the
electronic properties of oxygen vacancies and nitrogen substitution in cubic
HfO$_2$ using first principles calculations based on density functional theory
(DFT) and many-body diffusion Monte Carlo (DMC) methods. We investigate five
different charge defect states of oxygen vacancies, as well as substitutional N
defects which can lead to local magnetic moments. Both DMC and DFT calculations
shows that an oxygen vacancy induces strong lattice relaxations around the
defect. Finally, we compare defect formation energies, charge and spin
densities obtained from DMC with results obtained using DFT. While the obtained
formation energies from DMC are 0.6~eV -- 1.5~eV larger than those from GGA+U,
the agreement for the most important defects, neutral and positively charged
oxygen vacancies, and nitrogen substitutional defect, under oxygen-poor
conditions are in reasonably good agreement. Our work confirms that nitrogen
can act to passivate cubic hafnia for applications in electronics.


  Subfactor theory provides a tool to analyze and construct extensions of
Quantum Field Theories, once the latter are formulated as local nets of von
Neumann algebras. We generalize some of the results of [LR95] to the case of
extensions with infinite Jones index. This case naturally arises in physics,
the canonical examples are given by global gauge theories with respect to a
compact (non-finite) group of internal symmetries. Building on the works of
Izumi, Longo, Popa [ILP98] and Fidaleo, Isola [FI99], we consider generalized
Q-systems (of intertwiners) for a semidiscrete inclusion of properly infinite
von Neumann algebras, which generalize ordinary Q-systems introduced by Longo
[Lon94] to the infinite index case. We characterize inclusions which admit
generalized Q-systems of intertwiners and define a braided product among the
latter, hence we construct examples of QFTs with defects (phase boundaries) of
infinite index, extending the family of boundaries in the grasp of [BKLR16].


  Background. The COVID-19 pandemic triggered a widespread transition to hybrid
work models (combinations of co-located and remote work) as software
professionals' demanded more flexibility and improved work-life balance.
However, hybrid work models reduce the spontaneous, informal face-to-face
interactions that promote group maturation, cohesion, and resilience. Little is
known about how software companies can successfully transition to a hybrid
workforce or the factors that influence the resilience of hybrid software
development teams. Goal. The purpose of this study is to explore the
relationship between hybrid work and team resilience in the context of software
development. Method. Constructivist Grounded Theory was used, based on
interviews of 26 software professionals. This sample included professionals of
different genders, ethnicities, sexual orientations, and levels of experience.
Interviewees came from eight different companies, 22 different projects, and
four different countries. Consistent with grounded theory methodology, data
collection, and analysis were conducted iteratively, in waves, using
theoretical sampling, constant comparison, and initial, focused, and
theoretical coding. Results. Software Team Resilience is the ability of a group
of software professionals to continue working together effectively under
adverse conditions. Resilience depends on the group's maturity. The
configuration of a hybrid team (who works where and when) can promote or hinder
group maturity depending on the level of intra-group interaction it supports.
Conclusion. This paper presents the first study on the resilience of hybrid
software teams. Software teams need resilience to maintain their performance in
the face of disruptions and crises. Software professionals strongly value
hybrid work; therefore, team resilience is a key factor to be considered in the
software industry.


  In this paper we combine WFC3/UVIS F275W, F336W, and F438W data from the "UV
Legacy Survey of Galactic Globular Clusters: Shedding Light on Their
Populations and Formation" (GO-13297) HST Treasury program with F606W, F625W,
F658N, and F814W ACS archive data for a multi-wavelength study of the globular
cluster NGC 6352. In the color-magnitude and two-color diagrams obtained with
appropriate combination of the photometry in the different bands we separate
two distinct stellar populations and trace them from the main sequence to the
subgiant, red giant, horizontal and asymptotic giant branches. We infer that
the two populations differ in He by Delta Y=0.029+/-0.006. With a new method,
we also estimate the age difference between the two sequences. Assuming no
difference in [Fe/H] and [alpha/Fe], and the uncertainties on Delta Y, we found
a difference in age between the two populations of 10+/-120 Myr. If we assume
[Fe/H] and [alpha/Fe] differences of 0.02 dex (well within the uncertainties of
spectroscopic measurements), the total uncertainty in the relative age rises to
~300 Myr.


  We report the discovery and analysis of the follow-up ROSAT pointed
observation, an ASCA observation and optical and radio observations of the
enigmatic soft X-ray AGN RX J0134.2-4258. In the optical, RX J0134.2-4258
appears as an extreme 'Narrow-Line Seyfert 1 galaxy' (NLS1), with very strong
FeII emission, very blue optical continuum spectrum and almost no Narrow Line
Region emission. While its spectrum was one of the softest observed from an AGN
during the ROSAT All-Sky Survey, its spectrum was found to be dramatically
harder during a pointed observation although the count rate remained constant.
We found in the pointed observation that the spectrum is softer when it is
fainter and spectral fitting demonstrates that it is the hard component that is
variable. The ASCA observation confirms the presence of a hard X-ray power law,
the slope of which is rather flat compared with other NLS1s. Survey and
followup radio observations reveal that RX J0134.2-4258 is also unusual in that
it is a member of the rare class of radio-loud NLS1s, and, with R=71, it holds
the current record for largest radio-to-optical ratio in NLS1s. We discuss
possible scenarios to explain its strange behaviour.


