  We investigate the potential of neural-network based classifiers for
discriminating gravitational wave bursts (GWBs) of a given canonical family
(e.g. core-collapse supernova waveforms) from typical transient instrumental
artifacts (glitches), in the data of a single detector. The further
classification of glitches into typical sets is explored.In order to provide a
proof of concept,we use the core-collapse supernova waveform catalog produced
by H. Dimmelmeier and co-Workers, and the data base of glitches observed in
laser interferometer gravitational wave observatory (LIGO) data maintained by
P. Saulson and co-Workers to construct datasets of (windowed) transient
waveforms (glitches and bursts) in additive (Gaussian and compound-Gaussian)
noise with different signal-tonoise ratios (SNR). Principal component analysis
(PCA) is next implemented for reducing data dimensionality, yielding results
consistent with, and extending those in the literature. Then, a multilayer
perceptron is trained by a backpropagation algorithm (MLP-BP) on a data subset,
and used to classify the transients as glitch or burst. A Self-Organizing Map
(SOM) architecture is finally used to classify the glitches. The glitch/burst
discrimination and glitch classification abilities are gauged in terms of the
related truth tables. Preliminary results suggest that the approach is
effective and robust throughout the SNR range of practical interest.
Perspective applications pertain both to distributed (network, multisensor)
detection of GWBs, where someintelligenceat the single node level can be
introduced, and instrument diagnostics/optimization, where spurious transients
can be identified, classified and hopefully traced back to their entry points


  We report a $\sim5.2\sigma$ detection of the kinetic Sunyaev-Zel'dovich (kSZ)
effect in Fourier space, by combining the DESI galaxy groups and the Planck
data. We use the density-weighted pairwise kSZ power spectrum as the summary
statistic, and the detailed procedure of its measurement is presented in this
paper. Meanwhile, we analyze the redshift space group density power spectrum to
constrain its bias parameters and photo-z uncertainties. These best-fitted
parameters are substituted to a nonlinear kSZ model, and we fit the measured
kSZ power spectrum with this model to constrain the group optical depth
$\bar{\tau}$. Selected by a varying lower mass threshold $M_{\rm th}$, the
galaxy group catalogs with different median masses ($\tilde{M}$) are
constructed from the DR9 data of the DESI Legacy Imaging Surveys. $\tilde{M}$
spans a wide range of $\sim10^{13}-10^{14}M_\odot/h$ and the heaviest
$\tilde{M}\sim10^{14} M_\odot/h$ is larger than those of most other kSZ
detections. When the aperture photometric filter radius $\theta_{\rm AP}$ is
set to be $4.2$ arcmin, the $\tilde{M}=1.75\times10^{13}M_\odot/h$ group sample
at the median redshift $\tilde{z}=0.64$ has the highest kSZ detection ${\rm
S/N}=5.2$. By fitting $\bar{\tau}$s from various samples against their
$\tilde{M}$s, we obtain a linear $\log\bar{\tau}-\log \tilde{M}$ relation:
$\log\bar{\tau} = \gamma(\log \tilde{M}-14)+\log\beta$, in which
$\gamma=0.55\pm0.1$. We also vary the aperture photometric filter radius and
measure the $\bar{\tau}$ profiles of group samples, whose constraints on the
baryon distribution within and around dark matter halos will be discussed in a
companion paper.


  The well-known M4 processes of Smith and Weissman are very flexible models
for asymptotically dependent multivariate data. Extended M4 of Heffernan
\emph{et al.} allows to also account for asymptotic independence. In this paper
we introduce a more general multivariate model comprising asymptotic dependence
and independence, which has the extended M4 class as a particular case. We
study properties of the proposed model. In particular, we compute the
multivariate extremal index, tail dependence and extremal coefficients.


  The combination of microwave and microfluidic technologies has the potential
to enable wireless monitoring and interaction with bioparticles, facilitating
in this way the exploration of a still largely uncharted territory at the
intersection of biology, communication engineering and microscale physics.
Opportunely, the scientific and technical requirements of microfluidics and
microwave techniques converge to the need of system miniaturization to achieve
the required sensitivity levels. This work, therefore, presents the design and
optimization of a measurement system for the detection of bioparticles over the
frequency range $0.01$ to $10$ $\mathrm{GHz}$, with different coplanar
electrodes configurations on a microfluidic platform. The design of the
measurement signal-chain setup is optimized for a novel real-time
superheterodyne microwave detection system. In particular, signal integrity is
achieved by means of a microwave-shielded chamber, which is protected from
external electromagnetic interference that may potentially impact the coplanar
electrodes mounted on the microfluidic device. Additionally, analytical
expressions and experimental validation of the system-level performance are
provided and discussed for the different designs of the coplanar electrodes.
This technique is applied to measure the electrical field perturbation produced
by $10$ $\mathrm{\mu m}$ polystyrene beads with a concentration of $10^5$
$\mathrm{beads/mL}$, and flowing at a rate of $10$ $\mathrm{\mu L/m}$. The
achieved SNR is in the order of $40$ $\mathrm{dB}$ for the three coplanar
electrodes considered.


  Detection pre-training methods for the DETR series detector have been
extensively studied in natural scenes, e.g., DETReg. However, the detection
pre-training remains unexplored in remote sensing scenes. In existing
pre-training methods, alignment between object embeddings extracted from a
pre-trained backbone and detector features is significant. However, due to
differences in feature extraction methods, a pronounced feature discrepancy
still exists and hinders the pre-training performance. The remote sensing
images with complex environments and more densely distributed objects
exacerbate the discrepancy. In this work, we propose a novel Mutually
optimizing pre-training framework for remote sensing object Detection, dubbed
as MutDet. In MutDet, we propose a systemic solution against this challenge.
Firstly, we propose a mutual enhancement module, which fuses the object
embeddings and detector features bidirectionally in the last encoder layer,
enhancing their information interaction.Secondly, contrastive alignment loss is
employed to guide this alignment process softly and simultaneously enhances
detector features' discriminativity. Finally, we design an auxiliary siamese
head to mitigate the task gap arising from the introduction of enhancement
module. Comprehensive experiments on various settings show new state-of-the-art
transfer performance. The improvement is particularly pronounced when data
quantity is limited. When using 10% of the DIOR-R data, MutDet improves DetReg
by 6.1% in AP50. Codes and models are available at:
https://github.com/floatingstarZ/MutDet.


  The integrated Sachs-Wolfe (ISW) effect provides us the information of the
time evolution of gravitational potential. The cross-correlation between the
cosmic microwave background (CMB) and the large scale structure (LSS) is known
as a promising way to extract the ISW effect. Compared to CMB, the matter
fluctuation can grow non-linearly and this is represented in the gravitational
potential. Compared to the linear ISW effect, this non-linear ISW effect known
as the Rees-Sciama (RS) effect shows the unique behavior by changing the
anti-correlated cross correlation between the CMB and the mass tracer into the
positively correlated cross correlation. We show that the dependence of this
flipping scale on dark energy models and it might be used as a new method to
investigate dark energy models.


  Gravitational wave detectors in space, particularly the LISA project, can
study a rich variety of astronomical systems whose gravitational radiation is
not detectable from the ground, because it is emitted in the low-frequency
gravitational wave band (0.1 mHz to 1 Hz) that is inaccessible to ground-based
detectors. Sources include binary systems in our Galaxy and massive black holes
in distant galaxies. The radiation from many of these sources will be so strong
that it will be possible to make remarkably detailed studies of the physics of
the systems. These studies will have importance both for astrophysics (most
notably in binary evolution theory and models for active galaxies) and for
fundamental physics. In particular, it should be possible to make decisive
measurements to confirm the existence of black holes and to test, with
accuracies better than 1%, general relativity's description of them. Other
observations can have fundamental implications for cosmology and for physical
theories of the unification of forces. In order to understand these
conclusions, one must know how to estimate the gravitational radiation produced
by different sources. In the first part of this lecture I review the dynamics
of gravitational wave sources, and I derive simple formulas for estimating wave
amplitudes and the reaction effects on sources of producing this radiation.
With these formulas one can estimate, usually to much better than an order of
magnitude, the physics of most of the interesting low-frequency sources. In the
second part of the lecture I use these estimates to discuss, in the context of
the expected sensitivity of LISA, what we can learn by from observations of
binary systems, massive black holes, and the early Universe itself.


  In this letter, we propose a symbol-wise puncturing scheme to support hybrid
automatic repeat request (HARQ) integrated probabilistic amplitude shaping
(PAS). To prevent the probability distribution distortion caused by the
traditional sequential puncturing and realize the promised gain of PAS, we
perform symbol-wise puncturing on the label sequence of the shaped modulation
symbols. Our simulation results indicate that the proposed puncturing scheme
achieves a stable shaping gain across the signal-to-noise ratio of at least 0.6
dB compared with the uniform case under the same throughput, while the gain of
sequential puncturing drops rapidly with retransmissions. Moreover, in each
transmission, the proposed scheme is able to reduce the distribution distortion
that achieves over 1.2 dB gain at a block error rate (BLER) of ${10^{-3}}$. In
contrast, for sequential puncturing, the distribution is severely distorted and
the BLER performance is even worse than that of the uniform case in
retransmissions.


  We show how to define biproducts up to isomorphism in an arbitrary category
without assuming any enrichment. The resulting notion coincides with the usual
definitions whenever all binary biproducts exist or the category is suitably
enriched, resulting in a modest yet strict generalization otherwise. We also
characterize when a category has all binary biproducts in terms of an
ambidextrous adjunction. Finally, we give some new examples of biproducts that
our definition recognizes.


  Non-Intrusive Load Monitoring (NILM) is pivotal in today's energy landscape,
offering vital solutions for energy conservation and efficient management. Its
growing importance in enhancing energy savings and understanding consumer
behavior makes it a pivotal technology for addressing global energy challenges.
This paper delivers an in-depth review of NILM, highlighting its critical role
in smart homes and smart grids. The significant contributions of this study are
threefold: Firstly, it compiles a comprehensive global dataset table, providing
a valuable tool for researchers and engineers to select appropriate datasets
for their NILM studies. Secondly, it categorizes NILM approaches, simplifying
the understanding of various algorithms by focusing on technologies, label data
requirements, feature usage, and monitoring states. Lastly, by identifying gaps
in current NILM research, this work sets a clear direction for future studies,
discussing potential areas of innovation.


