  Since 1996 the blast wave driven by SN 1987A has been interacting with the
dense circumstellar material, which provides us with a unique opportunity to
study the early evolution of a newborn supernova remnant (SNR). Based on the
XMM-Newton RGS and EPIC-pn X-ray observations from 2007 to 2019, we
investigated the post-impact evolution of the X-ray emitting gas in SNR 1987A.
The hot plasma is represented by two non-equilibrium ionization components with
temperature of $\sim0.6$ keV and $\sim2.5$ keV. The low-temperature plasma has
a density $\sim2400$ cm$^{-3}$, which is likely dominated by the lower density
gas inside the equatorial ring (ER). The high-temperature plasma with a density
$\sim550$ cm$^{-3}$ could be dominated by the H II region and the high-latitude
material beyond the ring. In the last few years, the emission measure of the
low-temperature plasma has been decreasing, indicating that the blast wave has
left the main ER. But the blast wave is still propagating into the
high-latitude gas, resulting in the steadily increase of the high-temperature
emission measure. In the meantime, the average abundances of N, O, Ne, and Mg
are found to be declining, which may reflect the different chemical
compositions between two plasma components. We also detected the Fe K lines in
most of the observations, showing increasing flux and centroid energy. We
interpret the Fe K lines as from a third hot component, which may come from the
reflected shock-heated gas or originate from Fe-rich ejecta clumps, shocked by
the reverse shock.


  We study the quantization of chiral QED by means of an extended
Slavnov-Taylor (ST) identity under which an external source and the ghost field
form a doublet. This ST identity incorporates the Adler-Bardeen anomaly. We
prove that the cohomology classes of the extended classical linearized ST
operator ${\cal S}'_0$ are modified with respect to the ones of the classical
BRST differential in the FP neutral sector (physical observables). This
provides a counterexample showing that the introduction of a doublet can modify
the cohomology of the model, as a consequence of the fact that the counting
operator for the doublet does not commute with ${\cal S}'_0$.


  In the context of a localization and tracking application, we developed a
stereo vision system based on cheap low-resolution 80x60 pixels thermal
cameras. We proposed a threefold sub-pixel stereo matching framework (called ST
for Subpixel Thermal): 1) robust features extraction method based on phase
congruency, 2) rough matching of these features in pixel precision, and 3)
refined matching in sub-pixel accuracy based on local phase coherence. We
performed experiments on our very low-resolution thermal images (acquired using
a stereo system we manufactured) as for high-resolution images from a benchmark
dataset. Even if phase congruency computation time is high, it was able to
extract two times more features than state-of-the-art methods such as ORB or
SURF. We proposed a modified version of the phase correlation applied in the
phase congruency feature space for sub-pixel matching. Using simulated stereo,
we investigated how the phase congruency threshold and the sub-image size of
sub-pixel matching can influence the accuracy. We then proved that given our
stereo setup and the resolution of our images, being wrong of 1 pixel leads to
a 500 mm error in the Z position of the point. Finally, we showed that our
method could extract four times more matches than a baseline method ORB +
OpenCV KNN matching on low-resolution images. Moreover, our matches were more
robust. More precisely, when projecting points of a standing person, ST got a
standard deviation of 300 mm when ORB + OpenCV KNN gave more than 1000 mm.


  A crossover from $d$ to $d-1$, and then back to $d$-dimensional critical
behavior is argued to be a generic feature characterizing ordering in a
$d$-dimensional superlattice composed of atomically {\em thick} films of two
ferromagnets. The crossover leads to anomalous changes in the amplitudes of
critical singularities. In $d=3$ Heisenberg and $XY$ superlattices large scale
critical fluctuations persist over a wide temperature range.


  Additive manufacturing is a process that has facilitated the cost effective
production of complicated designs. Objects fabricated via additive
manufacturing technologies often suffer from dimensional accuracy issues and
other part specific problems such as thin part robustness, overhang geometries
that may collapse, support structures that cannot be removed, engraved and
embossed details that are indistinguishable. In this work we present an
approach to predict the dimensional accuracy per vertex and per part.
Furthermore, we provide a framework for estimating the probability that a model
is fabricated correctly via an additive manufacturing technology for a specific
application. This framework can be applied to several 3D printing technologies
and applications. In the context of this paper, a thorough experimental
evaluation is presented for binder jetting technology and applications.


  The spectral determinant is usually defined using the spectral zeta function
that is meromorphically continued to zero. In this definition, the complex
logarithms of the eigenvalues appear. Hence the notion of the spectral
determinant depends on the way how one chooses the branch cut in the definition
of the logarithm. We give results for the non-self-adjoint operators that state
when the determinant can and cannot be defined and how its value differs
depending on the choice of the branch cut.


  Agent-based transportation modelling has become the standard to simulate
travel behaviour, mobility choices and activity preferences using disaggregate
travel demand data for entire populations, data that are not typically readily
available. Various methods have been proposed to synthesize population data for
this purpose. We present a Composite Travel Generative Adversarial Network
(CTGAN), a novel deep generative model to estimate the underlying joint
distribution of a population, that is capable of reconstructing composite
synthetic agents having tabular (e.g. age and sex) as well as sequential
mobility data (e.g. trip trajectory and sequence). The CTGAN model is compared
with other recently proposed methods such as the Variational Autoencoders (VAE)
method, which has shown success in high dimensional tabular population
synthesis. We evaluate the performance of the synthesized outputs based on
distribution similarity, multi-variate correlations and spatio-temporal
metrics. The results show the consistent and accurate generation of synthetic
populations and their tabular and spatially sequential attributes, generated
over varying spatial scales and dimensions.


  In multi-user optical wireless communication (OWC) systems interference
between users and cells can significantly affect the quality of OWC links.
Thus, in this paper, a mixed-integer linear programming (MILP) model is
developed to establish the optimum resource allocation in wavelength division
multiple access (WDMA) optical wireless systems. Consideration is given to the
optimum allocation of wavelengths and access points (APs) to each user to
support multiple users in an environment where Micro, Pico and Atto Cells
co-exist for downlink communication. The high directionality of light rays in
small cells, such as Pico and Atto cells, can offer a very high signal to noise
and interference ratio (SINR) at high data rates. Consideration is given in
this work to visible light communication links which utilise four wavelengths
per access point (red, green, yellow and blue) for Pico and Atto cells systems,
while the Micro cell system uses an infrared (IR) transmitter. Two 10-user
scenarios are considered in this work. All users in both scenarios achieve a
high optical channel bandwidth beyond 7.8 GHz. In addition, all users in the
two scenarios achieve high SINR beyond the threshold (15.6 dB) needed for 10-9
on off keying (OOK) bit error rate at a data rate of 7.1 Gbps.


  The propagation of damage on the square Ising lattice with a corner geometry
is studied by means of Monte Carlo simulations. It is found that, just at
$T=T_f (h)$ (critical temperature of the filling transition) the damage
initially propagates along the interface of the competing domains, according to
a power law given by $D(t) \propto t^{\eta}$. The value obtained for the
dynamic exponent ($\eta^{*} = 0.89(1)$) is in agreement with that corresponding
to the wetting transition in the slit geometry (Abraham Model) given by
$\eta^{WT} = 0.91(1)$. However, for later times the propagation crosses to a
new regime such as $\eta^{**} = 0.40 \pm 0.02$, which is due to the propagation
of the damage into the bulk of the magnetic domains. This result can be
understood due to the constraints imposed to the propagation of damage by the
corner geometry of the system that cause healing at the corners where the
interface is attached.


  The emergence of a Kalb-Ramond field and string charge in the lattice is
discussed. The local bosonic model with rotor variables placed on the faces of
a cubic lattice is considered. The coupling model consisting of the Maxwell
fields and the Kalb-Ramond field is given. This construction naturally
incorporates the emerging coupling between both gauge and string fields. In the
process, an object that resembles to a D-brane on the lattice is introduced.


  A lattice $\Lambda$ is said to be an extension of a sublattice $L$ of smaller
rank if $L$ is equal to the intersection of $\Lambda$ with the subspace spanned
by $L$. The goal of this paper is to initiate a systematic study of the
geometry of lattice extensions. We start by proving the existence of a
small-determinant extension of a given lattice, and then look at successive
minima and covering radius. To this end, we investigate extensions (within an
ambient lattice) preserving the successive minima of the given lattice, as well
as extensions preserving the covering radius. We also exhibit some interesting
arithmetic properties of deep holes of planar lattices.