  For many practical problems and applications, it is not feasible to create a
vast and accurately labeled dataset, which restricts the application of deep
learning in many areas. Semi-supervised learning algorithms intend to improve
performance by also leveraging unlabeled data. This is very valuable for
2D-pose estimation task where data labeling requires substantial time and is
subject to noise. This work aims to investigate if semi-supervised learning
techniques can achieve acceptable performance level that makes using these
algorithms during training justifiable. To this end, a lightweight network
architecture is introduced and mean teacher, virtual adversarial training and
pseudo-labeling algorithms are evaluated on 2D-pose estimation for surgical
instruments. For the applicability of pseudo-labelling algorithm, we propose a
novel confidence measure, total variation. Experimental results show that
utilization of semi-supervised learning improves the performance on unseen
geometries drastically while maintaining high accuracy for seen geometries. For
RMIT benchmark, our lightweight architecture outperforms state-of-the-art with
supervised learning. For Endovis benchmark, pseudo-labelling algorithm improves
the supervised baseline achieving the new state-of-the-art performance.


  The magnetic structure for the newly discovered iron-arsenide compound
\CaFeAs has been studied by neutron powder diffraction. Long-range magnetic
order is detected below 85K, with an incommensurate modulation described by the
propagation vector k=(0,$\delta$,0), $\delta\sim$ 0.39. Below $\sim$ 25K, our
measurements detect a first-order phase transition where $\delta$ locks into
the commensurate value 3/8. A model of the magnetic structure is proposed for
both temperature regimes, based on Rietveld refinements of the powder data and
symmetry considerations. The structures correspond to longitudinal
spin-density-waves with magnetic moments directed along the \textit{b}-axis. A
Landau analysis captures the change in thermodynamic quantities observed at the
two magnetic transitions, in particular the drop in resistivity at the lock-in
transition.


  Motion planning and control in autonomous car racing are one of the most
challenging and safety-critical tasks due to high speed and dynamism. The
lower-level control nodes are expected to be highly optimized due to resource
constraints of onboard embedded processing units, although there are strict
latency requirements. Some of these guarantees can be provided at the
application level, such as using ROS2's Real-Time executors. However, the
performance can be far from satisfactory as many modern control algorithms
(such as Model Predictive Control) rely on solving complicated online
optimization problems at each iteration. In this paper, we present a simple yet
effective multi-threading technique to optimize the throughput of
online-control algorithms for resource-constrained autonomous racing platforms.
We achieve this by maintaining a systematic pool of worker threads solving the
optimization problem in parallel which can improve the system performance by
reducing latency between control input commands. We further demonstrate the
effectiveness of our method using the Model Predictive Contouring Control
(MPCC) algorithm running on Nvidia's Xavier AGX platform.


  We consider the fourth order Schr\"odinger operator $H=\Delta^2+V$ and show
that if there are no eigenvalues or resonances in the absolutely continuous
spectrum of $H$ that the solution operator $e^{-itH}$ satisfies a large time
integrable $|t|^{-\frac54}$ decay rate between weighted spaces. This bound
improves what is possible for the free case in two directions; both better time
decay and smaller spatial weights. In the case of a mild resonance at zero
energy, we derive the operator-valued expansion $e^{-itH}P_{ac}(H)=t^{-\frac34}
A_0+t^{-\frac54}A_1$ where $A_0:L^1\to L^\infty$ is an operator of rank at most
four and $A_1$ maps between polynomially weighted spaces.


  Recent work has shown that convolutional networks can be substantially
deeper, more accurate, and efficient to train if they contain shorter
connections between layers close to the input and those close to the output. In
this paper, we embrace this observation and introduce the Dense Convolutional
Network (DenseNet), which connects each layer to every other layer in a
feed-forward fashion.Whereas traditional convolutional networks with L layers
have L connections - one between each layer and its subsequent layer - our
network has L(L+1)/2 direct connections. For each layer, the feature-maps of
all preceding layers are used as inputs, and its own feature-maps are used as
inputs into all subsequent layers. DenseNets have several compelling
advantages: they alleviate the vanishing-gradient problem, encourage feature
reuse and substantially improve parameter efficiency. We evaluate our proposed
architecture on four highly competitive object recognition benchmark tasks
(CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant
improvements over the state-of-the-art on most of them, whilst requiring less
parameters and computation to achieve high performance.