  Unbiased quantum Monte-Carlo simulations are performed on the
nearest-neighbor spin-$\frac{1}{2}$ pyrochlore XXZ model with an
antiferromagnetic longitudinal and a weak ferromagnetic transverse exchange
couplings, $J$ and $J_\perp$. The specific heat exhibits a broad peak at
$T_{\mathrm{CSI}}\sim0.2J$ associated with a crossover to a classical Coulomb
liquid regime showing a suppressed spin-ice monopole density, a broadened
pinch-point singularity, and the Pauling entropy for $|J_\perp|\ll J$, as in
classical spin ice. On further cooling, the entropy restarts decaying for
$J_\perp>J_{\perp c}\sim-0.104J$, producing another broad specific heat peak
for a crossover to a bosonic quantum Coulomb liquid, where the spin correlation
contains both photon and quantum spin-ice monopole contributions. With
negatively increasing $J_\perp$ across $J_{\perp c}$, a first-order thermal
phase transition occurs from the quantum Coulomb liquid to an XY ferromagnet.
Relevance to magnetic rare-earth pyrochlore oxides is discussed.


  We show how supersymmetric QCD in a slice of AdS can naturally acquire
metastable vacua. The formulation closely follows that of Intriligator, Seiberg
and Shih (ISS), with an "electric" sector on the UV brane and a "magnetic"
sector on the IR brane. However the 't Hooft anomaly matching that constrains
the Seiberg duality central to ISS is replaced by anomaly inflow and
cancellation, and the source of strong coupling is the CFT to which the theory
couples rather than the gauge groups. The theory contains an anomaly free
R-symmetry that, when broken by UV effects, leads to an O'Raifeartaigh model on
the IR brane. In contrast to ISS, the R-symmetry breaking in the UV can be
maximal, and yet the R-symmetry breaking in the IR theory remains under strict
control: there is no need for retrofitting of small parameters.


  To measure the floor in interplanetary magnetic field and estimate the time-
invariant open magnetic flux of Sun, it is necessary to know a part of magnetic
field of Sun carried away by CMEs. In contrast with previous papers, we did not
use global solar parameters: we identified different large-scale types of solar
wind for 1976-2000 interval, obtained a fraction of interplanetary CMEs (ICMEs)
and calculated magnitude of interplanetary magnetic field B averaged over 2
Carrington rotations. The floor of magnetic field is estimated as B value at
solar cycle minimum when the ICMEs were not observed and it was calculated to
be 4,65 \pm 6,0 nT. Obtained value is in a good agreement with previous
results.


  We study the problem of novel view synthesis from sparse source observations
of a scene comprised of 3D objects. We propose a simple yet effective approach
that is neither continuous nor implicit, challenging recent trends on view
synthesis. Our approach explicitly encodes observations into a volumetric
representation that enables amortized rendering. We demonstrate that although
continuous radiance field representations have gained a lot of attention due to
their expressive power, our simple approach obtains comparable or even better
novel view reconstruction quality comparing with state-of-the-art baselines
while increasing rendering speed by over 400x. Our model is trained in a
category-agnostic manner and does not require scene-specific optimization.
Therefore, it is able to generalize novel view synthesis to object categories
not seen during training. In addition, we show that with our simple
formulation, we can use view synthesis as a self-supervision signal for
efficient learning of 3D geometry without explicit 3D supervision.


  Models of the consensus of the individual state in social systems have been
the subject of recent researches in the physics literature. We investigate how
network structures coevolve with the individual state under the framework of
social identity theory. And we propose an adaptive network model to achieve
state consensus or local structural adjustment of individuals by evaluating the
homogeneity among them. Specifically, the similarity threshold significantly
affects the evolution of the network with different initial conditions, and
thus there emerges obvious community structure and polarization. More
importantly, there exists a critical point of phase transition, at which the
network may evolve into a significant community structure and state-consistent
group.


  We develop a simple and unified framework for nonlinear variable selection
that incorporates uncertainty in the prediction function and is compatible with
a wide range of machine learning models (e.g., tree ensembles, kernel methods,
neural networks, etc). In particular, for a learned nonlinear model
$f(\mathbf{x})$, we consider quantifying the importance of an input variable
$\mathbf{x}^j$ using the integrated partial derivative $\Psi_j = \Vert
\frac{\partial}{\partial \mathbf{x}^j} f(\mathbf{x})\Vert^2_{P_\mathcal{X}}$.
We then (1) provide a principled approach for quantifying variable selection
uncertainty by deriving its posterior distribution, and (2) show that the
approach is generalizable even to non-differentiable models such as tree
ensembles. Rigorous Bayesian nonparametric theorems are derived to guarantee
the posterior consistency and asymptotic uncertainty of the proposed approach.
Extensive simulations and experiments on healthcare benchmark datasets confirm
that the proposed algorithm outperforms existing classic and recent variable
selection methods.


  A class of long Gamma-Ray Bursts (GRBs) presenting light curves with an
extended plateau phase in their X-ray afterglows obeys a correlation between
the rest frame end time of the plateau, $T_a$, and its corresponding X-ray
luminosity, $L_{a}$, Dainotti et al. (2008). In this work we perform an
analysis of a total sample of 176 {\it Swift} GRBs with known redshifts,
exhibiting afterglow plateaus. By adding a third parameter, that is the peak
luminosity in the prompt emission, $L_{peak}$, we discover the existence of a
new three parameter correlation. The scatter of data about this plane becomes
smaller when a class-specific GRB sample is defined. This sample of 122 GRBs is
selected from the total sample by excluding GRBs with associated Supernovae
(SNe), X-ray flashes and short GRBs with extended emission. {\bf With this
sample the three parameter correlation identifies a GRB `fundamental plane'}.
Moreover, we further limit our analysis to GRBs with lightcurves having good
data coverage and almost flat plateaus, 40 GRBs forming our `gold sample'. The
intrinsic scatter, $\sigma_{int}=0.27 \pm 0.04$, for the three-parameter
correlation for this last subclass is more than twice smaller than the value
for the $L_{a}-T_a$ one, making this the tightest three parameter correlation
involving the afterglow plateau phase. Finally, we also show that a slightly
less tight correlation is present between $L_{peak}$ and a proxy for the total
energy emitted during the plateau phase, $L_a T_a$, confirming the existence of
an energy scaling between prompt and afterglow phases.


  Experiments with vortex beams have shown a surge of interest in controlling
cold atoms. Most of the controlling protocols are dominated by circularly
polarized light due to its ability to induce vector polarization at atoms,
which is impossible for paraxial linearly polarized light. Here we develop a
theory for frequency dependent polarizability of an atomic state interacting
with a focused linearly polarized vortex beam. The naturally induced spin-orbit
coupling in this type of linearly polarized beam produces vector component of
the valence polarizability to an atomic state, obeying the total angular
momentum conservation of the beam. The theory is employed on $^{87}$Sr$^{+}$
ion to accurately calculate the magic wavelengths for the clock transitions and
tune-out wavelengths for the clock states using relativistic coupled-cluster
method. The induced vector component in the dynamic polarizability due to the
linearly polarized focused vortex beam promotes fictitious magnetic field to
the atomic state. We demonstrate that this fictitious magnetic field, depending
on the focusing angle and OAM of the beam, improves the flexibility of the
coherent heteronuclear spin oscillations in a spin-1 mixture of $^{87}$Rb and
$^{23}$Na atoms.


  The zero-degree calorimeters (ZDC) installed in the ALICE, ATLAS and CMS
experiments at the LHC, make possible simultaneous detection of
forward-backward leading neutrons, pp=>nXn. Such data with sufficiently high
statistics could be a source of information about the pion-pion total cross
section at high energies, provided that the absorption corrections, which are
expected to be strong, are well understood. Otherwise, making a plausible
assumption about the magnitude of the pion-pion cross section, one can consider
such measurements as a way to study the absorption effects, which is the main
focus of the present paper. These effects introduced at the amplitude level,
are found to be different for the pion fluxes, which either conserve or flip
the nucleon helicity. The pion fluxes from both colliding protons are
essentially reduced by absorption, moreover, there is a common absorption
suppression factor, which breaks down the factorized form of the cross section.
We also evaluate the feed-down corrections related to the initial/final state
inelastic processes possessing a rapidity gap, and found them to be small in
the kinematic range under consideration. The contribution of other iso-vector
Reggeons, spin-flip natural parity rho and a2, and spin non-flip unnatural
parity a1 are also evaluated and found to be rather small.


  Despite a wealth of experimental data for high p_T processes in heavy-ion
collisions, discriminating between different models of hard parton-medium
interactions has been difficult. One important reason is that the pQCD parton
spectrum at RHIC kinematics is so steeply falling that distinguishing even a
moderate shift in parton energy from complete parton absorption is almost
impossible in observable quantities. In essence, energy loss models are
effectively only probed in the vicinity of zero energy loss and as a result,
only the pathlength dependence of energy loss offers some discriminating power
at RHIC kinematics. At LHC, this is no longer the case: Due to the much flatter
shape of the parton spectra originating from 2.76 AGeV collisions, the
available observables probe much deeper into the model dynamics. A simultaneous
fit of the nuclear suppression both at RHIC and LHC kinematics has thus a huge
potential to discriminate between various models with equally good description
of RHIC data alone.


  We establish an inequality which involves a non-negative function defined on
the vertices of a finite $m$-ary regular rooted tree. The inequality may be
thought of as relating an interaction energy defined on the free vertices of
the tree summed over automorphisms of the tree, to a product of sums of powers
of the function over vertices at certain levels of the tree. Conjugate powers
arise naturally in the inequality, indeed, H\"{o}lder's inequality is a key
tool in the proof which uses induction on subgroups of the automorphism group
of the tree.


  With the unprecedented reliance on cloud computing as the backbone for
storing today's big data, we argue in this paper that the role of the cloud
should be reshaped from being a passive virtual market to become an active
platform for monetizing the big data through Artificial Intelligence (AI)
services. The objective is to enable the cloud to be an active platform that
can help big data service providers reach a wider set of customers and cloud
users (i.e., data consumers) to be exposed to a larger and richer variety of
data to run their data analytic tasks. To achieve this vision, we propose a
novel game theoretical model, which consists of a mix of cooperative and
competitive strategies. The players of the game are the big data service
providers, cloud computing platform, and cloud users. The strategies of the
players are modeled using the two-sided market theory that takes into
consideration the network effects among involved parties, while integrating the
externalities between the cloud resources and consumer demands into the design
of the game. Simulations conducted using Amazon and google clustered data show
that the proposed model improves the total surplus of all the involved parties
in terms of cloud resources provision and monetary profits compared to the
current merchant model.