  Even though face recognition in frontal view and normal lighting condition
works very well, the performance degenerates sharply in extreme conditions.
Recently there are many work dealing with pose and illumination problems,
respectively. However both the lighting and pose variation will always be
encountered at the same time. Accordingly we propose an end-to-end face
recognition method to deal with pose and illumination simultaneously based on
convolutional networks where the discriminative nonlinear features that are
invariant to pose and illumination are extracted. Normally the global structure
for images taken in different views is quite diverse. Therefore we propose to
use the 1*1 convolutional kernel to extract the local features. Furthermore the
parallel multi-stream multi-layer 1*1 convolution network is developed to
extract multi-hierarchy features. In the experiments we obtained the average
face recognition rate of 96.9% on multiPIE dataset,which improves the
state-of-the-art of face recognition across poses and illumination by 7.5%.
Especially for profile-wise positions, the average recognition rate of our
proposed network is 97.8%, which increases the state-of-the-art recognition
rate by 19%.


  We investigate the effect of anharmonicity and interactions on the dynamics
of an initially Gaussian wavepacket in a weakly anharmonic potential. We note
that depending on the strength and sign of interactions and anharmonicity, the
quantum state can be either localized or delocalized in the potential. We
formulate a classical model of this phenomenon and compare it to quantum
simulations done for a self consistent potential given by the Gross-Pitaevskii
Equation.


  We have observed the 8-13 $\mu$m spectrum (R$\sim$250) of the Vega-like star
candidate HD145263 using Subaru/COMICS. The spectrum of HD145263 shows the
broad trapezoidal silicate feature with the shoulders at 9.3 $\mu$m and 11.44
$\mu$m, indicating the presence of crystalline silicate grains. This detection
implies that crystalline silicate may also be commonly present around Vega-like
stars. The 11.44 $\mu$m feature is slightly shifted to a longer wavelength
compared to the usual 11.2-3 $\mu$m crystalline forsterite feature detected
toward Herbig Ae/Be stars and T Tauri stars. Although the peak shift due to the
effects of the grain size can not be ruled out, we suggest that Fe-bearing
crystalline olivine explains the observed peak wavelength fairly well.
Fe-bearing silicates are commonly found in meteorites and most interplanetary
dust particles, which originate from planetesimal-like asteroids. According to
studies of meteorites, Fe-bearing silicate must have been formed in asteroidal
planetesimals, supporting the scenario that dust grains around Vega-like stars
are of planetesimal origin, if the observed 11.44 $\mu$m peak is due to
Fe-bearing silicates.


  We give a pedagogical introduction to hard photoproduction processes at HERA,
including the production of jets, hadrons, and prompt photons. Recent
theoretical developments in the three areas are reviewed.


  Automatic few-shot font generation (AFFG), aiming at generating new fonts
with only a few glyph references, reduces the labor cost of manually designing
fonts. However, the traditional AFFG paradigm of style-content disentanglement
cannot capture the diverse local details of different fonts. So, many
component-based approaches are proposed to tackle this problem. The issue with
component-based approaches is that they usually require special pre-defined
glyph components, e.g., strokes and radicals, which is infeasible for AFFG of
different languages. In this paper, we present a novel font generation approach
by aggregating styles from character similarity-guided global features and
stylized component-level representations. We calculate the similarity scores of
the target character and the referenced samples by measuring the distance along
the corresponding channels from the content features, and assigning them as the
weights for aggregating the global style features. To better capture the local
styles, a cross-attention-based style transfer module is adopted to transfer
the styles of reference glyphs to the components, where the components are
self-learned discrete latent codes through vector quantization without manual
definition. With these designs, our AFFG method could obtain a complete set of
component-level style representations, and also control the global glyph
characteristics. The experimental results reflect the effectiveness and
generalization of the proposed method on different linguistic scripts, and also
show its superiority when compared with other state-of-the-art methods. The
source code can be found at https://github.com/awei669/VQ-Font.


  The likelihood ratio test (LRT) and the related $F$ test, do not (even
asymptotically) adhere to their nominal $\chi^2$ and $F$ distributions in many
statistical tests common in astrophysics, thereby casting many marginal line or
source detections and non-detections into doubt. Although there are many
legitimate uses of these statistics, in some important cases it can be
impossible to compute the correct false positive rate. For example, it has
become common practice to use the LRT or the $F$ test for detecting a line in a
spectral model or a source above background despite the lack of certain
required regularity conditions. In these and other settings that involve
testing a hypothesis that is on the boundary of the parameter space, {\it
contrary to common practice, the nominal $\chi^2$ distribution for the LRT or
the $F$ distribution for the $F$ test should not be used}. In this paper, we
characterize an important class of problems where the LRT and the $F$ test fail
and illustrate this non-standard behavior. We briefly sketch several possible
acceptable alternatives, focusing on Bayesian posterior predictive
probability-values. We present this method in some detail, as it is a simple,
robust, and intuitive approach. This alternative method is illustrated using
the gamma-ray burst of May 8, 1997 (GRB 970508) to investigate the presence of
an Fe K emission line during the initial phase of the observation.


  The partial linear Cox model for interval-censoring is well-studied under the
additive assumption but is still under-investigated without this assumption. In
this paper, we propose to use a deep ReLU neural network to estimate the
nonparametric components of a partial linear Cox model for interval-censored
data. This model not only retains the nice interpretability of the parametric
component but also improves the predictive power compared to the partial linear
additive Cox model. We derive the convergence rate of the proposed estimator
and show that it can break the curse of dimensionality under some certain
smoothness assumptions. Based on such rate, the asymptotic normality and the
semiparametric efficiency are also established. Intensive simulation studies
are carried out to demonstrate the finite sample performance on both estimation
and prediction. The proposed estimation procedure is illustrated on a real
dataset.


  The present solar and atmospheric neutrino data together with the LSND
results and the presence of hot dark matter (HDM) suggest the existence of a
sterile neutrino at the eV scale. We have reanalysed the effect of resonant
{\sl sterile} neutrino conversions induced by neutrino magnetic moments in a
type-II supernova. We analyse the implications of $\nu_e-\nu_s$ and
$\bar{\nu}_e-\bar{\nu}_s$ ($\nu_s$ denotes sterile neutrino) conversions for
the supernova shock re-heating, the detected $\bar\nu_e$ signal from SN1987A
and the $r$-process nucleosynthesis hypothesis. Using reasonable magnetic field
profiles we determine the sensitivity of these three arguments to the relevant
neutrino parameters, i.e. the value of the transition magnetic moment and the
$\nu_e-\nu_s$ mass difference $\Delta m^2 \equiv \Delta m^2_{LSND/HDM}$


  We present the first global analysis of the Constrained NMSSM that
investigates the impact of the recent discovery of a 126 GeV Higgs-like boson,
of the observation of a signal for BR(B_s->\mu^+\mu^-), and of constraints on
supersymmetry from ~5/fb of data accumulated at the LHC, as well as of other
relevant constraints from colliders, flavor physics and dark matter. We
consider three possible cases, assuming in turn that the discovered Higgs boson
is: i) the lightest Higgs boson of the model; ii) the next-to-lightest Higgs
boson; and iii) a combination of both roughly degenerate in mass. The
likelihood function for the Higgs signal uses signal rates in the \gamma\gamma\
and ZZ --> 4l channels, while that for the Higgs exclusion limits assumes decay
through the \gamma\gamma, \tau\tau, ZZ and W^+W^- channels. In all cases
considered we identify the 68% and 95% credible posterior probability regions
in a Bayesian approach. We find that, when the constraints are applied with
their respective uncertainties, the first case shows strong CMSSM-like
behavior, with the stau coannihilation region featuring highest posterior
probability, the best-fit point, a correct mass of the lightest Higgs boson and
the lighter stop mass in the ballpark of 1 TeV. The second and the third cases,
on the other hand, while allowed are disfavored by the constraints from direct
detection of dark matter and from BR(B_s->\mu^+\mu^-). Without the anomalous
magnetic moment of the muon the fit improves considerably, especially for
negative effective \mu\ parameter. We discuss how the considered scenarios
could be tested furtherly at the LHC and in dark matter searches.


