  This paper is devoted to establishing the compactness and existence results
of the solutions to the prescribing fractional $Q$-curvatures problem of order
$2\sigma$ on $n$-dimensional standard sphere when $ n-2\sigma=2$,
$\sigma=1+m/2,$ $m\in \mathbb{N}_{+}.$ The compactness results are novel and
optimal. In addition, we prove a degree-counting formula of all solutions to
achieve the existence. From our results, we can know where blow up occur.
Furthermore, the sequence of solutions that blow up precisely at any finite
distinct location can be constructed. It is worth noting that our results
include the case of multiple harmonic.


  An additive quaternary $[n,k,d]$-code (length $n,$ quaternary dimension $k,$
minimum distance $d$) is a $2k$-dimensional F_2-vector space of $n$-tuples with
entries in $Z_2\times Z_2$ (the $2$-dimensional vector space over F_2) with
minimum Hamming distance $d.$ We determine the optimal parameters of additive
quaternary codes of dimension $k\leq 3.$ The most challenging case is dimension
$k=2.5.$ We prove that an additive quaternary $[n,2.5,d]$-code where $d<n-1$
exists if and only if $3(n-d)\geq \lceil d/2\rceil +\lceil d/4\rceil +\lceil
d/8\rceil$. In particular we construct new optimal $2.5$-dimensional additive
quaternary codes. As a by-product we give a direct proof for the fact that a
binary linear $[3m,5,2e]_2$-code for $e<m-1$ exists if and only if the Griesmer
bound $3(m-e)\geq \lceil e/2\rceil +\lceil e/4\rceil+\lceil e/8\rceil$ is
satisfied.


  The vibrational motion of molecules in dissipative environments, such as
solvation and protein molecules, is composed of contributions from both
intermolecular and intramolecular modes. The existence of these collective
modes introduces difficulty into quantum simulations of chemical and biological
processes. In order to describe the complex molecular motion of the environment
in a simple manner, we introduce a system-bath model in which the
intramolecular modes with anharmonic mode-mode couplings are described by a
system Hamiltonian, while the other degrees of freedom, arising from the
environmental molecules, are described by heat bath. Employing a
machine-learning based approach, we determine not only the system parameters of
the intramolecular modes but also the spectral distribution of the system-bath
coupling to describe the intermolecular modes, using the atomic trajectories
obtained from molecular dynamics (MD) simulations. The capabilities of the
present approach are demonstrated for liquid water using MD trajectories
calculated from the SPC/E model and the polarizable water model for
intramolecular and intermolecular vibrational spectroscopies (POLI2VS) by
determining the system parameters describing the symmetric-stretch,
asymmetric-stretch and bend modes with intramolecular interactions and the bath
spectral distribution functions for each intramolecular mode representing the
interaction with the intra-molecular modes. From these results, we were able to
elucidate the energy relaxation pathway between the intramolecular modes and
the intermolecular modes in a non-intuitive manner.


  Singlet fission, the molecular process through which photons are effectively
converted into pairs of lower energy triplet excitons, holds promise as a means
of boosting photovoltaic device efficiencies. In the preceding article of this
series, we formulated a vibronic theory of singlet fission, inspired by
previous experimental and theoretical studies suggesting that vibronic coupling
plays an important role in fission dynamics. Here, we extend our model in order
to simulate two-dimensional electronic spectra, through which the theory is
further validated based on a comparison to recent measurements on pentacene
crystals. Moreover, by means of such spectral simulations, we provide new
insights into the nature of the correlated triplet pair state, the first
product intermediate in the fission process. In particular, we address a
disagreement in the literature regarding the identification, energies, and
transition dipole moments of its optical transitions towards higher-lying
triplet states.


  We study the evolution of dwarf (L_H < 10^{9.6} L_Ho) star forming and
quiescent galaxies in the Virgo cluster by comparing their UV to radio
centimetric properties to the predictions of multizone chemo-spectrophotometric
models of galaxy evolution especially tuned to take into account the
perturbations induced by the interaction with the cluster intergalactic medium.
Our models simulate one or multiple ram pressure stripping events and galaxy
starvation. Models predict that all star forming dwarf galaxies entering the
cluster for the first time loose most, if not all, of their atomic gas content,
quenching on short time scales (< 150 Myr) their activity of star formation.
These dwarf galaxies soon become red and quiescent, gas metal-rich objects with
spectrophotometric and structural properties similar to those of dwarf
ellipticals. Young, low luminosity, high surface brightness star forming
galaxies such as late-type spirals and BCDs are probably the progenitors of
relatively massive dwarf ellipticals, while it is likely that low surface
brightness magellanic irregulars evolve into very low surface brightness
quiescent objects hardly detectable in ground based imaging surveys. The small
number of dwarf galaxies with physical properties intermediate between those of
star forming and quiescent systems is consistent with a rapid (< 1 Gyr)
transitional phase between the two dwarf galaxies populations. These results,
combined with statistical considerations, are consistent with the idea that
most of the dwarf ellipticals dominating the faint end of the Virgo luminosity
function were initially star forming systems, accreted by the cluster and
stripped of their gas by one or subsequent ram pressure stripping events.


  This paper presents a conforming finite element semi-discretization of the
streamfunction form of the one-layer unsteady quasi-geostrophic equations,
which are a commonly used model for large-scale wind-driven ocean circulation.
We derive optimal error estimates and present numerical results.


  Recent advancements in visual localization and mapping have demonstrated
considerable success in integrating point and line features. However, expanding
the localization framework to include additional mapping components frequently
results in increased demand for memory and computational resources dedicated to
matching tasks. In this study, we show how a lightweight neural network can
learn to represent both 3D point and line features, and exhibit leading pose
accuracy by harnessing the power of multiple learned mappings. Specifically, we
utilize a single transformer block to encode line features, effectively
transforming them into distinctive point-like descriptors. Subsequently, we
treat these point and line descriptor sets as distinct yet interconnected
feature sets. Through the integration of self- and cross-attention within
several graph layers, our method effectively refines each feature before
regressing 3D maps using two simple MLPs. In comprehensive experiments, our
indoor localization findings surpass those of Hloc and Limap across both
point-based and line-assisted configurations. Moreover, in outdoor scenarios,
our method secures a significant lead, marking the most considerable
enhancement over state-of-the-art learning-based methodologies. The source code
and demo videos of this work are publicly available at:
https://thpjp.github.io/pl2map/


  A novel fault-tolerant computation technique based on array Belief
Propagation (BP)-decodable XOR (BP-XOR) codes is proposed for distributed
matrix-matrix multiplication. The proposed scheme is shown to be configurable
and suited for modern hierarchical compute architectures such as Graphical
Processing Units (GPUs) equipped with multiple nodes, whereby each has many
small independent processing units with increased core-to-core communications.
The proposed scheme is shown to outperform a few of the well--known earlier
strategies in terms of total end-to-end execution time while in presence of
slow nodes, called $stragglers$. This performance advantage is due to the
careful design of array codes which distributes the encoding operation over the
cluster (slave) nodes at the expense of increased master-slave communication.
An interesting trade-off between end-to-end latency and total communication
cost is precisely described. In addition, to be able to address an identified
problem of scaling stragglers, an asymptotic version of array BP-XOR codes
based on projection geometry is proposed at the expense of some computation
overhead. A thorough latency analysis is conducted for all schemes to
demonstrate that the proposed scheme achieves order-optimal computation in both
the sublinear as well as the linear regimes in the size of the computed product
from an end-to-end delay perspective.


  The emerging age of connected, digital world means that there are tons of
data, distributed to various organizations and their databases. Since this data
can be confidential in nature, it cannot always be openly shared in seek of
artificial intelligence (AI) and machine learning (ML) solutions. Instead, we
need integration mechanisms, analogous to integration patterns in information
systems, to create multi-organization AI/ML systems. In this paper, we present
two real-world cases. First, we study integration between two organizations in
detail. Second, we address scaling of AI/ML to multi-organization context. The
setup we assume is that of continuous deployment, often referred to DevOps in
software development. When also ML components are deployed in a similar
fashion, term MLOps is used. Towards the end of the paper, we list the main
observations and draw some final conclusions. Finally, we propose some
directions for future work.


  During the period when the Sun was intensely active on October-November 2003,
two remarkable solar neutron events were observed by the ground-based neutron
monitors. On October 28, 2003, in association with an X17.2 large flare, solar
neutrons were detected with high statistical significance (6.4 sigma) by the
neutron monitor at Tsumeb, Namibia. On November 4, 2003, in association with an
X28 class flare, relativistic solar neutrons were observed by the neutron
monitors at Haleakala in Hawaii and Mexico City, and by the solar neutron
telescope at Mauna Kea in Hawaii simultaneously. Clear excesses were observed
at the same time by these detectors, with the significance calculated as 7.5
sigma for Haleakala, and 5.2 sigma for Mexico City. The detector onboard the
INTEGRAL satellite observed a high flux of hard X-rays and gamma-rays at the
same time in these events. By using the time profiles of the gamma-ray lines,
we can explain the time profile of the neutron monitor. It appears that
neutrons were produced at the same time as the gamma-ray emission.


  While emission-selected galaxy surveys are biased towards the most luminous
part of the galaxy population, absorption selection is a potentially unbiased
galaxy selection technique with respect to luminosity. However, the physical
properties of absorption-selected galaxies are not well characterised. Here we
study the excitation conditions in the interstellar medium (ISM) in damped
Ly$\alpha$ (DLA) absorption-selected galaxies. We present a study of the CO
spectral line energy distribution (SLED) in four high-metallicity
absorption-selected galaxies with previously reported CO detections at
intermediate ($z \sim 0.7$) and high ($z \sim 2$) redshifts. We find further
evidence for a wide variety of ISM conditions in these galaxies. Two out of the
four galaxies show CO SLEDs consistent with that of the Milky Way inner disk.
Interestingly, one of these galaxies is at $z \sim 2$ and has a CO SLED below
that of main-sequence galaxies at similar redshifts. The other two galaxies at
$z>2$ show more excited ISM conditions, with one of them showing thermal
excitation of the mid-$J$ (J$=3, 4$) levels, similar to that seen in two
massive main-sequence galaxies at these redshifts. Overall, we find that
absorption selection traces a diverse population of galaxies.