  Linear Programming (LP) is an important decoding technique for binary linear
codes. However, the advantages of LP decoding, such as low error floor and
strong theoretical guarantee, etc., come at the cost of high computational
complexity and poor performance at the low signal-to-noise ratio (SNR) region.
In this letter, we adopt the penalty dual decomposition (PDD) framework and
propose a PDD algorithm to address the fundamental polytope based maximum
likelihood (ML) decoding problem. Furthermore, we propose to integrate machine
learning techniques into the most time-consuming part of the PDD decoding
algorithm, i.e., check polytope projection (CPP). Inspired by the fact that a
multi-layer perception (MLP) can theoretically approximate any nonlinear
mapping function, we present a specially designed neural CPP (NCPP) algorithm
to decrease the decoding latency. Simulation results demonstrate the
effectiveness of the proposed algorithms.


  The IsoDAR antineutrino source, which produces a flux from 8Li isotope decay
at rest, when paired with the proposed JUNO (Jiangmen Underground Neutrino
Observatory) detector, has unprecedented sensitivity to anti-nu_e disappearance
for oscillations at high Delta m^2. Assuming CPT invariance, the sensitive
region for anti-nu_e disappearance can be used to restrict the allowed
parameter space of an anti-nu_mu to anti-nu_e appearance signal. The 5 sigma
sensitivity of this experiment covers the entire anti-nu_mu to anti-nu_e
allowed parameter space from a combined fit to short-baseline experiments. This
represents a decisive test of the LSND and MiniBooNE antineutrino appearance
signals within all models that are $CPT$ invariant. Running IsoDAR at KamLAND
restricts a large part of the appearance signal region in a similar way.


  A primitive Calabi-Yau threefold is a non-singular Calabi-Yau threefold which
cannot be written as a crepant resolution of a singular fibre of a degeneration
of Calabi-Yau threefolds. These should be thought as the most basic Calabi-Yau
manifolds; all others should arise through degenerations of these. This paper
first continues the study of smoothability of Calabi-Yau threefolds with
canonical singularities begun in the author's previous paper, ``Deforming
Calabi-Yau Threefolds.'' (alg-geom 9506022). If X' is a non-singular Calabi-Yau
threefold, and f:X'->X is a contraction of a divisor to a curve, we obtain
results on when X is smoothable. We then discuss applications of this result to
the classification of primitive Calabi-Yau threefolds. Combining the
deformation theoretic results of this paper with those of ``Deforming
Calabi-Yau Threefolds,'' we obtain strong restrictions on the class of
primitive Calabi-Yau threefolds. We also speculate on how such work might yield
results on connecting together all moduli spaces of Calabi-Yau threefolds.


  We have improved the tree-level model of Ref arXiv:1004.5484 [hep-ph] for
weak production of kaons off nucleons by partially restoring unitarity. This is
achieved by imposing Watson's theorem to the dominant vector and axial-vector
contributions in appropriate angular momentum and isospin quantum number
sectors. The observable consequences of this procedure are investigated.


  Following the outburst of the unusual variable star V838 Monocerotis in 2002,
a spectacular light echo appeared. A light echo provides the possibility of
direct geometric distance determination, because it should contain a ring of
highly linearly polarized light at a linear radius of ct, where t is the time
since the outburst. We present imaging polarimetry of the V838 Mon light echo,
obtained in 2002 and 2005 with the Advanced Camera for Surveys onboard the
Hubble Space Telescope, which confirms the presence of the highly polarized
ring. Based on detailed modeling that takes into account the outburst light
curve, the paraboloidal echo geometry, and the physics of dust scattering and
polarization, we find a distance of 6.1+-0.6 kpc. The error is dominated by the
systematic uncertainty in the scattering angle of maximum linear polarization,
taken to be theta_{max}=90^o +- 5^o. The polarimetric distance agrees
remarkably well with a distance of 6.2+-1.5 kpc obtained from the entirely
independent method of main-sequence fitting to a sparse star cluster associated
with V838 Mon. At this distance, V838 Mon at maximum light had M_V\simeq-9.8,
making it temporarily one of the most luminous stars in the Local Group. Our
validation of the polarimetric method offers promise for measurement of
extragalactic distances using supernova light echoes.


  We discuss modification in the rapidity distribution of the transverse energy
flow as a signal of medium-induced partonic energy loss in ultrarelativistic
heavy ion collisions. A scan of the global energy flow and jet rates in the
wide rapidity region might provide the important information about the
pseudo-rapidity size of a dense medium created in heavy ion collisions at the
LHC.


  Self-supervised visual representation learning has recently attracted
significant research interest. While a common way to evaluate self-supervised
representations is through transfer to various downstream tasks, we instead
investigate the problem of measuring their interpretability, i.e. understanding
the semantics encoded in raw representations. We formulate the latter as
estimating the mutual information between the representation and a space of
manually labelled concepts. To quantify this we introduce a decoding
bottleneck: information must be captured by simple predictors, mapping concepts
to clusters in representation space. This approach, which we call reverse
linear probing, provides a single number sensitive to the semanticity of the
representation. This measure is also able to detect when the representation
contains combinations of concepts (e.g., "red apple") instead of just
individual attributes ("red" and "apple" independently). Finally, we propose to
use supervised classifiers to automatically label large datasets in order to
enrich the space of concepts used for probing. We use our method to evaluate a
large number of self-supervised representations, ranking them by
interpretability, highlight the differences that emerge compared to the
standard evaluation with linear probes and discuss several qualitative
insights. Code at: {\scriptsize{\url{https://github.com/iro-cp/ssl-qrp}}}.


  Super-Kamiokande (SK) can search for weakly interacting massive particles
(WIMPs) by detecting neutrinos produced from WIMP annihilations occurring
inside the Sun. In this analysis, we include neutrino events with interaction
vertices in the detector in addition to upward-going muons produced in the
surrounding rock. Compared to the previous result, which used the upward-going
muons only, the signal acceptances for light (few-GeV/$c^2$ $\sim$
200-GeV/$c^2$) WIMPs are significantly increased. We fit 3903 days of SK data
to search for the contribution of neutrinos from WIMP annihilation in the Sun.
We found no significant excess over expected atmospheric-neutrino background
and the result is interpreted in terms of upper limits on WIMP-nucleon elastic
scattering cross sections under different assumptions about the annihilation
channel. We set the current best limits on the spin-dependent (SD) WIMP-proton
cross section for WIMP masses below 200 GeV/$c^2$ (at 10 GeV/$c^2$, 1.49$\times
10^{-39}$ cm$^2$ for $\chi\chi\rightarrow b \bar{b}$ and 1.31$\times 10^{-40}$
cm$^2$ for $\chi\chi\rightarrow\tau^+\tau^-$ annihilation channels), also
ruling out some fraction of WIMP candidates with spin-independent (SI) coupling
in the few-GeV/$c^2$ mass range.


  We examine spin density wave and triplet superconductivity as possible ground
states of the Bernal bilayer graphene. The spin density wave is stable for the
unbiased and undoped bilayer. Both the doping and the applied bias voltage
destroy this phase. We show that, when biased and slightly doped, bilayer can
host a triplet superconducting phase. The mechanisms for both ordered phases
rely on the renormalized Coulomb interaction. Consistency of our theoretical
conclusions with recent experimental results are discussed.


  We study the photon self-energy in magnetized chiral plasma by solving the
response of electromagnetic field perturbations in chiral kinetic theory with
Landau level states. With lowest Landau level approximation and in
collisionless limit, we find solutions for three particular perturbations:
parallel electric field, static perpendicular electric and magnetic field,
corresponding to chiral magnetic wave, drift state and tilted state, from which
we extract components of photon self-energy in different kinematics. We show no
solution is possible for more general field perturbations. We argue this is an
artifact of the collisionless limit: while static solution corresponding to
drift state and tilted state can be found, they cannot be realized dynamically
without interaction between Landau levels. We also discuss possible
manifestation of side-jump effect due to both boost and rotation, with the
latter due to the presence of background magnetic field.


  Rehabilitation research focuses on determining the components of a treatment
intervention, the mechanism of how these components lead to recovery and
rehabilitation, and ultimately the optimal intervention strategies to maximize
patients' physical, psychologic, and social functioning. Traditional randomized
clinical trials that study and establish new interventions face several
challenges, such as high cost and time commitment. Observational studies that
use existing clinical data to observe the effect of an intervention have shown
several advantages over RCTs. Electronic Health Records (EHRs) have become an
increasingly important resource for conducting observational studies. To
support these studies, we developed a clinical research datamart, called
ReDWINE (Rehabilitation Datamart With Informatics iNfrastructure for rEsearch),
that transforms the rehabilitation-related EHR data collected from the UPMC
health care system to the Observational Health Data Sciences and Informatics
(OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model
(CDM) to facilitate rehabilitation research. The standardized EHR data stored
in ReDWINE will further reduce the time and effort required by investigators to
pool, harmonize, clean, and analyze data from multiple sources, leading to more
robust and comprehensive research findings. ReDWINE also includes deployment of
data visualization and data analytics tools to facilitate cohort definition and
clinical data analysis. These include among others the Open Health Natural
Language Processing (OHNLP) toolkit, a high-throughput NLP pipeline, to provide
text analytical capabilities at scale in ReDWINE. Using this comprehensive
representation of patient data in ReDWINE for rehabilitation research will
facilitate real-world evidence for health interventions and outcomes.


  A possibility to observe the persistent voltage in a superconducting ring of
different widths of the arms is experimentally investigated. It was earlier
found that switching of the arms between superconducting and normal states by
ac current induces the dc voltage oscillation in magnetic field with a period
corresponding to the flux quantum inside the ring. We use systems with a large
number of asymmetric rings connected in series in order to investigate the
possibility to observe this quantum phenomenon near the superconducting
transition where thermal fluctuations switch ring segments without external
influence and the persistent current is much smaller than in the
superconducting state.


