  Here we report on new, multi-wavelength radio observations of the unusual
point source "N3" that appears to be located in the vicinity of the Galactic
Center. VLA observations between 2 and 50 GHz reveal that N3 is a compact and
bright source (56 mJy at 10 GHz) with a non-thermal spectrum superimposed upon
the non-thermal radio filaments (NTFs) of the Radio Arc. Our highest frequency
observations place a strict upper limit of 65 x 28 mas on the size of N3. We
compare our observations to those of Yusef-Zadeh & Morris (1987) and Lang et
al. (1997) and conclude that N3 is variable over long time scales.
Additionally, we present the detection of a compact molecular cloud located
adjacent to N3 in projection. CH3CN, CH3OH, CS, HC3N, HNCO, SiO, SO, and NH3
are detected in the cloud and most transitions have FWHM line widths of ~20
km/s. The rotational temperature determined from the metastable NH3 transitions
ranges from 79 K to 183 K depending on the transitions used. We present
evidence that this molecular cloud is interacting with N3. After exploring the
relationship between the NTFs, molecular cloud, and N3, we conclude that N3
likely lies within the Galactic Center. We are able to rule out the HII region,
young supernova, active star, AGN, and micro-quasar hypotheses for N3. While a
micro-blazar may provide a viable explanation for N3, additional observations
are needed to determine the physical counterpart of this mysterious source.


  Using the mathematical theory of shape calculus as tool, we perform a
rigorous investigation of the virtual work principle for the computation of
electromagnetic forces in static settings. The main goal is to shed light on
the widely held belief that the virtual work principle entails passively
advecting the fields with the virtual displacement when computing
deformation-dependent field energies gradients.
  The adjoint approach to differentiation of functionals under variational
constraints provides a mathematical justification for this belief. However, it
also shows that passively advecting the actual electromagnetic fields is
sufficient only in the case of linear materials. In the general case, also
fields arising as solutions of adjoint variational problems have to be taken
into account.


  Continuous-time autoregressive moving average (CARMA) process driven by
simple semi-L\'evy process has periodically correlated property with many
potential application in finance. In this paper, we study on the estimation of
the parameters of the simple semi-L\'evy CARMA (SSLCARMA) process based on the
Kalman recursion technique. We implement this method in conjunction with the
state-space representation of the associated process. The accuracy of
estimation procedure is assessed in a simulated study. We fit a SSLCARMA(2,1)
process to intraday realized volatility of Dow Jones Industrial Average data.
Finally, We show that this process provides better in-sample forecasts of these
data than the L\'evy driven CARMA process after de-seasonalized them.


  We reconsider the model of the randomly pinned planar flux array (or the
two-dimensional XY-model with random fields and no vortices) in extensive Monte
Carlo simulations. We show that contrary to what has been reported recently
[Phys.\ Rev.\ Lett.\ {\bf 72}, 4133 (1994)] the disorder-averaged displacement
correlation function is different from the pure case for larger values of the
disorder. Its slope as a function of the logarithm of disorder is compatible
with newer RG-calculation results.


  Automatic image colorization is inherently an ill-posed problem with
uncertainty, which requires an accurate semantic understanding of scenes to
estimate reasonable colors for grayscale images. Although recent
interaction-based methods have achieved impressive performance, it is still a
very difficult task to infer realistic and accurate colors for automatic
colorization. To reduce the difficulty of semantic understanding of grayscale
scenes, this paper tries to utilize corresponding audio, which naturally
contains extra semantic information about the same scene. Specifically, a novel
audio-infused automatic image colorization (AIAIC) network is proposed, which
consists of three stages. First, we take color image semantics as a bridge and
pretrain a colorization network guided by color image semantics. Second, the
natural co-occurrence of audio and video is utilized to learn the color
semantic correlations between audio and visual scenes. Third, the implicit
audio semantic representation is fed into the pretrained network to finally
realize the audio-guided colorization. The whole process is trained in a
self-supervised manner without human annotation. In addition, an audiovisual
colorization dataset is established for training and testing. Experiments
demonstrate that audio guidance can effectively improve the performance of
automatic colorization, especially for some scenes that are difficult to
understand only from visual modality.


  We provide a general method to analyze the asymptotic properties of a variety
of estimators of continuous time diffusion processes when the data are not only
discretely sampled in time but the time separating successive observations may
possibly be random. We introduce a new operator, the generalized infinitesimal
generator, to obtain Taylor expansions of the asymptotic moments of the
estimators. As a special case, our results apply to the situation where the
data are discretely sampled at a fixed nonrandom time interval. We include as
specific examples estimators based on maximum-likelihood and discrete
approximations such as the Euler scheme.


  Recent work has proposed stochastic Plackett-Luce (PL) ranking models as a
robust choice for optimizing relevance and fairness metrics. Unlike their
deterministic counterparts that require heuristic optimization algorithms, PL
models are fully differentiable. Theoretically, they can be used to optimize
ranking metrics via stochastic gradient descent. However, in practice, the
computation of the gradient is infeasible because it requires one to iterate
over all possible permutations of items. Consequently, actual applications rely
on approximating the gradient via sampling techniques. In this paper, we
introduce a novel algorithm: PL-Rank, that estimates the gradient of a PL
ranking model w.r.t. both relevance and fairness metrics. Unlike existing
approaches that are based on policy gradients, PL-Rank makes use of the
specific structure of PL models and ranking metrics. Our experimental analysis
shows that PL-Rank has a greater sample-efficiency and is computationally less
costly than existing policy gradients, resulting in faster convergence at
higher performance. PL-Rank further enables the industry to apply PL models for
more relevant and fairer real-world ranking systems.


  We investigate the blue and optical rest-frame sizes (lambda~2300A-4000A) of
three compact star-forming regions in a galaxy at z=4 strongly lensed (x30,
x45, x100) by the Hubble Frontier Field galaxy cluster A2744 using GLASS-ERS
JWST/NIRISS imaging at 1.15um, 1.50mu and 2.0mu with PSF < 0.1". In particular,
the Balmer break is probed in detail for all multiply-imaged sources of the
system. With ages of a few tens of Myr, stellar masses in the range (0.7-4.0) x
10^6 Msun and optical/ultraviolet effective radii spanning the interval 3 <
R_eff < 20 pc, such objects are currently the highest redshift
(spectroscopically-confirmed) gravitationally-bound young massive star clusters
(YMCs), with stellar mass surface densities resembling those of local globular
clusters. Optical (4000A, JWST-based) and ultraviolet (1600A, HST-based) sizes
are fully compatible. The contribution to the ultraviolet underlying continuum
emission (1600A) is ~30%, which decreases by a factor of two in the optical for
two of the YMCs (~4000A rest-frame), reflecting the young ages (<30 Myr)
inferred from the SED fitting and supported by the presence of high-ionization
lines secured with VLT/MUSE. Such bursty forming regions enhance the sSFR of
the galaxy, which is ~10 Gyr^-1. This galaxy would be among the extreme analogs
observed in the local Universe having high star formation rate surface density
and high occurrence of massive stellar clusters in formation.


  The WW production cross section measured at the LHC has been consistently
exhibiting a mild excess beyond the SM prediction, in both ATLAS and CMS at
both 7-TeV and 8-TeV runs. We provide an explanation of the excess in terms of
resummation of large logarithms that arise from a jet-veto condition, i.e., the
rejection of high-pT jets with pT > pT(veto) that is imposed in the
experimental analyses to reduce backgrounds. Jet veto introduces a second mass
scale pT(veto) to the problem in addition to the invariant mass of the WW pair.
This gives rise to large logarithms of the ratio of the two scales that need to
be resummed. Such resummation may not be properly accounted for by the Monte
Carlo simulations used in the ATLAS and CMS studies. Those logarithms are also
accompanied by large pi^2 terms when the standard, positive sign is chosen for
the squared renormalization scale. We analytically resum the large logarithms
including the pi^2 terms in the framework of soft collinear effective theory
(SCET), and demonstrate that the SCET calculation not only reduces the scale
uncertainties of the SM prediction significantly but also renders the theory
prediction well compatible with the experiment. We find that resummation of the
large logarithms and that of the pi^2 terms are both comparably important.


  We study one-dimensional quantum walks in a homogeneous electric field. The
field is given by a phase which depends linearly on position and is applied
after each step. The long time propagation properties of this system, such as
revivals, ballistic expansion and Anderson localization, depend very
sensitively on the value of the electric field $\Phi$, e.g., on whether
$\Phi/(2\pi)$ is rational or irrational. We relate these properties to the
continued fraction expansion of the field. When the field is given only with
finite accuracy, the beginning of the expansion allows analogous conclusions
about the behavior on finite time scales.


  High resolution (0."037-0."13 [10-35 pc]) e-MERLIN ($\lambda6-18$ cm) and
(0."024 [6.5 pc]) ALMA ($\lambda 1.1$ mm) observations have been used to image
OH (hydroxyl) and H$_2$CO (formaldehyde) megamaser emission, and HCN 3->2
emission towards the nuclear (<100 pc) region of the luminous infrared galaxy
Zw049.057. Zw049.057 hosts a compact obscured nucleus (CON), thus representing
a class of galaxies that are often associated with inflow and outflow motions.
Formaldehyde megamaser emission is detected towards the nuclear region, <30 pc
(<0."1), and traces a structure along the disk major axis. OH megamaser (OHM)
emission is detected along the minor axis of the disk, ~30 pc (0."1) from the
nucleus, where it exhibits a velocity gradient with extrema of -20 km/s
south-east (SE) of the disk and -110 km/s north-west (NW) of the disk. HCN 3->2
emission reveals extended emission, along the disk minor axis out to ~60 pc
(0."2). Analysis of the minor axis HCN emission reveals high-velocity features,
extending out to 600 km/s, redshifted on the SE side and blueshifted on the NW
side. We propose that the high-velocity HCN emission traces a fast >250 km/s
and collimated outflow, that is enveloped by a wide-angle and slow ~50 km/s
outflow that is traced by the OHM emission. Analysis of the outflow kinematics
suggests that the slow wide-angle outflow will not reach escape velocity and
instead will fall back to the galaxy disk, evolving as a so-called fountain
flow, while the fast collimated outflow traced by HCN emission will likely
escape the nuclear region. We suggest that the absence of OHM emission in the
nuclear region is due to high densities there. Even though OHMs associated with
outflows are an exception to conventional OHM emission, we expect them to be
common in CON sources that host both OHM and H$_2$CO megamasers.