  We consider geodesics motion in a particular Kundt type III spacetime in
which Einstein-Yang-Mills equations admit solutions. On a particular surface as
constraint we project the geodesics into the (x,y) plane and treat the problem
as a 2-dimensional one. Our numerical study shows that chaotic behavior emerges
under reasonable conditions.


  The phenomenon of magnetic catalysis of chiral symmetry breaking in the
quantum chromodynamic theory in the framework of logarithmic quark sigma model
is studied. Thermodynamic properties are calculated in the mean-field
approximation such as the pressure, the entropy density, the energy density,
and measure interaction. The pressure, the entropy density, and the energy
density increase with increasing temperature and an external magnetic field.
The critical temperature increases with increasing an external magnetic field.
In addition, the chiral phase transition is crossover in the presence of an
external magnetic field with absent of baryonic chemical potential when
explicit symmetry breaking is included. A comparison is presented with the
original sigma model and other works. A conclusion indicates that the
logarithmic quark model enhances the magnetic catalysis phenomenon.


  We develop representation theoretic techniques to construct three dimensional
non-semisimple topological quantum field theories which model homologically
truncated topological B-twists of abelian Gaiotto-Witten theory with linear
matter. Our constructions are based on relative modular structures on the
category of weight modules over an unrolled quantization of a Lie superalgebra.
The Lie superalgebra, originally defined by Gaiotto and Witten, is associated
to a complex symplectic representation of a metric abelian Lie algebra. The
physical theories we model admit alternative realizations as
Chern-Simons-Rozansky-Witten theories and supergroup Chern-Simons theories and
include as particular examples global forms of $\mathfrak{gl}(1 \vert
1)$-Chern-Simons theory and toral Chern-Simons theory. Fundamental to our
approach is the systematic incorporation of non-genuine line operators which
source flat connections for the topological flavour symmetry of the theory.


  We develop a method for investigating the relationship between the shape of a
1-particle distribution and non-linear electrostatic oscillations in a
collisionless plasma, incorporating transverse thermal motion. A general
expression is found for the maximum sustainable electric field, and is
evaluated for a particular highly anisotropic distribution.


  Future lepton colliders will be precision machines whose physics program
includes close study of the Higgs sector and searches for new physics via
polarised beams. The luminosity requirements of such machines entail very
intense lepton bunches at the interaction point with associated strong
electromagnetic fields. These strong fields not only lead to obvious phenomena
such as beamstrahlung, but also potentially affect every particle physics
process via virtual exchange with the bunch fields. For precision studies,
strong field effects have to be understood to the sub-percent level. Strong
external field effects can be taken into account exactly via the Furry Picture
or, in certain limits, via the Quasi-classical Operator method . Significant
theoretical development is in progress and here we outline the current state of
play.


  We derive dispersive representations for the anomalous process gamma pi -->
pi pi with the pi pi P-wave phase shift as input. We investigate how in this
framework the chiral anomaly can be extracted from a cross-section measurement
using all data up to 1 GeV, and discuss the importance of a precise
representation of the gamma pi --> pi pi amplitude for the hadronic
light-by-light contribution to the anomalous magnetic moment of the muon.


  We show that we can interpret concatenation theories in arithmetical theories
without coding sequences.


  Context: Diffuse radio emission, in the form of radio halos and relics,
traces regions in clusters with shocks or turbulence, probably produced by
cluster mergers. Some models of diffuse radio emission in clusters indicate
that virtually all clusters should contain diffuse radio sources with a steep
spectrum. External accretion shocks associated with filamentary structures of
galaxies could also accelerate electrons to relativistic energies and hence
produce diffuse synchrotron emitting regions. Here we report on Giant Metrewave
Radio Telescope (GMRT) observations of a sample of steep spectrum sources from
the 74 MHz VLSS survey. These sources are diffuse and not associated with
nearby galaxies.
  Aims: The main aim of the observations is to search for diffuse radio
emission associated with galaxy clusters or the cosmic web.
  Methods: We carried out GMRT 610 MHz continuum observations of unidentified
diffuse steep spectrum sources.
  Results: We have constructed a sample of diffuse steep spectrum sources,
selected from the 74 MHz VLSS survey. We identified eight diffuse radio sources
probably all located in clusters. We found five radio relics, one cluster with
a giant radio halo and a radio relic, and one radio mini-halo. By complementing
our observations with measurements from the literature we find correlations
between the physical size of relics and the spectral index, in the sense that
smaller relics have steeper spectra. Furthermore, larger relics are mostly
located in the outskirts of clusters while smaller relics are located closer to
the cluster center.


  Because the LHC is a proton-proton collider, sizable production of two
positively charged W-bosons in association with two jets is possible. This
process leads to a distinct signature of same sign high-pt leptons, missing
energy and jets. We compute the NLO QCD corrections to the QCD-mediated part of
pp -> W+W+jj. These corrections reduce the dependence of the production
cross-section on the renormalization and factorization scale to about +- 10
percent. We find that a large number of W+W+jj events contain a relatively hard
third jet. The presence of this jet should help to either pick up the W+W+jj
signal or to reject it as an unwanted background.