  This paper is devoted to the development of adaptive control schemes for
uncertain discrete-time systems, which guarantee robust, global, exponential
convergence to the desired equilibrium point of the system. The proposed
control scheme consists of a nominal feedback law, which achieves robust,
global, exponential stability properties when the vector of the parameters is
known, in conjunction with a nonlinear, dead-beat observer. The obtained
results are applicable to highly nonlinear, uncertain discrete-time systems
with unknown constant parameters. The applicability of the obtained results to
real control problems is demonstrated by the rigorous application of the
proposed adaptive control scheme to uncertain freeway models.


  We report the detection of methyl ketene towards TMC-1 with the QUIJOTE line
survey. Nineteen rotational transitions with rotational quantum numbers ranging
from J = 3 up to J = 5 and Ka =< 2 were identified in the frequency range
32.0-50.4 GHz, 11 of which arise above the 3{\sigma} level. We derived a column
density for CH3CHCO of N=1.5x10^11 cm-2 and a rotational temperature of 9 K.
Hence, the abundance ratio between ketene and methyl ketene, CH2CO/CH3CHCO, is
93. This species is the second C3H4O isomer detected. The other, trans-propenal
(CH2CHCHO), corresponds to the most stable isomer and has a column density of
N=(2.2+-0.3)x10^11 cm-2, which results in an abundance ratio CH2CHCHO/CH3CHCO
of 1.5. The next non-detected isomer with the lowest energy is cis-propenal,
which is therefore a good candidate for future discovery. We have carried out
an in-depth study of the possible gas-phase chemical reactions involving methyl
ketene to explain the abundance detected, achieving good agreement between
chemical models and observations.


  Let $W$ denote the Brownian motion. For any exponentially bounded Borel
function $g$ the function $u$ defined by $u(t,x)= \mathbb{E}[g(x{+}\sigma
W_{T-t})]$ is the stochastic solution of the backward heat equation with
terminal condition $g$. Let $u^n(t,x)$ denote the corresponding approximation
generated by a simple symmetric random walk with time steps $2T/n$ and space
steps $\pm \sigma \sqrt{T/n}$ where $\sigma > 0$. For quite irregular terminal
conditions $g$ (bounded variation on compact intervals, locally H\"older
continuous) the rate of convergence of $u^n(t,x)$ to $u(t,x)$ is considered,
and also the behavior of the error $u^n(t,x)-u(t,x)$ as $t$ tends to $T$


  This paper considers economic intelligence contribution to exploit individual
and collective images of change, in ICT design decision-making. Technical
devices meeting with real use situations often gives the opportunity to emerge
mental images, that a innovation process, through its unprecedented nature, can
not anticipate. Although methodologies exists for quality and design project
management, the survey we conduct among small ICT publishers, show how they are
not very suitable for small firms. This elements taken into account, we try to
build a proposition of exploration ? analyze ? sum up process, adapted to this
type of actors decisional process.


  The initial-boundary value problems for linear non-autonomous first order
evolution equations are examined. Our assumptions provide a unified treatment
which is applicable to many situations, where the domains of the operators may
change with time. We study existence, uniqueness and maximal regularity of
solutions in Sobolev spaces. In contrast to the previous results we use only
the continuity assumption on the operators in the main part of the equation.


  It is shown that an application of optical theorem for the non-unitary
S-matrix can lead to the qualitative error in the result.


  We investigate the internal structure of charged black holes with a
spherically symmetric model in the double-null coordinate system. Hawking
radiation is considered using the S-wave approximation of semiclassical
back-reaction and discharge is simulated by supplying an oppositely charged
matter to the black hole. In the stage of formation, the internal structure is
determined by the mass and charge of collapsing matter. When the charge-mass
ratio is small, a wormhole-like internal structure is observed. The structure
becomes analogous to the static limit as the ratio reaches unity. After the
formation, mass inflation induces large curvature in the internal structure,
which makes the structure insensitive to the late-time perturbations. The
internal structure determined from the formation seems to be maintained during
a substantial mass reduction. The discharge and neutralization of charged black
holes is also investigated for both non-evaporating and evaporating cases.
Finally, we discuss the implications of the wormhole-like structure inside of
charged black holes.


  We study the merger history of primordial black holes (PBHs) in a scenario
where they represent the dominant dark matter component of a typical dwarf
galaxies' core. We investigate the possibility of a sequence of collisions
resulting in a hierarchical merger of black holes and look at the final mass
spectrum in such {\it clusters}, which initially present a monochromatic
(single-mass) PBH population. Our study shows that the merging process results
in the transfer of about $40\%$ of the total mass of the core to the merger
products regardless of the initial mass of PBHs, with about $5\%$ of energy
radiated out in the form of gravitational waves. We find that, in the lighter
mass limit, black holes up to eight times more massive than the original
population can be formed within a Hubble time.


  For a finite group $G$, (semi-)Mackey functors and (semi-)Tambara functors
are regarded as $G$-bivariant analogs of (semi-)groups and (semi-)rings
respectively. In fact if $G$ is trivial, they agree with the ordinary
(semi-)groups and (semi-)rings, and many naive algebraic properties concerning
rings and groups have been extended to these $G$-bivariant analogous notions.
  In this article, we investigate a $G$-bivariant analog of the semi-group
rings with coefficients. Just as a coefficient ring $R$ and a monoid $Q$ yield
the semi-group ring $R[Q]$, our constrcution enables us to make a Tambara
functor $T[M]$ out of a semi-Mackey functor $M$, and a coefficient Tambara
functor $T$. This construction is a composant of the Tambarization and the
Dress construction.
  As expected, this construction is the one uniquely determined by the
righteous adjoint property. Besides in analogy with the trivial group case, if
$M$ is a Mackey functor, then $T[M]$ is equipped with a natural Hopf structure.
  Moreover, as an application of the above construction, we also obtain some
$G$-bivariant analogs of the polynomial rings.


  A new theory describing the interaction between atoms and a conductor with
small densities of current carriers is presented. The theory takes into account
the penetration of the static component of the thermally fluctuating field in
the conductor and generalizes the Lifshitz theory in the presence of a spatial
dispersion. The equation obtained for the force describes the continuous
crossover between the Lifshitz results for dielectrics and metals.


  We make an in-depth study of the known border rank (i.e. approximate)
algorithms for the matrix multiplication tensor encoding the multiplication of
an n x 2 matrix by a 2 x 2 matrix.


  When following the growth of structure in the Universe, we propose replacing
merger trees with merger graphs, in which haloes can both merge and split into
separate pieces. We show that this leads to smoother mass growth and eliminates
catastrophic failures in which massive haloes have no progenitors or
descendants. For those who prefer to stick with merger trees, we find that
trees derived from our merger graphs have similar mass growth properties to
previous methods, but again without catastrophic failures. For future galaxy
formation modelling, two different density thresholds can be used to
distinguish host haloes (extended galactic haloes, groups and clusters) from
higher-density subhaloes: sites of galaxy formation.


  Antimicrobial resistance is an important public health concern that has
implications in the practice of medicine worldwide. Accurately predicting
resistance phenotypes from genome sequences shows great promise in promoting
better use of antimicrobial agents, by determining which antibiotics are likely
to be effective in specific clinical cases. In healthcare, this would allow for
the design of treatment plans tailored for specific individuals, likely
resulting in better clinical outcomes for patients with bacterial infections.
In this work, we present the recent work of Drouin et al. (2016) on using Set
Covering Machines to learn highly interpretable models of antibiotic resistance
and complement it by providing a large scale application of their method to the
entire PATRIC database. We report prediction results for 36 new datasets and
present the Kover AMR platform, a new web-based tool allowing the visualization
and interpretation of the generated models.


  Two theorems of Weyl tell us that the algebra of Lorentz- (and parity-)
invariant polynomials in the momenta of $n$ particles are generated by the dot
products and that the redundancies which arise when $n$ exceeds the spacetime
dimension $d$ are generated by the $(d+1)$-minors of the $n \times n$ matrix of
dot products. Here, we use the Cohen-Macaulay structure of the invariant
algebra to provide a more direct characterisation in terms of a Hironaka
decomposition. Among the benefits of this approach is that it can be
generalized straightforwardly to cases where a permutation group acts on the
particles, such as when some of the particles are identical. In the first
non-trivial case, $n=d+1$, we give a homogeneous system of parameters that is
valid for the action of an arbitrary permutation symmetry and make a conjecture
for the full Hironaka decomposition in the case without permutation symmetry.
An appendix gives formul\ae\ for the computation of the relevant Hilbert series
for $d \leq 4$.


  The global pandemic of COVID-19 over the last 2.5 years have produced an
enormous amount of epidemic/public health datasets, which may also be useful
for studying the underlying structure of our globally connected world. Here we
used the Johns Hopkins University COVID-19 dataset to construct a correlation
network of countries/regions and studied its global community structure.
Specifically, we selected countries/regions that had at least 100,000
cumulative positive cases from the dataset and generated a 7-day moving average
time series of new positive cases reported for each country/region. We then
calculated a time series of daily change exponents by taking the day-to-day
difference in log of the number of new positive cases. We constructed a
correlation network by connecting countries/regions that had positive
correlations in their daily change exponent time series using their Pearson
correlation coefficient as the edge weight. Applying the modularity
maximization method revealed that there were three major communities: (1)
Mainly Europe + North America + Southeast Asia that showed similar six-peak
patterns during the pandemic, (2) mainly Near/Middle East + Central/South Asia
+ Central/South America that loosely followed Community 1 but had a notable
increase of activities because of the Delta variant and was later impacted
significantly by the Omicron variant, and (3) mainly Africa + Central/East
Canada + Australia that did not have much activities until a huge spike was
caused by the Omicron variant. These three communities were robustly detected
under varied settings. Constructing a 3D "phase space" by using the median
curves in those three communities for x-y-z coordinates generated an effective
summary trajectory of how the global pandemic progressed.


  We study the Lepton Flavor Violating (LFV) $e(\mu)-\tau$ conversion in Deep
Inelastic Scattering (DIS) of electron (muon) on fixed-target nuclei. Our
model-independent analysis is based on the set of the low-energy effective
four-fermion LFV operators composed of leptons and quarks with the
corresponding mass scales $\Lambda_{k}$ for each operator. Using the estimated
sensitivity of the search for this LFV process in events with large missing
energy in the NA64 experiment at the CERN SPS, we derive lower limits for
$\Lambda_{k}$ and compared them with the corresponding limits existing in the
literature. We show that the DIS $e(\mu)-\tau$ conversion is able to provide a
plenty of new limits as yet non-existing in the literature. We also analyzed
the energy spectrum of the final-state $\tau$ and discussed viability of the
observation of this process in the NA64 experiment and ones akin to it. The
case of polarized beams and targets is also discussed.