  The nature of dark matter in the Universe is still an open question in
astrophysics and cosmology. Axions and axion-like particles (ALPs) offer a
compelling solution, and traditionally ground-based experiments have eagerly,
but to date unsuccessfully, searched for these hypothetical low-mass particles
that are expected to be produced in large quantities in the strong
electromagnetic fields in the interior of stars. This work offers a fresh look
at axions and ALPs by leveraging their conversion into X-rays in the magnetic
field of the Sun's atmosphere rather than a laboratory magnetic field. Unique
data acquired with the Nuclear Spectroscopic Telescope Array (NuSTAR) during
the solar minimum in 2020 allows us to set stringent limits on the coupling of
axions to photons using state-of-the-art magnetic field models of the solar
atmosphere. We report pioneering limits on the axion-photon coupling strength
of $6.9\times 10^{-12}$ GeV$^{-1}$ at 95\% confidence level for axion masses
$m_a \lesssim 2\times 10^{-7}$ eV, surpassing current ground-based searches and
further probing unexplored regions of the axion-photon coupling parameter space
up to axion masses of $m_a \lesssim 5\times 10^{-4}$ eV.


  The word stable is used to describe a situation when mathematical objects
that almost satisfy an equation are close to objects satisfying it exactly. We
study operator-algebraic forms of stability for unitary representations of
groups and quantum synchronous strategies for non-local games. We observe in
particular that simple spectral gap estimates can lead to strong quantitative
forms of stability. For example, we prove that the direct product of two
(flexibly) Hilbert-Schmidt stable groups is again (flexibly) Hilbert-Schmidt
stable, provided that one of them has Kazhdan's property (T). We also provide a
simple form and simple analysis of a non-local game with few questions, with
the property that synchronous strategies with large value are close to perfect
strategies involving large Pauli matrices. This simplifies one of the steps
(the question reduction) in the recent announced resolution of Connes'
embedding problem by Ji, Natarajan, Vidick, Wright and Yuen.


  Some differential implications of classical Marx-Strohh\"acker theorem are
extended for multivalent functions. These results are also generalized for
functions with fixed second coefficient by using the theory of first order
differential subordination which in turn, corrects the results of Selvaraj and
Stelin [On multivalent functions associated with fixed second coefficient and
the principle of subordination, Int. J. Math. Anal. {\bf 9} (2015), no.~18,
883--895].


  M82 X-1 is one of the brightest ultraluminous X-ray sources (ULXs) known,
which, assuming Eddington-limited accretion and other considerations, makes it
one of the best intermediate-mass black hole (IMBH) candidates. However, the
ULX may still be explained by super-Eddington accretion onto a stellar-remnant
black hole. We present simultaneous NuSTAR, Chandra and Swift/XRT observations
during the peak of a flaring episode with the aim of modeling the emission of
M82 X-1 and yielding insights into its nature. We find that thin-accretion disk
models all require accretion rates at or above the Eddington limit in order to
reproduce the spectral shape, given a range of black hole masses and spins.
Since at these high Eddington ratios the thin-disk model breaks down due to
radial advection in the disk, we discard the results of the thin-disk models as
unphysical. We find that the temperature profile as a function of disk radius
($T(r)\propto r^{-p}$) is significantly flatter ($p=0.55^{+ 0.07}_{- 0.04}$)
than expected for a standard thin disk ($p=0.75$). A flatter profile is instead
characteristic of a slim disk which is highly suggestive of super-Eddington
accretion. Furthermore, radiation hydrodynamical simulations of super-Eddington
accretion have shown that the predicted spectra of these systems are very
similar to what we observe for M82 X-1. We therefore conclude that M82 X-1 is a
super-Eddington accretor. Our mass estimates inferred from the inner disk
radius imply a stellar-remnant black hole ($M_{\rm BH}=26^{+9}_{-6} M_{\odot}$)
when assuming zero spin, or an IMBH ($M_{\rm BH}=125^{+45}_{-30} M_{\odot}$)
when assuming maximal spin.


  Dualising the construction of a polyhedral product, we introduce the notion
of a polyhedral coproduct as a certain homotopy limit over the face poset of a
simplicial complex. We begin a study of the basic properties of polyhedral
coproducts, surveying the Eckmann-Hilton duals of various familiar examples and
properties of polyhedral products. In particular, we show that polyhedral
coproducts give a functorial interpolation between the wedge and cartesian
product of spaces which differs from the one given by polyhedral products, and
we establish a general loop space decomposition for these spaces which is dual
to the suspension splitting of a polyhedral product due to Bahri, Bendersky,
Cohen and Gitler.


  A new proof of an old theorem of Drinfeld concerning the representability of
the moduli problem of special formal $\mathcal{O}_{D}$-modules by Deligne's
$p$-adic formal model of Drinfeld's upper half-plane is given for $d=2.$ The
display associated to the universal object is constructed explicitly.


  Deformation twinning is a form of permanent deformation that is commonly
observed in low symmetry crystals such as hexagonal close-packed (hcp) metals.
With recent increased interest in using hcp metals, such as magnesium, in
structural, automotive, and armor applications due to their high strength to
weight ratio, there is a need for a comprehensive understanding of deformation
twinning and its interaction with dislocation slip. A great deal has been
learned at the microscopic level where individual dislocations interact with
twin boundaries through atomistic simulations, and at the macroscopic level by
ignoring morphology and treating twinning as `pseudo-slip'. However, twins form
collectively across multiple grains with complex morphology that affects the
bulk behavior. These mesoscale aspects have been less studied and are the focus
of this paper. We present a model that describes the twin and slip morphology,
its evolution, and interactions in a unified manner at the scale of several
grains and use it to study the implications on macroscopic behavior. The key
ideas are to combine a phase-field model of twinning with a crystal plasticity
model of slip, and to implement it in parallel on graphic processing units for
fast computations.


  Although extreme or freak waves are repeatedly measured in the oceans, their
origin is largely unknown. The interaction of different water waves is seen as
one reason for their emergence. One way to consider nonlinear waves in deep
water is to look at solutions of the nonlinear Schr\"odinger equation, which
plays an important role in the determination of extreme waves. One specific
solution is the soliton solution. Therefore the question arises, how nonlinear
waves behave as they interact or collide. Using a relaxation pseudo spectral
scheme for the computation of solutions of the nonlinear Schr\"odinger
equation, the behavior of colliding solitons is studied. Thereby, different
wave amplitudes and angles of collision are considered. In addition to this,
the influence of an initial perturbation by random waves is studied, which is
generated using a Pierson-Moskowitz spectrum.


  Recently, the use of digital images in various fields is increasing rapidly.
To increase the number of images stored and get faster transmission of them, it
is necessary to reduce the size of these images. Single bitmap block truncation
coding (SBBTC) schemes are compression techniques, which are used to generate a
common bitmap to quantize the R, G and B planes in color image. As one of the
traditional SBBTC schemes, weighted plane (W-plane) method is famous for its
simplicity and low time consumption. However, the W-plane method also has poor
performance in visual quality. This paper proposes an improved SBBTC scheme
based on W-plane method using parallel computing and hill climbing algorithm.
Compared with various schemes, the simulation results of the proposed scheme
are better than that of the reference schemes in visual quality and time
consumption.


  The diffusion of rumors on microblogs generally follows a propagation tree
structure, that provides valuable clues on how an original message is
transmitted and responded by users over time. Recent studies reveal that rumor
detection and stance detection are two different but relevant tasks which can
jointly enhance each other, e.g., rumors can be debunked by cross-checking the
stances conveyed by their relevant microblog posts, and stances are also
conditioned on the nature of the rumor. However, most stance detection methods
require enormous post-level stance labels for training, which are
labor-intensive given a large number of posts. Enlightened by Multiple Instance
Learning (MIL) scheme, we first represent the diffusion of claims with
bottom-up and top-down trees, then propose two tree-structured weakly
supervised frameworks to jointly classify rumors and stances, where only the
bag-level labels concerning claim's veracity are needed. Specifically, we
convert the multi-class problem into a multiple MIL-based binary classification
problem where each binary model focuses on differentiating a target stance or
rumor type and other types. Finally, we propose a hierarchical attention
mechanism to aggregate the binary predictions, including (1) a bottom-up or
top-down tree attention layer to aggregate binary stances into binary veracity;
and (2) a discriminative attention layer to aggregate the binary class into
finer-grained classes. Extensive experiments conducted on three Twitter-based
datasets demonstrate promising performance of our model on both claim-level
rumor detection and post-level stance classification compared with
state-of-the-art methods.


  By analysing K2 short-cadence observations we detect starspots on WASP-85A,
the host star of the hot Jupiter WASP-85Ab. The detection of recurring starspot
occultation events indicates that the planet's orbit is aligned with the star's
rotational axis ($\lambda<14^{\circ}$) and suggests a stellar rotational period
of $15.1 \pm 0.6$ d. The K2 lightcurve reveals a rotational modulation with a
period of $13.6 \pm 1.6$ d, consistent with the period determined from
starspots. There are no significant transit-timing variations and thus no
evidence of any additional planet in the system. Given the pronounced
rotational modulation we are only able to place an upper limit of 100 parts per
million for any phase-curve modulations and the secondary eclipse.


  Short review on entanglement, as seen from a quantum information perspective,
and some simple applications to many-body quantum systems. Special emphasis in
area laws, cold atoms, and efficient descriptions using tensor network states.


  Stationary Wavelet Transform (SWT) is an efficient tool for edge analysis.
This paper a new edge detection technique using SWT based Hidden Markov Model
(WHMM) along with the expectation-maximization (EM) algorithm is proposed. The
SWT coefficients contain a hidden state and they indicate the SWT coefficient
fits into an edge model or not. Laplacian and Gaussian model is used to check
the information of the state is an edge or no edge. This model is trained by an
EM algorithm and the Viterbi algorithm is employed to recover the state. This
algorithm can be applied to noisy images efficiently.


  Multistage coupling of laser-wakefield accelerators is essential to overcome
laser energy depletion for high-energy applications such as TeV level
electron-positron colliders. Current staging schemes feed subsequent laser
pulses into stages using plasma mirrors, while controlling electron beam
focusing with plasma lenses. Here a more compact and efficient scheme is
proposed to realize simultaneous coupling of the electron beam and the laser
pulse into a second stage. A curved channel with transition segment is used to
guide a fresh laser pulse into a subsequent straight channel, while allowing
the electrons to propagate in a straight channel. This scheme benefits from a
shorter coupling distance and continuous guiding of the electrons in plasma,
while suppressing transverse beam dispersion. With moderate laser parameters,
particle-in-cell simulations demonstrate that the electron beam from a previous
stage can be efficiently injected into a subsequent stage for further
acceleration, while maintaining high capture efficiency, stability, and beam
quality.