  This work presents the design, implementation and validation of learning
techniques based on the kNN scheme for gesture detection in prosthetic control.
To cope with high computational demands in instance-based prediction, methods
of dataset reduction are evaluated considering real-time determinism to allow
for the reliable integration into battery-powered portable devices. The
influence of parameterization and varying proportionality schemes is analyzed,
utilizing an eight-channel-sEMG armband. Besides offline cross-validation
accuracy, success rates in real-time pilot experiments (online target
achievement tests) are determined. Based on the assessment of specific dataset
reduction techniques' adequacy for embedded control applications regarding
accuracy and timing behaviour, Decision Surface Mapping (DSM) proves itself
promising when applying kNN on the reduced set. A randomized, double-blind user
study was conducted to evaluate the respective methods (kNN and kNN with
DSM-reduction) against Ridge Regression (RR) and RR with Random Fourier
Features (RR-RFF). The kNN-based methods performed significantly better
(p<0.0005) than the regression techniques. Between DSM-kNN and kNN, there was
no statistically significant difference (significance level 0.05). This is
remarkable in consideration of only one sample per class in the reduced set,
thus yielding a reduction rate of over 99% while preserving success rate. The
same behaviour could be confirmed in an extended user study. With k=1, which
turned out to be an excellent choice, the runtime complexity of both kNN (in
every prediction step) as well as DSM-kNN (in the training phase) becomes
linear concerning the number of original samples, favouring dependable wearable
prosthesis applications.


  Let $X$ be a measure space and $T:X\to X$ a measurable transformation. For
any measurable $E\subseteq X$ and $x\in E$, the possibly infinite return time
is $n_E(x):=\inf\{n>0: T^n x\in E\}$. If $T$ is an ergodic tranformation of the
probability space $X$, and $\mu(E)>0$, then a theorem of M. Kac states that
$\int_E n_E d\mu=1$. We generalize this to any invertible measure preserving
transformation $T$ on a finite measure space $X$, by proving independently, and
nearly trivially that for any measurable $E\subseteq X$ one has $\int_E n_E
d\mu=\mu(I_E)$, where $I_E$ is the smallest invariant set containing $E$. In
particular this also provides a simpler proof of Poincar\'{e}'s recurrence
theorem.


  We have measured the field dependence of the Zero Bias Conductance Peak
(ZBCP) of tunnel junctions prepared on (110) oriented Y1Ba2Cu3O7-d films of
various thickness. We have found that the field splitting of the ZBCP, believed
to be due to the Time Reversal Symmetry Breaking (TRSB) of Andreev surface
bound states, is independent of film thickness. This rules out that Meissner
screening currents are at the origin of TRSB. We conclude that TRSB must,
instead, be due to a field-induced modification of the Order parameter.
Different possibilities involving an imaginary component are discussed.


  We study spectrum inclusion regions for complex Jacobi matrices which are
compact perturbations of real periodic Jacobi matrices. The condition
sufficient for the lack of discrete spectrum for such matrices is given


  Multi-Access Edge Computing (MEC) is a paradigm for handling delay sensitive
services that require ultra-low latency at the access network. With it,
computing and communications are performed within one Base Station (BS) site,
where the computation resources are in the form of Virtual Machines (VMs)
(computer emulators) in the MEC server. MEC and Energy Harvesting (EH) BSs,
i.e., BSs equipped with EH equipments, are foreseen as a key towards
next-generation mobile networks. In fact, EH systems are expected to decrease
the energy drained from the electricity grid and facilitate the deployment of
BSs in remote places, extending network coverage and making energy
self-sufficiency possible in remote/rural sites. In this paper, we propose an
online optimization algorithm called ENergy Aware and Adaptive Management
(ENAAM), for managing remote BS sites through foresighted control policies
exploiting (short-term) traffic load and harvested energy forecasts. Our
numerical results reveal that ENAAM achieves energy savings with respect to the
case where no energy management is applied, ranging from 56% to 66% through the
scaling of computing resources, and keeps the server utilization factor between
30% and 96% over time (with an average of 75%). Notable benefits are also found
against heuristic energy management techniques.


  Recent successful deep reinforcement learning algorithms, such as Trust
Region Policy Optimization (TRPO) or Proximal Policy Optimization (PPO), are
fundamentally variations of conservative policy iteration (CPI). These
algorithms iterate policy evaluation followed by a softened policy improvement
step. As so, they are naturally on-policy. In this paper, we propose to combine
(any kind of) soft greediness with Modified Policy Iteration (MPI). The
proposed abstract framework applies repeatedly: (i) a partial policy evaluation
step that allows off-policy learning and (ii) any softened greedy step. Our
contribution can be seen as a new generic tool for the deep reinforcement
learning toolbox. As a proof of concept, we instantiate this framework with the
PPO greediness. Comparison to the original PPO shows that our algorithm is much
more sample efficient. We also show that it is competitive with the
state-of-art off-policy algorithm Soft Actor Critic (SAC).