  Recent work of Hopkins establishes that the lattice of order ideals of a
minuscule poset satisfies the coincidental down-degree expectations property of
Reiner, Tenner, and Yong. His approach appeals to the classification of
minuscule posets. A uniform proof is presented herein.
  The blueprint follows that of Rush and Wang in their uniform proof that
various cardinality statistics are homomesic on orbits of order ideals of
minuscule posets under the Fon-Der-Flaass action. The underpinning remains the
original insight of Rush and Shi into the structure of the isomorphism between
the weight lattice of a minuscule representation of a complex simple Lie
algebra and the lattice of order ideals of the corresponding minuscule poset.


  We classify braided generalized near-group fusion categories whose global
dimension is not an integer; there are exactly two up to Grothendieck
equivalence and taking products with braided pointed fusion categories.


  We suggest a natural mapping between bipartite states and quantum evolutions
of local states, which is a Jamiolkowski map. It is shown that spatial
correlations of weak measurements in bipartite systems precisely coincide with
temporal correlations of local systems. This mapping has several practical and
conceptual implications on the correspondence between Bell and Leggett-Garg
inequalities, the statistical properties of evolutions in large systems,
temporal decoherence and computational gain, in evaluation of spatial
correlations of large systems.


  In this note, we study the infinitesimal forms of Deligne cycle class maps.
As an application, we prove that the infinitesimal form of a conjecture by
Beilinson is true.


  We report duration dependence of superconductivity in a 43 \% humidity
atmosphere and $^{59}$Co nuclear quadrupole resonance (NQR) of polycrystalline
samples of $bi$layer hydrated Na$_x$CoO$_2 \cdot y$H$_2$O. We found a reentrant
behavior of superconductivity with respect to the time duration. We obtained
the universal phase diagram where two superconducting phases and an in-between
magnetic phase are classified by $^{59}$Co NQR frequency. Zero field $^{59}$Co
nuclear spin-lattice relaxation rates $1/T_1$ indicated a magnetic critical
slowing down at 5 K in the magnetic phase, excluding a charge density wave
ordering, and an enhancement at and just above $T_\mathrm{c}$ in a new
superconducting phase, suggesting a coexistence of magnetic ordering.


  Hadron-nucleus amplitudes at high energies are studied in the "toy" Regge
model in zero transverse dimension for finite nuclei, when the standard series
of fan diagrams is converted into a finite sum and looses physical sense at
quite low energies. Taking into account all the loop contributions by numerical
methods we find a physically meaningful amplitudes at all energies. They
practically coincide with the amplitudes for infinite nuclei. A surprizing
result is that for finite nuclei and small enough triple pomeron coupling the
infinite series of fan diagrams describes the amplitude quite well in spite of
the fact that in reality the series should be cut and as such deprived of any
physical sense at high energies.


  The fluctuations in wind power entering an electrical grid (Irish grid) were
analyzed and found to exhibit correlated fluctuations with a self-similar
structure, a signature of large-scale correlations in atmospheric turbulence.
The statistical structure of temporal correlations for fluctuations in
generated and forecast time series was used to quantify two types of forecast
error: a timescale error ($e_{\tau}$) that quantifies the deviations between
the high frequency components of the forecast and the generated time series,
and a scaling error ($e_{\zeta}$) that quantifies the degree to which the
models fail to predict temporal correlations in the fluctuations of the
generated power. With no $a$ $priori$ knowledge of the forecast models, we
suggest a simple memory kernel that reduces both the timescale error
($e_{\tau}$) and the scaling error ($e_{\zeta}$).


  Stimulated by recent advances in isolating graphene, we discovered that
quantum dot can be trapped in Z-shaped graphene nanoribbon junciton. The
topological structure of the junction can confine electronic states completely.
By varying junction length, we can alter the spatial confinement and the number
of discrete levels within the junction. In addition, quantum dot can be
realized regardless of substrate induced static disorder or irregular edges of
the junction. This device can be used to easily design quantum dot devices.
This platform can also be used to design zero-dimensional functional nanoscale
electronic devices using graphene ribbons.


  We discuss the non-decoupling effects on the loop-induced $H^\pm W^\mp Z^0$
vertex in the general two Higgs-doublet model. The decay process $H^\pm \to
W^\pm Z^0$ is analyzed at one-loop level and possible enhancement of the decay
width is explored. We find that a novel enhancement can be realized by the
Higgs non-decoupling effects with large mass difference between charged and
CP-odd Higgs bosons. The branching ratio of the process can be a few % $\sim$
10%. Therefore the decay mode may be detectable at LHC or future $e^+e^-$
linear colliders (LC's).


  The PHENIX experiement has measured the electron-positron pair mass spectrum
from 0 to 8 GeV/c^2 in p+p collisions at sqrt(s)=200 GeV. The contributions
from light meson decays to e^+e^- pairs have been determined based on
measurements of hadron production cross sections by PHENIX. They account for
nearly all e^+e^- pairs in the mass region below 1 GeV/c^2. The e^+e^- pair
yield remaining after subtracting these contributions is dominated by
semileptonic decays of charmed hadrons correlated through flavor conservation.
Using the spectral shape predicted by PYTHIA, we estimate the charm production
cross section to be 544 +/- 39(stat) +/- 142(syst) +/- 200(model) \mu b, which
is consistent with QCD calculations and measurements of single leptons by
PHENIX.


  In this paper, we describe the "implicit autoencoder" (IAE), a generative
autoencoder in which both the generative path and the recognition path are
parametrized by implicit distributions. We use two generative adversarial
networks to define the reconstruction and the regularization cost functions of
the implicit autoencoder, and derive the learning rules based on
maximum-likelihood learning. Using implicit distributions allows us to learn
more expressive posterior and conditional likelihood distributions for the
autoencoder. Learning an expressive conditional likelihood distribution enables
the latent code to only capture the abstract and high-level information of the
data, while the remaining low-level information is captured by the implicit
conditional likelihood distribution. We show the applications of implicit
autoencoders in disentangling content and style information, clustering,
semi-supervised classification, learning expressive variational distributions,
and multimodal image-to-image translation from unpaired data.


  We determine the structure of $\{C_3, C_5\}$-free graphs with $n$ vertices
and minimum degree larger than $n/5$: such graphs are homomorphic to the graph
obtained from a $(5k - 3)$-cycle by adding all chords of length $1$ mod $5$,
for some $k$. This answers a question of Messuti and Schacht. We deduce that
the homomorphism threshold of $\{C_3, C_5\}$-free graphs is $1/5$, thus
answering a question of Oberkampf and Schacht.


  Recent work suggests that fundamental and Dirichlet strings, and their (p,q)
bound states, may be observed as cosmic strings. The evolution of cosmic string
networks, and therefore their observational signals, depends on what happens
when two strings collide. We study this in string perturbation theory for
collisions between all possible pairs of strings; different cases involve
sphere, disk, and annulus amplitudes. The result also depends on the details of
compactification; the dependence on ratios of scales is only logarithmic, but
this is still numerically important. We study a range of models and parameters,
and find that in most cases these strings can be distinguished from cosmic
strings that arise as gauge theory solitons.


  We report the experimental resource-efficient implementation of the
variational quantum eigensolver (VQE) using four-dimensional photonic quantum
states of single-photons. The four-dimensional quantum states are implemented
by utilizing polarization and path degrees of freedom of a single-photon. Our
photonic VQE is equipped with the quantum error mitigation (QEM) protocol that
efficiently reduces the effects of Pauli noise in the quantum processing unit.
We apply our photonic VQE to estimate the ground state energy of He--H$^{+}$
cation. The simulation and experimental results demonstrate that our
resource-efficient photonic VQE can accurately estimate the bond dissociation
curve, even in the presence of large noise in the quantum processing unit.


  We re-visit the issue of the temperature dependence of the specific heat C(T)
for interacting fermions in 1D. The charge component C_c(T) scales linearly
with T, but the spin component C_s (T) displays a more complex behavior with T
as it depends on the backscattering amplitude, g_1, which scales down under RG
transformation and eventually behaves as g_1 (T) \sim 1/\log T. We show,
however, by direct perturbative calculations that C_s(T) is strictly linear in
T to order g^2_1 as it contains the renormalized backscattering amplitude not
on the scale of T, but at the cutoff scale set by the momentum dependence of
the interaction around 2k_F. The running amplitude g_1 (T) appears only at
third order and gives rise to an extra T/\log^3 T term in C_s (T). This agrees
with the results obtained by a variety of bosonization techniques. We also show
how to obtain the same expansion in g_1 within the sine-Gordon model.


  On 3 July 2016, several instruments on board ESA's Rosetta spacecraft
detected signs of an outburst event on comet 67P, at a heliocentric distance of
3.32 AU from the sun, outbound from perihelion. We here report on the inferred
properties of the ejected dust and the surface change at the site of the
outburst. The activity coincided with the local sunrise and continued over a
time interval of 14 - 68 minutes. It left a 10m-sized icy patch on the surface.
The ejected material comprised refractory grains of several hundred microns in
size, and sub-micron-sized water ice grains. The high dust mass production rate
is incompatible with the free sublimation of crystalline water ice under solar
illumination as the only acceleration process. Additional energy stored near
the surface must have increased the gas density. We suggest a pressurized
sub-surface gas reservoir, or the crystallization of amorphous water ice as
possible causes.


  We derive a novel, provably robust, and closed-form Bayesian update rule for
online filtering in state-space models in the presence of outliers and
misspecified measurement models. Our method combines generalised Bayesian
inference with filtering methods such as the extended and ensemble Kalman
filter. We use the former to show robustness and the latter to ensure
computational efficiency in the case of nonlinear models. Our method matches or
outperforms other robust filtering methods (such as those based on variational
Bayes) at a much lower computational cost. We show this empirically on a range
of filtering problems with outlier measurements, such as object tracking, state
estimation in high-dimensional chaotic systems, and online learning of neural
networks.


  The Neggers-Stanley conjecture (also known as the Poset conjecture) asserts
that the polynomial counting the linear extensions of a partially ordered set
on $\{1,2,...,p\}$ by their number of descents has real zeros only. We provide
counterexamples to this conjecture.