  In this paper, we give polynomial-time algorithms that can take a graph G
with a given combinatorial embedding on an orientable surface S of genus g and
produce a planar drawing of G in R^2, with a bounding face defined by a
polygonal schema P for S. Our drawings are planar, but they allow for multiple
copies of vertices and edges on P's boundary, which is a common way of
visualizing higher-genus graphs in the plane. Our drawings can be defined with
respect to either a canonical polygonal schema or a polygonal cutset schema,
which provides an interesting tradeoff, since canonical schemas have fewer
sides, and have a nice topological structure, but they can have many more
repeated vertices and edges than general polygonal cutsets. As a side note, we
show that it is NP-complete to determine whether a given graph embedded in a
genus-g surface has a set of 2g fundamental cycles with vertex-disjoint
interiors, which would be desirable from a graph-drawing perspective.


  Webly supervised learning becomes attractive recently for its efficiency in
data expansion without expensive human labeling. However, adopting search
queries or hashtags as web labels of images for training brings massive noise
that degrades the performance of DNNs. Especially, due to the semantic
confusion of query words, the images retrieved by one query may contain
tremendous images belonging to other concepts. For example, searching `tiger
cat' on Flickr will return a dominating number of tiger images rather than the
cat images. These realistic noisy samples usually have clear visual semantic
clusters in the visual space that mislead DNNs from learning accurate semantic
labels. To correct real-world noisy labels, expensive human annotations seem
indispensable. Fortunately, we find that metadata can provide extra knowledge
to discover clean web labels in a labor-free fashion, making it feasible to
automatically provide correct semantic guidance among the massive label-noisy
web data. In this paper, we propose an automatic label corrector VSGraph-LC
based on the visual-semantic graph. VSGraph-LC starts from anchor selection
referring to the semantic similarity between metadata and correct label
concepts, and then propagates correct labels from anchors on a visual graph
using graph neural network (GNN). Experiments on realistic webly supervised
learning datasets Webvision-1000 and NUS-81-Web show the effectiveness and
robustness of VSGraph-LC. Moreover, VSGraph-LC reveals its advantage on the
open-set validation set.


  Timing channels are a significant and growing security threat in computer
systems, with no established solution. We have recently argued that the OS must
provide time protection, in analogy to the established memory protection, to
protect applications from information leakage through timing channels. Based on
a recently-proposed implementation of time protection in the seL4 microkernel,
we investigate how such an implementation could be formally proved to prevent
timing channels. We postulate that this should be possible by reasoning about a
highly abstracted representation of the shared hardware resources that cause
timing channels.


  An upper bound on the first S^1 invariant eigenvalue of the Laplacian for
invariant metrics on the 2-sphere is used to find obstructions to the existence
of isometric embeddings of such metrics in (R^3,can). As a corollary we prove:
If the first four distinct eigenvalues have even multiplicities then the
surface of revolution cannot be isometrically embedded in (R^3,can). This leads
to a generalization of a classical result in the theory of surfaces.


  PSR B1259-63/LS2883 is a binary system composed of a pulsar and a Be star.
The Be star has an equatorial circumstellar disk (CD). The {\it Fermi}
satellite discovered unexpected gamma-ray flares around 30 days after the last
two periastron passages. The origin of the flares remain puzzling. In this
work, we explore the possibility that, the GeV flares are consequences of
inverse Compton-scattering of soft photons by the pulsar wind. The soft photons
are from an accretion disk around the pulsar, which is composed by the matter
from CD captured by the pulsar's gravity at disk-crossing before the
periastron. At the other disk-crossing after the periastron, the density of the
CD is not high enough so that accretion is prevented by the pulsar wind shock.
This model can reproduce the observed SEDs and light curves satisfactorily.


  In this paper, we first propose a method that can efficiently compute the
maximal robust controlled invariant set for discrete-time linear systems with
pure delay in input. The key to this method is to construct an auxiliary linear
system (without delay) with the same state-space dimension of the original
system in consideration and to relate the maximal invariant set of the
auxiliary system to that of the original system. When the system is subject to
disturbances, guaranteeing safety is harder for systems with input delays.
Ability to incorporate any additional information about the disturbance becomes
more critical in these cases. Motivated by this observation, in the second part
of the paper, we generalize the proposed method to take into account additional
preview information on the disturbances, while maintaining computational
efficiency. Compared with the naive approach of constructing a higher
dimensional system by appending the state-space with the delayed inputs and
previewed disturbances, the proposed approach is demonstrated to scale much
better with the increasing delay time.


  In recent years, the introduction of self-supervised contrastive learning
(SSCL) has demonstrated remarkable improvements in representation learning
across various domains, including natural language processing and computer
vision. By leveraging the inherent benefits of self-supervision, SSCL enables
the pre-training of representation models using vast amounts of unlabeled data.
Despite these advances, there remains a significant gap in understanding the
impact of different SSCL strategies on time series forecasting performance, as
well as the specific benefits that SSCL can bring. This paper aims to address
these gaps by conducting a comprehensive analysis of the effectiveness of
various training variables, including different SSCL algorithms, learning
strategies, model architectures, and their interplay. Additionally, to gain
deeper insights into the improvements brought about by SSCL in the context of
time-series forecasting, a qualitative analysis of the empirical receptive
field is performed. Through our experiments, we demonstrate that the end-to-end
training of a Transformer model using the Mean Squared Error (MSE) loss and
SSCL emerges as the most effective approach in time series forecasting.
Notably, the incorporation of the contrastive objective enables the model to
prioritize more pertinent information for forecasting, such as scale and
periodic relationships. These findings contribute to a better understanding of
the benefits of SSCL in time series forecasting and provide valuable insights
for future research in this area. Our codes are available at
https://github.com/chiyuzhang94/contrastive_learning_time-series_e2e.


  In this paper, we define and apply representational stability analysis
(ReStA), an intuitive way of analyzing neural language models. ReStA is a
variant of the popular representational similarity analysis (RSA) in cognitive
neuroscience. While RSA can be used to compare representations in models, model
components, and human brains, ReStA compares instances of the same model, while
systematically varying single model parameter. Using ReStA, we study four
recent and successful neural language models, and evaluate how sensitive their
internal representations are to the amount of prior context. Using RSA, we
perform a systematic study of how similar the representational spaces in the
first and second (or higher) layers of these models are to each other and to
patterns of activation in the human brain. Our results reveal surprisingly
strong differences between language models, and give insights into where the
deep linguistic processing, that integrates information over multiple
sentences, is happening in these models. The combination of ReStA and RSA on
models and brains allows us to start addressing the important question of what
kind of linguistic processes we can hope to observe in fMRI brain imaging data.
In particular, our results suggest that the data on story reading from Wehbe et
al. (2014) contains a signal of shallow linguistic processing, but show no
evidence on the more interesting deep linguistic processing.


  Two-dimensional integrable differential calculi for classes of Ore extensions
of the polynomial ring and the Laurent polynomial ring in one variable are
constructed. Thus it is concluded that all affine pointed Hopf domains of
Gelfand-Kirillov dimension two which are not polynomial identity rings are
differentially smooth.


  Metaplectic Wigner distributions generalize the most popular time-frequency
representations, such as the short-time Fourier transform (STFT) and
$\tau$-Wigner distributions, using metaplectic operators. However, in order for
a metaplectic Wigner distribution to measure local time-frequency concentration
of signals, the additional property of shift-invertibility is fundamental. In
addition, metaplectic atoms provide different ways to model signals. Namely,
signals can be written as discrete superpositions of these operators, providing
original ways to represent signals, with applications to machine learning,
signal analysis, theory of pseudodifferential operators, to mention a few.
Among all shift-invertible distributions, Wigner-decomposable metaplectic
Wigner distributions provide the most straightforward generalization of the
STFT. In this work, we focus on metaplectic atoms of Wigner-decomposable
shift-invertible metaplectic distributions and characterize the associated
metaplectic Gabor frames.


  We investigate the emergence of strange baryons in the dynamical collapse of
a non-rotating massive star to a black hole by the neutrino-radiation
hydrodynamical simulations in general relativity. By following the dynamical
formation and collapse of nascent proto-neutron star from the gravitational
collapse of a 40Msun star adopting a new hyperonic EOS table, we show that the
hyperons do not appear at the core bounce but populate quickly at ~0.5-0.7 s
after the bounce to trigger the re-collapse to a black hole. They start to show
up off center owing to high temperatures and later prevail at center when the
central density becomes high enough. The neutrino emission from the accreting
proto-neutron star with the hyperonic EOS stops much earlier than the
corresponding case with a nucleonic EOS while the average energies and
luminosities are quite similar between them. These features of neutrino signal
are a potential probe of the emergence of new degrees of freedom inside the
black hole forming collapse.