  Designing an autonomous precise controller for ships requires accurate and
reliable ship models, including the ship dynamic model and actuator model.
However, selecting a suitable model for controller design and determining its
parameters pose a significant challenge, considering factors such as ship
actuation, input constraints, environmental disturbances, and others. This
challenge is further amplified for underactuated ships, as obtaining decoupled
experiment data is not feasible, and the limited data available may not
adequately represent the motion characteristics of ships. To address this
issue, we propose a novel model-based parameter estimation approach, called
MBPE-LOGO, for underactuated ship motion models. This method combines local
optimization and global optimization methods to solve the model identification
problem using a dataset generated from real experiments. The effectiveness of
the identified model is verified through extensive comparisons of different
trajectories and prediction steps.


  We show that the structure factor $S(q)$ of water can be obtained from X-ray
synchrotron experiments at grazing angle of incidence (in reflection mode) by
using a liquid surface diffractometer. The corrections used to obtain $S(q)$
self-consistently are described. Applying these corrections to scans at
different incident beam angles (above the critical angle) collapses the
measured intensities into a single master curve, without fitting parameters,
which within a scale factor yields $S(q)$. Performing the measurements below
the critical angle for total reflectivity yields the structure factor of the
top most layers of the water/vapor interface. Our results indicate water
restructuring at the vapor/water interface. We also introduce a new approach to
extract $g(r)$, the pair distribution function (PDF), by expressing the PDF as
a linear sum of Error functions whose parameters are refined by applying a
non-linear least square fit method. This approach enables a straightforward
determination of the inherent uncertainties in the PDF. Implications of our
results to previously measured and theoretical predictions of the PDF are also
discussed.


  The recent development of subwavelength photonic and phononic crystals shows
the possibility of controlling wave propagation at deep subwavelength scales.
Subwavelength bandgap phononic crystals are typically created using a periodic
arrangement of subwavelength resonators, in our case small gas bubbles in a
liquid. In this work, a waveguide is created by modifying the sizes of the
bubbles along a line in a dilute two-dimensional bubbly crystal, thereby
creating a line defect. Our aim is to prove that the line defect indeed acts as
a waveguide; waves of certain frequencies will be localized to, and guided
along, the line defect. The key result is an original formula for the
frequencies of the defect modes. Moreover, these frequencies are numerically
computed using the multipole method, which numerically illustrates our main
results.


  The paper deals with a problem of control of a system characterized by the
fact that the influence of controls on the dynamics of certain functions of
state variables (called observables) is relatively weak and the rates of change
of these observables are much slower than the rates of change of the state
variables themselves. The contributions of the paper are twofold. Firstly, the
averaged system whose solutions approximate the trajectories of the slow
observables is introduced, and it is shown that the optimal value of the
problem of optimal control with time discounting criterion considered on the
solutions of the system with slow observables (this problem is referred to as
perturbed) converges to the optimal value of the corresponding problem of
optimal control of the averaged system. Secondly, a way how an asymptotically
optimal control of the perturbed problem can be constructed on the basis of an
optimal solution of the averaged problem is indicated, sufficient and necessary
optimality conditions for the averaged problem are stated, and a way how a near
optimal solution of the latter can be constructed numerically is outlined (the
construction being illustrated with an example).


  We investigate analytically and numerically the asymptotic behavior of the
Nielsen-Olesen vortex solutions and show that they approach their asymptotic
values exponentially but with exponents that differ from the ones quoted in the
literature. In particular, it is shown that for values of the Higgs
self-coupling that are larger than a critical value, both the Higgs field and
the gauge field approach exponentially their asymptotic values with {\it equal}
exponents that are {\it independent} of the Higgs self-coupling.


  Obtaining the exciton dynamics of large photosynthetic complexes by using
mixed quantum mechanics/molecular mechanics (QM/MM) is computationally
demanding. We propose a machine learning technique, multi-layer perceptrons, as
a tool to reduce the time required to compute excited state energies. With this
approach we predict time-dependent density functional theory (TDDFT) excited
state energies of bacteriochlorophylls in the Fenna-Matthews-Olson (FMO)
complex. Additionally we compute spectral densities and exciton populations
from the predictions. Different methods to determine multi-layer perceptron
training sets are introduced, leading to several initial data selections. In
addition, we compute spectral densities and exciton populations. Once
multi-layer perceptrons are trained, predicting excited state energies was
found to be significantly faster than the corresponding QM/MM calculations. We
showed that multi-layer perceptrons can successfully reproduce the energies of
QM/MM calculations to a high degree of accuracy with prediction errors
contained within 0.01 eV (0.5%). Spectral densities and exciton dynamics are
also in agreement with the TDDFT results. The acceleration and accurate
prediction of dynamics strongly encourage the combination of machine learning
techniques with ab-initio methods.