  Fake news spread widely on social media in various domains, which lead to
real-world threats in many aspects like politics, disasters, and finance. Most
existing approaches focus on single-domain fake news detection (SFND), which
leads to unsatisfying performance when these methods are applied to
multi-domain fake news detection. As an emerging field, multi-domain fake news
detection (MFND) is increasingly attracting attention. However, data
distributions, such as word frequency and propagation patterns, vary from
domain to domain, namely domain shift. Facing the challenge of serious domain
shift, existing fake news detection techniques perform poorly for multi-domain
scenarios. Therefore, it is demanding to design a specialized model for MFND.
In this paper, we first design a benchmark of fake news dataset for MFND with
domain label annotated, namely Weibo21, which consists of 4,488 fake news and
4,640 real news from 9 different domains. We further propose an effective
Multi-domain Fake News Detection Model (MDFEND) by utilizing a domain gate to
aggregate multiple representations extracted by a mixture of experts. The
experiments show that MDFEND can significantly improve the performance of
multi-domain fake news detection. Our dataset and code are available at
https://github.com/kennqiang/MDFEND-Weibo21.


  The last decade has witnessed remarkable progress in the development of
quantum technologies. Although fault-tolerant devices likely remain years away,
the noisy intermediate-scale quantum devices of today may be leveraged for
other purposes. Leading candidates are variational quantum algorithms (VQAs),
which have been developed for applications including chemistry, optimization,
and machine learning, but whose implementations on quantum devices have yet to
demonstrate improvements over classical capabilities. In this Perspective, we
propose a variety of ways that the performance of VQAs could be informed by
quantum optimal control theory. To set the stage, we identify VQAs and quantum
optimal control as formulations of variational optimization at the circuit
level and pulse level, respectively, where these represent just two levels in a
broader hierarchy of abstractions that we consider. In this unified picture, we
suggest several ways that the different levels of abstraction may be connected,
in order to facilitate the application of quantum optimal control theory to VQA
challenges associated with ansatz selection, optimization landscapes, noise,
and robustness. A major theme throughout is the need for sufficient control
resources in VQA implementations; we discuss different ways this need can
manifest, outline a variety of open questions, and conclude with a look to the
future.


  We analyze the submillimeter emission surrounding the new FU Orionis-type
object, HBC 722. We present the first epoch of observations of the active
environs of HBC 722, with imaging and spectroscopy from PACS, SPIRE, and HIFI
aboard the Herschel Space Observatory, as well as CO J= 2-1 and 350 um imaging
(SHARC-II) with the Caltech Submillimeter Observatory. The primary source of
submillimeter continuum emission in the region -- 2MASS 20581767+4353310 -- is
located 16$\arcsec$ south-southeast of the optical flaring source while the
optical and near-IR emission is dominated by HBC 722. A bipolar outflow extends
over HBC 722; the most likely driver is the submillimeter source. We detect
warm (100 K) and hot (246 K) CO emission in the surrounding region, evidence of
outflow-driven heating in the vicinity. The region around HBC 722 itself shows
little evidence of heating driven by the new outbursting source itself.


  We explore the effect of magnetorotational turbulence on the dynamics and
concentrations of boulders in local box simulations of a sub-Keplerian
protoplanetary disc. The solids are treated as particles each with an
independent space coordinate and velocity. We find that the turbulence has two
effects on the solids. 1) Meter and decameter bodies are strongly concentrated,
locally up to a factor 100 times the average dust density, whereas decimeter
bodies only experience a moderate density increase. The concentrations are
located in large scale radial gas density enhancements that arise from a
combination of turbulence and shear. 2) For meter-sized boulders, the
concentrations cause the average radial drift speed to be reduced by 40%. We
find that the densest clumps of solids are gravitationally unstable under
physically reasonable values for the gas column density and for the dust-to-gas
ratio due to sedimentation. We speculate that planetesimals can form in a dust
layer that is not in itself dense enough to undergo gravitational
fragmentation, and that fragmentation happens in turbulent density fluctuations
in this sublayer.


  In this paper, we analyze the achievable rate of the uplink of a single-cell
multi-user distributed massive multiple-input-multiple-output (MIMO) system.
The multiple users are equipped with single antenna and the base station (BS)
is equipped with a large number of distributed antennas. We derive an
analytical expression for the asymptotic ergodic achievable rate of the system
under zero-forcing (ZF) detector. In particular, we consider circular antenna
array, where the distributed BS antennas are located evenly on a circle, and
derive an analytical expression and closed-form tight bounds for the achievable
rate of an arbitrarily located user. Subsequently, closed-form bounds on the
average achievable rate per user are obtained under the assumption that the
users are uniformly located in the cell. Based on the bounds, we can understand
the behavior of the system rate with respect to different parameters and find
the optimal location of the circular BS antenna array that maximizes the
average rate. Numerical results are provided to assess our analytical results
and examine the impact of the number and the location of the BS antennas, the
transmit power, and the path-loss exponent on system performance. It is shown
that circularly distributed massive MIMO system largely outperforms centralized
massive MIMO system.


  Given the extremely large pool of events and stories available, media outlets
need to focus on a subset of issues and aspects to convey to their audience.
Outlets are often accused of exhibiting a systematic bias in this selection
process, with different outlets portraying different versions of reality.
However, in the absence of objective measures and empirical evidence, the
direction and extent of systematicity remains widely disputed.
  In this paper we propose a framework based on quoting patterns for
quantifying and characterizing the degree to which media outlets exhibit
systematic bias. We apply this framework to a massive dataset of news articles
spanning the six years of Obama's presidency and all of his speeches, and
reveal that a systematic pattern does indeed emerge from the outlet's quoting
behavior. Moreover, we show that this pattern can be successfully exploited in
an unsupervised prediction setting, to determine which new quotes an outlet
will select to broadcast. By encoding bias patterns in a low-rank space we
provide an analysis of the structure of political media coverage. This reveals
a latent media bias space that aligns surprisingly well with political ideology
and outlet type. A linguistic analysis exposes striking differences across
these latent dimensions, showing how the different types of media outlets
portray different realities even when reporting on the same events. For
example, outlets mapped to the mainstream conservative side of the latent space
focus on quotes that portray a presidential persona disproportionately
characterized by negativity.


  Photometric calibration is currently the dominant source of systematic
uncertainty in exploiting type Ia supernovae to determine the nature of the
dark energy. We review our ongoing program to address this calibration
challenge by performing measurements of both the instrumental response function
and the optical transmission function of the atmosphere. A key aspect of this
approach is to complement standard star observations by using NIST-calibrated
photodiodes as a metrology foundation for optical flux measurements. We present
our first attempt to assess photometric consistency between synthetic
photometry and observations, by comparing predictions based on a
NIST-diode-based determination of the PanSTARRS-1 instrumental response and
empirical atmospheric transmission measurements, with fluxes we obtained from
observing spectrophotometric standards.


  Axial algebras are commutative algebras generated by idempotents; they
generalise associative algebras by allowing the idempotents to have additional
eigenvectors, controlled by fusion rules. If the fusion rules are
$\mathbb{Z}/2$-graded, axial algebras afford representations of transposition
groups. We consider axial representations of Weyl groups of simply-laced root
systems, which are examples of regular $3$-transposition groups. We introduce
coset axes, a special class of idempotents based on embeddings of transposition
groups, and use them to study the propagation of fusion rules in axial
algebras, for root system ${\rm A}_n$. This is related to the construction of
lattice vertex operator algebras and we show it reflects on the fusion of
modules for the Virasoro algebra when we specialise our construction.


  Aimed at geometric applications, we prove the homology cobordism invariance
of the $L^2$-betti numbers and $L^2$-signature defects associated to the class
of amenable groups lying in Strebel's class $D(R)$, which includes some
interesting infinite/finitenon-torsion-free groups. The proofs include the only
prior known condition, that $\Gamma$ is a poly-torsion-free abelian group (or
potentially a finite $p$-group.) We define a new commutator-type series which
refines Harvey's torsion-free derived series of groups, using the localizations
of groups and rings of Bousfield, Vogel, and Cohn. The series, called the local
derived series, has versions for homology with arbitrary coefficients, and
satisfies functoriality and an injectivity theorem. We combine these two new
tools to give some applications to distinct homology cobordism types within the
same simple homotopy type in higher dimensions, to concordance of knots in
three manifolds, and to spherical space forms in dimension three.


  We propose a hierarchical normalizing flow model for generating molecular
graphs. The model produces new molecular structures from a single-node graph by
recursively splitting every node into two. All operations are invertible and
can be used as plug-and-play modules. The hierarchical nature of the latent
codes allows for precise changes in the resulting graph: perturbations in the
top layer cause global structural changes, while perturbations in the
consequent layers change the resulting molecule marginally. The proposed model
outperforms existing generative graph models on the distribution learning task.
We also show successful experiments on global and constrained optimization of
chemical properties using latent codes of the model.


  We are developing a consistent method to combine tree-level event generators
for hadron collision interactions with those including one additional QCD
radiation from the initial-state partons, based on the limited leading-log
(LLL) subtraction method, aiming at an application to NLO event generators. In
this method, a boundary between non-radiative and radiative processes
necessarily appears at the factorization scale (mu_F). The radiation effects
are simulated using a parton shower (PS) in non-radiative processes. It is
therefore crucial in our method to apply a PS which well reproduces the
radiation activities evaluated from the matrix-element (ME) calculations for
radiative processes. The PS activity depends on the applied kinematics model.
In this paper we introduce two models for our simple initial-state leading-log
PS: a model similar to the "old" PYTHIA-PS and a p_T-prefixed model motivated
by ME calculations. PS simulations employing these models are tested using
W-boson production at LHC as an example. Both simulations show a smooth
matching to the LLL-subtracted W + 1 jet simulation in the p_T distribution of
W bosons, and the summed p_T spectra are stable against a variation of mu_F,
despite that the p_T-prefixed PS results in an apparently harder p_T spectrum.


  We exhibit a singly generated, semisimple commutative operator algebra with a
contractive approximate identity, such that the spectrum of the generator is a
null sequence and zero, but the algebra is not the closed linear span of the
idempotents associated with the null sequence and obtained from the analytic
functional calculus. Moreover the multiplication on the algebra is neither
compact nor weakly compact. Thus we construct a `large' operator algebra of
orthogonal idempotents, which may be viewed as a dense subalgebra of c0.