  We discuss the evolution of the interstellar medium of quiescent galaxies,
currently emerging from recent analyses, with the help of a simple model based
on well-established empirical relations such as the stellar mass functions and
the main sequence of star formation. This model is meant to describe observed
quantities without making specific assumptions on the nature of quenching
processes, but relying on their observable consequences. We find that the high
gas fractions seen or suggested at high redshift in quiescent galaxies, and
their apparent mild evolution at early times, can be mostly attributed to a
progenitor effect where recently quenched galaxies with ~10% gas fractions
dominate the quiescent galaxy population until z~1. In the same context, the
much lower gas and dust fractions measured in local early-type galaxies are
interpreted as the product of the steady depletion of their interstellar medium
on a ~2 Gyr timescale, coupled with a higher fraction of more gas-exhaustive
events.


  Online app search optimization (ASO) platforms that provide bulk installs and
fake reviews for paying app developers in order to fraudulently boost their
search rank in app stores, were shown to employ diverse and complex strategies
that successfully evade state-of-the-art detection methods. In this paper we
introduce RacketStore, a platform to collect data from Android devices of
participating ASO providers and regular users, on their interactions with apps
which they install from the Google Play Store. We present measurements from a
study of 943 installs of RacketStore on 803 unique devices controlled by ASO
providers and regular users, that consists of 58,362,249 data snapshots
collected from these devices, the 12,341 apps installed on them and their
110,511,637 Google Play reviews. We reveal significant differences between ASO
providers and regular users in terms of the number and types of user accounts
registered on their devices, the number of apps they review, and the intervals
between the installation times of apps and their review times. We leverage
these insights to introduce features that model the usage of apps and devices,
and show that they can train supervised learning algorithms to detect paid app
installs and fake reviews with an F1-measure of 99.72% (AUC above 0.99), and
detect devices controlled by ASO providers with an F1-measure of 95.29% (AUC =
0.95). We discuss the costs associated with evading detection by our
classifiers and also the potential for app stores to use our approach to detect
ASO work with privacy.


  In this paper, an LSTM autoencoder-based architecture is utilized for
drowsiness detection with ResNet-34 as feature extractor. The problem is
considered as anomaly detection for a single subject; therefore, only the
normal driving representations are learned and it is expected that drowsiness
representations, yielding higher reconstruction losses, are to be distinguished
according to the knowledge of the network. In our study, the confidence levels
of normal and anomaly clips are investigated through the methodology of label
assignment such that training performance of LSTM autoencoder and
interpretation of anomalies encountered during testing are analyzed under
varying confidence rates. Our method is experimented on NTHU-DDD and
benchmarked with a state-of-the-art anomaly detection method for driver
drowsiness. Results show that the proposed model achieves detection rate of
0.8740 area under curve (AUC) and is able to provide significant improvements
on certain scenarios.


  Radiology reports are one of the main forms of communication between
radiologists and other clinicians and contain important information for patient
care. In order to use this information for research and automated patient care
programs, it is necessary to convert the raw text into structured data suitable
for analysis. State-of-the-art natural language processing (NLP)
domain-specific contextual word embeddings have been shown to achieve
impressive accuracy for these tasks in medicine, but have yet to be utilized
for section structure segmentation. In this work, we pre-trained a contextual
embedding BERT model using breast radiology reports and developed a classifier
that incorporated the embedding with auxiliary global textual features in order
to perform section segmentation. This model achieved a 98% accuracy at
segregating free text reports sentence by sentence into sections of information
outlined in the Breast Imaging Reporting and Data System (BI-RADS) lexicon, a
significant improvement over the Classic BERT model without auxiliary
information. We then evaluated whether using section segmentation improved the
downstream extraction of clinically relevant information such as
modality/procedure, previous cancer, menopausal status, the purpose of the
exam, breast density, and breast MRI background parenchymal enhancement. Using
the BERT model pre-trained on breast radiology reports combined with section
segmentation resulted in an overall accuracy of 95.9% in the field extraction
tasks. This is a 17% improvement compared to an overall accuracy of 78.9% for
field extraction with models using Classic BERT embeddings and not using
section segmentation. Our work shows the strength of using BERT in radiology
report analysis and the advantages of section segmentation in identifying key
features of patient factors recorded in breast radiology reports.


  We study quantum electrodynamics with 2+1 dimensional massless Dirac fermion
around a Coulomb impurity. Around a large charge with atomic number Z > 137,
the QED vacuum is expected to collapse due to the strong Coulombic force. While
the relativistic quantum mechanics fails to make reliable predictions for the
fate of the vacuum, the heavy ion collision experiment also does not give clear
understanding of this system. Recently, the "atomic collapse" resonances were
observed on graphene where an artificial nuclei can be made. In this paper, we
present our nonperturbative study of the vacuum structure of the quasiparticles
in graphene with a charge impurity which contains multi-body effect using
bosonization method.


  A distinguishability operator is defined for the continuous-time quantum walk
(CTQW) of a bipartite quantum walker on two simply connected graphs,
$W_{G_i,G_j} = U_{G_i}\left(t\right) \otimes U_{G_j}\left(t'\right) -
U_{G_j}\left(t'\right) \otimes U_{G_i}\left(t\right)$, where
$U_{G_i}\left(t\right)$ is the unitary CTQW operator for a labeled graph $G_i$
over a time interval $t$. The null space of $W_{G_i,G_j}$ defines the vector
space of initial bipartite states whose time development is either constant or
only dependent on $t + t'$ and is invariant to which quantum walker subsystem
goes with each graph. The set of null spaces corresponding with a set of
$W_{G_i,G_j}$ have interesting relations as subspaces, intersections between
subspaces, and subspaces of intersections. These relations are depicted as
Euler diagrams for labeled graphs of three and four vertices.


  With the widespread application of convolutional neural networks (CNNs), the
traditional model based denoising algorithms are now outperformed. However,
CNNs face two problems. First, they are computationally demanding, which makes
their deployment especially difficult for mobile terminals. Second,
experimental evidence shows that CNNs often over-smooth regular textures
present in images, in contrast to traditional non-local models. In this letter,
we propose a solution to both issues by combining a nonlocal algorithm with a
lightweight residual CNN. This solution gives full latitude to the advantages
of both models. We apply this framework to two GPU implementations of classic
nonlocal algorithms (NLM and BM3D) and observe a substantial gain in both
cases, performing better than the state-of-the-art with low computational
requirements. Our solution is between 10 and 20 times faster than CNNs with
equivalent performance and attains higher PSNR. In addition the final method
shows a notable gain on images containing complex textures like the ones of the
MIT Moire dataset.


  The quantum capacitance of the monolayer graphene for arbitrary carrier
density, magnetic field and temperature is found. The density dependence of the
quantum capacitance is analyzed for magnetic field(temperature) is
fixed(varied) and vice versa. The theory is compared with the experimental
data.


  We report H-alpha observations of zeta Tauri, taken between late 2000 and
early 2006. Next to extending existing long-term montioring of the disk state
of this star we report an intermediate timescale of about 69 days to be present
in the V/R variations of the Halpha line. The observational data will be
published together with this manuscript.


  We describe a simple approach to factorize non-commutative (nc) polynomials,
that is, elements in free associative algebras (over a commutative field), into
atoms (irreducible elements) based on (a special form of) their minimal linear
representations. To be more specific, a correspondence between factorizations
of an element and upper right blocks of zeros in the system matrix (of its
representation) is established. The problem is then reduced to solving a system
of polynomial equations (with at most quadratic terms) with commuting unknowns
to compute appropriate transformation matrices (if possible).


  We discuss the action of a subgroup on small nilpotent orbits, and prove a
bounded multiplicity property for the restriction of minimal representations of
real reductive Lie groups with respect to arbitrary reductive symmetric pairs.


  Higher-order topological phases are characterized by protected states
localized at the corners or hinges of the system. By applying time-periodic
quenches to a two-dimensional lattice with balanced gain and loss, we obtain a
rich variety of non-Hermitian Floquet second order topological insulating
phases. Each of the phases is characterized by a pair of integer topological
invariants, which predict the numbers of Floquet corner modes at zero and $\pi$
quasienergies. We establish the topological phase diagram of the model, and
find a series of non-Hermiticity induced transitions between different Floquet
second order topological phases. We further generalize the mean chiral
displacement to two-dimensional non-Hermitian systems, and use it to extract
the topological invariants of our model dynamically. This work thus extend the
study of higher-order topological matter to more generic nonequilibrium
settings, in which the interplay between Floquet engineering and
non-Hermiticity yields fascinating new phases.


  We revisit the principal arguments for interpreting the free quantum fields
on anti-de Sitter or de Sitter spacetimes of any dimensions as oscillators or
respectively anti-oscillators. In addition, we point out that there exists a
chart on the de Sitter background where the free Dirac field becomes a genuine
anti-oscillator in the non-relativistic limit in the sense of special
relativity.


  We investigate the steady state of heat conduction in general relativity
using a variational approach for two-fluid dynamics. We adopt coordinates based
on the Landau-Lifschitz observer because it allows us to describe
thermodynamics with heat, formulated in the Eckart decomposition, on a static
geometry. Through our analysis, we demonstrate that the stability condition of
a thermal equilibrium state arises from the fundamental principle that heat
cannot propagate faster than the speed of light. We then formulate the
equations governing steady-state heat conduction and introduce a binormal
equilibrium condition that the Tolman temperature gradient holds for the
directions orthogonal to the heat flow. As an example, we consider radial heat
conductions in a spherically symmetric spacetime. We find that the total
diffusion over a spherical surface satisfies a red-shifted form, $J(r)
\sqrt{-g_{tt}} =$ constant. We also discuss the behavior of local temperature
around an event horizon and specify the condition that the local temperature is
finite there.


  Trilinear $Z$ boson interactions are sensitive probes both of new sources of
$CP$ violation in physics Beyond the Standard Model and of new particle
thresholds. Measurements of trilinear $Z$ interactions are typically
interpreted in the frameworks of anomalous couplings and effective field
theory, both of which require care in interpretation. To obtain a quantitative
picture of the power of these measurements when interpreted in a TeV-scale
context, we investigate the anatomy of $ZZZ$ interactions and consider two
minimal and perturbative simplified models which induce such interactions
through new scalar and fermion loops at the weak scale, focusing on $ZZ$ and
vector boson fusion-induced $Zjj$ production at the LHC and $ZZ$ production at
a future $e^+ e^-$ collider. We show that both threshold and non-threshold
effects often are small compared to the sensitivity of the LHC, while the
increased sensitivity of a future lepton collider should allow us to constrain
such scenarios through associated electroweak precision effects complementary
to direct searches at hadron colliders.