  Locally Differentially Private (LDP) Reports are commonly used for collection
of statistics and machine learning in the federated setting. In many cases the
best known LDP algorithms require sending prohibitively large messages from the
client device to the server (such as when constructing histograms over large
domain or learning a high-dimensional model). This has led to significant
efforts on reducing the communication cost of LDP algorithms.
  At the same time LDP reports are known to have relatively little information
about the user's data due to randomization. Several schemes are known that
exploit this fact to design low-communication versions of LDP algorithm but all
of them do so at the expense of a significant loss in utility. Here we
demonstrate a general approach that, under standard cryptographic assumptions,
compresses every efficient LDP algorithm with negligible loss in privacy and
utility guarantees. The practical implication of our result is that in typical
applications the message can be compressed to the size of the server's
pseudo-random generator seed. More generally, we relate the properties of an
LDP randomizer to the power of a pseudo-random generator that suffices for
compressing the LDP randomizer. From this general approach we derive
low-communication algorithms for the problems of frequency estimation and
high-dimensional mean estimation. Our algorithms are simpler and more accurate
than existing low-communication LDP algorithms for these well-studied problems.


  In the previous papers we present a construction of the set ${\cal U}_{SBS}$
in the direct product ${\cal B}_S \times \mathbb{P} \Gamma (M, L)$ of the
moduli space of Bohr - Sommerfeld lagrangian submanifolds of fixed topological
type and the projectivized space of smooth sections of the prequantization
bundle $L \to M$ over a given compact simply connected symplectic manifold $M$.
Canonical projections $p: {\cal U}_{SBS} \to \mathbb{P} \Gamma (M, L)$ and $q:
{\cal U}_{SBS} \to {\cal B}_S$ are studied in the present text: first, we show
that the differential ${\rm d} p$ at a given point is an isomorphism, which
implies that a natural complex structure can be defined on ${\cal U}_{SBS}$;
second, the projection $q: {\cal U}_{SBS} \to {\cal B}_S$ splits as the
combination ${\cal U}_{SBS} \to T {\cal B}_S \to {\cal B}_S$ such that the
fibers of the first map are complex subsets in ${\cal U}_{SBS}$. This implies
that an appropriate section of the first map should define a complex structure
on $T {\cal B}_S$; therefore it can be seen as a complexification of the moduli
space ${\cal B}_S$. The construction can be exploited in the Lagrangian
approach to Geometric Quantization


  We consider efficient methods for computing solutions to and estimating
uncertainties in dynamic inverse problems, where the parameters of interest may
change during the measurement procedure. Compared to static inverse problems,
incorporating prior information in both space and time in a Bayesian framework
can become computationally intensive, in part, due to the large number of
unknown parameters. In these problems, explicit computation of the square root
and/or inverse of the prior covariance matrix is not possible. In this work, we
develop efficient, iterative, matrix-free methods based on the generalized
Golub-Kahan bidiagonalization that allow automatic regularization parameter and
variance estimation. We demonstrate that these methods can be more flexible
than standard methods and develop efficient implementations that can exploit
structure in the prior, as well as possible structure in the forward model.
Numerical examples from photoacoustic tomography, deblurring, and passive
seismic tomography demonstrate the range of applicability and effectiveness of
the described approaches. Specifically, in passive seismic tomography, we
demonstrate our approach on both synthetic and real data. To demonstrate the
scalability of our algorithm, we solve a dynamic inverse problem with
approximately $43,000$ measurements and $7.8$ million unknowns in under $40$
seconds on a standard desktop.


  Neural Network Language Models (NNLMs) generate probability distributions by
applying a softmax function to a distance metric formed by taking the dot
product of a prediction vector with all word vectors in a high-dimensional
embedding space. The dot-product distance metric forms part of the inductive
bias of NNLMs. Although NNLMs optimize well with this inductive bias, we show
that this results in a sub-optimal ordering of the embedding space that
structurally impoverishes some words at the expense of others when assigning
probability. We present numerical, theoretical and empirical analyses showing
that words on the interior of the convex hull in the embedding space have their
probability bounded by the probabilities of the words on the hull.


  We consider an open manifold which is the interior of a compact manifold with
boundary. Assuming gauge invariance, we classify magnetic fields with compact
support into being trapping or non-trapping. We study spectral properties of
the associated magnetic Laplacian for a class of Riemannian metrics which
includes complete hyperbolic metrics of finite volume. When $B$ is
non-trapping, the magnetic Laplacian has nonempty essential spectrum. Using
Mourre theory, we show the absence of singular continuous spectrum and the
local finiteness of the point spectrum. When $B$ is trapping, the spectrum is
discrete and obeys the Weyl law. The existence of trapping magnetic fields with
compact support depends on cohomological conditions, indicating a new and very
strong long-range effect.
  In the non-gauge invariant case, we exhibit a strong Aharonov-Bohm effect. On
hyperbolic surfaces with at least two cusps, we show that the magnetic
Laplacian associated to every magnetic field with compact support has purely
discrete spectrum for some choices of the vector potential, while other choices
lead to a situation of limit absorption principle.
  We also study perturbations of the metric. We show that in the Mourre theory
it is not necessary to require a decay of the derivatives of the perturbation.
This very singular perturbation is then brought closer to the perturbation of a
potential.


  A non-dichotomic Bell-type inequality for macroscopic photon numbers in two
spatially separated beams is derived from local realistic assumptions. This
inequality is shown to be possible to violate with the macroscopic Bell states
recently obtained in experiment.


  For many years, the distributed systems community has struggled to smooth the
transition from local to remote computing. Transparency means concealing the
complexities of distributed programming like remote locations, failures or
scaling. For us, full transparency implies that we can compile, debug and run
unmodified single-machine code over effectively unlimited compute, storage, and
memory resources. We elaborate in this article why resource disaggregation in
serverless computing is the definitive catalyst to enable full transparency in
the Cloud. We demonstrate with two experiments that we can achieve transparency
today over disaggregated serverless resources and obtain comparable performance
to local executions. We also show that locality cannot be neglected for many
problems and we present five open research challenges: granular middleware and
locality, memory disaggregation, virtualization, elastic programming models,
and optimized deployment. If full transparency is possible, who needs explicit
use of middleware if you can treat remote entities as local ones? Can we close
the curtains of distributed systems complexity for the majority of users?


  Given a family $\mathcal F\subset 2^{[n]}$, its diversity is the number of
sets not containing an element with the highest degree. The concept of
diversity has proven to be very useful in the context of $k$-uniform
intersecting families. In this paper, we study (different notions of) diversity
in the context of other extremal set theory problems. One of the main results
of the paper is a sharp stability result for cross-intersecting families in
terms of diversity and, slightly more generally, sharp stability for the
Kruskal--Katona theorem.


  In this paper, we introduce the applications of third-order reduced
biquaternion tensors in color video processing. We first develop algorithms for
computing the singular value decomposition (SVD) of a third-order reduced
biquaternion tensor via a new Ht-product. As theoretical applications, we
define the Moore-Penrose inverse of a third-order reduced biquaternion tensor
and develop its characterizations. In addition, we discuss the general (or
Hermitian) solutions to reduced biquaternion tensor equation
$\mathcal{A}\ast_{Ht} \mathcal{X}=\mathcal{B}$ as well as its least-square
solution. Finally, we compress the color video by this SVD, and the
experimental data shows that our method is faster than the compared scheme.


  We report the detection of a magnetic field on Vega through
spectropolarimetric observations. We acquired 257 Stokes V, high
signal-to-noise and high-resolution echelle spectra during four consecutive
nights with the NARVAL spectropolarimeter at the 2-m Telescope Bernard Lyot of
Observatoire du Pic du Midi (France). A circularly polarized signal in line
profiles is unambiguously detected after combining the contribution of about
1200 spectral lines for each spectrum and summing the signal over the 257
spectra. Due to the low amplitude of the polarized signal, various tests have
been performed to discard the possibility of a spurious polarized signal. They
all point towards a stellar origin of the polarized signal. Interpreting this
polarization as a Zeeman signature leads to a value of $-0.6 \pm 0.3$ G for the
disk-averaged line-of-sight component of the surface magnetic field. This is
the first strong evidence of a magnetic field in an A-type star which is not an
Ap chemically peculiar star. Moreover, this longitudinal magnetic field is
smaller by about two orders of magnitude than the longitudinal magnetic field
(taken at its maximum phase) of the most weakly magnetic Ap stars. Magnetic
fields similar to the Vega magnetic field could be present but still undetected
in many other A-type stars.


  This paper describes the ESPnet-ST group's IWSLT 2021 submission in the
offline speech translation track. This year we made various efforts on training
data, architecture, and audio segmentation. On the data side, we investigated
sequence-level knowledge distillation (SeqKD) for end-to-end (E2E) speech
translation. Specifically, we used multi-referenced SeqKD from multiple
teachers trained on different amounts of bitext. On the architecture side, we
adopted the Conformer encoder and the Multi-Decoder architecture, which equips
dedicated decoders for speech recognition and translation tasks in a unified
encoder-decoder model and enables search in both source and target language
spaces during inference. We also significantly improved audio segmentation by
using the pyannote.audio toolkit and merging multiple short segments for long
context modeling. Experimental evaluations showed that each of them contributed
to large improvements in translation performance. Our best E2E system combined
all the above techniques with model ensembling and achieved 31.4 BLEU on the
2-ref of tst2021 and 21.2 BLEU and 19.3 BLEU on the two single references of
tst2021.