  In this paper, we present the connection of two concepts as induced
representation and partially reduced irreducible representations (PRIR) appear
in the context of port-based teleportation protocols. Namely, for a given
finite group $G$ with arbitrary subgroup $H$, we consider a particular case of
matrix irreducible representations, whose restriction to the subgroup $H$, as a
matrix representation of $H$, is completely reduced to diagonal block form with
an irreducible representation of $H$ in the blocks. The basic properties of
such representations are given. Then as an application of this concept, we show
that the spectrum of the port-based teleportation operator acting on $n$
systems is connected in a very simple way with the spectrum of the
corresponding Jucys-Murphy operator for the symmetric group $S(n-1)\subset
S(n)$. This shows on the technical level relation between teleporation and one
of the basic objects from the point of view of the representation theory of the
symmetric group. This shows a deep connection between the central object
describing properties of deterministic PBT schemes and objects appearing
naturally in the abstract representation theory of the symmetric group. In
particular, we present a new expression for the eigenvalues of the Jucys-Murphy
operators based on the irreducible characters of the symmetric group. As an
additional but not trivial result, we give also purely matrix proof of the
Frobenius reciprocity theorem for characters with explicit construction of the
unitary matrix that realizes the reduction of the natural basis of induced
representation to the reduced one.


  The rapid growth of the e-commerce market in Indonesia, making various
e-commerce companies appear and there has been high competition among them.
Marketing intelligence is an important activity to measure competitive
position. One element of marketing intelligence is to assess customer
satisfaction. Many Indonesian customers express their sense of satisfaction or
dissatisfaction towards the company through social media. Hence, using social
media data provides a new practical way to measure marketing intelligence
effort. This research performs sentiment analysis using the naive bayes
classifier classification method with TF-IDF weighting. We compare the
sentiments towards of top-3 e-commerce sites visited companies, are Bukalapak,
Tokopedia, and Elevenia. We use Twitter data for sentiment analysis because
it's faster, cheaper, and easier from both the customer and the researcher
side. The purpose of this research is to find out how to process the huge
customer sentiment Twitter to become useful information for the e-commerce
company, and which of those top-3 e-commerce companies has the highest level of
customer satisfaction. The experiment results show the method can be used to
classify customer sentiments in social media Twitter automatically and Elevenia
is the highest e-commerce with customer satisfaction.


  We show that we are able to extract the pion electromagnetic form factor by
using the recent charged pion electroproduction data from JLab and a simple
t-channel diagram. For this purpose we have performed the Q2-independent and
dependent analyses. The result of the first analysis is in good agreement with
those of previous works and fit the Maris and Tandy model as well as the
monopole parameterization which describes a pion radius of 0.672 fm. The result
of the second analysis corroborates the findings in the first analysis. Our
findings therefore provide a direct proof that at the given kinematics the
t-channel diagram really dominates the process. This could also set a new
constraint to the phenomenological models that try to describe the process.


  Conferences and workshops shape scientific discourse. The Kavli Institute for
Theoretical Physics (KITP) hosts long-term workshops to stimulate scientific
collaboration that would not otherwise have taken place. One goal of KITP
programs is to increase diversity in the next generation of scientists. In this
analysis, we examine gender trends in authorship of papers generated as a
result of the KITP program \textit{Probes of Transport in Stars}, which ran
from October 11th, 2021 to December 17th, 2021. While 38\% of workshop
participants were women, only 19\% of publications produced between December
1st, 2021 and June 3rd, 2022 had female first-authors. Further, of these early
publications, 61\% had all-male author lists. Among publications resulting from
the KITP program, the proportions of both male first-author papers and papers
with all-male author lists are higher than predicted by models that take into
account the gender distribution of the KITP participants. These results
motivate more thorough investigations of collaboration networks at scientific
conferences and workshops. Importantly, they also suggest that programs,
conferences, and workshops of any kind need to take steps beyond those
implemented in this KITP program to enable more diverse collaborations and
address gender disparities in science.


  Document clustering and topic modeling are two closely related tasks which
can mutually benefit each other. Topic modeling can project documents into a
topic space which facilitates effective document clustering. Cluster labels
discovered by document clustering can be incorporated into topic models to
extract local topics specific to each cluster and global topics shared by all
clusters. In this paper, we propose a multi-grain clustering topic model
(MGCTM) which integrates document clustering and topic modeling into a unified
framework and jointly performs the two tasks to achieve the overall best
performance. Our model tightly couples two components: a mixture component used
for discovering latent groups in document collection and a topic model
component used for mining multi-grain topics including local topics specific to
each cluster and global topics shared across clusters.We employ variational
inference to approximate the posterior of hidden variables and learn model
parameters. Experiments on two datasets demonstrate the effectiveness of our
model.


  We describe the design of a new family of high spectral brightness narrow
linewidth (NLW) mid-infrared (MIR) lasers of < 1 MHz anticipated linewidths
with potential for operation at any target wavelength between 2.5 and 9.5 um.
More specifically, we analyze the potential performance characteristics of
mid-infrared distributed feedback (DFB) Raman fiber lasers (RFLs) based on
Pi-phase-shifted (PPS) Fiber Bragg Gratings (FBGs) written in appropriately
chosen low-phonon-energy glass fibers. In particular, we calculate anticipated
threshold pump powers for optimal laser designs and pump wavelengths for single
frequency (fundamental mode) operation of specific mid-infrared DFB-RFLs
operating at chosen target wavelengths, and show that these pump powers can be
as low as a few milliWatts for MIR DFB-RFLs fabricated with appropriate
low-loss small mode area single mode fibers. As such, we clearly establish the
PPS-DFB RFL platform as a very practical approach for constructing a broad
range of narrow linewidth MIR coherent sources for numerous applications,
including proximal and remote sensing of molecules and various high spectral
brightness and long coherence length MIR applications.


  We explore liquid crystal order in a cell with a "dirty" substrate imposing a
random surface pinning. Modeling such systems by a random-field xy-model with
surface heterogeneity, we find that orientational order in the
three-dimensional system is marginally unstable to such surface pinning. We
compute the Larkin length scale, and the corresponding surface and bulk
distortions. On longer scales we calculate correlation functions using the
functional renormalization group and matching methods, finding a universal
logarithmic and double-logarithmic roughness in two and three dimensions,
respectively. For a finite thickness cell, we explore the interplay of
homogeneous-heterogeneous substrate pair and detail crossovers as a function of
disorder strength and cell thickness.


  By assuming certain analytic properties of the propagator, it is shown that
universal features of the spectral function including threshold enhancement
arise if a pole describing a particle at high temperature approaches in the
complex energy plane the threshold position of its two-body decay with the
variation of T. The case is considered, when one can disregard any other decay
processes. The quality of the proposed description is demonstrated by comparing
it with the detailed large N solution of the linear sigma model around the
pole-threshold coincidence.


  Programming or scripting languages used in real-world systems are seldom
designed with a formal semantics in mind from the outset. Therefore, developing
well-founded analysis tools for these systems requires reverse-engineering a
formal semantics as a first step. This can take months or years of effort.
  Can we (at least partially) automate this process? Though desirable,
automatically reverse-engineering semantics rules from an implementation is
very challenging, as found by Krishnamurthi et al. [2019]. In this paper, we
highlight that scaling methods with the size of the language is very difficult
due to state space explosion, so we propose to learn semantics incrementally.
We give a formalisation of Krishnamurthi et al.'s desugaring learning framework
in order to clarify the assumptions necessary for an incremental learning
algorithm to be feasible.
  We show that this reformulation allows us to extend the search space and
express rules that Krishnamurthi et al. described as challenging, while still
retaining feasibility. We evaluate enumerative synthesis as a baseline
algorithm, and demonstrate that, with our reformulation of the problem, it is
possible to learn correct desugaring rules for the example source and core
languages proposed by Krishnamurthi et al., in most cases identical to the
intended rules. In addition, with user guidance, our system was able to
synthesize rules for desugaring list comprehensions and try/catch/finally
constructs.


  Standard pretrained language models operate on sequences of subword tokens
without direct access to the characters that compose each token's string
representation. We probe the embedding layer of pretrained language models and
show that models learn the internal character composition of whole word and
subword tokens to a surprising extent, without ever seeing the characters
coupled with the tokens. Our results show that the embedding layer of RoBERTa
holds enough information to accurately spell up to a third of the vocabulary
and reach high average character ngram overlap on all token types. We further
test whether enriching subword models with additional character information can
improve language modeling, and observe that this method has a near-identical
learning curve as training without spelling-based enrichment. Overall, our
results suggest that language modeling objectives incentivize the model to
implicitly learn some notion of spelling, and that explicitly teaching the
model how to spell does not appear to enhance its performance on such tasks.


  In the standard Cold Dark Matter (CDM) theory of structure formation,
virialized minihalos (with T_vir}<10^4 K) form in abundance at high redshift
(z>6), during the cosmic ``dark ages.'' The hydrogen in these minihalos, the
first nonlinear baryonic structures to form in the universe, is mostly neutral
and sufficiently hot and dense to emit strongly at the 21-cm line. We calculate
the emission from individual minihalos and the radiation background contributed
by their combined effect. Minihalos create a ``21-cm forest'' of emission
lines. We predict that the angular fluctuations in this 21-cm background should
be detectable with the planned LOFAR and SKA radio arrays, thus providing a
direct probe of structure formation during the ``dark ages.'' Such a detection
will serve to confirm the basic CDM paradigm while constraining the shape of
the power-spectrum of primordial density fluctuations down to much smaller
scales than have previously been constrained, the onset and duration of the
reionization epoch, and the conditions which led to the first stars and
quasars. We present results here for the currently-favored, flat $\Lambda$CDM
model, for different tilts of the primordial power spectrum.


  Colored tensor models have recently burst onto the scene as a promising
conceptual and computational tool in the investigation of problems of random
geometry in dimension three and higher. We present a snapshot of the cutting
edge in this rapidly expanding research field. Colored tensor models have been
shown to share many of the properties of their direct ancestor, matrix models,
which encode a theory of fluctuating two-dimensional surfaces. These features
include the possession of Feynman graphs encoding topological spaces, a 1/N
expansion of graph amplitudes, embedded matrix models inside the tensor
structure, a resumable leading order with critical behavior and a continuum
large volume limit, Schwinger-Dyson equations satisfying a Lie algebra (akin to
the Virasoro algebra in two dimensions), non-trivial classical solutions and so
on. In this review, we give a detailed introduction of colored tensor models
and pointers to current and future research directions.