  In this work, we define quasicrystalline spin networks as a subspace within
the standard Hilbert space of loop quantum gravity, effectively constraining
the states to coherent states that align with quasicrystal geometry structures.
We introduce quasicrystalline spin foam amplitudes, a variation of the EPRL
spin foam model, in which the internal spin labels are constrained to
correspond to the boundary data of quasicrystalline spin networks. Within this
framework, the quasicrystalline spin foam amplitudes encode the dynamics of
quantum geometries that exhibit aperiodic structures. Additionally, we
investigate the coupling of fermions within the quasicrystalline spin foam
amplitudes. We present calculations for three-dimensional examples and then
explore the 600-cell construction, which is a fundamental component of the
four-dimensional Elser-Sloane quasicrystal derived from the E8 root lattice.


  In this paper, we focus on exploring the robustness of the 3D object
detection in point clouds, which has been rarely discussed in existing
approaches. We observe two crucial phenomena: 1) the detection accuracy of the
hard objects, e.g., Pedestrians, is unsatisfactory, 2) when adding additional
noise points, the performance of existing approaches decreases rapidly. To
alleviate these problems, a novel TANet is introduced in this paper, which
mainly contains a Triple Attention (TA) module, and a Coarse-to-Fine Regression
(CFR) module. By considering the channel-wise, point-wise and voxel-wise
attention jointly, the TA module enhances the crucial information of the target
while suppresses the unstable cloud points. Besides, the novel stacked TA
further exploits the multi-level feature attention. In addition, the CFR module
boosts the accuracy of localization without excessive computation cost.
Experimental results on the validation set of KITTI dataset demonstrate that,
in the challenging noisy cases, i.e., adding additional random noisy points
around each object,the presented approach goes far beyond state-of-the-art
approaches. Furthermore, for the 3D object detection task of the KITTI
benchmark, our approach ranks the first place on Pedestrian class, by using the
point clouds as the only input. The running speed is around 29 frames per
second.


  An N=4 super-Schwarzian theory is formulated by the coadjoint orbit method.
It is discovered that the action has symmetry under PSU(1,1|2).


  We establish consequences of the moving form of Schmidt's Subspace Theorem.
Indeed, we obtain inequalities that bound the logarithmic greatest common
divisor of moving multivariable polynomials evaluated at moving $S$-unit
arguments. In doing so, we complement recent work of Levin. As an additional
application, we obtain results that pertain to the greatest common divisor
problem for algebraic linear recurrence sequences. These observations are
motivated by previous related works of Corvaja-Zannier, Levin and others.


  Differentiable planning promises end-to-end differentiability and adaptivity.
However, an issue prevents it from scaling up to larger-scale problems: they
need to differentiate through forward iteration layers to compute gradients,
which couples forward computation and backpropagation, and needs to balance
forward planner performance and computational cost of the backward pass. To
alleviate this issue, we propose to differentiate through the Bellman
fixed-point equation to decouple forward and backward passes for Value
Iteration Network and its variants, which enables constant backward cost (in
planning horizon) and flexible forward budget and helps scale up to large
tasks. We study the convergence stability, scalability, and efficiency of the
proposed implicit version of VIN and its variants and demonstrate their
superiorities on a range of planning tasks: 2D navigation, visual navigation,
and 2-DOF manipulation in configuration space and workspace.


  The latest experimental [Noll et al. Atmos. Chem. Phys. 20(2020)5269] and
theoretical [Brooke et al. J. Quant. Spectr. Rad. Transfer 168(2016)142] data
on the OH emission intensities are analyzed with use of the NIDL. It is found
that the calculated intensities of the $\Delta v>6$ transitions should not be
trusted.
  The analysis of the OH data revealed that the NIDL theory is not applicable
to the satellite bands. The effect of small reduced mass previously discovered
in H$_2$ [Ushakov et al. J. Mol. Spectrosc. 399(2024)111863], causing the NIDL
straight-line slope to be larger than the one associated with the repulsive
branch of the potential, is demonstrated, and the same should be true of all
the diatomic hydrides. We performed ab initio calculations of the OH repulsive
branch and compared it with the one of Brooke et al. and the other due to
Varandas and Voronin [Chem. Phys. 194(1995)91]. We found that the ab initio PEF
closely follows the Varandas-Voronin potential in the repulsive region
important for calculating the overtone intensities [Medvedev, J. Chem. Phys.
137(2012)174307]. Assumption is made that different potentials can be used to
calculate transition frequencies and intensities for spectroscopic databases.


  Layered video streaming in peer-to-peer (P2P) networks has drawn great
interest, since it can not only accommodate large numbers of users, but also
handle peer heterogeneity. However, there's still a lack of comprehensive
studies on chunk scheduling for the smooth playout of layered streams in P2P
networks. In these situations, a playout smoothing mechanism can be used to
ensure the uniform delivery of the layered stream. This can be achieved by
reducing the quality changes that the stream undergoes when adapting to
changing network conditions. This paper complements previous efforts in
throughput maximization and delay minimization for P2P streaming by considering
the consequences of playout smoothing on the scheduling mechanisms for stream
layer acquisition. The two main problems to be considered when designing a
playout smoothing mechanism for P2P streaming are the fluctuation in available
bandwidth between peers and the unreliability of user-contributed
resources--particularly peer churn. Since the consideration of these two
factors in the selection and scheduling of stream layers is crucial to maintain
smooth stream playout, the main objective of our smoothing mechanism becomes
the determination of how many layers


  We present a spatio-temporal AI framework that concurrently exploits both the
spatial and time-variable features of gravitationally lensed supernovae in
optical images to ultimately aid in future discoveries of such exotic
transients in wide-field surveys. Our spatio-temporal engine is designed using
recurrent convolutional layers, while drawing from recent advances in
variational inference to quantify approximate Bayesian uncertainties via a
confidence score. Using simulated Young Supernova Experiment (YSE) images of
lensed and non-lensed supernovae as a showcase, we find that the use of
time-series images adds relevant information from time variability of spatial
light distribution of partially blended images of lensed supernova, yielding a
substantial gain of around 20 per cent in classification accuracy over
single-epoch observations. Preliminary application of our network to mock
observations from the Legacy Survey of Space and Time (LSST) results in
detections with accuracy reaching around 99 percent. Our innovative deep
learning machinery is versatile and can be employed to search for any class of
sources which exhibit variability both in flux and spatial distribution of
light.


  The ADM and Bondi mass for the RST model have been first discussed from
Hawking and Horowitz's argument. Expressing the localized RST action in terms
of the ADM formulation, the RST Hamiltonian can be derived, meanwhile keeping
track of all boundary terms. Then the total boundary terms can be taken as the
total energy for the RST model. It has been found that there is a new
contribution to the ADM and Bondi mass from the RST boundary due to the
existence of the hidden dynamical field. The ADM and Bondi mass have been
discussed respectively in detail, and some new properties have been found.


  Let $K$ be a local field with residue characteristic $p$ and let $L/K$ be a
totally ramified extension of degree $p^k$. In this paper we show that if $L/K$
has only two distinct indices of inseparability then there exists a uniformizer
$\pi_L$ for $L$ whose minimum polynomial over $K$ has at most three terms. This
leads to an explicit classification of extensions with two indices of
inseparability. Our classification extends work of Amano, who considered the
case $k=1$.


  In Application layer multicast (ALM) also called Overlay Multicast,
multicast-related functionalities are moved to end-hosts. The key advantages,
overlays offers, are flexibility, adaptability and ease of deployment [1].
Application layer multicast builds a peer-to-peer (P2P) overlay multicast tree
topology consisting of end-to-end unicast connections between end-hosts. End
users self organize themselves into logical overlay networks for efficient data
delivery. Major concern in designing ALM protocol is how to build and maintain
a topology, to route data efficiently and reliably. We propose here a scheme in
which the topology is built incrementally while maintaining dual feeds of the
media stream to any node from the source with minimum differential delay in
receiving packets from both alternatives. We have made the assumption of
availability of a P2P query search network. This enables building of multicast
tree directly as an overlay. There is no need of maintaining an overlaid mesh
and running multicast routing protocol to maintain a multicast tree in this
mesh. Thus the scheme is much more simplified than in the earlier work on
multicast overlay mesh creation and management and then creation and management
of multicast trees using this mesh.


  GRO J1655--40 is a galactic microquasar, i.e. a short-period binary with
relativistic radio-jets and where the companion is a black-hole. A little after
its discovery in 1994 by $BATSE$, a distance of 3.2$\pm$0.2 kpc has been
estimated, and at which the radio jets appeared superluminal. Since that time,
this distance of GRO J1655--40 has been discussed in many studies, often
strengthening the value of 3.2 kpc, and used in numerous models. However,
recently, \citet{Foellmi-etal-2006b-astroph} used new VLT-UVES and {\it
published} photometric data to show that GRO J1655--40 must be closer than 1.7
kpc and that the accuracy of the main distance estimators for this source can
be questioned. Still, the details on how really the distance of 3.2 kpc has
been build was not fully clear. It is the purpose of this article to show that
while the {\it upper} limit to the distance is rather firm, the lower limit is
not. We draw some conclusions about the new understanding we have of GRO
J1655--40, and finally present a new and promising method that can be used to
determine the distance of GRO J1655--40, and maybe many faint and embedded
stars.


  We publish as open source a high performance file writer library to store
large images in the IMS format for high performance visualization and analysis.
The library is capable of writing images at high speed. In just over 1 minute
it can write a 100GB image to disk. The library takes care of all the details
of multi-resolution resampling, chunking, compression, and multi-threading. It
makes high performance writing of images in a useful format very easy.