  Quantum dots (QDs) defined with electrostatic gates are a leading platform
for a scalable quantum computing implementation. However, with increasing
numbers of qubits, the complexity of the control parameter space also grows.
Traditional measurement techniques, relying on complete or near-complete
exploration via two-parameter scans (images) of the device response, quickly
become impractical with increasing numbers of gates. Here we propose to
circumvent this challenge by introducing a measurement technique relying on
one-dimensional projections of the device response in the multidimensional
parameter space. Dubbed the ``ray-based classification (RBC) framework,'' we
use this machine learning approach to implement a classifier for QD states,
enabling automated recognition of qubit-relevant parameter regimes. We show
that RBC surpasses the 82 % accuracy benchmark from the experimental
implementation of image-based classification techniques from prior work while
reducing the number of measurement points needed by up to 70 %. The reduction
in measurement cost is a significant gain for time-intensive QD measurements
and is a step forward toward the scalability of these devices. We also discuss
how the RBC-based optimizer, which tunes the device to a multiqubit regime,
performs when tuning in the two-dimensional and three-dimensional parameter
spaces defined by plunger and barrier gates that control the QDs.This work
provides experimental validation of both efficient state identification and
optimization with machine learning techniques for non-traditional measurements
in quantum systems with high-dimensional parameter spaces and time-intensive
measurements.


  We study the spectral function of the 2D Hubbard model using cluster
perturbation theory, and the density matrix renormalization group as a cluster
solver. We reconstruct the two-dimensional dispersion at, and away from
half-filling using 2xL ladders, with L up to 80 sites, yielding results with
unprecedented resolution in excellent agreement with quantum Monte Carlo. The
main features of the spectrum can be described with a mean-field dispersion,
while kinks and pseudogap traced back to scattering between spin and charge
degrees of freedom.


  In this paper we introduce a novel statistical framework based on the first
two quantile conditional moments that facilitates effective goodness-of-fit
testing for one-sided L\'evy distributions. The scale-ratio framework
introduced in this paper extends our previous results in which we have shown
how to extract unique distribution features using conditional variance ratio
for the generic class of {\alpha}-stable distributions. We show that the
conditional moment-based goodness-of-fit statistics are a good alternative to
other methods introduced in the literature tailored to the one-sided L\'evy
distributions. The usefulness of our approach is verified using an empirical
test power study. For completeness, we also derive the asymptotic distributions
of the test statistics and show how to apply our framework to real data.


  The gravitational radiation originating from a compact binary system in
circular orbit is usually expressed as an infinite sum over radiative multipole
moments. In a slow-motion approximation, each multipole moment is then
expressed as a post-Newtonian expansion in powers of v/c, the ratio of the
orbital velocity to the speed of light. The bare multipole truncation of the
radiation consists in keeping only the leading-order term in the post-Newtonian
expansion of each moment, but summing over all the multipole moments. In the
case of binary systems with small mass ratios, the bare multipole series was
shown in a previous paper to converge for all values v/c < 2/e, where e is the
base of natural logarithms. In this paper, we extend the analysis to a dressed
multipole truncation of the radiation, in which the leading-order moments are
corrected with terms of relative order (v/c)^2 and (v/c)^3. We find that the
dressed multipole series converges also for all values v/c < 2/e, and that it
coincides (within 1%) with the numerically ``exact'' results for v/c < 0.2.


  Several mathematical models of tumor growth are now commonly used to explain
medical observations and predict cancer evolution based on images. These models
incorporate mechanical laws for tissue compression combined with rules for
nutrients availability which can differ depending on the situation under
consideration, in vivo or in vitro. Numerical solutions exhibit, as expected
from medical observations, a proliferative rim and a necrotic core. However,
their precise profiles are rather complex, both in one and two dimensions.


  In learning-to-rank for information retrieval, a ranking model is
automatically learned from the data and then utilized to rank the sets of
retrieved documents. Therefore, an ideal ranking model would be a mapping from
a document set to a permutation on the set, and should satisfy two critical
requirements: (1)~it should have the ability to model cross-document
interactions so as to capture local context information in a query; (2)~it
should be permutation-invariant, which means that any permutation of the
inputted documents would not change the output ranking. Previous studies on
learning-to-rank either design uni-variate scoring functions that score each
document separately, and thus failed to model the cross-document interactions;
or construct multivariate scoring functions that score documents sequentially,
which inevitably sacrifice the permutation invariance requirement. In this
paper, we propose a neural learning-to-rank model called SetRank which directly
learns a permutation-invariant ranking model defined on document sets of any
size. SetRank employs a stack of (induced) multi-head self attention blocks as
its key component for learning the embeddings for all of the retrieved
documents jointly. The self-attention mechanism not only helps SetRank to
capture the local context information from cross-document interactions, but
also to learn permutation-equivariant representations for the inputted
documents, which therefore achieving a permutation-invariant ranking model.
Experimental results on three large scale benchmarks showed that the SetRank
significantly outperformed the baselines include the traditional
learning-to-rank models and state-of-the-art Neural IR models.


  The physical interpretation of the appearance of resonant transmission
through single-point barriers is discussed on the basis of a double-layer
heterostructure in the squeezing limit as both the thickness of the layers and
the distance between them tend to zero simultaneously. In this limit, the
electron transmission through a barrier-well structure is derived to be
non-zero at certain discrete values of the system parameters forming the
so-called resonance set, while beyond this set, the structure behaves as a
perfectly reflecting wall. The origin of this phenomenon is shown to result
from the reflection coefficients at the interfaces in the inter-layer space.
The transmission amplitude is computed as a set function defined on the
trihedral angle surface in a three-dimensional parameter space.


  The family of 2-level matroids, that is, matroids whose base polytope is
2-level, has been recently studied and characterized by means of combinatorial
properties. 2-level matroids generalize series-parallel graphs, which have been
already successfully analyzed from the enumerative perspective.
  We bring to light some structural properties of 2-level matroids and exploit
them for enumerative purposes. Moreover, the counting results are used to show
that the number of combinatorially non-equivalent (n-1)-dimensional 2-level
polytopes is bounded from below by $c \cdot n^{-5/2} \cdot \rho^{-n}$, where
$c\approx 0.03791727 $ and $\rho^{-1} \approx 4.88052854$.


  We describe a novel scheme to implement scalable quantum information
processing using Li-Cs molecular state to entangle $^{6}$Li and $^{133}$Cs
ultracold atoms held in independent optical lattices. The $^{6}$Li atoms will
act as quantum bits to store information, and $^{133}$Cs atoms will serve as
messenger bits that aid in quantum gate operations and mediate entanglement
between distant qubit atoms. Each atomic species is held in a separate optical
lattice and the atoms can be overlapped by translating the lattices with
respect to each other. When the messenger and qubit atoms are overlapped,
targeted single spin operations and entangling operations can be performed by
coupling the atomic states to a molecular state with radio-frequency pulses. By
controlling the frequency and duration of the radio-frequency pulses,
entanglement can either be created or swapped between a qubit messenger pair.
We estimate operation fidelities for entangling two distant qubits and discuss
scalability of this scheme and constraints on the optical lattice lasers.


  Hard photodisintegration of the deuteron has been extensively studied in
order to understand the dynamics of the transition from hadronic to quark-gluon
descriptions of the strong interaction. In this work, we discuss the extension
of this program to hard photodisintegration of a $pp$ pair in the $^3$He
nucleus. Experimental confirmation of new features predicted here for the
suggested reaction would advance our understanding of hard nuclear reactions. A
main prediction, in contrast with low-energy observations, is that the $pp$
breakup cross section is not much smaller than the one for $pn$ break up.In
some models, the energy-dependent oscillations observed for $pp$ scattering are
predicted to appear in the $\gamma ^3{\rm He} \to p p + n$ reaction. Such an
observation would open up a completely new field in studies of color coherence
phenomena in hard nuclear reactions. We also demonstrate that, in addition to
the energy dependence, the measurement of the light-cone momentum distribution
of the recoil neutron provides an independent test of the underlying dynamics
of hard disintegration.


  The fundamental sensitivity limit of atomic force microscopy is strongly
correlated to the thermal noise of the cantilever oscillation. A method to
suppress this unwanted noise is to reduce the bandwidth of the measurement, but
this approach is limited by the speed of the measurement and the width of the
cantilever resonance, commonly defined through the quality factor Q. However,
it has been shown that optomechanical resonances in interferometers might
affect the cantilever oscillations resulting in an effective quality factor
Q$_{eff}$ . When the laser power is sufficiently increased the cantilever
oscillations might even reach the regime of self-oscillation. In this
self-oscillation state, the noise of the system is partially determined by the
interaction with the laser light far from equilibrium. Here, we show and
discuss how tuning of the laser power leads to nonlinear optomechanical effects
that can dramatically increase the effective quality factor of the cantilever
leading to out-of-equilibrium noise. We model the effects using a fourth order
nonlinearity of the damping coefficient.


  We present a calculation of diffractive charm jet production at hadron
colliders in perturbative QCD based on the two-gluon exchange model. Differing
from the previous calculations, we abandon the use of the effective
color-singlet vitual gluon simplification, and use the real gluon in the
calculations for the partonic process. We find the final result is free of the
linear singularities due to the small transverse momenta of the exchanged two
gluons. In the leading logarithmic approximation (LLA) in QCD, this process is
related to the off-diagonal gluon density in the proton. As a result, this
process may provide a wide window for testing the two-gluon exchange model, and
may be particularly useful in studying the small $x$ physics. In comparison,
the diffractive bottom jet production is also discussed, and is found to be
important only for large transverse momentum of the jet.


  We continue the studies of Moutard-type transform for generalized analytic
functions started in our previous paper: arXiv:1510.08764. In particular, we
suggest an interpretation of generalized analytic functions as spinor fields
and show that in the framework of this approach Moutard-type transforms for the
aforementioned functions commute with holomorphic changes of variables.


  We investigate the effect of hydrostatic scale heights ${\lambda}(T)$ in
coronal loops on the determination of the vertical temperature structure $T(h)$
of the solar corona. Every method that determines an average temperature at a
particular line-of-sight from optically thin emission (e.g. in EUV or soft
X-ray wavelengths) of a mutli-temperature plasma, is subject to the emission
measure-weighted contributions $dEM(T)/dT$ from different temperatures. Because
most of the coronal structures (along open or closed field lines) are close to
hydrostatic equilibrium, the hydrostatic temperature scale height introduces a
height-dependent weighting function that causes a systematic bias in the
determination of the temperature structure $T(h)$ as function of altitude $h$.
The net effect is that the averaged temperature seems to increase with
altitude, $dT(h)/dh > 0$, even if every coronal loop (of a multi-temperature
ensemble) is isothermal in itself. We simulate this effect with differential
emission measure distributions observed by {\sl SERTS} for an instrument with a
broadband temperature filter such as {\sl Yohkoh/SXT} and find that the
apparent temperature increase due to hydrostatic weighting is of order $\Delta
T \approx T_0 \times h/r_{\sun}$. We suggest that this effect largely explains
the systematic temperature increase in the upper corona reported in recent
studies (e.g. by Sturrock et al., Wheatland et al., or Priest et al.), rather
than being an intrinsic signature of a coronal heating mechanism.