  The main result of this article is the fact that the currents defined by
Levin give a description of the polylogarithm of an abelian scheme at the
topological level. This result was a conjecture of Levin. This provides a
method to explicit the Eisenstein classes of an abelian scheme at the
topological level. These classes are of special interest since they have a
motivic origin by a theorem of Kings. In a forthcoming article, we use the main
result of this paper to prove that the Eisenstein classes of the universal
abelian scheme over an Hilbert-Blumenthal variety degenerate at the boundary of
the Baily-Borel compactification of the base in a special value of an
$L$-function associated to the underlying totally real number field. As a
corollary, we get a non vanishing result for some of these Eisenstein classes
in this geometric situation.


  Cross sections for production of 6He, 6Li, 7Li, and 7Be in the alpha+alpha
reaction were measured at bombarding energies of 159.3, 279.6, and 619.8 MeV,
and are found to decrease rapidly with increasing energy. These cross sections
are essential for the calculation of the rate of nucleosynthesis of the lithium
isotopes in the cosmic rays and thereby play a key role in our understanding of
the synthesis of Li, Be, and B. The results for 6Li differ significantly from
the tabulated values commonly used in cosmic-ray production calculations and
lead to lower production of 6Li.


  The Toronto Red-Sequence Cluster Survey (TRCS) is a new galaxy cluster survey
designed to provide a large sample of optically selected 0.1 < z < 1.4
clusters. The planned survey data is 100 square degrees of two color (R and z')
imaging, with a 5-sigma depth ~2 mag past M* at z=1. The primary scientific
drivers of the survey are a derivation of Omega_m and sigma_8 (from N(M,z) for
clusters) and a study of cluster galaxy evolution with a complete sample. This
paper gives a brief outline of the TRCS survey parameters and sketches the
methods by which we intend to pursue the main scientific goals, including an
explicit calculation of the expected survey completeness limits. Some
preliminary results from the first set of data (~6 deg^2) are also given. These
preliminary results provide new examples of rich z~1 clusters, strong cluster
lensing, and a possible filament at z~1.


  Ratios of cross sections, $\sigma$(Z+c jets) / $\sigma$(Z+jets), $\sigma$(Z+b
jets) / $\sigma$(Z+jets), and $\sigma$(Z+c jets) / $\sigma$(Z+b jets) in the
associated production of a Z boson with at least one charm or bottom quark jet
are measured in proton-proton collisions at $\sqrt{s} =$ 13 TeV. The data
sample, collected by the CMS experiment at the CERN LHC, corresponds to an
integrated luminosity of 35.9 fb$^{-1}$, with a fiducial volume of
$p_\mathrm{T}$ $>$ 30 GeV and $|\eta|$ $<$ 2.4 for the jets, where
$p_\mathrm{T}$ and $\eta$ represent transverse momentum and pseudorapidity,
respectively. The Z boson candidates come from leptonic decays into electrons
or muons with $p_\mathrm{T}$ $>$ 25 GeV and $|\eta|$ $<$ 2.4, and the dilepton
mass satisfies 71 $\lt$ $m_\mathrm{Z}$ $\lt$ 111 GeV. The measured values are
$\sigma$(Z+c jets) / $\sigma$(Z+jets) = 0.102 $\pm$ 0.002 $\pm$ 0.009,
$\sigma$(Z+b jets) / $\sigma$(Z+jets) = 0.0633 $\pm$ 0.0004 $\pm$ 0.0015, and
$\sigma$(Z+c jets) / $\sigma$(Z+b jets) = 1.62 $\pm$ 0.03 $\pm$ 0.15. Results
on the inclusive and differential cross section ratios as functions of jet and
Z boson transverse momentum are compared with predictions from leading and
next-to-leading order perturbative quantum chromodynamics calculations. These
are the first measurements of the cross section ratios at 13 TeV.


  Facing a heavy task, any single person can only make a limited contribution
and team cooperation is needed. As one enjoys the benefit of the public goods,
the potential benefits of the project are not always maximized and may be
partly wasted. By incorporating individual ability and project benefit into the
original public goods game, we study the coupling effect of the four
parameters, the upper limit of individual contribution, the upper limit of
individual benefit, the needed project cost and the upper limit of project
benefit on the evolution of cooperation. Coevolving with the individual-level
group size preferences, an increase in the upper limit of individual benefit
promotes cooperation while an increase in the upper limit of individual
contribution inhibits cooperation. The coupling of the upper limit of
individual contribution and the needed project cost determines the critical
point of the upper limit of project benefit, where the equilibrium frequency of
cooperators reaches its highest level. Above the critical point, an increase in
the upper limit of project benefit inhibits cooperation. The evolution of
cooperation is closely related to the preferred group-size distribution. A
functional relation between the frequency of cooperators and the dominant group
size is found.


  The growing study of time series, especially those related to nonlinear
systems, has challenged the methodologies to characterize and classify
dynamical structures of a signal. Here we conceive a new diagnostic tool for
time series based on the concept of information entropy, in which the
probabilities are associated to microstates defined from the recurrence phase
space. Recurrence properties can properly be studied using recurrence plots, a
methodology based on binary matrices where trajec- tories in phase space of
dynamical systems are evaluated against other embedded trajectory. Our novel
entropy methodology has several advantages compared to the traditional
recurrence entropy defined in the literature, namely, the correct evaluation of
the chaoticity level of the signal, the weak dependence on parameters, correct
evaluation of periodic time series properties and more sensitivity to noise
level of time series. Furthermore, the new entropy quantifier developed in this
manuscript also fixes inconsistent results of the traditional recurrence
entropy concept, reproducing classical results with novel insights.


  Two-dimensional single-crystal metals are highly sought after for
next-generation technologies. Here, we report large-area (>10^4 {\mu}m2),
single-crystal two-dimensional gold with thicknesses down to a single-nanometer
level, employing an atomic-level-precision chemical etching approach. The
ultrathin thickness and single-crystal quality endow two-dimensional gold with
unique properties including significantly quantum-confinement-augmented optical
nonlinearity, low sheet resistance, high transparency and excellent mechanical
flexibility. By patterning the two-dimensional gold into nanoribbon arrays,
extremely-confined near-infrared plasmonic resonances are further demonstrated
with quality factors up to 5. The freestanding nature of two-dimensional gold
allows its straightforward manipulation and transfer-printing for integration
with other structures. The developed two-dimensional gold provides an emerging
platform for fundamental studies in various disciplines and opens up new
opportunities for applications in high-performance ultrathin optoelectronic,
photonic and quantum devices.


  Future human action forecasting from partial observations of activities is an
important problem in many practical applications such as assistive robotics,
video surveillance and security. We present a method to forecast actions for
the unseen future of the video using a neural machine translation technique
that uses encoder-decoder architecture. The input to this model is the observed
RGB video, and the objective is to forecast the correct future symbolic action
sequence. Unlike prior methods that make action predictions for some unseen
percentage of video one for each frame, we predict the complete action sequence
that is required to accomplish the activity. We coin this task action sequence
forecasting. To cater for two types of uncertainty in the future predictions,
we propose a novel loss function. We show a combination of optimal transport
and future uncertainty losses help to improve results.
  We extend our action sequence forecasting model to perform weakly supervised
action forecasting on two challenging datasets, the Breakfast and the 50Salads.
Specifically, we propose a model to predict actions of future unseen frames
without using frame level annotations during training. Using Fisher vector
features, our supervised model outperforms the state-of-the-art action
forecasting model by 0.83% and 7.09% on the Breakfast and the 50Salads datasets
respectively. Our weakly supervised model is only 0.6% behind the most recent
state-of-the-art supervised model and obtains comparable results to other
published fully supervised methods, and sometimes even outperforms them on the
Breakfast dataset. Most interestingly, our weakly supervised model outperforms
prior models by 1.04% leveraging on proposed weakly supervised architecture,
and effective use of attention mechanism and loss functions.


  Turbulent-laminar patterns are ubiquitous near transition in wall-bounded
shear flows. Despite recent progress in describing their dynamics in analogy to
non-equilibrium phase transitions, there is no theory explaining their
emergence. Dynamical-system approaches suggest that invariant solutions to the
Navier--Stokes equations, such as traveling waves and relative periodic orbits
in pipe flow, act as building blocks of the disordered dynamics. While recent
studies have shown how transient chaos arises from such solutions, the ensuing
dynamics lacks the strong fluctuations in size, shape and speed of the
turbulent spots observed in experiments. We here show that chaotic spots with
distinct dynamical and kinematic properties merge in phase space and give rise
to the enhanced spatio-temporal patterns observed in pipe flow. This paves the
way for a dynamical-system foundation to the phenomenology of turbulent-laminar
patterns in wall-bounded extended shear flows.


  Dynamical system models of complex biochemical reaction networks are usually
high-dimensional, nonlinear, and contain many unknown parameters. In some cases
the reaction network structure dictates that positive equilibria must be unique
for all values of the parameters in the model. In other cases multiple
equilibria exist if and only if special relationships between these parameters
are satisfied. We describe methods based on homotopy invariance of degree which
allow us to determine the number of equilibria for complex biochemical reaction
networks and how this number depends on parameters in the model.


  As artificial intelligence (AI) / machine learning (ML) gain widespread
adoption, practitioners are increasingly seeking means to quantify and control
the risk these systems incur. This challenge is especially salient when such
systems have autonomy to collect their own data, such as in black-box
optimization and active learning, where their actions induce sequential
feedback-loop shifts in the data distribution. Conformal prediction is a
promising approach to uncertainty and risk quantification, but prior variants'
validity guarantees have assumed some form of ``quasi-exchangeability'' on the
data distribution, thereby excluding many types of sequential shifts. In this
paper we prove that conformal prediction can theoretically be extended to
\textit{any} joint data distribution, not just exchangeable or
quasi-exchangeable ones. Although the most general case is exceedingly
impractical to compute, for concrete practical applications we outline a
procedure for deriving specific conformal algorithms for any data distribution,
and we use this procedure to derive tractable algorithms for a series of
AI/ML-agent-induced covariate shifts. We evaluate the proposed algorithms
empirically on synthetic black-box optimization and active learning tasks.