  We prove the following variant of Marstrand's theorem about projections of
cartesian products of sets:
  Consider the space $\Lambda_m=\set{(t,O), t\in\R, O\in SO(m)}$ with the
natural measure and set $\Lambda=\Lambda_{m_1}\times\ppp\times\Lambda_{m_n}$.
For every $\la=(t_1,O_1,\ppp,t_n,O_n)\in\Lambda$ and every
$x=(x^1,\ppp,x^n)\in\R^{m_1}\times\ppp\times\R^{m_n}$ we define
$\pi_\la(x)=\pi(t_1O_1x^1,\ppp,t_nO_nx^n)$. Suppose that $\pi$ is surjective
and set $$\mathfrak{m}:=\min\set{\sum_{i\in I}\dim_H(K_i) +
\dim\pi(\bigoplus_{i\in I^c}\R^{m_i}), I\subset\set{1,\ppp,n},
I\ne\emptyset}.$$ Then we have
  {thm*}
  \emph{(i)} If $\mathfrak{m}>k$, then $\pi_\la(K_1\times\ppp\times K_n)$ has
positive $k$-dimensional Lebesgue measure for almost every $\la\in\Lambda$.
  \emph{(ii)} If $\mathfrak{m}\leq k$ and $\dim_H(K_1\times\ppp\times
K_n)=\dim_H(K_1)+\ppp+\dim_H(K_n)$, then $\dim_H(\pi_\la(K_1\times\ppp\times
K_n))=\mathfrak{m}$ for almost every $\la\in\Lambda$. {thm*}


  The Straight-Through Estimator (STE) is widely used for back-propagating
gradients through the quantization function, but the STE technique lacks a
complete theoretical understanding. We propose an alternative methodology
called alpha-blending (AB), which quantizes neural networks to low-precision
using stochastic gradient descent (SGD). Our method (AB) avoids STE
approximation by replacing the quantized weight in the loss function by an
affine combination of the quantized weight w_q and the corresponding
full-precision weight w with non-trainable scalar coefficient $\alpha$ and
$1-\alpha$. During training, $\alpha$ is gradually increased from 0 to 1; the
gradient updates to the weights are through the full-precision term,
$(1-\alpha)w$, of the affine combination; the model is converted from
full-precision to low-precision progressively. To evaluate the method, a 1-bit
BinaryNet on CIFAR10 dataset and 8-bits, 4-bits MobileNet v1, ResNet_50 v1/2 on
ImageNet dataset are trained using the alpha-blending approach, and the
evaluation indicates that AB improves top-1 accuracy by 0.9%, 0.82% and 2.93%
respectively compared to the results of STE based quantization.


  During the last decade, entity embeddings have become ubiquitous in
Artificial Intelligence. Such embeddings essentially serve as compact but
semantically meaningful representations of the entities of interest. In most
approaches, vectors are used for representing the entities themselves, as well
as for representing their associated attributes. An important advantage of
using attribute embeddings is that (some of the) semantic dependencies between
the attributes can thus be captured. However, little is known about what kinds
of semantic dependencies can be modelled in this way. The aim of this paper is
to shed light on this question, focusing on settings where the embedding of an
entity is obtained by pooling the embeddings of its known attributes. Our
particular focus is on studying the theoretical limitations of different
embedding strategies, rather than their ability to effectively learn attribute
dependencies in practice. We first show a number of negative results, revealing
that some of the most popular embedding models are not able to capture even
basic Horn rules. However, we also find that some embedding strategies are
capable, in principle, of modelling both monotonic and non-monotonic attribute
dependencies.


  We present a new staggered discretization of the Dirac operator. Doubling
gives only a doublet of Dirac fermions which we propose to interpret as a
physical (lepton or quark) doublet. If coupled with gauge fields, an
$(1+\gamma^5)$ chiral interaction appears in a natural way. We define a
generalization for curved background which does not require tetrad variables.
The approach suggests a natural explanation for the three fermion families.


  Machine unlearning, a novel area within artificial intelligence, focuses on
addressing the challenge of selectively forgetting or reducing undesirable
knowledge or behaviors in machine learning models, particularly in the context
of large language models (LLMs). This paper introduces a methodology to align
LLMs, such as Open Pre-trained Transformer Language Models, with ethical,
privacy, and safety standards by leveraging the gradient ascent algorithm for
knowledge unlearning. Our approach aims to selectively erase or modify learned
information in LLMs, targeting harmful responses and copyrighted content. This
paper presents a dual-pronged approach to enhance the ethical and safe behavior
of large language models (LLMs) by addressing the issues of harmful responses
and copyrighted content. To mitigate harmful responses, we applied gradient
ascent on the PKU dataset, achieving a 75\% reduction in harmful responses for
Open Pre-trained Transformer Language Models (OPT1.3b and OPT2.7b)
\citet{zhang2022opt} while retaining previous knowledge using the TruthfulQA
dataset \citet{DBLP:journals/corr/abs-2109-07958}. For handling copyrighted
content, we constructed a custom dataset based on the Lord of the Rings corpus
and aligned LLMs (OPT1.3b and OPT2.7b) \citet{zhang2022opt} through LoRA:
Low-Rank Adaptation of Large Language Models
\citet{DBLP:journals/corr/abs-2106-09685} finetuning. Subsequently, we employed
gradient ascent to unlearn the Lord of the Rings content, resulting in a
remarkable reduction in the presence of copyrighted material. To maintain a
diverse knowledge base, we utilized the Book Corpus dataset. Additionally, we
propose a new evaluation technique for assessing the effectiveness of harmful
unlearning.


  We derive a new class of exact time dependent solutions in a warped six
dimensional supergravity model. Under the assumptions we make for the form of
the underlying moduli fields, we show that the only consistent time dependent
solutions lead to all six dimensions evolving in time, implying the eventual
decompactification or collapse of the extra dimensions. We also show how the
dynamics affects the quantization of the deficit angle.


  We present a framework that enables to write a family of convex
approximations of complex contact models. Within this framework, we show that
we can incorporate well established and experimentally validated contact models
such as the Hunt & Crossley model. Moreover, we show how to incorporate
Coulomb's law and the principle of maximum dissipation using a regularized
model of friction. Contrary to common wisdom that favors the use of rigid
contact models, our convex formulation is robust and performant even at high
stiffness values far beyond that of materials such as steel. Therefore, the
same formulation enables the modeling of compliant surfaces such as rubber
gripper pads or robot feet as well as hard objects. We characterize and
evaluate our approximations in a number of tests cases. We report their
properties and highlight limitations.
  Finally, we demonstrate robust simulation of robotic tasks at interactive
rates, with accurately resolved stiction and contact transitions, as required
for meaningful sim-to-real transfer. Our method is implemented in the open
source robotics toolkit Drake.


  We theoretically investigate photothermal heating of ultra-flexible
metamaterials, which are obtained by randomly mixing TiN nanoparticles in
polydimethylsiloxane (PDMS). Due to the plasmonic properties of TiN
nanoparticles, incident light is perfectly absorbed in a broadband range
(300-3000 nm) to generate heat within these metamaterials. Under irradiation of
an 808 nm near-infrared laser with different intensities, our predicted
temperature rises as a function of time agree well with recent experimental
data. For a given laser intensity, the temperature rise varies
non-monotonically with concentration of TiN nanoparticles because the
enhancement of thermal conductivity and absorbed energy as adding plasmonic
nanostructures leads to opposite effects on the heating process. When the model
is extended to solar heating, photothermal behaviors are qualitatively similar
but the temperature increase is less than 13 $K$. Our studies would provide
good guidance for future experimental studies on the photo-to-heat conversion
of broadband perfect absorbers.


  PASCO scientific 012-05187C Precision Interferometer is used in Michelson
mode to investigate wavelengths and refractive indices. From varying the
distance of the movable mirror in the Michelson setup the wavelength of the
HeNe laser beam is found to be $630.6\pm7.9~\mathrm{nm}$; $0.28$ sigmas away
from the accepted $632.8~\mathrm{nm}$ and agreeing with it. Then after
considering the fact that the index of refraction for low pressure gasses
varies linearly with pressure we place a vacuum cell in front of the movable
mirror and pump out the air within it to find the individual slopes. By
extrapolating the average slope we calculate the index of refraction for air to
be $n=1.000226\pm0.000026$. This is $1.44$ sigmas away from the manufacturer's
measured $1.000263$ and barely agreeing with it as we underestimated our error
in the fringe count (which is caused by the change in pressure). Furthermore,
the vacuum cell is replaced by a crown glass plate which is rotated to vary the
length at which the EM wave travels in the Michelson interferometer. The angle
of rotation is measured and utilized to find the refractive index of glass to
be $1.514\pm0.006$; agreeing with the accepted value of $1.515$ as it is
$0.125$ sigmas away from it. Thus, along with the wavelength observation this
experiment is conducted successfully. Although the experiment to find the
refractive index of air is conducted less successfully, it is also prosperous
as the measured value is close to the accepted.


  Using time-dependent density functional theory, applied to valence electrons,
coupled non-adiabatically to molecular dynamics of the ions, we study the
induced dynamics of ethylene subjected to the laser field. We demonstrate the
reliable quality of such an approach in comparison to the experimental data on
atomic and molecular properties. The impact of ionic motion on the ionization
is discussed showing the importance of dealing with electronic and ionic
degrees of freedom simultaneously. We explore the various excitation scenarios
of ethylene as a function of the laser parameters. We find that the Coulomb
fragmentation depends sensitively on the laser frequency. The high laser
intensity can cause brute-force Coulomb explosion and the laser pulse length
actually has influence on the excitation dynamics of ethylene.


  I consider relaxation of the pairing amplitude in a disordered
Bardeen-Cooper-Schrieffer (BCS) superconductor in the absence of the
two-particle collisions. My main assumption is that nonmagnetic and magnetic
disorder scattering rates are much smaller than the value of the
superconducting pairing gap $\Delta_0$. I derive a system of nonlinear
equations which describe the collisionless relaxation of the pairing amplitude
following a quench of the pairing strength. I find that in a superconductor in
which scattering on paramagnetic impurities is dominant, the pairing amplitude
in a steady state varies periodically with time even for small deviations from
equilibrium. It is shown that such a steady state emerges due to scattering on
paramagnetic impurities which leads to a decrease in the value of the resonant
frequency of the amplitude mode below $2\Delta_0$.