  Learning maps between data samples is fundamental. Applications range from
representation learning, image translation and generative modeling, to the
estimation of spatial deformations. Such maps relate feature vectors, or map
between feature spaces. Well-behaved maps should be regular, which can be
imposed explicitly or may emanate from the data itself. We explore what induces
regularity for spatial transformations, e.g., when computing image
registrations. Classical optimization-based models compute maps between pairs
of samples and rely on an appropriate regularizer for well-posedness. Recent
deep learning approaches have attempted to avoid using such regularizers
altogether by relying on the sample population instead. We explore if it is
possible to obtain spatial regularity using an inverse consistency loss only
and elucidate what explains map regularity in such a context. We find that deep
networks combined with an inverse consistency loss and randomized off-grid
interpolation yield well behaved, approximately diffeomorphic, spatial
transformations. Despite the simplicity of this approach, our experiments
present compelling evidence, on both synthetic and real data, that regular maps
can be obtained without carefully tuned explicit regularizers, while achieving
competitive registration performance.


  KTaO_3-based solid solutions exhibit a variety of interesting physical
phenomena. To better understand these phenomena, we performed first-principles
calculations on [K_{1-x}, Li_x]TaO_3 (KLT) and K[Ta_{1-x}, Nb_x]O_3 (KTN)
supercells. Our results show Li displacements and potential barrier heights in
KLT that are in excellent agreement with values obtained from experimental
fits. Dramatic changes in B-site dynamical charges occur in KTN in response to
changes in near neighbor (nn) coordination. These effects can be explained by
heterogeneity in the local electronic dielectric permittivity.


  This work presents a new deep learning approach for keystroke biometrics
based on a novel Distance Metric Learning method (DML). DML maps input data
into a learned representation space that reveals a "semantic" structure based
on distances. In this work, we propose a novel DML method specifically designed
to address the challenges associated to free-text keystroke identification
where the classes used in learning and inference are disjoint. The proposed
SetMargin Loss (SM-L) extends traditional DML approaches with a learning
process guided by pairs of sets instead of pairs of samples, as done
traditionally. The proposed learning strategy allows to enlarge inter-class
distances while maintaining the intra-class structure of keystroke dynamics. We
analyze the resulting representation space using the mathematical problem known
as Circle Packing, which provides neighbourhood structures with a theoretical
maximum inter-class distance. We finally prove experimentally the effectiveness
of the proposed approach on a challenging task: keystroke biometric
identification over a large set of 78,000 subjects. Our method achieves
state-of-the-art accuracy on a comparison performed with the best existing
approaches.


  We revisit Kaluza-Klein compactification of 11-d supergravity on S^7/Z_k
using group theory techniques that may find application in other flux vacua
with internal coset spaces. Among the SO(2) neutral states, we identify
marginal deformations and fields that couple to the recently discussed
world-sheet instanton of Type IIA on CP^3. We also discuss charged states, dual
to monopole operators, and the Z_k projection of the Osp(4|8) singleton and its
tensor products. In particular, we show that the doubleton spectrum may account
for N=6 higher spin symmetry enhancement in the limit of vanishing 't Hooft
coupling in the boundary Chern-Simons theory.


  The collective ground-state correlations stemming from low-lying quadrupole
excitations are computed microscopically. To that end, the self-consistent
mean-field model is employed on the basis of the Skyrme-Hartre-Fock (SHF)
functional augmented by BCS pairing. The microscopic-macroscopic mapping is
achieved by quadrupole-constrained mean-field calculations which are processed
further in the generator-coordinate method (GCM) at the level of the Gaussian
overlap approximation (GOA).
  We study the correlation effects on energy, charge radii, and surface
thickness for a great variety of semi-magic nuclei. A key issue is to work out
the influence of variations of the SHF functional. We find that collective
ground-state correlations (GSC) are robust under change of nuclear bulk
properties (e.g., effective mass, symmetry energy) or of spin-orbit coupling.
Some dependence on the pairing strength is observed. This, however, does not
change the general conclusion that collective GSC obey a general pattern and
that their magnitudes are rather independent of the actual SHF parameters.


  We prove the convergence of Araki-Haag detectors in any Haag-Kastler quantum
field theory with an upper and lower mass gap. We cover the case of a single
Araki-Haag detector on states of bounded energy, which are selected from the
absolutely continuous part of the energy-momentum spectrum sufficiently close
to the lower boundary of the multi-particle spectrum. These states essentially
encompass those states in the multi-particle spectrum lying below the
three-particle threshold. In our proof, we draw on insights from proofs of
asymptotic completeness in quantum mechanics. Notably, we apply Mourre's
conjugate operator method for the first time within the framework of
Haag-Kastler quantum field theory. Furthermore, we discuss applications of our
findings for the problem of asymptotic completeness in local relativistic
quantum field theory.


  In this paper, a time substitution as used by Duru and Kleinert in their
treatment of the hydrogen atom with path integrals is performed to price timer
options under stochastic volatility models. We present general pricing formulas
for both the perpetual timer call options and the finite time-horizon timer
call options. These general results allow us to find closed-form pricing
formulas for both the perpetual and the finite time-horizon timer options under
the 3/2 stochastic volatility model as well as under the Heston stochastic
volatility model. For the treatment of timer option under the 3/2 model we will
rely on the path integral for the Morse potential, with the Heston model we
will rely on the Kratzer potential.


  Program synthesis is an active research field in academia, national labs, and
industry. Yet, work directly applicable to scientific computing, while having
some impressive successes, has been limited. This report reviews the relevant
areas of program synthesis work for scientific computing, discusses successes
to date, and outlines opportunities for future work. This report is the result
of the Workshop on Program Synthesis for Scientific Computing was held
virtually on August 4-5 2020 (https://prog-synth-science.github.io/2020/).


  Transcorrelated coupled cluster and distinguishable cluster methods are
presented. The Hamiltonian is similarity transformed with a Jastrow factor in
the first quantisation, which results in up to three-body integrals. The
coupled cluster with singles and doubles equations on this transformed
Hamiltonian are formulated and implemented. It is demonstrated that the
resulting methods have a superior basis set convergence and accuracy to the
corresponding conventional and explicitly correlated methods. Additionally,
approximations for three-body integrals are suggested and tested.


  We derive an explicit expression for the transition amplitude from black to
white hole horizon at the end of Hawking evaporation using covariant loop
quantum gravity.


  Taking an interaction network oriented perspective in informatics raises the
challenge to describe deterministic finite systems which take part in networks
of nondeterministic interactions. The traditional approach to describe
processes as stepwise executable activities which are not based on the
ordinarily nondeterministic interaction shows strong centralization tendencies.
As suggested in this article, viewing processes and their interactions as
complementary can circumvent these centralization tendencies.
  The description of both, processes and their interactions is based on the
same building blocks, namely finite input output automata (or transducers).
Processes are viewed as finite systems that take part in multiple, ordinarily
nondeterministic interactions. The interactions between processes are described
as protocols.
  The effects of communication between processes as well as the necessary
coordination of different interactions within a processes are both based on the
restriction of the transition relation of product automata. The channel based
outer coupling represents the causal relation between the output and the input
of different systems. The coordination condition based inner coupling
represents the causal relation between the input and output of a single system.
  All steps are illustrated with the example of a network of resource
administration processes which is supposed to provide requesting user processes
exclusive access to a single resource.


  In the last few years, many advances have been made in the demonstration of
high-average power pulsed THz sources; however, little effort has been made to
study compatible sensitive field-resolved detectors. Here, we investigate
ErAs:InAlGaAs photoconductive receivers optimized for a probe wavelength of
1030 nm and thus suitable for the new class of high-power ultrafast
Ytterbium-based laser sources for THz generation and detection. The performance
of the receiver is tested with a few-cycle THz source with high average power
up to 20 mW and the dynamic range and saturation behavior of the receiver is
thoroughly characterized. Under optimized settings, a dynamic range of more
than 115 dB is reached in a 120 s measurement time with 20 mW of THz average
power, which is among the highest reported values to date. By reviewing the
state-of-the art in TDS measurement and post-processing technology, we identify
current limitations and guidelines for further increasing the dynamic range
towards 150 dB in short measurement times using high average power THz systems.