  Recent studies have shown that the environment where people eat can affect
their nutritional behaviour. In this work, we provide automatic tools for a
personalised analysis of a person's health habits by the examination of daily
recorded egocentric photo-streams. Specifically, we propose a new automatic
approach for the classification of food-related environments, that is able to
classify up to 15 such scenes. In this way, people can monitor the context
around their food intake in order to get an objective insight into their daily
eating routine. We propose a model that classifies food-related scenes
organized in a semantic hierarchy. Additionally, we present and make available
a new egocentric dataset composed of more than 33000 images recorded by a
wearable camera, over which our proposed model has been tested. Our approach
obtains an accuracy and F-score of 56\% and 65\%, respectively, clearly
outperforming the baseline methods.


  We give a formula for the number of lozenge tilings of a hexagon on the
triangular lattice with unit triangles removed from arbitrary positions along
two non-adjacent, non-opposite sides. Our formula implies that for certain
families of such regions, the ratios of their numbers of tilings are given by
simple product formulas.


  We show that the large sieve is optimal for almost all exponential sums, thus
proving a conjecture by Erd\"os and Renyi.


  A scan of the top production threshold at a future electron-positron collider
provides the possibility for a precise measurement of the top quark mass in
theoretically well-defined mass schemes. With statistical uncertainties of 20
MeV or below, systematics will likely dominate the total uncertainty of the
measurement. This contribution presents a first look at the impact of the
renormalization scale uncertainties in recent NNNLO calculations of the top
pair production cross section in the threshold region on the measurement of the
top quark mass at the International Linear Collider.


  The collisional dynamics of test impurity ions in spherical tokamak plasmas
with strongly-sheared radial electric fields is investigated by means of a test
particle full orbit simulation code. The strength of the shear is such that the
standard drift ordering can no longer be assumed and a full orbit approach is
required. The effect of radial electric field shear on neoclassical particle
transport is quantified for a range of test particle mass and charge numbers
and electric field parameters. It is shown that the effect of a sheared
electric field is to enhance the confinement of impurity species above the
level observed in the absence of such a field. The effect may be explained in
terms of a collisional drag force drift, which is proportional to particle
charge number but independent of particle mass. This drift acts inwards for
negative radial electric fields and outwards for positive fields, implying
strongly enhanced confinement of highly ionized impurity ions in the presence
of a negative radial electric field.


  In this work, we study the Cauchy problem for the radially symmetric
spatially homogeneous Boltzmann equation with Debye-Yukawa potential. We prove
that this Cauchy problem enjoys the same smoothing effect as the Cauchy problem
defined by the evolution equation associated to a fractional logarithmic
harmonic oscillator. To be specific, we can prove the solution of the Cauchy
problem belongs to Shubin spaces.


  Context. Open clusters are groups of coeval stars sharing properties such as
distance and metallicity, and they are key to understanding stellar evolution.
Aims. Our main goal is to study the evolution of open clusters with a special
focus on the universality of the luminosity function. Methods. We applied an
upgraded version of the convergent point technique on about 50 open clusters.
The selection of cluster members was based purely on the exquisite astrometry
of the Gaia DR3 and Hipparcos catalogues in the five-dimensional or full
six-dimensional space. Results. We present updated lists of bona fide members
of ~50 open clusters within 500 pc and younger than 1 Gyr, exploiting the full
depth of the third Gaia data release complemented by Hipparcos at the bright
end, excluding regions in the Galactic plane. Our catalogues also are
complemented by optical and infrared photometry from the major large-scale
public surveys. All the data will be made available on a dedicated webpage with
interactive plots and a direct link to Aladin and Vizier hosted at the Centre
de Donn\'ees de Strasbourg. We derived luminosity functions for all bound
clusters and compared them in three age groups of ~50 Myr, ~150 Myr, and ~600
Myr, discussing similarities and differences to constrain their dynamical
evolution. Conclusions. Luminosity functions of clusters at 50 Myr are more
likely similar to each other and show a greater degree of similarity than older
clusters. We explain this observation with the universal luminosity function
within the volume of our sample (500 pc). Luminosity functions of clusters with
ages similar to the Pleiades or Hyades are more diverse, perhaps due to
internal dynamical evolution, but more work is needed to provide additional
evidence.


  We generate new AdS_4 solutions of D=11 supergravity starting from AdS_4 x
X_7 solutions where X_7 has U(1)^3 isometry. We consider examples where X_7 is
weak G_2, Sasaki-Einstein or tri-Sasakian, corresponding to d=3 SCFTs with
N=1,2 or 3 supersymmetry, respectively, and where the deformed solutions
preserve N=1,2 or 1 supersymmetry, respectively. For the special cases when X_7
is M(3,2), Q(1,1,1) or N(1,1)_I we identify the exactly marginal deformation in
the dual field theory. We also show that the volume of supersymmetric 5-cycles
of N(1,1)_I agrees with the conformal dimension predicted by the baryons of the
dual field theory.


  Diagnosing epilepsy is a problem of crucial importance. So analysing EEG data
is of much importance to help this diagnosis. Assembling the Feigenbaum graphs
for EEG signals. And calculating their average clustering, average degree, and
average shortest path length. We manage to characterize two different data sets
from each other. Each data set consisted of focal and non-focal activity, from
where epileptic regions could be identified. This method yields good results
for identifying sets of data from epileptic zones. Suggesting our approach
could be used to aid physicians with diagnosing epilepsy from EEG data.


  General relativity predicts that black holes are described by the Kerr
metric, which has integrable geodesics. This property is crucial to produce
accurate waveforms from extreme-mass-ratio inspirals. Astrophysical
environments, modifications of gravity and new fundamental fields may lead to
nonintegrable geodesics, inducing chaotic effects. We study geodesics around
self-interacting rotating boson stars and find robust evidence of
nonintegrability and chaos. We identify islands of stability around resonant
orbits, where the orbital radial and polar oscillation frequency ratios, known
as rotation numbers, remain constant throughout the island. These islands are
generically present both in the exterior and the interior of compact boson
stars. A monotonicity change of rotation curves takes place as orbits travel
from the exterior to the interior of the star. Therefore, configurations with
neutron-star-like compactness can support degenerate resonant islands. This
anomaly is reported here for the first time and it is not present in black
holes. Such configurations can also support extremely prolonged resonant
islands that span from the exterior to the interior of the star and are
shielded by thick chaotic layers. We adiabatically evolve inspirals using
approximated post-Newtonian fluxes and find time-dependent plateaus in the
rotation curves which are associated with island-crossing orbits. Crossings of
external islands give rise to typical gravitational-wave glitches found in
non-Kerr objects. Furthermore, when an inspiral is traversing an internal
island that is surrounded by a thick chaotic layer, a new type of simultaneous
multifrequency glitch occurs that may be detectable with space interferometers
such as LISA, and can serve as evidence of an extreme-mass-ratio inspiral
around a supermassive boson star.


  Nano-electronic integrated circuit technology is exclusively based on MOSFET
transistor due to its scalability down to the nanometer range. On the other
hand, Bipolar Junction Transistor (BJT), which provides unmatched analog
characteristics and frequency response, cannot be scaled to nanometer regime
without the loss of transistor action. Here a versatile nanoscale transistor is
introduced that provides identical BJT behavior and expands its capabilities.
The new transistor uses CMOS fabrication technology and creates BJT emitter,
base, and collector via electric fields. By allowing carrier modulation during
operation, its current gain can be changed at least by five orders of
magnitude. This property introduces novel adaptive, variable gain, and
programmable analog modules into existing electronic circuit design and
manufacturing. A NOT gate version of this device with the critical dimension of
7 nm operates at 730 GHz, and its three-stage ring oscillator exhibits a
frequency of 240 GHz. With proper gate biasing, it can also operate as a
nanoscale MOSFET, easily alleviating short-channel effects.


  Standard Direction of Arrival (DOA) estimation methods are typically derived
based on the Gaussian noise assumption, making them highly sensitive to
outliers. Therefore, in the presence of impulsive noise, the performance of
these methods may significantly deteriorate. In this paper, we model impulsive
noise as Gaussian noise mixed with sparse outliers. By exploiting their
statistical differences, we propose a novel DOA estimation method based on
sparse signal recovery (SSR). Furthermore, to address the issue of grid
mismatch, we utilize an alternating optimization approach that relies on the
estimated outlier matrix and the on-grid DOA estimates to obtain the off-grid
DOA estimates. Simulation results demonstrate that the proposed method exhibits
robustness against large outliers.


  We study spatial permutations with cycle weights that are bounded or slowly
diverging. We show that a phase transition occurs at an explicit critical
density. The long cycles are macroscopic and their cycle lengths satisfy a
Poisson-Dirichlet law.


  Topological defects can act as local impurities that seed cosmological phase
transitions. In this paper we study the case of domain walls, and how they can
affect the electroweak phase transition in the Standard Model extended with a
$Z_2$-odd scalar singlet. When the transition is two-step, the early breaking
of the $Z_2$ symmetry implies the formation of domain walls which can then act
as nucleation sites for the second step. We develop a method based on
dimensional reduction to calculate the rate of the catalyzed phase transition
within the 3d theory on the domain wall surface. By comparison with the
standard homogeneous rate, we conclude that the seeded phase transition is
generically faster and it ultimately determines the way the phase transition is
completed. We comment on the phenomenological implications for gravitational
waves and baryogenesis.


  Action classification in still images is an important task in computer
vision. It is challenging as the appearances of ac- tions may vary depending on
their context (e.g. associated objects). Manually labeling of context
information would be time consuming and difficult to scale up. To address this
challenge, we propose a method to automatically discover and cluster action
concepts, and learn their classifiers from weakly supervised image-sentence
corpora. It obtains candidate action concepts by extracting verb-object pairs
from sentences and verifies their visualness with the associated images.
Candidate action concepts are then clustered by using a multi-modal
representation with image embeddings from deep convolutional networks and text
embeddings from word2vec. More than one hundred human action concept
classifiers are learned from the Flickr 30k dataset with no additional human
effort and promising classification results are obtained. We further apply the
AdaBoost algorithm to automatically select and combine relevant action concepts
given an action query. Promising results have been shown on the PASCAL VOC 2012
action classification benchmark, which has zero overlap with Flickr30k.


  A QED-based bootstrap mechanism, appearing at sufficiently small space-time
scales, is suggested as an explanation for the "dark" vacuum energy that may be
able to accelerate the universe. Very small-scale vacuum currents are allowed
to generate small-scale electromagnetic fields, which become significant near
the light cone. The resulting lepton-pair production, some of which may be
tachyonic, has ramifications for the convergence of all QED perturbative
processes, as well as providing possible mechanisms for "dark" matter, galactic
gamma-ray bursts, and ultra-high-energy cosmic rays.