  We extend the Global Compactness result by M. Struwe (Math. Z, 1984) to any
fractional Sobolev spaces $\dot{H}^s(\Omega)$ for $0<s<N/2$ and $\Omega \subset
\mathbb{R}^N$ a bounded domain with smooth boundary. The proof is a simple
direct consequence of the so-called Profile Decomposition of P. Gerard (ESAIM:
Control, Optimisation and Calculus of Variations, 1998).


  This article studies a parabolic-elliptic system modelling the pattern
formation in E. coli bacteria in response to a chemoattractant known as
acylhomoserine lactone concentration (AHL). The system takes into account
certain bacterial strains with motility regulation, and the parameters of the
equations represent the bacterial logistic growth, AHL diffusion and the rates
of production and degradation of AHL. We consider the numerical solution to the
system using the Generalized Finite Difference (GFD) Method, a meshless method
known to effectively compute numerical solutions to nonlinear problems. The
paper is organized to first explain the derivation of the explicit formulae of
the method, followed by the study of the convergence of the explicit scheme.
Then, several examples over regular and irregular meshes are given.


  Generalising the well-known correspondence between two-sided adjunctions and
Frobenius algebras, we establish a one-to-one correspondence between local
adjunctions of C*-correspondences, as defined and studied in prior work with P.
Clare and N. Higson; and Frobenius C*-algebras, a natural C*-algebraic
adaptation of the standard notion of Frobenius algebras that we introduce here.


  Optical vortices as topological objects exist ubiquitously in nature. In this
paper, by making use of the $\phi$-mapping topological current theory, we
investigate the topology in the closed and knotted optical vortices. The
topological inner structure of the optical vortices are obtained, and the
linking of the knotted optical vortices is also given.


  In their original study of conformal gravity, a candidate alternate
gravitational theory, Mannheim and Kazanas showed that in any empty vacuum
region exterior to a localized static spherically symmetric gravitational
source, the geometry would reduce to the standard attractive gravity
Schwarzschild geometry on solar system distance scales. In a recent paper
Flanagan has argued that this would not be the case if the source has
associated with it a macroscopic scalar field which makes a non-zero
contribution to the energy-momentum tensor in the otherwise empty exterior
region. In this paper we examine Flanagan's analysis and show that even with
such long range scalar fields, the standard Schwarzschild phenomenology is
still recovered.


  We extend the relation between random matrices and free probability theory
from the level of expectations to the level of fluctuations. We show how the
concept of "second order freeness", which was introduced in Part I, allows one
to understand global fluctuations of Haar distributed unitary random matrices.
In particular, independence between the unitary ensemble and another ensemble
goes in the large $N$ limit over into asymptotic second order freeness. Two
important consequences of our general theory are: (i) we obtain a natural
generalization of a theorem of Diaconis and Shahshahani to the case of several
independent unitary matrices; (ii) we can show that global fluctuations in
unitarily invariant multi-matrix models are not universal.


  Predictive modeling plays key role in providing accurate prognosis and
enables us to take a step closer to personalized treatment. We identified two
potential sources of human induced biases that can lead to disparate
conclusions. We illustrate through a complex phenotype that robust results can
still be drawn after accounting for such biases.
  Often predictive models build based in high dimensional data suffers from the
drawback of lack of interpretability. To achieve interpretability in the form
of description of the organism level phenomena in term of molecular or cellular
level activities, functional and pathway information is often augmented.
Functional information can greatly facilitate the interpretation of the results
of the predictive model.
  However an important aspect of (vertical) data augmentation is routinely
ignored, that is there could be several stages of analysis where such
information could be meaningfully integrated. There is no know criteria to
enable us to assess the effect of such augmentation. A novel aspect of the
proposed work is in exploring possibilities of stages of analysis where
functional information may be incorporated and in assessing the extent to which
the ultimate conclusions would differ depending on level of amalgamation.
  To boost our confidence on the key findings a first level of meta-analysis is
done by exploring different levels of data augmentation. This is followed by
comparison of predictive models across different definitions of the same
phenotype developed by different groups, which is also an extended form of
meta-analysis.
  We have used real life data on a complex phenotype to illustrate the above.
The data pertains to Chronic Fatigue Syndrome (CFS) and another novel aspect of
the current work is in modeling the underlying continuous symptom measurements
for CFS, which is the first for this disease to our knowledge.


  Recently, the semiclassical theory of the anomalous Hall effect induced by
the Berry curvature in Bloch bands has been introduced. The theory operates
only with gauge invariant concepts, that have a simple semiclassical
interpretation and provides a clear distinction among various contributions to
the Hall current. While the construction of such an approach to the anomalous
Hall effect problem has been long sought, only the new semiclassical theory
demonstrated the agreement with quantitative results of rigorous approaches
based on the Green function techniques. The purpose of this work is to review
the semiclassical approach including the early ideas and the recent
achievements.