  We present 3D magnetohydrodynamic numerical simulations of the adiabatic
interaction of a shock with a dense, filamentary cloud. We investigate the
effects of various filament lengths and orientations on the interaction using
different orientations of the magnetic field, and vary the Mach number of the
shock, the density contrast of the filament, and the plasma beta, in order to
determine their effect on the evolution and lifetime of the filament. We find
that in a parallel magnetic field filaments have longer lifetimes if they are
orientated more 'broadside' to the shock front, and that an increase in the
density contrast hastens the destruction of the cloud, in terms of the modified
cloud-crushing time-scale, tcs. The combination of a mild shock and a
perpendicular or oblique field provides the best condition for extending the
life of the filament, with some filaments able to survive almost indefinitely
since they are cocooned by the magnetic field. A high value for the density
contrast does not initiate large turbulent instabilities in either the
perpendicular or oblique field cases but rather draws the filament out into
long tendrils which may eventually fragment. In addition, flux ropes are only
formed in parallel magnetic fields. The length of the filament is, however, not
as important for the evolution and destruction of a filament.


  We discuss the gravitational wave emission and the orbital evolution of a
hierarchical triple system composed of an inner binary black hole (BBH) and an
outer tertiary. Depending on the kick velocity at the merger, the merged BBH
could tidally disrupt the tertiary. Even though the fraction of BBH mergers
accompanied by such disruptions is expected to be much smaller than unity, the
existence of a tertiary and its basic parameters (e.g. semimajor axis,
projected mass) can be examined for more than 1000 BBHs with the space GW
detector LISA and its follow-on missions. This allows us to efficiently
prescreen the targets for the follow-up searches for the tidal disruption
events (TDEs). The TDE probability would be significantly higher for triple
systems with aligned orbital- and spin-angular momenta, compared with random
configurations.


  In 2004, Muzereau et al. showed how to use a reduction algorithm of the
discrete logarithm problem to Diffie-Hellman problem in order to estimate lower
bound on Diffie-Hellman problem on elliptic curves. They presented their
estimates for various elliptic curves that are used in practical applications.
In this paper, we show that a much tighter lower bound for Diffie-Hellman
problem on those curves can be achieved, if one uses the multiplicative group
of a finite field as an auxiliary group. Moreover, improved lower bound
estimates on Diffie-Hellman problem for various recommended curves are also
given which are the tightest; thus, leading us towards the equivalence of
Diffie-Hellman problem and the discrete logarithm problem for these recommended
elliptic curves.


  Selecting attractive photos from a human action shot sequence is quite
challenging, because of the subjective nature of the "attractiveness", which is
mainly a combined factor of human pose in action and the background. Prior
works have actively studied high-level image attributes including
interestingness, memorability, popularity, and aesthetics. However, none of
them has ever studied the "attractiveness" of human action shot. In this paper,
we present the first study of the "attractiveness" of human action shots by
taking a systematic data-driven approach. Specifically, we create a new
action-shot dataset composed of about 8000 high quality action-shot photos. We
further conduct rich crowd-sourced human judge studies on Amazon Mechanical
Turk(AMT) in terms of global attractiveness of a single photo, and relative
attractiveness of a pair of photos. A deep Siamese network with a novel hybrid
distribution matching loss was further proposed to fully exploit both types of
ratings. Extensive experiments reveal that (1) the property of action shot
attractiveness is subjective but predicable (2) our proposed method is both
efficient and effective for predicting the attractive human action shots.


  Thirteen transitions within the ground 3d^2 configuration of Fe VII are
identified in ultraviolet and optical spectra of the symbiotic star RR
Telescopii obtained with the STIS instrument of the Hubble Space Telescope. The
line fluxes are compared with theoretical data computed with the recent atomic
data of K.A. Berrington et al., and high resolution optical spectra from
VLT/UVES are used to identify blends. Seven branching ratios are measured, with
three in good agreement with theory and one affected by blending. The
lambda5277/lambda4943 branching ratio is discrepant by > 3 sigma, indicating
errors in the atomic data for the lambda5277 line. A least-squares minimization
scheme is used to simultaneously derive the temperature, T, and density, N_e,
of the RR Tel nebula, and the interstellar extinction, E(B-V), towards RR Tel
from the complete set of emission lines. The derived values are: log T/K = 4.50
+/- 0.23, log N_e/cm^-3=7.25 +/- 0.05, and E(B-V)<0.27. The extinction is not
well-constrained by the Fe VII lines, but is consistent with the more accurate
value E(B-V)=0.109^{+0.052}_{-0.059} derived here from the Ne V
lambda2974/lambda1574 ratio in the STIS spectrum. Large differences between the
K.A. Berrington et al. electron excitation data and the earlier F.P. Keenan &
P.H. Norrington data-set are demonstrated, and the latter is shown to give
worse agreement with observations.


  The minimal Kolmogorov complexity of a total computable function that exceeds
everywhere all total computable functions of complexity at most $n$, is
$2^{n+O(1)}$. If we replace "everywhere" by "for all sufficiently large
inputs", the answer is $n+O(1)$.


  We show that particle-hole pairing is realized in the background of a charged
black hole in magnetic field. The pairing instability occurs for sufficiently
large fermion charges, which correspond to the Fermi liquid regime. The
critical temperature for Fermi liquids is proportinal to the magnetic field and
vanishes as we approach the non-Fermi liquid state. The pairing order parameter
leads to a relative shift of the Fermi surfaces corresponding to the bulk
fermions with spin up and down. The value of the shift in Fermi momentum $k_F$
and the critical temperature $T_c$ are proportional to the effective density of
states at the Fermi surface. Our one-loop calculations provide a dual
description of the magnetic catalysis for the lowest Landau level in graphene.
This analyses may be relevant for the antiferromagnetic behavior in the cuprate
superconductors and for the chiral spirals in the chiral magnetic effect.
  We also discuss thermodynamic and transport properties of a system at the
boundary at zero magnetic field. The scaling behavior of the specific heat is
$c\sim T$ for Fermi liquid and $c\sim T^{2\nu}$ for non-Fermi liquid, while the
behavior of the DC conductivity is the same $\sigma\sim T^{-2\nu}$ in both
cases. While it can be difficult to extract transport and hydrodynamic from the
lattice, the $AdS/CFT$ approach provides a robust frame for nonperturbative
calculation of these properties.


  As artificial intelligence (AI) applications continue to expand, there is a
growing need for deep neural network (DNN) models. Although DNN models deployed
at the edge are promising to provide AI as a service with low latency, their
cooperation is yet to be explored. In this paper, we consider the DNN service
providers share their computing resources as well as their models' parameters
and allow other DNNs to offload their computations without mirroring. We
propose a novel algorithm called coordinated DNNs on edge (\textbf{CoDE}) that
facilitates coordination among DNN services by creating multi-task DNNs out of
individual models. CoDE aims to find the optimal path that results in the
lowest possible cost, where the cost reflects the inference delay, model
accuracy, and local computation workload. With CoDE, DNN models can make new
paths for inference by using their own or other models' parameters. We then
evaluate the performance of CoDE through numerical experiments. The results
demonstrate a $75\%$ reduction in the local service computation workload while
degrading the accuracy by only $2\%$ and having the same inference time in a
balanced load condition. Under heavy load, CoDE can further decrease the
inference time by $30\%$ while the accuracy is reduced by only $4\%$.


  We propose a method to predict the value of the external strain where a
generic amorphous solid will fail by a plastic response (i.e. an irreversible
deformation), solely on the basis of measurements of the nonlinear elastic
moduli. While usually considered fundamentally different, with the elastic
properties describing reversible phenomena and plastic failure epitomizing
irreversible behavior, we show that the knowledge of some nonlinear elastic
moduli is enough to predict where plasticity sets in.