  Radial velocity observations of the F8 V star Upsilon Andromedae taken at
Lick and at Whipple Observatories have revealed evidence of three periodicities
in the line-of-sight velocity of the star. These periodicities have been
interpreted as evidence for at least three low mass companions (LMCs) revolving
around Upsilon Andromedae. The mass and orbital parameters inferred for these
companions raise questions about the dynamical stability of the system. We
report here results from our independent analysis of the published radial
velocity data as well as new unpublished data taken at Lick Observatory. Our
results confirm the finding of three periods in the data. Our best fits to the
data, on the assumption that these periods arise from the gravitational
perturbations of companions in keplerian orbits, is also generally in
agreement, but with some differences, from the earlier findings. We find that
the available data do not constrain well the orbital eccentricity of the middle
companion in a three-companion model of the data. We also find that in order
for our best-fit model to the Lick data to be dynamically stable over the
lifetime of the star (~2 billion years), the system must have a mean
inclination to the plane of the sky greater than 13 degrees. The corresponding
minimum inclination for the best fit to the Whipple data set is 19 degrees.
These values imply that the maximum mass for the outer companion can be no
greater than about 20 Jupiter masses. Our analysis of the stability of the
putative systems also places constraints on the relative inclinations of the
orbital planes of the companions. We comment on global versus local (i.e.,
method of steepest descent) means of finding best-fit orbits from radial
velocity data sets.


  In spite of prolonged polemics, the agreement on the existence of N(1710) P11
resonance has not until now been reached, and the Particle Data Group declares
it as a 3-star resonance only. We show that the proper inclusion of inelastic
channels in the coupled-channel formalism indisputably demands the existence of
N(1710) P11 state, and that it presumably stays "hidden" within the continuum
ambiguity of any typical single channel partial wave analyses. Consequently,
its Particle Data Group confidence rating should be raised to a 4-star
resonance.


  In this article we fully classify regular tubular surfaces in Euclidean,
Lorentzian and hyperbolic 3-spaces whose Gaussian and mean curvatures $K$ and
$H$ verify a polynomial relation. More precisely, we determine the set $S(Q)$
of all regular tubular surfaces whose curvatures verify a given polynomial
relation $Q(K,H)=0$, and the set $Q(S)$ of all polynomial relations vanished by
the curvatures of a given tubular surface $S$.


  Magic state distillation (MSD) is a purification protocol that plays a
central role in fault tolerant quantum computation. Repeated iteration of the
steps of a MSD protocol, generates pure single non-stabilizer states, or magic
states, from multiple copies of a mixed resource state using stabilizer
operations only. Thus mixed resource states promote the stabilizer operations
to full universality. Magic state distillation was introduced for qubit-based
quantum computation, but little has been known concerning MSD in higher
dimensional qudit-based computation. Here, we describe a general approach for
studying MSD in higher dimensions. We use it to investigate the features of a
qutrit MSD protocol based on the 5-qutrit stabilizer code. We show that this
protocol distills non-stabilizer magic states, and identify two types of
states, that are attractors of this iteration map. Finally, we show how these
states may be converted, via stabilizer circuits alone, into a state suitable
for state injected implementation of a non-Clifford phase gate, enabling
non-Clifford unitary computation.


  Encapsulating few-layer graphene (FLG) in hexagonal boron nitride (hBN) can
cause nanoscale inhomogeneities in the FLG, including changes in stacking
domains and topographic defects. Due to the diffraction limit, characterizing
these inhomogeneities is challenging. Recently, the visualization of stacking
domains in encapsulated four-layer graphene (4LG) has been demonstrated with
phonon polariton (PhP)-assisted near-field imaging. However, the underlying
coupling mechanism and ability to image subdiffractional-sized inhomogeneities
remain unknown. Here, we retrieve direct replicas and magnified images of
subdiffractional-sized inhomogeneities in hBN-covered trilayer graphene (TLG)
and encapsulated 4LG, enabled by the hyperlensing effect. This hyperlensing
effect is mediated by hBN's hyperbolic PhP that couple to the FLG's plasmon
polaritons. Using near-field microscopy, we identify the coupling by
determining the polariton dispersion in hBN-covered TLG to be
stacking-dependent. Our work demonstrates super-resolution and magnified
imaging of inhomogeneities, paving the way for the realization of homogeneous
encapsulated FLG transport samples to study correlated physics.


  Besides the well-known quasinormal modes, the gravitational spectrum of a
Schwarzschild black hole also has a continuum part on the negative imaginary
frequency axis. The latter is studied numerically for quadrupole waves. The
results show unexpected striking behavior near the algebraically special
frequency $\Omega=-4i$. This reveals a pair of unconventional damped modes very
near $\Omega$, confirmed analytically.


  Partitions wherein the even parts appear in two different colours are known
as cubic partitions. Recently, Merca introduced and studied the function
$A(n)$, which is defined as the difference between the number of cubic
partitions of $n$ into an even number of parts and the number of cubic
partitions of $n$ into an odd number of parts. In particular, using Smoot's
\textsf{RaduRK} Mathematica package, Merca proved the following congruences by
finding the exact generating functions of the respective functions. For all
$n\ge0$, \begin{align*}A(9n+5)\equiv 0\pmod 3,\\ A(27n+26)\equiv 0\pmod 3.
\end{align*} By using generating function manipulations and dissections, da
Silva and Sellers proved these congruences and two infinite families of
congruences modulo 3 arising from these congruences. In this paper, by
employing Ramanujan's theta function identities, we present simplified formulas
of the generating functions from which proofs of the congruences of Merca as
well as those of da Silva and Sellers follow quite naturally. We also study
analogous partition functions wherein multiples of $k$ appear in two different
colours, where $k\in\{3,5,7,23\}$.


  Discovering fine-grained categories from coarsely labeled data is a practical
and challenging task, which can bridge the gap between the demand for
fine-grained analysis and the high annotation cost. Previous works mainly focus
on instance-level discrimination to learn low-level features, but ignore
semantic similarities between data, which may prevent these models learning
compact cluster representations. In this paper, we propose Denoised
Neighborhood Aggregation (DNA), a self-supervised framework that encodes
semantic structures of data into the embedding space. Specifically, we retrieve
k-nearest neighbors of a query as its positive keys to capture semantic
similarities between data and then aggregate information from the neighbors to
learn compact cluster representations, which can make fine-grained categories
more separatable. However, the retrieved neighbors can be noisy and contain
many false-positive keys, which can degrade the quality of learned embeddings.
To cope with this challenge, we propose three principles to filter out these
false neighbors for better representation learning. Furthermore, we
theoretically justify that the learning objective of our framework is
equivalent to a clustering loss, which can capture semantic similarities
between data to form compact fine-grained clusters. Extensive experiments on
three benchmark datasets show that our method can retrieve more accurate
neighbors (21.31% accuracy improvement) and outperform state-of-the-art models
by a large margin (average 9.96% improvement on three metrics). Our code and
data are available at https://github.com/Lackel/DNA.


  This paper analyzes the effect of a discrete treatment Z on a duration T. The
treatment is not randomly assigned. The confounding issue is treated using a
discrete instrumental variable explaining the treatment and independent of the
error term of the model. Our framework is nonparametric and allows for random
right censoring. This specification generates a nonlinear inverse problem and
the average treatment effect is derived from its solution. We provide local and
global identification properties that rely on a nonlinear system of equations.
We propose an estimation procedure to solve this system and derive rates of
convergence and conditions under which the estimator is asymptotically normal.
When censoring makes identification fail, we develop partial identification
results. Our estimators exhibit good finite sample properties in simulations.
We also apply our methodology to the Illinois Reemployment Bonus Experiment.


  We prove that any surjective homomorphism of simplicial Maltsev algebras is a
Kan fibration.


  This is a review of a decade-long effort to develop novel tools for exploring
magnetism in cold astrophysical media and to establish a new field of molecular
spectropolarimetry since Berdyugina et al. (2000). It is most directly
applicable to the Sun, cool stars, substellar objects, planets and other minor
bodies as well as interstellar and circumstellar matter. It is close to being a
mature field with developed theoretical tools poised to uncover new insights
into the magnetic structures in cooler environments. Here I attempt a broad
description of the literature and present some recent exciting results. In
particular, following my programmatic review at Solar Polarization Workshop 3,
I discuss advances in molecular magnetic diagnostics which are based on the
modeling of about a dozen diatomic molecules with various electronic
transitions and states, including the most challenging - FeH. The applications
stretch from sunspots to starspots, small-scale and turbulent solar magnetic
fields, red and white dwarfs, and spin-offs such as polarimetry of
protoplanetary disks and exoplanets.


  Neural radiance fields (NeRFs) have become a ubiquitous tool for modeling
scene appearance and geometry from multiview imagery. Recent work has also
begun to explore how to use additional supervision from lidar or depth sensor
measurements in the NeRF framework. However, previous lidar-supervised NeRFs
focus on rendering conventional camera imagery and use lidar-derived point
cloud data as auxiliary supervision; thus, they fail to incorporate the
underlying image formation model of the lidar. Here, we propose a novel method
for rendering transient NeRFs that take as input the raw, time-resolved photon
count histograms measured by a single-photon lidar system, and we seek to
render such histograms from novel views. Different from conventional NeRFs, the
approach relies on a time-resolved version of the volume rendering equation to
render the lidar measurements and capture transient light transport phenomena
at picosecond timescales. We evaluate our method on a first-of-its-kind dataset
of simulated and captured transient multiview scans from a prototype
single-photon lidar. Overall, our work brings NeRFs to a new dimension of
imaging at transient timescales, newly enabling rendering of transient imagery
from novel views. Additionally, we show that our approach recovers improved
geometry and conventional appearance compared to point cloud-based supervision
when training on few input viewpoints. Transient NeRFs may be especially useful
for applications which seek to simulate raw lidar measurements for downstream
tasks in autonomous driving, robotics, and remote sensing.


  Optically active defect centers in diamond are of considerable interest, and
ab initio calculations have provided valuable insight into the physics of these
systems. Candidate structures for the Xe center in diamond, for which little
structural information is known, are modeled using density functional theory.
The relative thermodynamic stabilities were calculated for two likely
structural arrangements. The split-vacancy structure is found to be the most
stable for all temperatures up to 1500 K. A vibrational analysis was also
carried out, predicting Raman- and IR-active modes which may aid in
distinguishing between center structures.


  In the field of privacy protection, publishing complete data (especially
high-dimensional data sets) is one of the most challenging problems. The common
encryption technology can not deal with the attacker to take differential
attack to obtain sensitive information, while the existing differential privacy
protection algorithm model takes a long time for high-dimensional calculation
and needs to add noise to reduce data accuracy, which is not suitable for
high-dimensional large data sets. In view of this situation, this paper designs
a complete data synthesis scheme to protect data privacy around the concept of
"plausible denial". Firstly, the paper provides the theoretical support for the
difference between "plausible data" and "plausible data". In the process of
scheme designing, this paper decomposes the scheme design into construction
data synthesis module and privacy test module, then designs algorithm models
for them respectively and realizes the function of privacy protection. When
evaluating the feasibility of the scheme, the paper selects the Results of the
2013 community census in the United States as the high-dimensional data set,
uses the simulation program that is based on Python to test and analyzes the
efficiency and reliability of the data synthesis scheme. This portion focuses
on the evaluation of the privacy protection effectiveness of the scheme.