  With the advent of neuroimaging and microsurgery, there is a rising need for
capturing images through an optical fiber. We present an approach of imaging
through a single fiber without mechanical scanning by implementing
spatial-spectral encoding. The spectral encoding is achieved through a
microfabricated spectral filter array, where light from different spatial
pixels is coded with a highly orthogonal spectrum. The image is then
computationally recovered via pseudo inverse of the encoding process. We
demonstrate imaging of a $4 \times 4$ binary object at the proximity of the
spectral filter array using $560-625nm$ wavelength band. The recovered image
maintains an error rate of $<11\%$ when measured using a spectrometer with a
spectral resolution of $1.5nm$. The image remains unchanged with fiber bending
or moving. Thus our approach shows a more robust way to image through a single
optical fiber, with potential applications in compact endoscopes and
angioscopes.


  We present results based on the first 20 Chandra images obtained in a survey
of jets in radio selected flat-spectrum quasars (FSRQs), along with new
sub-arcsecond radio maps and optical images. We discover jet X-ray flux in 12
sources despite short exposures, establishing that FSRQ jets are often X-ray
bright. The X-ray morphology typically matches the radio and fades rapidly
after the first sharp radio bend, but there are notable exceptions. Optical
non-detections rule out simple synchrotron models for jet X-ray emission,
implying these systems are dominated by inverse Compton (IC) scattering. Models
of IC scattering of the cosmic microwave background (CMB) constrain the bulk
flow and magnetic field, suggesting the jets are oriented close to our line of
sight, with deprojected lengths often \gg 100 kpc.


  The late universe contains a wealth of information about fundamental physics
and gravity, wrapped up in non-Gaussian fields. To make use of as much
information as possible it is necessary to go beyond two-point statistics.
Rather than going to higher order N-point correlation functions, we demonstrate
that the probability distribution function (PDF) of spheres in the matter field
(a one-point function) already contains a significant amount of this
non-Gaussian information. The matter PDF dissects different density
environments which are lumped together in two-point statistics, making it
particularly useful for probing modifications of gravity or expansion history.
Our approach in Cataneo et. al. 2021 extends the success of Large Deviation
Theory for predicting the matter PDF in $\Lambda$CDM in these ''extended''
cosmologies. A Fisher forecast demonstrates the information content in the
matter PDF via constraints for a Euclid-like survey volume combining the 3D
matter PDF with the 3D matter power spectrum. Adding the matter PDF halves the
uncertainties on parameters in an evolving dark energy model, relative to the
power spectrum alone. Additionally, the matter PDF contains enough non-linear
information to substantially increase the detection significance of departures
from General Relativity, with improvements up to six times the power spectrum
alone. This analysis demonstrates that the matter PDF is a promising
non-Gaussian statistic for extracting cosmological information, particularly
for beyond $\Lambda$CDM models.


  In this paper we have used one 2 variable Boolean function called Rule 6 to
define another beautiful transformation named as Extended Rule-6. Using this
function we have explored the algebraic beauties and its application to an
efficient Round Robin Tournament (RRT) routine for 2k (k is any natural number)
number of teams. At the end, we have thrown some light towards any number of
teams of the form nk where n, k are natural numbers.


  In this note, we study the singular mean field equation defined on a Riemann
surface with a sign-changing potential function. We prove if some singular
sources happen to be placed on the zero-level curve of the potential function,
a priori estimate can still be obtained. As a consequence of this estimate,
existence and multiplicity results can still be obtained based on the topology
of the manifold.


  An equational condition is a set of equations in an algebraic language, and
an algebraic structure satisfies such a condition if it possesses terms that
meet the required equations. We find a single nontrivial equational condition
which is implied by any nontrivial idempotent equational condition.


  In this note the relation between the range-renewal speed and entropy for
i.i.d. models is discussed.


  We propose a novel approach to optimize the design of heterogeneous
materials, with the goal of enhancing their effective fracture toughness under
mode-I loading. The method employs a Gaussian processes-based Bayesian
optimization framework to determine the optimal shapes and locations of stiff
elliptical inclusions within a periodic microstructure in two dimensions. To
model crack propagation, the phase-field fracture method with an efficient
interior-point monolithic solver and adaptive mesh refinement, is used. To
account for the high sensitivity of fracture properties to initial crack
location with respect to heterogeneities, we consider multiple cases of initial
crack and optimize the material for the worst-case scenario. We also impose a
minimum clearance constraint between the inclusions to ensure design
feasibility. Numerical experiments demonstrate that the method significantly
improves the fracture toughness of the material compared to the homogeneous
case.


  We present an improved form of the algorithm for constructing Jacobi
rotations. This is simultaneously a more accurate code for finding the
eigenvalues and eigenvectors of a real symmetric 2x2 matrix.


  The search for artificial structure with tunable topological properties is an
interesting research direction of today's topological physics. Here, we
introduce a scheme to realize `topological semimetal states' with a
three-dimensional periodic inductor-capacitor (LC) circuit lattice, where the
topological nodal-line state and Weyl state can be achieved by tuning the
parameters of inductors and capacitors. A tight-binding-like model is derived
to analyze the topological properties of the LC circuit lattice. The key
characters of the topological states, such as the drumhead-like surface bands
for nodal-line state and the Fermi-arc-like surface bands for Weyl state, are
found in these systems. We also show that the Weyl points are stable with the
fabrication errors of electric devices.


  We derive an analytic expression for the distribution of velocities of
multiple interacting active particles which we test by numerical simulations.
In clear contrast with equilibrium we find that the velocities are coupled to
positions. Our model shows that, even for two particles only, the individual
velocities display a variance depending on the interparticle separation and the
emergence of correlations between the velocities of the particles. When
considering systems composed of many particles we find an analytic expression
connecting the overall velocity variance to density, at the mean-field level,
and to the pair distribution function valid in the limit of small noise
correlation times. Finally we discuss the intriguing analogies and main
differences between our effective free energy functional and the theoretical
scenario proposed so far for phase-separating active particles.


  The properties of an F=1 spinor Bose-Einstein condensate trapped in a
double-well potential are discussed using both a mean-field two-mode approach
and a simplified two-site Bose-Hubbard Hamiltonian. We focus in the region of
phase space in which spin effects lead to a symmetry breaking of the system,
favoring the spatial localization of the condensate in one well. To model this
transition we derive, using perturbation theory, an effective Hamiltonian that
describes N/2 spin singlets confined in a double-well potential.


  We discuss and solve a model for a game with many players, where a subset of
truely deciding players is embedded into a hierarchy of dependent agents.
  These interdependencies modify the game matrix and the Nash equilibria for
the deciding players. In a concrete example, we recognize the partition
function of the Ising model and for high dependency we observe a phase
transition to a new Nash equilibrium, which is the Pareto-efficient outcome.
  An example we have in mind is the game theory for major shareholders in a
stock market, where intermediate companies decide according to a majority vote
of their owners and compete for the final profit. In our model, these
interdependency eventually forces cooperation.


  Reductions in natural habitats urge that we better understand species'
interconnection and how biological communities respond to environmental
changes. However, ecological studies of species' interactions are limited by
their geographic and taxonomic focus which can distort our understanding of
interaction dynamics. We focus on bird-plant interactions that refer to
situations of potential fruit consumption and seed dispersal. We develop an
approach for predicting species' interactions that accounts for errors in the
recorded interaction networks, addresses the geographic and taxonomic biases of
existing studies, is based on latent factors to increase flexibility and borrow
information across species, incorporates covariates in a flexible manner to
inform the latent factors, and uses a meta-analysis data set from 85 individual
studies. We focus on interactions among 232 birds and 511 plants in the
Atlantic Forest, and identify 5% of pairs of species with an unrecorded
interaction, but posterior probability that the interaction is possible over
80%. Finally, we develop a permutation-based variable importance procedure for
latent factor network models and identify that a bird's body mass and a plant's
fruit diameter are important in driving the presence of species interactions,
with a multiplicative relationship that exhibits both a thresholding and a
matching behavior.


  In the paper, the thermodynamic properties of the superconducting state in
the $\rm{B_{2}H_{6}}$ compound have been characterized. The pressure of 360 GPa
has been taken into account. The calculations have been carried out in the
framework of the Eliashberg formalism for the wide range of the Coulomb
pseudopotential: $\mu^{\star}\in<0.1,0.3>$. It has been found that the critical
temperature ($T_{C}$) varies in the range from 147 K to 87 K, depending on the
assumed value of the Coulomb pseudopotential. The ratio of the energy gap to
the critical temperature ($R_{\Delta}\equiv 2\Delta(0)/k_{B}T_{C}$)
significantly exceeds the value predicted by the BCS theory:
$R_{\Delta}\in<4.24,3.98>$. In the similar manner behaves the ratio of the
specific heat jump to the heat of the normal state ($R_{C}\equiv\Delta
C(T_{C})/C^{N}(T_{C})$), namely: $R_{C}\in<2.33,2.17>$. The parameter
$R_{H}\equiv T_{C}C^{N}(T_{C})/H^{2}_{C}(0)$, where $H_{C}(0)$ is the
thermodynamic critical field, ranges from 0.144 to 0.168.


  Multi-view clustering has attracted increasing attentions recently by
utilizing information from multiple views. However, existing multi-view
clustering methods are either with high computation and space complexities, or
lack of representation capability. To address these issues, we propose deep
embedded multi-view clustering with collaborative training (DEMVC) in this
paper. Firstly, the embedded representations of multiple views are learned
individually by deep autoencoders. Then, both consensus and complementary of
multiple views are taken into account and a novel collaborative training scheme
is proposed. Concretely, the feature representations and cluster assignments of
all views are learned collaboratively. A new consistency strategy for cluster
centers initialization is further developed to improve the multi-view
clustering performance with collaborative training. Experimental results on
several popular multi-view datasets show that DEMVC achieves significant
improvements over state-of-the-art methods.