  In the mapping of the local ISM it is of some interest to know where the
first indications of the boundary of the Local Bubble can be measured. The
Hipparcos distances combined to B-V photometry and some sort of spectral
classification permit mapping of the spatial extinction distribution.
Photometry is available for almost the complete Hipparcos sample and Michigan
Classification is available for brighter stars south of delta = +5 deg (1900).
For the northern and fainter stars spectral types, e.g. the HD types, are given
but a luminosity class is often missing. The B-V photometry and the parallax
do, however, permit a dwarf/giant separation due to the value of the slope of
the reddening vector compared to the gradient of the main sequence in a color
magnitude diagram, in the form: B-V vs. M_V+A_V = V+5(1+log(pi)), together with
the rather shallow extinction present in the Hipparcos sample. We present the
distribution of median A_V(l, b) for stars with Hipparcos 2 distances less than
55 pc. The northern part of the first and second quadrant has most extinction,
up to 0.2 mag and the southern part of the third and fourth quadrant the
slightest extinction, 0.05 mag. The boundary of the extinction minimum appears
rather coherent on an angular resolution of a few degrees


  Space biology research aims to understand fundamental effects of spaceflight
on organisms, develop foundational knowledge to support deep space exploration,
and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem
of plants, crops, microbes, animals, and humans for sustained multi-planetary
life. To advance these aims, the field leverages experiments, platforms, data,
and model organisms from both spaceborne and ground-analog studies. As research
is extended beyond low Earth orbit, experiments and platforms must be maximally
autonomous, light, agile, and intelligent to expedite knowledge discovery. Here
we present a summary of recommendations from a workshop organized by the
National Aeronautics and Space Administration on artificial intelligence,
machine learning, and modeling applications which offer key solutions toward
these space biology challenges. In the next decade, the synthesis of artificial
intelligence into the field of space biology will deepen the biological
understanding of spaceflight effects, facilitate predictive modeling and
analytics, support maximally autonomous and reproducible experiments, and
efficiently manage spaceborne data and metadata, all with the goal to enable
life to thrive in deep space.


  We present detailed semi-empiric models for rotational modulations observed
in ultraviolet wind lines of B0.5 supergiant HD 64760. We model the Rotational
Modulation Regions (RMRs) with advanced 3-D radiative transfer calculations in
the stellar wind and quantitatively fit the time-evolution of the Si IV 1395
resonance line. We find that the RMRs are due to linearly-shaped narrow
sector-like density enhancements in the equatorial wind. Unlike the Co-rotating
Interaction Regions (CIRs) which produce Discrete Absorption Components in the
line, the RMRs do not spread out with larger distance above the stellar
surface. The detailed best fit shows that the RMRs of HD 64760 have maximum
density enhancements of ~17% above the surrounding smooth wind density, about
twice smaller than the hydrodynamic models of CIRs that warp around the star.
The semi-empiric 3-D transfer modelling reveals that the narrow spoke-like RMRs
must co-exist with broader and curved large-scale CIR wind density structures
in the equatorial plane of this fast rotating Ib-supergiant.


  Given a closed hyperbolic Riemannian surface, the aim of the present paper is
to describe an explicit construction of smooth deformations of the hyperbolic
metric into Finsler metrics that are not Riemannian and whose properties are
such that the classical Riemannian results about entropy rigidity, marked
length spectrum rigidity and boundary rigidity all fail to extend to the
Finsler category.


  Next location prediction is a discipline that involves predicting a users
next location. Its applications include resource allocation, quality of
service, energy efficiency, and traffic management. This paper proposes an
energy-efficient, small, and low parameter machine learning (ML) architecture
for accurate next location prediction, deployable on modest base stations and
edge devices. To accomplish this we ran a hundred hyperparameter experiments on
the full human mobility patterns of an entire city, to determine an exact ML
architecture that reached a plateau of accuracy with the least amount of model
parameters. We successfully achieved a reduction in the number of model
parameters within published ML architectures from 202 million down to 2
million. This reduced the total size of the model parameters from 791 MB down
to 8 MB. Additionally, this decreased the training time by a factor of four,
the amount of graphics processing unit (GPU) memory needed for training by a
factor of twenty, and the overall accuracy was increased from 80.16% to 82.54%.
This improvement allows for modest base stations and edge devices which do not
have a large amount of memory or storage, to deploy and utilize the proposed ML
architecture for next location prediction.


  We study the behavior of one dimensional Kac spin glasses as function of the
interaction range. We verify by Montecarlo numerical simulations the crossover
from local mean field behavior to global paramagnetism. We investigate the
behavior of correlations and find that in the low temperature phase
correlations grow at a faster rate then the interaction range. We completely
characterize the growth of correlations in the vicinity of the mean-field
critical region.


  The introduction and emergence of agriculture into Eastern North America
(ENA) and Europe proceeded very differently in both subcontinents: it varied in
timing, speed, and mechanism. Common to both regions, agricultural subsistence
profited from the introduction of major staple crops which had been
domesticated elsewhere; in both regions, the temperate climate and originally
predominant forest vegetation provided an environmental context conducive to
agriculture. To understand the different paths to agricultural subsistence, an
integrated view of technological innovation, domestication and exchange of
domesticates, migration and trade is required within the constraints imposed by
the environmental context and geography. This study makes use of a numerical
model of regional socio-technological evolution which builds on adaptation of
important characteristics of prehistoric societies such as technology and
subsistence diversity. With this Global Land Use and Technological Evolution
Simulator (GLUES), regional transitions from foraging to agropastoralism, and
associated land use and demographic changes are realistically hindcasted. I
show that the model is capable of explaining the different timing, speed, and
transition mechanisms: Europe received a large package of foreign domesticates
and converted rapidly to agriculture, this fast transition can be best
explained with demic and cultural diffusion followed by fast adoption by
resident foragers. In contrast, ENA trajectories show a more gradual
transition. Hunting-gathering and agropastoral life style coexisted for a long
time and agriculture was adopted slowly into the existing subsistence scheme.


  We present a re-analysis of the Geneva-Copenhagen survey, which benefits from
the infrared flux method to improve the accuracy of the derived stellar
effective temperatures and uses the latter to build a consistent and improved
metallicity scale. Metallicities are calibrated on high-resolution spectroscopy
and checked against four open clusters and a moving group, showing excellent
consistency. The new temperature and metallicity scales provide a better match
to theoretical isochrones, which are used for a Bayesian analysis of stellar
ages. With respect to previous analyses, our stars are on average 100 K hotter
and 0.1 dex more metal rich, which shift the peak of the metallicity
distribution function around the solar value. From Stromgren photometry we are
able to derive for the first time a proxy for alpha elements, which enables us
to perform a tentative dissection of the chemical thin and thick disc. We find
evidence for the latter being composed of an old, mildly but systematically
alpha-enhanced population that extends to super solar metallicities, in
agreement with spectroscopic studies. Our revision offers the largest existing
kinematically unbiased sample of the solar neighbourhood that contains full
information on kinematics, metallicities, and ages and thus provides better
constraints on the physical processes relevant in the build-up of the Milky Way
disc, enabling a better understanding of the Sun in a Galactic context.


  Gravity takes care of both inflation and subsequent reheating in
Starobinsky's R^2-model. The latter is due to inflaton gravitation decays
dominated by scalar particle production. It is tempting to suggest that dark
matter particles are also produced in this process. Since free scalars being
too hot cannot serve as viable dark matter (Phys.Lett.B700:157-162,2011), we
further study the issue considering two options: scalars with non-minimal
coupling to gravity and superheavy scalars generated at inflationary stage. We
found that the first option allows for viable warm or cold dark matter if
scalar mass exceeds 0.8 MeV. The second option implies supercold dark matter
with particle mass 10^16 GeV, which production is saturated at the end of
inflation when inflaton-dependent scalar mass rapidly changes and violates
adiabaticity. Similar result holds for superheavy fermion dark matter.


  This work investigates the influence of pulse modulation frequency ranging
from 50 Hz- 10 kHz on the helium RF atmospheric pressure plasma jet's
fundamental characteristics. The impact of modulation frequency on plasma jet
discharge behavior, geometrical variation, reactive species emission, and
plasma parameters (gas temperature Tg, electron excitation temperature Texc,
and electron density (ne) are studied using various diagnostics such as optical
imaging, emission spectra, and thermal diagnostics. From the experiments, it is
observed that operating the plasma jet at low pulse modulation frequencies
(around 50 Hz) provides enhanced plasma dimensions, higher electron densities
and greater optical emission from reactive species (viz., He I, O, OH, N2+,
etc.) as compared to the higher modulation frequencies. Besides the low power
consumption, the three times less gas temperature of the modulated plasma jet
than the continuous wave mode makes it more advantageous for the applications.
Moreover, the influence of duty cycle (D) and applied RF power (P) on the
plasma jet characteristics are also discussed. It is found that 10- 40% duty
cycle operation provides the most favorable attributes. More importantly, the
concern of shorter plasma length in RF plasma jets is overcome by operating at
10- 20% duty cycle with increased applied power. This work thoroughly
characterizes helium atmospheric pressure RF plasma jet with a wide range of
pulse mode operating parameters, which could help select appropriate operating
conditions for various industrial and biomedical applications.