  The ubiquitous planes and structural consistency are the most apparent
features of indoor multi-story Buildings compared with outdoor environments. In
this paper, we propose a tightly coupled LiDAR-Inertial 3D SLAM framework with
plane features for the multi-story building. The framework we proposed is
mainly composed of three parts: tightly coupled LiDAR-Inertial odometry,
extraction of representative planes of the structure, and factor graph
optimization. By building a local map and inertial measurement unit (IMU)
pre-integration, we get LiDAR scan-to-local-map matching and IMU measurements,
respectively. Minimize the joint cost function to obtain the LiDAR-Inertial
odometry information. Once a new keyframe is added to the graph, all the planes
of this keyframe that can represent structural features are extracted to find
the constraint between different poses and stories. A keyframe-based factor
graph is conducted with the constraint of planes, and LiDAR-Inertial odometry
for keyframe poses refinement. The experimental results show that our algorithm
has outstanding performance in accuracy compared with the state-of-the-art
algorithms.


  Loop interference (LI) in wireless communications, is a notion resulting from
the full-duplex (FD) operation. In a large-scale network, FD also increases the
multiuser interference due to the large number of active wireless links that
exist. Hence, in order to realize the FD potentials, this interference needs to
be restricted. This paper presents a stochastic geometry model of FD cellular
networks where the users and base stations employ directional antennas. Based
on previous experimental results, we model the passive suppression of the LI at
each FD terminal as a function of the angle between the two antennas and show
the significant gains that can be achieved by this method. Together with the
reduction of multiuser interference resulting from antenna directionality, our
model demonstrates that FD can potentially be implemented in large-scale
directional networks.


  Radar imaging is crucial in remote sensing and has many applications in
detection and autonomous driving. However, the received radar signal for
imaging is enormous and redundant, which degrades the speed of real-time radar
quantitative imaging and leads to obstacles in the downlink applications. In
this paper, we propose a physics-assisted deep learning method for radar
quantitative imaging with the advantage of compressed sensing (CS).
Specifically, the signal model for frequency-modulated continuous-wave (FMCW)
radar imaging which only uses four antennas and parts of frequency components
is formulated in terms of matrices multiplication. The learned fast iterative
shrinkage-thresholding algorithm with residual neural network (L-FISTA-ResNet)
is proposed for solving the quantitative imaging problem. The L-FISTA is
developed to ensure the basic solution and ResNet is attached to enhance the
image quality. Simulation results show that our proposed method has higher
reconstruction accuracy than the traditional optimization method and pure
neural networks. The effectiveness and generalization performance of the
proposed strategy is verified in unseen target imaging, denoising, and
frequency migration tasks.


  Let $\G(v,e)$ be the set of all simple graphs with $v$ vertices and $e$ edges
and let $P_2(G)=\sum d_i^2$ denote the sum of the squares of the degrees, $d_1,
>..., d_v$, of the vertices of $G$.
  It is known that the maximum value of $P_2(G)$ for $G \in \G(v,e)$ occurs at
one or both of two special graphs in $\G(v,e)$--the \qs graph or the \qc graph.
For each pair $(v,e)$, we determine which of these two graphs has the larger
value of $P_2(G)$. We also determine all pairs $(v,e)$ for which the values of
$P_2(G)$ are the same for the \qs and the \qc graph. In addition to the \qs and
\qc graphs, we find all other graphs in $\G(v,e)$ for which the maximum value
of $P_2(G)$ is attained. Density questions posed by previous authors are
examined.


  Equilibrium gels of colloidal particles can be realized through the
introduction of a second species, a linker that mediates the bonds between the
colloids. A gel forming binary mixture whose linkers can self-assemble into
linear chains while still promoting the aggregation of particles is considered
in this work. The particles are patchy particles with $f_C$ patches of type $C$
and the linkers are patchy particles with $2$ patches of type $A$ and $f_B$
patches of type B. The bonds between patches of type $A$ ($AA$ bonds) promote
the formation of linear chains of linkers. Two different ways (model A and
model B) of bonding the linkers to the particles - or inducing branching - are
studied. In model A, there is a competition between chaining and branching,
since the bonding between linkers and particles is done through $AC$ bonds
only. In model B linkers aggregate to particles through bonds $BC$ only, making
chaining and branching independent. The percolation behaviour of these two
models is studied in detail, employing a generalized Flory-Stockmayer theory
and Monte Carlo simulations. The self-assembly of linkers into chains reduces
the fraction of particles needed for percolation to occur (models A and B) and
induces percolation when the fraction of particles is high (model B).
Percolation by heating and percolation loops in temperature composition
diagrams are obtained when the formation of chains is energetically favourable,
by increasing the entropic gain of branching (model A). Chaining and branching
are found to follow a model dependent relation at percolation, which shows
that, for the same composition, longer chains require less branching for
percolation to occur.


  Vector inflation is a newly established model where inflation is driven by
non-minimally coupled massive vector fields with a potential term. This model
is similar to the model of chaotic inflation with scalar fields, except that
for vector fields the isotropy of expansion is achieved either by considering a
triplet of orthogonal vector fields or $N$ randomly oriented independent vector
fields. We introduce a new version of vector inflation where the vector field
has no potential term but is non-minimally coupled to gravity through the
kinetic term. The non-minimal coupling is established by introducing the
Einstein tensor besides the metric tensor within the kinetic term of the vector
field.


  We demonstrate that CP violation in the Higgs sector, \eg\ of a multi-doublet
model, can be directly probed using gluon-gluon collisions at the SSC. %
requires phyzzx.tex macro package


  Pig vital sign monitoring (e.g., estimating the heart rate (HR) and
respiratory rate (RR)) is essential to understand the stress level of the sow
and detect the onset of parturition. It helps to maximize peri-natal survival
and improve animal well-being in swine production. The existing approach mainly
relies on manual measurement, which is labor-intensive and only provides a few
points of information. Other sensing modalities such as wearables and cameras
are developed to enable more continuous measurement, but are still limited due
to animal discomfort, data transfer, and storage challenges. In this paper, we
introduce PigV$^2$, the first system to monitor pig heart rate and respiratory
rate through ground vibrations. Our approach leverages the insight that both
heartbeat and respiration generate ground vibrations when the sow is lying on
the floor. We infer vital information by sensing and analyzing these
vibrations. The main challenge in developing PigV$^2$ is the overlap of vital-
and non-vital-related information in the vibration signals, including pig
movements, pig postures, pig-to-sensor distances, and so on. To address this
issue, we first characterize their effects, extract their current status, and
then reduce their impact by adaptively interpolating vital rates over multiple
sensors. PigV$^2$ is evaluated through a real-world deployment with 30 pigs. It
has 3.4% and 8.3% average errors in monitoring the HR and RR of the sows,
respectively.


  The zeroth line bundle cohomology on Calabi-Yau three-folds encodes
information about the existence of flop transitions and the genus zero
Gromov-Witten invariants. We illustrate this claim by studying several Picard
number 2 Calabi-Yau three-folds realised as complete intersections in products
of projective spaces. Many of these manifolds exhibit certain symmetries on the
Picard lattice which preserve the zeroth cohomology.


  Science is an inherently quantitative endeavor, and general education science
courses are taken by a majority of college students. As such, they are a
powerful venue for advancing students' skills and attitudes toward mathematics.
This article reports on the development and validation of the Quantitative
Reasoning for College Science (QuaRCS) Assessment, a numeracy assessment
instrument designed for college-level general education science students. It
has been administered to more than four thousand students over eight semesters
of refinement. We show that the QuaRCS is able to distinguish varying levels of
quantitative literacy and present performance statistics for both individual
items and the instrument as a whole. Responses from a survey of forty-eight
Astronomy and Mathematics educators show that these two groups share views
regarding which quantitative skills are most important in the contexts of
science literacy and educated citizenship, and the skills assessed with the
QuaRCS are drawn from these rankings. The fully-developed QuaRCS assessment was
administered to nearly two thousand students in nineteen general education
science courses and one STEM major course in early 2015, and results reveal
that the instrument is valid for both populations.


  Faint, star-forming galaxies likely play a dominant role in cosmic
reionisation. Strides have been made in recent years to characterise these
populations at high redshifts ($z>3$). Now for the first time, with JWST
photometry beyond 1$\,\mu m$ in the rest frame, we can derive accurate stellar
masses and position these galaxies on the galaxy main sequence. We seek to
assess the place of 96 individual Lyman-alpha emitters (LAEs) selected behind
the A2744 lensing cluster with MUSE spectroscopy on the galaxy main sequence.
We also compare derived stellar masses to Lyman-alpha luminosities and
equivalent widths to better quantify the relationship between the Lyman-alpha
emission and the host galaxy. These 96 LAEs lie in the redshift range
$2.9<z<6.7$, and their range of masses extends down to
$10^6\,\mathrm{M_{\odot}}$ (over half with
$\mathrm{M_{\star}}<10^8\,\mathrm{M_{\odot}}$). We use the JWST/NIRCam and HST
photometric catalogs from the UNCOVER project, giving us excellent wavelength
coverage from $450\,\mathrm{nm}$ to $4.5\,\mu m$. We find a main sequence
relation for these low mass LAEs of the form: $\mathrm{log\,SFR}=(0.88\pm0.07 -
0.030\pm0.027\times t)\,\mathrm{log\,M_{\star}} - ( 6.31\pm0.41 -
0.08\pm0.37\times t)$. This is in approximate agreement with best-fits of
previous collated studies, however, with a steeper slope and a higher
normalisation. This indicates that low-mass LAEs towards the epoch of
reionisation lie above typical literature main sequence relations derived at
lower redshift and higher masses. Additionally, comparing our results to
UV-selected samples, we see that while low-mass LAEs lie above these typical
main sequence relations, they are likely not singular in this respect at these
masses and redshifts. While low-mass galaxies have been shown to play a
significant role in cosmic reionisation, our results point to no special
position for LAEs in this regard.