  A cryptocurrency is a decentralized digital currency that is designed for
secure and private asset transfer and storage. As a currency, it should be
difficult to counterfeit and double-spend. In this paper, we review and analyze
the major security and privacy issues of Bitcoin. In particular, we focus on
its underlying foundation, blockchain technology. First, we present a
comprehensive background of Bitcoin and the preliminary on security. Second,
the major security threats and countermeasures of Bitcoin are investigated. We
analyze the risk of double-spending attacks, evaluate the probability of
success in performing the attacks and derive the profitability for the attacker
to perform such attacks. Third, we analyze the underlying Bitcoin peer-to-peer
network security risks and Bitcoin storage security. We compare three types of
Bitcoin wallets in terms of security, type of services and their trade-offs.
Finally, we discuss the security and privacy features of alternative
cryptocurrencies and present an overview of emerging technologies today. Our
results can help Bitcoin users to determine a trade-off between the risk of
double-spending attempts and the transaction time delay or confidence before
accepting transactions. These results can also assist miners to develop
suitable strategies to get involved in the mining process and maximize their
profits.


  To address the long-standing $(g-2)_{\mu}$ anomaly via a light boson, in Ref.
[1] we proposed to extend the standard model (SM) by the local $(B-L)_{23}$,
under which only the second and third generations of fermions are charged. It
predicts an invisible $Z'$ with mass ${\cal O}(100)$ MeV, and moreover it has
flavor-changing neutral current (FCNC) couplings to the up-type quarks at tree
level. Such a $Z'$, via $K_L \to \pi^0 + Z'(\to \nu \bar{\nu})$ at loop level,
may be a natural candidate to account for the recent KOTO anomaly. In this
article, we investigate this possibility, to find that $Z'$ can readily do this
job if it is no longer responsible for the $(g-2)_{\mu}$ anomaly. We further
find that both anomalies can be explained with moderate tuning of the CP
violation, but may contradict the $B$ meson decays.


  We obtain a measure theoretical characterization of polynomials among
rational functions on $\mathbb{P}^1$, which generalizes a theorem of Lopes. Our
proof applies both classical and dynamically weighted potential theory.


  Cosmological and astrophysical observations suggest that 85\% of the total
matter of the Universe is made of Dark Matter (DM). However, its nature remains
one of the most challenging and fundamental open questions of particle physics.
Assuming particle DM, this exotic form of matter cannot consist of Standard
Model (SM) particles. Many models have been developed to attempt unraveling the
nature of DM such as Weakly Interacting Massive Particles (WIMPs), the most
favored particle candidates. WIMP annihilations and decay could produce SM
particles which in turn hadronize and decay to give SM secondaries such as high
energy $\gamma$ rays. In the framework of indirect DM search, observations of
promising targets are used to search for signatures of DM annihilation. Among
these, the dwarf spheroidal galaxies (dSphs) are commonly favored owing to
their expected high DM content and negligible astrophysical background. In this
work, we present the very first combination of 20 dSph observations, performed
by the Fermi-LAT, HAWC, H.E.S.S., MAGIC, and VERITAS collaborations in order to
maximize the sensitivity of DM searches and improve the current results. We use
a joint maximum likelihood approach combining each experiment's individual
analysis to derive more constraining upper limits on the WIMP DM
self-annihilation cross-section as a function of DM particle mass. We present
new DM constraints over the widest mass range ever reported, extending from 5
GeV to 100 TeV thanks to the combination of these five different $\gamma$-ray
instruments.


  To optimize materials' stability, automated high-throughput workflows are of
increasing interest. However, many of those workflows use processes not
suitable for large-area depositions which limits the transferability of
results. While combinatorial approaches based on vapour-based depositions are
inherently scalable, their potential for controlled stability assessments has
yet to be exploited. Based on MAPbI3 thin-films as a prototypical system, we
demonstrate a combinatorial inert-gas workflow to study materials degradation
based on intrinsic factors only, closely resembling conditions in encapsulated
de-vices. Through a comprehensive set of automated X-Ray fluorescence (XRF),
X-Ray diffraction (XRD) and UV-Vis characterizations, we aim to obtain a
holistic understanding of thin-film properties of pristine and aged thin-films.
From phase changes derived from XRD characterizations before and after aging,
we observe simi-lar aging behaviours for MAPbI3 thin-films with varying PbI2
residuals. Using a custom-designed in-situ UV-Vis aging setup, the
combinatorial libraries are exposed to relevant aging conditions, such as heat
or light-bias exposure. Simultaneously, UV-Vis photospectroscopy is performed
to gain kinetic insights into the aging process which can be linked to
intrinsic degradation processes such as autocatalytic decomposition. Despite
scattering effects, which complicate the conventional interpretation of in-situ
UV-Vis results, we demonstrate how a machine learning model trained on the
comprehensive characterization data before and after the aging process can link
optical changes to phase changes during aging. Consequently, this approach does
not only enable semi-quantitative comparisons of materials' stability but also
provides detailed insights into the underlying degradation processes which are
otherwise mostly reported for investigations on single samples.


  We propose a simple and general method to regularize the fine-tuning of
Transformer-based encoders for text classification tasks. Specifically, during
fine-tuning we generate adversarial examples by perturbing the word embeddings
of the model and perform contrastive learning on clean and adversarial examples
in order to teach the model to learn noise-invariant representations. By
training on both clean and adversarial examples along with the additional
contrastive objective, we observe consistent improvement over standard
fine-tuning on clean examples. On several GLUE benchmark tasks, our fine-tuned
BERT Large model outperforms BERT Large baseline by 1.7% on average, and our
fine-tuned RoBERTa Large improves over RoBERTa Large baseline by 1.3%. We
additionally validate our method in different domains using three intent
classification datasets, where our fine-tuned RoBERTa Large outperforms RoBERTa
Large baseline by 1-2% on average.


  In recent years, large-scale Bayesian learning draws a great deal of
attention. However, in big-data era, the amount of data we face is growing much
faster than our ability to deal with it. Fortunately, it is observed that
large-scale datasets usually own rich internal structure and is somewhat
redundant. In this paper, we attempt to simplify the Bayesian posterior via
exploiting this structure. Specifically, we restrict our interest to the
so-called well-clustered datasets and construct an \emph{approximate posterior}
according to the clustering information. Fortunately, the clustering structure
can be efficiently obtained via a particular clustering algorithm. When
constructing the approximate posterior, the data points in the same cluster are
all replaced by the centroid of the cluster. As a result, the posterior can be
significantly simplified. Theoretically, we show that under certain conditions
the approximate posterior we construct is close (measured by KL divergence) to
the exact posterior. Furthermore, thorough experiments are conducted to
validate the fact that the constructed posterior is a good approximation to the
true posterior and much easier to sample from.


  We study learning algorithms that seek to minimize the conditional
value-at-risk (CVaR), when all the learner knows is that the losses incurred
may be heavy-tailed. We begin by studying a general-purpose estimator of CVaR
for potentially heavy-tailed random variables, which is easy to implement in
practice, and requires nothing more than finite variance and a distribution
function that does not change too fast or slow around just the quantile of
interest. With this estimator in hand, we then derive a new learning algorithm
which robustly chooses among candidates produced by stochastic gradient-driven
sub-processes. For this procedure we provide high-probability excess CVaR
bounds, and to complement the theory we conduct empirical tests of the
underlying CVaR estimator and the learning algorithm derived from it.


  A finite-difference time-domain (FDTD) simulation of broadband
electromagnetic metasurfaces based on direct in- corporation of Generalized
Sheet Transition Conditions (GSTCs) inside a conventional Yee-cell region has
been proposed, for arbitrary wave excitations. This is achieved by inserting a
zero thickness metasurface inside bulk nodes of the Yee-cell region, giving
rise to three distinct cell configurations - Symmetric Cell (SC), Asymmetric
Cell (AC) and Tight Asymmetric Cell (TAC). In addition, the metasurface is
modelled using electric and magnetic surface susceptibilities exhibiting a
broadband Lorentzian response. As a result, the proposed model guarantees a
physical and causal response from the metasurface. Several full- wave results
are shown, and compared with analytical Fourier propagation methods showing
excellent results, for both 1D and 2D fields simulations. It is found that the
TAC provides the fastest convergence among the three methods with minimum
error.


  We construct an example of a regular algebra over $\mathbb C$ of dimension
$d$ and a rank $r$ projective module over it which is not generated by $d+r-1$
elements. This strengthens an example by Swan over the field of real numbers.


  We use vector-bundle techniques in order to compute $\dim W^1_d(C)$ where $C$
is general and smooth in a linear system on an unnodal Enriques surface. We
furthermore find new examples of smooth curves on Enriques surfaces with an
infinite number of $g^1_{\gon(C)}$'s.


  Observations of magnetic clouds (MCs) are consistent with the presence of
flux ropes detected in the solar wind (SW) a few days after their expulsion
from the Sun as coronal mass ejections (CMEs). Both the \textit{in situ}
observations of plasma velocity profiles and the increase of their size with
solar distance show that MCs are typically expanding structures. The aim of
this work is to derive the expansion properties of MCs in the inner heliosphere
from 0.3 to 1 AU.We analyze MCs observed by the two Helios spacecraft using
\textit{in situ} magnetic field and velocity measurements. We split the sample
in two subsets: those MCs with a velocity profile that is significantly
perturbed from the expected linear profile and those that are not. From the
slope of the \textit{in situ} measured bulk velocity along the Sun-Earth
direction, we compute an expansion speed with respect to the cloud center for
each of the analyzed MCs. We analyze how the expansion speed depends on the MC
size, the translation velocity, and the heliocentric distance, finding that all
MCs in the subset of non-perturbed MCs expand with almost the same
non-dimensional expansion rate ($\zeta$). We find departures from this general
rule for $\zeta$ only for perturbed MCs, and we interpret the departures as the
consequence of a local and strong SW perturbation by SW fast streams, affecting
the MC even inside its interior, in addition to the direct interaction region
between the SW and the MC. We also compute the dependence of the mean total SW
pressure on the solar distance and we confirm that the decrease of the total SW
pressure with distance is the main origin of the observed MC expansion rate. We
found that $\zeta$ was $0.91\pm 0.23$ for non-perturbed MCs while $\zeta$ was
$0.48\pm 0.79$ for perturbed MCs, the larger spread in the last ones being due
to the influence of the environment conditions on the expansion.