  Involvement of the environment is indispensable for establishing the
statistical distribution of system. We analyze the statistical distribution of
a quantum system coupled strongly with a heat bath. This distribution is
determined by tracing over the bath's degrees of freedom for the equilibrium
system-plus-bath composite. The stability of system distribution is largely
affected by the system--bath interaction strength. We propose that the quantum
system exhibits a stable distribution only when its system response function in
the frequency domain satisfies $\tilde\chi(\omega = 0+)>0$. We show our results
by investigating the non-interacting bosonic impurity system from both the
thermodynamic and dynamic perspectives. Our study refines the theoretical
framework of canonical statistics, offering insights into thermodynamic
phenomena in small-scale systems.


  Reliable and reproducible performance of the more than 800 Relativistic Heavy
Ion Collider (RHIC) magnet power supplies is essential to successful RHIC
operation. In order to support power supply commissioning, a system was
developed to capture detailed power supply measurements from all the RHIC ring
power supplies during acceleration ramps. Diagnostic tools were developed to
allow experts to assess ramp reproducibility and rapidly identify problems. The
system has now become a routine part of RHIC operations, with data captured for
every acceleration ramp. This paper describes the RHIC power supply ramp
diagnostic system and considers its impact on RHIC operations.


  This article examines the advantages and disadvantages of Large Language
Models (LLMs) and Artificial Intelligence (AI) in research and education and
proposes the urgent need for an international statement to guide their
responsible use. LLMs and AI demonstrate remarkable natural language
processing, data analysis, and decision-making capabilities, offering potential
benefits such as improved efficiency and transformative solutions. However,
concerns regarding ethical considerations, bias, fake publications, and
malicious use also arise. The objectives of this paper are to critically
evaluate the utility of LLMs and AI in research and education, call for
discussions between stakeholders, and discuss the need for an international
statement. We identify advantages such as data processing, task automation, and
personalized experiences, alongside disadvantages like bias reinforcement,
interpretability challenges, inaccurate reporting, and plagiarism. Stakeholders
from academia, industry, government, and civil society must engage in open
discussions to address the ethical, legal, and societal implications. The
proposed international statement should emphasize transparency, accountability,
ongoing research, and risk mitigation. Monitoring, evaluation, user education,
and awareness are essential components. By fostering discussions and
establishing guidelines, we can ensure the responsible and ethical development
and use of LLMs and AI, maximizing benefits while minimizing risks.


  We consider the problem of recommending relevant content to users of an
internet platform in the form of lists of items, called slates. We introduce a
variational Bayesian Recurrent Neural Net recommender system that acts on time
series of interactions between the internet platform and the user, and which
scales to real world industrial situations. The recommender system is tested
both online on real users, and on an offline dataset collected from a Norwegian
web-based marketplace, FINN.no, that is made public for research. This is one
of the first publicly available datasets which includes all the slates that are
presented to users as well as which items (if any) in the slates were clicked
on. Such a data set allows us to move beyond the common assumption that
implicitly assumes that users are considering all possible items at each
interaction. Instead we build our likelihood using the items that are actually
in the slate, and evaluate the strengths and weaknesses of both approaches
theoretically and in experiments. We also introduce a hierarchical prior for
the item parameters based on group memberships. Both item parameters and user
preferences are learned probabilistically. Furthermore, we combine our model
with bandit strategies to ensure learning, and introduce `in-slate Thompson
Sampling' which makes use of the slates to maximise explorative opportunities.
We show experimentally that explorative recommender strategies perform on par
or above their greedy counterparts. Even without making use of exploration to
learn more effectively, click rates increase simply because of improved
diversity in the recommended slates.


  Oil refinery is one of industries that require huge energy consumption. The
today technology advance requires energy saving. Heat integration is a method
used to minimize the energy comsumption though the implementation of Heat
Exchanger Network (HEN). CPT is one of types of Heat Exchanger Network (HEN)
that functions to recover the heat in the flow of product or waste. HEN
comprises a number of heat exchangers (HEs) that are serially connected.
However, the presence of fouling in the heat exchanger has caused the decline
of the performance of both heat exchangers and all heat exchanger networks.
Fouling can not be avoided. However, it can be mitigated. In industry, periodic
heat exchanger cleaning is the most effective and widely used mitigation
technique. On the other side, a very frequent cleaning of heat exchanger can be
much costly in maintenance and lost of production. In this way, an accurate
optimization technique of cleaning schedule interval of heat exchanger is very
essential. Commonly, this technique involves three elements: model to simulate
the heat exchanger network, representative fouling model to describe the
fouling behavior and suitable optimization algorithm to solve the problem of
clening schedule interval for heat exchanger network. This paper describe the
optimization of interval cleaning schedule of HEN within the 44-month period
using PSO (particle swarm optimization). The number of iteration used to
achieve the convergent is 100 iterations and the fitness value in PSO
correlated with the amount of heat recovery, cleaning cost, and additional
pumping cost. The saving after the optimization of cleaning schedule of HEN in
this research achieved at $ 1.236 millions or 23% of maximum potential savings.


  Inthispaperwedescribeaconcept-wisemulti-preferencesemantics for description
logic which has its root in the preferential approach for modeling defeasible
reasoning in knowledge representation. We argue that this proposal, beside
satisfying some desired properties, such as KLM postulates, and avoiding the
drowning problem, also defines a plausible notion of semantics. We motivate the
plausibility of the concept-wise multi-preference semantics by developing a
logical semantics of self-organising maps, which have been proposed as possible
candidates to explain the psychological mechanisms underlying category
generalisation, in terms of multi-preference interpretations.


  There are several interesting filtrations on the Cartan subalgebra of a
complex simple Lie algebra coming from very different contexts: one is the
principal filtration coming from the Langlands dual, one is coming from the
Clifford algebra associated with a non-degenerate invariant bilinear form, one
is coming from the symmetric algebra and the Chevalley projection, and two
other ones are coming from the enveloping algebra and Harish-Chandra
projections. It is now known that all these filtrations coincide. This results
from a combination of works of several authors (Rohr, Joseph, Alekseev and the
second named author), and was essentially conjectured by Kostant. In this
paper, we establish a direct correspondence between the enveloping filtration
and the symmetric filtration for a simple Lie algebra of type A or C. Our proof
is very different from Rohr and Joseph approaches. The idea is to use an
explicit description of the symmetric and enveloping invariants in term of
combinatorial objects, called weighted paths, in the crystal graph of the
standard representation.


  A novel tracking paradigm for flying geometric trajectories using tethered
kites is presented. It is shown how the differential-geometric notion of
turning angle can be used as a one-dimensional representation of the kite
trajectory, and how this leads to a single-input single-output (SISO) tracking
problem. Based on this principle a Lyapunov-based nonlinear adaptive controller
is developed that only needs control derivatives of the kite aerodynamic model.
The resulting controller is validated using simulations with a point-mass kite
model.


  In this paper, we propose an efficient algorithm to directly restore a clear
image from a hazy input. The proposed algorithm hinges on an end-to-end
trainable neural network that consists of an encoder and a decoder. The encoder
is exploited to capture the context of the derived input images, while the
decoder is employed to estimate the contribution of each input to the final
dehazed result using the learned representations attributed to the encoder. The
constructed network adopts a novel fusion-based strategy which derives three
inputs from an original hazy image by applying White Balance (WB), Contrast
Enhancing (CE), and Gamma Correction (GC). We compute pixel-wise confidence
maps based on the appearance differences between these different inputs to
blend the information of the derived inputs and preserve the regions with
pleasant visibility. The final dehazed image is yielded by gating the important
features of the derived inputs. To train the network, we introduce a
multi-scale approach such that the halo artifacts can be avoided. Extensive
experimental results on both synthetic and real-world images demonstrate that
the proposed algorithm performs favorably against the state-of-the-art
algorithms.