  Motivated by the gluon-chain model of flux tube formation, we compute and
diagonalize the transfer matrix in lattice SU(2) gauge theory for states
containing heavy static quark-antiquark sources, with separations up to one
fermi. The elements of the transfer matrix are calculated by variational Monte
Carlo methods, in a basis of states obtained by acting on the vacuum state with
zero, one, and two-gluon operators in Coulomb gauge. The color Coulomb
potential is obtained from the zero gluon to zero gluon element of the transfer
matrix, and it is well-known that while this potential is asymptotically
linear, it has a slope which is two to three times larger than the standard
asymptotic string tension. We show that the addition of one and two gluon
states results in a potential which is still linear, but the disagreement with
the standard asymptotic string tension is reduced to 38% at the largest lattice
coupling we have studied.


  This work presents four high-amplitude variable YSOs ($\simeq$ 3 mag at near-
or mid-IR wavelengths) arising from the SPICY catalog. Three outbursts show a
duration that is longer than 1 year, and are still ongoing. And additional YSO
brightened over the last two epochs of NEOWISE observations and the duration of
the outburst is thus unclear. Analysis of the spectra of the four sources
confirms them as new members of the eruptive variable class. We find two YSOs
that can be firmly classified as bona fide FUors and one object that falls in
the V1647 Ori-like class. Given the uncertainty in the duration of its
outburst, an additional YSO can only be classified as a candidate FUor.
Continued monitoring and follow-up of these particular sources is important to
better understand the accretion process of YSOs.


  Using the techniques of equivariant bifurcation theory we prove the existence
of non-stationary periodic solutions of $\Gamma$-symmetric systems $\ddot
q(t)=-\nabla U(q(t))$ in any neighborhood of an isolated orbit of minima
$\Gamma(q_0)$ of the potential $U$. We show the strength of our result by
proving the existence of new families of periodic orbits in the Lennard-Jones
two- and three-body problems and in the Schwarzschild three-body problem.


  In this work we consider a discontinuous Galerkin method for the
discretization of the Stokes problem. We use $H(\textrm{div})$-conforming
finite elements as they provide major benefits such as exact mass conservation
and pressure-independent error estimates. The main aspect of this work lies in
the analysis of high order approximations. We show that the considered method
is uniformly stable with respect to the polynomial order $k$ and provides
optimal error estimates $ \| \boldsymbol{u} - \boldsymbol{u}_h \|_{1_h} + \|
\Pi^{Q_h}p-p_h \| \le c \left( h/k \right)^s \| \boldsymbol{u} \|_{s+1} $. To
derive those estimates, we prove a $k$-robust LBB condition. This proof is
based on a polynomial $H^2$-stable extension operator. This extension operator
itself is of interest for the numerical analysis of $C^0$-continuous
discontinuous Galerkin methods for $4^{th}$ order problems.


  In this paper, inspired by the idea that many real networks are composed by
different sorts of communities, we investigate the synchronization property of
oscillators on such networks. We identify the communities by the intrinsic
frequencies probability density $g(\omega)$ of Kuramoto oscillators. That is to
say, communities in different sorts are functional different. For a network
containing two sorts of communities, when the community strength is strong,
only the oscillators in the same community synchronize. With the weakening of
the community strength, an interesting phenomenon, \emph{Community Grouping},
appears: although the global synchronization is not achieved, oscillators in
the same sort of communities will synchronize. Global synchronization will
appear with the further reducing of the community strength, and the oscillators
will rotate around the average frequency.


  3D ultrasound (US) is widely used for its rich diagnostic information.
However, it is criticized for its limited field of view. 3D freehand US
reconstruction is promising in addressing the problem by providing broad range
and freeform scan. The existing deep learning based methods only focus on the
basic cases of skill sequences, and the model relies on the training data
heavily. The sequences in real clinical practice are a mix of diverse skills
and have complex scanning paths. Besides, deep models should adapt themselves
to the testing cases with prior knowledge for better robustness, rather than
only fit to the training cases. In this paper, we propose a novel approach to
sensorless freehand 3D US reconstruction considering the complex skill
sequences. Our contribution is three-fold. First, we advance a novel online
learning framework by designing a differentiable reconstruction algorithm. It
realizes an end-to-end optimization from section sequences to the reconstructed
volume. Second, a self-supervised learning method is developed to explore the
context information that reconstructed by the testing data itself, promoting
the perception of the model. Third, inspired by the effectiveness of shape
prior, we also introduce adversarial training to strengthen the learning of
anatomical shape prior in the reconstructed volume. By mining the context and
structural cues of the testing data, our online learning methods can drive the
model to handle complex skill sequences. Experimental results on developmental
dysplasia of the hip US and fetal US datasets show that, our proposed method
can outperform the start-of-the-art methods regarding the shift errors and path
similarities.


  Most theoretical studies for correlated light-matter systems are performed
within the long-wavelength limit, i.e., the electromagnetic field is assumed to
be spatially uniform. In this limit the so-called length-gauge transformation
for a fully quantized light-matter system gives rise to a dipole self-energy
term in the Hamiltonian, i.e., a harmonic potential of the total dipole matter
moment. In practice this term is often discarded as it is assumed to be
subsumed in the kinetic energy term. In this work we show the necessity of the
dipole self-energy term. First and foremost, without it the light-matter system
in the long-wavelength limit does not have a ground-state, i.e., the combined
light-matter system is unstable. Further, the mixing of matter and photon
degrees of freedom due to the length-gauge transformation, which also changes
the representation of the translation operator for matter, gives rise to the
Maxwell equations in matter and the omittance of the dipole self-energy leads
to a violation of these equations. Specifically we show that without the dipole
self-energy the so-called 'depolarization shift' is not properly described.
Finally we show that this term also arises if we perform the semi-classical
limit after the length-gauge transformation. In contrast to the standard
approach where the semi-classical limit is performed before the length-gauge
transformation, the resulting Hamiltonian is bounded from below and thus
supports ground-states. This is very important for practical calculations and
for density-functional variational implementations of the non-relativistic QED
formalism. For example, the existence of a combined light-matter ground-state
allows one to calculate the Stark shift non-perturbatively.


  Asymptotic Safety, based on a non-Gaussian fixed point of the gravitational
renormalization group flow, provides an elegant mechanism for completing the
gravitational force at sub-Planckian scales. At high energies the fixed point
controls the scaling of couplings such that unphysical divergences are absent
while the emergence of classical low-energy physics is linked to a crossover
between two renormalization group fixed points. These features make Asymptotic
Safety an attractive framework for cosmological model building. The resulting
scenarios may naturally give rise to a quantum gravity driven inflationary
phase in the very early universe and an almost scale-free fluctuation spectrum.
Moreover, effective descriptions arising from an renormalization group
improvement permit a direct comparison to cosmological observations as, e.g.
Planck data.


  For each ordinal $\xi$, we define the notions of $\xi$-asymptotically
uniformly smooth and $w^*$-$\xi$-asymptotically uniformly convex operators.
When $\xi=0$, these extend the notions of asymptotically uniformly smooth and
$w^*$-asymptotically uniformly convex Banach spaces. We give a complete
description of renorming results for these properties in terms of the Szlenk
index of the operator, as well as a complete description of the duality between
these two properties. We also define the notion of an operator with property
$(\beta)$ of Rolewicz which extends the notion of property $(\beta)$ for a
Banach space. We characterize those operators the domain and range of which can
be renormed so that the operator has property $(\beta)$ in terms of the Szlenk
index of the operator and its adjoint.


  We present a detailed spectroscopic analysis of horizontal branch stars in
the globular cluster NGC 3201. We collected optical (4580-5330 A), high
resolution (~34,000), high signal-to-noise ratio (~200) spectra for eleven RR
Lyrae stars and one red horizontal branch star with the multifiber spectrograph
M2FS at the 6.5m Magellan telescope at the Las Campanas Observatory. From
measured equivalent widths we derived atmospheric parameters and abundance
ratios for {\alpha} (Mg, Ca, Ti), iron peak (Sc, Cr, Ni, Zn) and s-process (Y)
elements. We found that NGC 3201 is a homogeneous, mono-metallic ([Fe/H]=-1.47
+- 0.04), {\alpha}-enhanced ([{\alpha}/Fe]=0.37 +- 0.04) cluster. The relative
abundances of the iron peak and s-process elements were found to be consistent
with solar values. In comparison with other large stellar samples, NGC 3201 RR
Lyraes have similar chemical enrichment histories as do those of other old
(t>10 Gyr) Halo components (globular clusters, red giants, blue and red
horizontal branch stars, RR Lyraes). We also provided a new average radial
velocity estimate for NGC 3201 by using a template velocity curve to overcome
the limit of single epoch measurements of variable stars: Vrad=494 +- 2 km
s-1({\sigma}=8 km s-1).


  Radar sensors are emerging as solutions for perceiving surroundings and
estimating ego-motion in extreme weather conditions. Unfortunately, radar
measurements are noisy and suffer from mutual interference, which degrades the
performance of feature extraction and matching, triggering imprecise matching
pairs, which are referred to as outliers. To tackle the effect of outliers on
radar odometry, a novel outlier-robust method called \textit{ORORA} is
proposed, which is an abbreviation of \textit{Outlier-RObust RAdar odometry}.
To this end, a novel decoupling-based method is proposed, which consists of
graduated non-convexity~(GNC)-based rotation estimation and anisotropic
component-wise translation estimation~(A-COTE). Furthermore, our method
leverages the anisotropic characteristics of radar measurements, each of whose
uncertainty along the azimuthal direction is somewhat larger than that along
the radial direction. As verified in the public dataset, it was demonstrated
that our proposed method yields robust ego-motion estimation performance
compared with other state-of-the-art methods. Our code is available at
https://github.com/url-kaist/outlier-robust-radar-odometry.


  Existing neural ranking models follow the text matching paradigm, where
document-to-query relevance is estimated through predicting the matching score.
Drawing from the rich literature of classical generative retrieval models, we
introduce and formalize the paradigm of deep generative retrieval models
defined via the cumulative probabilities of generating query terms. This
paradigm offers a grounded probabilistic view on relevance estimation while
still enabling the use of modern neural architectures. In contrast to the
matching paradigm, the probabilistic nature of generative rankers readily
offers a fine-grained measure of uncertainty. We adopt several current neural
generative models in our framework and introduce a novel generative ranker
(T-PGN), which combines the encoding capacity of Transformers with the Pointer
Generator Network model. We conduct an extensive set of evaluation experiments
on passage retrieval, leveraging the MS MARCO Passage Re-ranking and TREC Deep
Learning 2019 Passage Re-ranking collections. Our results show the
significantly higher performance of the T-PGN model when compared with other
generative models. Lastly, we demonstrate that exploiting the uncertainty
information of deep generative rankers opens new perspectives to
query/collection understanding, and significantly improves the cut-off
prediction task.