  Social media abounds with multimodal sarcasm, and identifying sarcasm targets
is particularly challenging due to the implicit incongruity not directly
evident in the text and image modalities. Current methods for Multimodal
Sarcasm Target Identification (MSTI) predominantly focus on superficial
indicators in an end-to-end manner, overlooking the nuanced understanding of
multimodal sarcasm conveyed through both the text and image. This paper
proposes a versatile MSTI framework with a coarse-to-fine paradigm, by
augmenting sarcasm explainability with reasoning and pre-training knowledge.
Inspired by the powerful capacity of Large Multimodal Models (LMMs) on
multimodal reasoning, we first engage LMMs to generate competing rationales for
coarser-grained pre-training of a small language model on multimodal sarcasm
detection. We then propose fine-tuning the model for finer-grained sarcasm
target identification. Our framework is thus empowered to adeptly unveil the
intricate targets within multimodal sarcasm and mitigate the negative impact
posed by potential noise inherently in LMMs. Experimental results demonstrate
that our model far outperforms state-of-the-art MSTI methods, and markedly
exhibits explainability in deciphering sarcasm as well.


  Nuclear magnetic resonance spectroscopy (MRS) allows for the determination of
atomic structures and concentrations of different chemicals in a biochemical
sample of interest. MRS is used in vivo clinically to aid in the diagnosis of
several pathologies that affect metabolic pathways in the body. Typically, this
experiment produces a one dimensional (1D) 1H spectrum containing several peaks
that are well associated with biochemicals, or metabolites. However, since many
of these peaks overlap, distinguishing chemicals with similar atomic structures
becomes much more challenging. One technique capable of overcoming this issue
is the localized correlated spectroscopy (L-COSY) experiment, which acquires a
second spectral dimension and spreads overlapping signal across this second
dimension. Unfortunately, the acquisition of a two dimensional (2D)
spectroscopy experiment is extremely time consuming. Furthermore, quantitation
of a 2D spectrum is more complex. Recently, artificial intelligence has emerged
in the field of medicine as a powerful force capable of diagnosing disease,
aiding in treatment, and even predicting treatment outcome. In this study, we
utilize deep learning to: 1) accelerate the L-COSY experiment and 2) quantify
L-COSY spectra. We demonstrate that our deep learning model greatly outperforms
compressed sensing based reconstruction of L-COSY spectra at higher
acceleration factors. Specifically, at four-fold acceleration, our method has
less than 5% normalized mean squared error, whereas compressed sensing yields
20% normalized mean squared error. We also show that at low SNR (25% noise
compared to maximum signal), our deep learning model has less than 8%
normalized mean squared error for quantitation of L-COSY spectra. These pilot
simulation results appear promising and may help improve the efficiency and
accuracy of L-COSY experiments in the future.


  Electric vehicles (EVs) have become a popular mode of transportation, with
their performance depending on the ageing of the Li-ion batteries used to power
them. However, it can be challenging and time-consuming to determine the
capacity retention of a battery in service. A rapid and reliable testing method
for state of health (SoH) determination is desired. Ultrasonic testing
techniques are promising due to their efficient, portable, and non-destructive
features. In this study, we demonstrate that ultrasonic speed decreases with
the degradation of the capacity of an LFP prismatic cell. We explain this
correlation through numerical simulation, which describes wave propagation in
porous media. We propose that the reduction of binder stiffness can be a
primary cause of the change in ultrasonic speed during battery ageing. This
work brings new insights into ultrasonic SoH estimation techniques.


  We have successfully used a new on-the-fly mapping technique with the Mopra
radiotelescope to image G305.2+0.2 in transitions of 13CO, HCO+, N2H+, CH3CN
and CH3OH. All these species appear to be concentrated towards the
infrared-quiet methanol maser site G305A (G305.21+0.21). We suggest that this
region contains an extremely deeply embedded site of massive star formation,
with comparable qualities to the low mass Class 0 stage. The infrared-bright
methanol maser site G305B (G305.21+0.20) also exhibits emission in all the
mapped transitions, but always at a lower level. We suggest this is because it
harbours a site of massive star formation older and more developed than
G305.21+0.21. All transitions appear to be extended beyond the size of the
Mopra beam (30"). 13CO and HCO+ line wings are suggestive of an outflow in the
region, but the spatial resolution of these data is insufficient to identify
the powering source. A narrow-lined (1.6 km/s compared to a typical line FWHM
of 6.4 km/s) N2H+ source (G305SW) is found 90" to the south-west of the main
star forming centres, which does not correspond to any CH3CN or CH3OH source,
nor does it correspond well to 13CO or HCO+ emission in the vicinity. We
suggest this may be a massive, cold, quiescent and possibly prestellar core.


  Typical technique in knowledge distillation (KD) is regularizing the learning
of a limited capacity model (student) by pushing its responses to match a
powerful model's (teacher). Albeit useful especially in the penultimate layer
and beyond, its action on student's feature transform is rather implicit,
limiting its practice in the intermediate layers. To explicitly embed the
teacher's knowledge in feature transform, we propose a learnable KD layer for
the student which improves KD with two distinct abilities: i) learning how to
leverage the teacher's knowledge, enabling to discard nuisance information, and
ii) feeding forward the transferred knowledge deeper. Thus, the student enjoys
the teacher's knowledge during the inference besides training. Formally, we
repurpose 1x1-BN-ReLU-1x1 convolution block to assign a semantic vector to each
local region according to the template (supervised by the teacher) that the
corresponding region of the student matches. To facilitate template learning in
the intermediate layers, we propose a novel form of supervision based on the
teacher's decisions. Through rigorous experimentation, we demonstrate the
effectiveness of our approach on 3 popular classification benchmarks. Code is
available at: https://github.com/adagorgun/letKD-framework


  Let $C: y^2=f(x)$ be a hyperelliptic curve of genus $g\geq 1$, defined over a
complete discretely valued field $K$, with ring of integers $O_K$. Under
certain conditions on $C$, mild when residue characteristic is not $2$, we
explicitly construct the minimal regular model with normal crossings
$\mathcal{C}/O_K$ of $C$. In the same setting we determine a basis of integral
differentials of $C$, that is an $O_K$-basis for the global sections of the
relative dualising sheaf $\omega_{\mathcal{C}/O_K}$.


  We report a measurement of the branching fraction of the exclusive charmless
semileptonic decay B+ --> omega l+ nu, where l is either an electron or a muon.
We use samples of B+ mesons tagged by a reconstructed charmed semileptonic
decay of the other B meson in the event. The measurement is based on a dataset
of 426.1 fb-1 of e+e- collisions at a center-of-mass energy of 10.58 GeV
recorded with the BABAR detector at the PEP-II asymmetric-energy e+e- storage
rings. We measure a branching fraction of BF(B+ --> omega l+ nu) = (1.35 +/-
0.21 +/- 0.11) x 10(-4), where the uncertainties are statistical and
systematic, respectively. We also present measurements of the partial branching
fractions in three bins of q2, the invariant-mass squared of the
lepton-neutrino system, and we compare them to theoretical predictions of the
form factors.


  The two main results of this note are on the one hand that if V is a
2-dimensional potentially trianguline representation of G_Qp then V satisfies
at least one of the following properties (1) V is split trianguline (2) V is a
direct sum of characters or an induced representation (3) V is a twist of a de
Rham representation, and on the other hand that there exists some 2-dimensional
representations of G_Qp which are not potentially trianguline.


  Remote state preparation is generation of a desired state by a remote
observer. In spite of causality, it is well known, according to the
Reeh-Schlieder theorem, that it is possible for relativistic quantum field
theories, and a "physical" process achieving this task, involving
superoscillatory functions, has recently been introduced. In this work we deal
with non-relativistic fields, and show that remote state preparation is also
possible for them, hence obtaining a Reeh-Schlieder-like result for general
fields. Interestingly, in the nonrelativistic case, the process may rely on
completely different resources than the ones used in the relativistic case.


  We report the implementation of a dilution-refrigerator-based scanning
microwave impedance microscope (MIM) with a base temperature of ~ 100 mK. The
vibration noise of our apparatus with tuning-fork feedback control is as low as
1 nm. Using this setup, we have demonstrated the imaging of quantum anomalous
Hall states in magnetically (Cr and V) doped (Bi, Sb)2Te3 thin films grown on
mica substrates. Both the conductive edge modes and topological phase
transitions near coercive fields of Cr-doped and V-doped layers are visualized
in the field-dependent results. Our work establishes the experimental platform
for investigating nanoscale quantum phenomena under ultralow temperatures.


  A central problem of algebraic topology is to understand the homotopy groups
$\pi_d(X)$ of a topological space $X$. For the computational version of the
problem, it is well known that there is no algorithm to decide whether the
fundamental group $\pi_1(X)$ of a given finite simplicial complex $X$ is
trivial. On the other hand, there are several algorithms that, given a finite
simplicial complex $X$ that is simply connected (i.e., with $\pi_1(X)$
trivial), compute the higher homotopy group $\pi_d(X)$ for any given $d\geq 2$.
%The first such algorithm was given by Brown, and more recently, \v{C}adek et
al.
  However, these algorithms come with a caveat: They compute the isomorphism
type of $\pi_d(X)$, $d\geq 2$ as an \emph{abstract} finitely generated abelian
group given by generators and relations, but they work with very implicit
representations of the elements of $\pi_d(X)$. Converting elements of this
abstract group into explicit geometric maps from the $d$-dimensional sphere
$S^d$ to $X$ has been one of the main unsolved problems in the emerging field
of computational homotopy theory.
  Here we present an algorithm that, given a~simply connected space $X$,
computes $\pi_d(X)$ and represents its elements as simplicial maps from a
suitable triangulation of the $d$-sphere $S^d$ to $X$. For fixed $d$, the
algorithm runs in time exponential in $size(X)$, the number of simplices of
$X$. Moreover, we prove that this is optimal: For every fixed $d\geq 2$, we
construct a family of simply connected spaces $X$ such that for any simplicial
map representing a generator of $\pi_d(X)$, the size of the triangulation of
$S^d$ on which the map is defined, is exponential in $size(X)$.