  An approach is presented treating decision theory as a probabilistic theory
based on quantum techniques. Accurate definitions are given and thorough
analysis is accomplished for the quantum probabilities describing the choice
between separate alternatives, sequential alternatives characterizing
conditional quantum probabilities, and behavioral quantum probabilities taking
into account rational-irrational duality of decision making. The comparison
between quantum and classical probabilities is explained. The analysis
demonstrates that quantum probabilities serve as an essentially more powerful
tool of characterizing various decision-making situations including the
influence of psychological behavioral effects.


  Following the canonical quantization procedure for a tachyon field, the usual
Hamiltonian and the creation and annihilation operators are obtained. The
observation that the mass hyperboloid $p^2-m^2=0$ is one-sheeted, as opposed to
the case of bradyons where $p^2+m^2=0$ is two-sheeted, leads to the
construction of a base which is unbounded for negative as well as for positive
energies. There is a zero-energy eigenfunction from which all other states can
be constructed by repeated application of decreasing or increasing operators,
within this Fock space the vacuum expectation value of the chronological
product of field operators is shown to coincide with Cauchy's principal-value
Green's function.


  This paper studies a spectrum sharing scenario between a cooperative relay
network (CRN) and a nearby ad-hoc network. In particular, we consider a dynamic
spectrum access and resource allocation problem of the CRN. Based on sensing
and predicting the ad-hoc transmission behaviors, the ergodic traffic collision
time between the CRN and ad-hoc network is minimized subject to an ergodic
uplink throughput requirement for the CRN. We focus on real-time implementation
of spectrum sharing policy under practical computation and signaling
limitations. In our spectrum sharing policy, most computation tasks are
accomplished off-line. Hence, little real-time calculation is required which
fits the requirement of practical applications. Moreover, the signaling
procedure and computation process are designed carefully to reduce the time
delay between spectrum sensing and data transmission, which is crucial for
enhancing the accuracy of traffic prediction and improving the performance of
interference mitigation. The benefits of spectrum sensing and cooperative relay
techniques are demonstrated by our numerical experiments.


  We explore the rigidity of generic frameworks in 3-dimensions whose
underlying graph is close to being planar. Specifically we consider apex
graphs, edge-apex graphs and their variants and prove independence results in
the generic 3-dimensional rigidity matroid adding to the short list of graph
classes for which 3-dimensional rigidity is understood. We then analyse global
rigidity for these graph classes and use our results to deduce bounds on the
maximum likelihood threshold of graphs in these nearly planar classes.


  Efficient and effective drug-target binding affinity (DTBA) prediction is a
challenging task due to the limited computational resources in practical
applications and is a crucial basis for drug screening. Inspired by the good
representation ability of graph neural networks (GNNs), we propose a
simple-structured GNN model named SS-GNN to accurately predict DTBA. By
constructing a single undirected graph based on a distance threshold to
represent protein-ligand interactions, the scale of the graph data is greatly
reduced. Moreover, ignoring covalent bonds in the protein further reduces the
computational cost of the model. The GNN-MLP module takes the latent feature
extraction of atoms and edges in the graph as two mutually independent
processes. We also develop an edge-based atom-pair feature aggregation method
to represent complex interactions and a graph pooling-based method to predict
the binding affinity of the complex. We achieve state-of-the-art prediction
performance using a simple model (with only 0.6M parameters) without
introducing complicated geometric feature descriptions. SS-GNN achieves
Pearson's Rp=0.853 on the PDBbind v2016 core set, outperforming
state-of-the-art GNN-based methods by 5.2%. Moreover, the simplified model
structure and concise data processing procedure improve the prediction
efficiency of the model. For a typical protein-ligand complex, affinity
prediction takes only 0.2 ms. All codes are freely accessible at
https://github.com/xianyuco/SS-GNN.


  We study the real and imaginary parts of the powers of the Volterra operator
on $L^2[0,1]$, specifically their eigenvalues, their norms and their numerical
ranges.


  The atmospheric aerosol monitoring system of the Pierre Auger Observatory has
been operating smoothly since 2004. Two laser facilities (Central Laser
Facility, CLF and eXtreme Laser Facility, XLF) fire sets of 50 shots four times
per hour during FD shifts to measure the highly variable hourly aerosol
attenuation to correct the longitudinal UV light profiles of the Extensive Air
Showers detected by the Fluorescence Detector. Hourly aerosol attenuation loads
(Vertical Aerosol Optical Depth) are used to correct the measured profiles. Two
techniques are used to determine the aerosol profiles, which have been proven
to be fully compatible. The uncertainty in the VAOD profiles measured
consequently leads to an uncertainty on the energy and on the estimation of the
depth at the maximum development of a shower (X max ) of the event in analysis.
To prove the validity of the aerosol attenuation measurements used in FD event
analysis, the flatness of the ratio of reconstructed SD to FD energy as a
function of the aerosol transmission to the depth of shower maximum has been
verified.