  An important component of human analysis of medical images and their context
is the ability to relate newly seen things to related instances in our memory.
In this paper we mimic this ability by using multi-modal retrieval augmentation
and apply it to several tasks in chest X-ray analysis. By retrieving similar
images and/or radiology reports we expand and regularize the case at hand with
additional knowledge, while maintaining factual knowledge consistency. The
method consists of two components. First, vision and language modalities are
aligned using a pre-trained CLIP model. To enforce that the retrieval focus
will be on detailed disease-related content instead of global visual appearance
it is fine-tuned using disease class information. Subsequently, we construct a
non-parametric retrieval index, which reaches state-of-the-art retrieval
levels. We use this index in our downstream tasks to augment image
representations through multi-head attention for disease classification and
report retrieval. We show that retrieval augmentation gives considerable
improvements on these tasks. Our downstream report retrieval even shows to be
competitive with dedicated report generation methods, paving the path for this
method in medical imaging.


  In this paper we show that a convex subcomplex of a spherical building of
type E6, E7 or E8 is a subbuilding or the automorphisms of the subcomplex fix a
point on it. Together with previous results of M\"uhlherr-Tits, and Leeb and
the author, this completes the proof of Tits' Center Conjecture for spherical
buildings without factors of type H4, in particular, for thick spherical
buildings.


  We report on the synthesis and physical properties of FeSe$_{1-x}$Te$_x$
single crystals with a low Te content (x = 0.17, 0.21, 0.25), where the
replacement of Se with Te partially suppresses superconductivity. Resistivity
and Hall effect measurements indicate weak anomalies at elevated temperatures
ascribed to nematic transitions. A quasi-classical analysis of transport data,
including in a pulsed magnetic field of up to 25 T, confirms the inversion of
majority carriers type from holes in FeSe to electrons in FeSe$_{1-x}$Te$_x$ at
x $>$ 0.17. The temperature-dependent term in the elastoresistance of the
studied compositions has a negative sign, which means that for substituted FeSe
compositions, the elastoresistance is positive for hole-doped materials and
negative for electron-doped materials just like in semiconductors such as
silicon and germanium.


  The conventional understanding that a bandstructure that produces a Dirac
delta function transport distribution (or transmission in the Landauer
framework) maximizes the thermoelectric figure of merit, ZT, is revisited.
Thermoelectric (TE) performance is evaluated using a simple tight binding (TB)
model for electron dispersion and three different scattering models: 1) a
constant scattering time, 2) a constant mean-free-path, and 3) a scattering
rate proportional to the density-of-states. We found that a Dirac
delta-function transmission never produces the maximum ZT. The best
bandstructure for maximizing ZT depends on the scattering physics. These
results demonstrate that the selection of bandstructure to maximize TE
performance is more complex than previously thought and that a high
density-of-states near the band edge does not necessarily improve TE
performance.


  Multi-instrument Automatic Music Transcription (AMT), or the decoding of a
musical recording into semantic musical content, is one of the holy grails of
Music Information Retrieval. Current AMT approaches are restricted to piano and
(some) guitar recordings, due to difficult data collection. In order to
overcome data collection barriers, previous AMT approaches attempt to employ
musical scores in the form of a digitized version of the same song or piece.
The scores are typically aligned using audio features and strenuous human
intervention to generate training labels. We introduce NoteEM, a method for
simultaneously training a transcriber and aligning the scores to their
corresponding performances, in a fully-automated process. Using this unaligned
supervision scheme, complemented by pseudo-labels and pitch-shift augmentation,
our method enables training on in-the-wild recordings with unprecedented
accuracy and instrumental variety. Using only synthetic data and unaligned
supervision, we report SOTA note-level accuracy of the MAPS dataset, and large
favorable margins on cross-dataset evaluations. We also demonstrate robustness
and ease of use; we report comparable results when training on a small, easily
obtainable, self-collected dataset, and we propose alternative labeling to the
MusicNet dataset, which we show to be more accurate. Our project page is
available at https://benadar293.github.io


  Several older and more recent results on the boundaries of solvability and
unsolvability in tag systems are surveyed. Emphasis will be put on the
significance of computer experiments in research on very small tag systems.


  In this paper, we introduce a novel formulation for camera motion estimation
that integrates RGB-D images and inertial data through scene flow. Our goal is
to accurately estimate the camera motion in a rigid 3D environment, along with
the state of the inertial measurement unit (IMU). Our proposed method offers
the flexibility to operate as a multi-frame optimization or to marginalize
older data, thus effectively utilizing past measurements. To assess the
performance of our method, we conducted evaluations using both synthetic data
from the ICL-NUIM dataset and real data sequences from the OpenLORIS-Scene
dataset. Our results show that the fusion of these two sensors enhances the
accuracy of camera motion estimation when compared to using only visual data.


  Cross-polarized absorption peaks of isolated single-walled carbon nanotubes
were observed by a polarized photoluminescence excitation (PLE) spectroscopy.
Using a simple theory for PL anisotropy, the observed PLE spectra are
decomposed into 'pure' components of the photoexcitation for incident light
polarized parallel and perpendicular to the SWNT axis. For several (n, m)
SWNTs, distinct peaks corresponding to perpendicular excitation were observed.
The measured transition energies for perpendicular excitations were
blue-shifted compared to the qualitative values predicted within a
single-particle theory. The results indicate a smaller exciton binding energy
for perpendicular excitations than for parallel excitations.


  The two lineal gravities --- based on the de Sitter group or a central
extension of the Poincar\'e group in 1+1 dimensions --- are shown to derive
classically from a unique topological gauge theory. This one is obtained after
a dimensional reduction of a Chern--Simons model, which describes pure gravity
in 2+1 dimensions, the gauge symmetry being given by an extension of ISO(2,1).


  Pretrained large-scale vision-language models like CLIP have exhibited strong
generalization over unseen tasks. Yet imperceptible adversarial perturbations
can significantly reduce CLIP's performance on new tasks. In this work, we
identify and explore the problem of \emph{adapting large-scale models for
zero-shot adversarial robustness}. We first identify two key factors during
model adaption -- training losses and adaptation methods -- that affect the
model's zero-shot adversarial robustness. We then propose a text-guided
contrastive adversarial training loss, which aligns the text embeddings and the
adversarial visual features with contrastive learning on a small set of
training data. We apply this training loss to two adaption methods, model
finetuning and visual prompt tuning. We find that visual prompt tuning is more
effective in the absence of texts, while finetuning wins in the existence of
text guidance. Overall, our approach significantly improves the zero-shot
adversarial robustness over CLIP, seeing an average improvement of over 31
points over ImageNet and 15 zero-shot datasets. We hope this work can shed
light on understanding the zero-shot adversarial robustness of large-scale
models.


  A novel approach to 3D surface imaging is proposed, allowing for the
continuous sampling of 3D surfaces to extract localized perspective
transformation coefficients from Fourier spectrum analysis of projected
patterns. The mathematical relationship for Spatial-Fourier Transformation
Pairs is derived, defining the transformation of spatial transformed planar
surfaces in the Discrete Fourier Transform spectrum. The mathematical
relationship for the twelve degrees of freedom in perspective transformation is
defined and validated, asserting congruity with independent and uniform
transform pairs for spatial Euclidean, similarity, affine and perspective
transformations. This work expands on previously derived affine Spatial-Fourier
Transformation Pairs and characterizes its implications towards 3D surface
imaging as a means of augmenting (X,Y,Z)-(R,G,B) point-clouds to include
additional information from localized sampling of pattern transformations.


  We study a poset of compositions restricted by part size under a partial
ordering introduced by Bj\"{o}rner and Stanley. We show that our composition
poset $C_{d+1}$ is isomorphic to the poset of words $A_d^*$. This allows us to
use techniques developed by Bj\"{o}rner to study the M\"{o}bius function of
$C_{d+1}$. We use counting arguments and shellability as avenues for proving
that the M\"{o}bius function is $\mu(u,w)=(-1)^{|u|+|w|}{w\choose u}_{dn}$,
where ${w\choose u}_{dn}$ is the number of $d$-normal embeddings of $u$ in $w$.
We then prove that the formal power series whose coefficients are given by the
zeta and the M\"{o}bius functions are both rational. Following in the footsteps
of Bj\"{o}rner and Reutenauer and Bj\"{o}rner and Sagan, we rely on definitions
to prove rationality in one case, and in another case we use finite-state
automata.


  The ability to measure small deformations or strains is useful for
understanding many aspects of materials. Here, a new analysis of speckle
diffraction peaks is presented in which the systematic shifts of the speckles
are analyzed allowing for strain (or flow) patterns to be inferred. This
speckle tracking technique measures strain patterns with a accuracy similar to
x-ray single crystal measurements but in amorphous or highly disordered
materials.


  In this study, we emphasize the integration of a pre-trained MICA model with
an imperfect face dataset, employing a self-supervised learning approach. We
present an innovative method for regenerating flawed facial structures,
yielding 3D printable outputs that effectively support physicians in their
patient treatment process. Our results highlight the model's capacity for
concealing scars and achieving comprehensive facial reconstructions without
discernible scarring. By capitalizing on pre-trained models and necessitating
only a few hours of supplementary training, our methodology adeptly devises an
optimal model for reconstructing damaged and imperfect facial features.
Harnessing contemporary 3D printing technology, we institute a standardized
protocol for fabricating realistic, camouflaging mask models for patients in a
laboratory environment.


  Image translation across domains for unpaired datasets has gained interest
and great improvement lately. In medical imaging, there are multiple imaging
modalities, with very different characteristics. Our goal is to use
cross-modality adaptation between CT and MRI whole cardiac scans for semantic
segmentation. We present a segmentation network using synthesised cardiac
volumes for extremely limited datasets. Our solution is based on a 3D
cross-modality generative adversarial network to share information between
modalities and generate synthesized data using unpaired datasets. Our network
utilizes semantic segmentation to improve generator shape consistency, thus
creating more realistic synthesised volumes to be used when re-training the
segmentation network. We show that improved segmentation can be achieved on
small datasets when using spatial augmentations to improve a generative
adversarial network. These augmentations improve the generator capabilities,
thus enhancing the performance of the Segmentor. Using only 16 CT and 16 MRI
cardiovascular volumes, improved results are shown over other segmentation
methods while using the suggested architecture.