  In this article we study the field of Hilbertian metrics and positive definit
(pd) kernels on probability measures, they have a real interest in kernel
methods. Firstly we will make a study based on the Alpha-Beta-divergence to
have a Hilbercan metric by proposing an improvement of this divergence by
constructing it so that its is symmetrical the Alpha-Beta-Symmetric-divergence
(ABS-divergence) and also do some studies on these properties but also propose
the kernels associated with this divergence. Secondly we will do mumerical
studies incorporating all proposed metrics/kernels into support vector machine
(SVM). Finally we presented a algorithm for image classification by using our
divergence.


  The consequences of natural disasters, such as earthquakes, are evident:
death, coordination problems, destruction of infrastructure, and displacement
of population. However, according to empirical research, the impact of a
natural disaster on economic activity is mixed. Natural disasters could have
significant economic effects, especially in developing economies. This is
particularly important for highly seismic countries such as Chile and New
Zealand. This paper contributes to the literature on natural disasters and
economic development by analyzing the cases of two affected regions within
these countries in the wake of major earthquakes experienced during 2010-2011:
Maule (Chile) and Canterbury (New Zealand). We examine the impact of natural
disasters on GDP per capita by applying the synthetic control method. Using the
synthetic approach, we assess the effects of these two earthquakes by building
counterfactuals to compare their recovery trajectories. We find that Chile and
New Zealand experienced opposite economic effects. The Canterbury region grew
10% more in three years than its synthetic counterfactual without the
earthquake, while the Maule region declined by 5%. We build synthetic controls
at a regional and economic-sector level, looking at aggregated and sectoral
effects. The difference in institutions, such as property rights and the large
amount of government spending given for reconstruction after the New Zealand
earthquake relative to Chile's, help to explain the difference in outcomes.


  A phenomenon of a periodic staircase of macroscopic jumps in the admitted
magnetic field has been observed, as the magnitude of an externally applied
magnetic field is smoothly increased or decreased upon a superconducting (SC)
loop of type II niobium-titanium wire which is coated with a
non-superconducting layer of copper. Large temperature spikes were observed to
occur simultaneously with the jumps, suggesting brief transitions to the normal
state, caused by en masse motions of Abrikosov vortices. An experiment that
exploits this phenomenon to explore the global decoherence of a large
superconducting system will be discussed, and preliminary data will be
presented. Though further experimentation is required to determine the actual
decoherence rate across the superconducting system, multiple classical
processes are ruled out, suggesting that jumps in magnetic flux are fully
quantum mechanical processes which may correspond to large group velocities
within the global Cooper pair wavefunction.


  The dust abundance of the interstellar medium plays an important role in
galaxy physics, the chemical evolution of matter and the absorption and
re-emission of stellar light. The last years have seen a surge in observational
and theoretical studies constraining the dust-abundance of galaxies up to
$z\sim5$. In this work we gather the latest observational measurements (with a
focus on absorption studies covering metallicities in the range $6.8 < 12 +
\log{(O/H)}<9$) and theoretical predictions (from six different galaxy
formation models) for the dust-to-gas (DTG) and dust-to-metal (DTM) ratio of
galaxies. The observed trend between DTG and DTM and gas-phase metallicity can
be described by a linear relation and shows no evolution from $0<z<5$.
Importantly, the fit to the DTG-metallicity relation provides a refined tool
for robust dust-based gas mass estimates inferred from millimeter
dust-continuum observations. The lack of evolution in the observed relations
are indicative of a quickly reached balance (already when the Universe was 1.2
Gyr old) between the formation and destruction of dust and a constant timescale
for star-formation at fixed metallicities over cosmic time. None of the models
is able to reproduce the observed trends over the entire range in metallicity
and redshift probed. The comparison between models and simulations furthermore
rules out some of the current implementations for the growth and destruction of
dust in galaxy formation models and places tight constraints on the predicted
timescale for star-formation.


  In this paper, we establish the coincidence of two classes of $L^p$-Kato
class measures in the framework of symmetric Markov processes admitting upper
and lower estimates of heat kernel under mild conditions. One class of
$L^p$-Kato class measures is defined by the $p$-th power of positive order
resolvent kernel, another is defined in terms of the $p$-th power of Green
kernel depending on some exponents related to the heat kernel estimates. We
also prove that $q$-th integrable functions on balls with radius $1$ having
uniformity of its norm with respect to centers are of $L^p$-Kato class if $q$
is greater than a constant related to $p$ and the constants appeared in the
upper and lower estimates of the heat kernel. These are complete extensions of
some results by Aizenman-Simon and the recent results by the second named
author in the framework of Brownian motions on Euclidean space. We further give
necessary and sufficient conditions for a Radon measure with Ahlfors regularity
to belong to $L^p$-Kato class. Our results can be applicable to many examples,
for instance, symmetric (relativistic) stable processes, jump processes on
$d$-sets, Brownian motions on Riemannian manifolds, diffusions on fractals and
so on.