  This paper investigates thermal transport in a nanocomposite system "porous
silicon matrix filled with ionic liquid". First, the thermal conductivity and
heat capacity of two imidazolium and one ammonium ionic liquids were evaluated
using the photoacoustic approach in piezoelectric configuration and
differential scanning calorimetry, respectively. Then, the thermal transport
properties of the composite system "ionic liquid confined inside porous silicon
matrix" were investigated with the photoacoustic approach in gas-microphone
configuration. It was found that a significant enhancement of the thermal
conductivity of the composite when compared to the individual components, i.e.
(i) more than two times for pristine porous silicon and (ii) more than eight
times for ionic liquids. These results provide new paths for innovative
solutions in the field of thermal management in highly efficient energy storage
devices.


  Various models based on StyleGAN have gained significant traction in the
field of image synthesis, attributed to their robust training stability and
superior performances. Within the StyleGAN framework, the adoption of image
skip connection is favored over the traditional residual connection. However,
this preference is just based on empirical observations; there has not been any
in-depth mathematical analysis on it yet. To rectify this situation, this brief
aims to elucidate the mathematical meaning of the image skip connection and
introduce a groundbreaking methodology, termed the image squeeze connection,
which significantly improves the quality of image synthesis. Specifically, we
analyze the image skip connection technique to reveal its problem and introduce
the proposed method which not only effectively boosts the GAN performance but
also reduces the required number of network parameters. Extensive experiments
on various datasets demonstrate that the proposed method consistently enhances
the performance of state-of-the-art models based on StyleGAN. We believe that
our findings represent a vital advancement in the field of image synthesis,
suggesting a novel direction for future research and applications.


  Kinetic transport equations are notoriously difficult to simulate because of
their complex multiscale behaviors and the need to numerically resolve a high
dimensional probability density function. Past literature has focused on
building reduced order models (ROM) by analytical methods. In recent years,
there is a surge of interest in developing ROM using data-driven or
computational tools that offer more applicability and flexibility. This paper
is a work towards that direction.
  Motivated by our previous work of designing ROM for the stationary radiative
transfer equation in [30] by leveraging the low-rank structure of the solution
manifold induced by the angular variable, we here further advance the
methodology to the time-dependent model. Particularly, we take the celebrated
reduced basis method (RBM) approach and propose a novel micro-macro decomposed
reduced basis method (MMD-RBM). The MMD-RBM is constructed by exploiting, in a
greedy fashion, the low-rank structures of both the micro- and macro-solution
manifolds with respect to the angular and temporal variables. Our reduced order
surrogate consists of: reduced bases for reduced order subspaces and a reduced
quadrature rule in the angular space. The proposed MMD-RBM features several
structure-preserving components: 1) an equilibrium-respecting strategy to
construct reduced order subspaces which better utilize the structure of the
decomposed system, and 2) a recipe for preserving positivity of the quadrature
weights thus to maintain the stability of the underlying reduced solver. The
resulting ROM can be used to achieve a fast online solve for the angular flux
in angular directions outside the training set and for arbitrary order moment
of the angular flux.


  Nonlinear absorption in four and five energy level systems have been studied
with the aid of steady-state rate equation approach. We report on the
tunability of saturable and reverse saturable absorption as a function of
spectroscopic parameters (lifetimes and absorption cross-sections). Detailed
information is given on the estimation of spectroscopic parameters in the
nonlinear absorption spectroscopy. The exhaustive graphical analysis of this
article can provide the brief idea about transmittance curves of the nonlinear
absorption to the experimentalists. In four and five level cascade models,
simultaneously saturable and reverse saturable absorption can be generated.


  Regime switching processes have proved to be indispensable in the modeling of
various phenomena, allowing model parameters that traditionally were considered
to be constant to fluctuate in a Markovian manner in line with empirical
findings. We study diffusion processes of Ornstein-Uhlenbeck type where the
drift and diffusion coefficients $a$ and $b$ are functions of a Markov process
with a stationary distribution $\pi$ on a countable state space. Exact long
time behavior is determined for the three regimes corresponding to the expected
drift: $E_{\pi}a(\cdot)>0,=0,<0$, respectively. Alongside we provide exact time
limit results for integrals of form
$\int_{0}^{t}b^{2}(X_{s})e^{-2\int_{s}^{t}a(X_{r})dr}ds$ for the three
different regimes. Finally, we demonstrate natural applications of the findings
in terms of Cox-Ingersoll-Ross diffusion and deterministic SIS epidemic models
in Markovian environments. Exact long time behaviors are naturally expressed in
terms of solutions to the well-studied fixed-point equation in law
$X\stackrel{d}{=}AX+B$ with $X \indep (A,B)$.


  We analyze the range of validity of Thomas Fermi theory for describing charge
density modulations induced by external potentials in neutral graphene. We
compare exact results obtained from a tight-binding calculation with those of
linear response theory and the Thomas Fermi approximation. For experimentally
interesting ranges of size and density amplitudes (electron densities less than
$\sim 10 ^{11} cm ^{-2}$, and spatial length scales below $\sim 20 nm$), linear
response is significantly more accurate than Thomas Fermi theory.


  In this work, we provide a mathematical formulation for error propagation in
flow trajectory prediction using data-driven turbulence closure modeling. Under
the assumption that the predicted state of a large eddy simulation prediction
must be close to that of a subsampled direct numerical simulation, we retrieve
an upper bound for the prediction error when utilizing a data-driven closure
model. We also demonstrate that this error is significantly affected by the
time step size and the Jacobian which play a role in amplifying the initial
one-step error made by using the closure. Our analysis also shows that the
error propagates exponentially with rollout time and the upper bound of the
system Jacobian which is itself influenced by the Jacobian of the closure
formulation. These findings could enable the development of new regularization
techniques for ML models based on the identified error-bound terms, improving
their robustness and reducing error propagation.


  A symmetry-preserving continuum approach to the two valence-body bound-state
problem is used to calculate the valence, glue and sea distributions within the
pion; unifying them with, inter alia, electromagnetic pion elastic and
transition form factors. The analysis reveals the following momentum fractions
at the scale $\zeta_2:=2\,$GeV: $\langle x_{\rm valence} \rangle = 0.48(3)$,
$\langle x_{\rm glue} \rangle = 0.41(2)$, $\langle x_{\rm sea} \rangle =
0.11(2)$; and despite hardening induced by the emergent phenomenon of dynamical
chiral symmetry breaking, the valence-quark distribution function,
${q}^\pi(x)$, exhibits the $x\simeq 1$ behaviour predicted by quantum
chromodynamics (QCD). After evolution to $\zeta=5.2\,$GeV, the prediction for
${q}^\pi(x)$ matches that obtained using lattice-regularised QCD. This
confluence should both stimulate improved analyses of existing data and aid in
planning efforts to obtain new data on the pion distribution functions.


  Full sky coverage Adaptive Optics on Extremely Large Telescopes requires the
adoption of several Laser Guide Stars as references.With such large apertures,
the apparent elongation of the beacons is absolutely significant.With few
exceptions,WaveFront Sensors designed for Natural Guide Stars are adapted and
used in suboptimal mode in this context. We analyse and describe the
geometrical properties of a class of WaveFront Sensors that are specifically
designed to deal with Laser Guide Stars propagated from a location in the
immediate vicinity of the telescope aperture. We describe in three dimensions
the loci where the light of the Laser Guide Stars would focus in the focal
volume located behind the focal plane (where astronomical objects are
reimaged). We also describe the properties of several types of optomechanical
devices that, through refraction and reflections, act as perturbers for this
new class of pupil plane sensors, which we call ingot WaveFront Sensor. We give
the recipes both for the most reasonable complex version of these WaveFront
Sensors, with 6 pupils, and for the simplest one, with only 3 pupils. Both of
them are referred to the ELT case. Elements to have a qualitative idea of how
the sensitivity of such a new class of sensors compared to conventional ones
are outlined. We present a new class of WaveFront Sensors, by carrying out the
extension to the case of elongated sources at finite distance of the pyramid
WaveFront Sensor and pointing out which advantages of the pyramid are retained
and how it can be adopted to optimize the sensing.


  A rigorous generation of spin-adapted (spin-free) substitution operators for
high spin ($S=S_z$) references of arbitrary substitution order and spin quantum
number $S$ is presented. The generated operators lead to linearly independent
but non-orthogonal CSFs when applied to the reference and span the complete
spin space. To incorporate spin completeness, spectating substitutions (as e.g.
$\hat{E}_{iv}^{va}$) are introduced. The presented procedure utilizes
L\"owdin's projection operator method of spin eigenfunction generation to
ensure spin completeness. The generated operators are explicitly checked for
(i) their linear independence and (ii) their spin completeness for up to
10-fold substitutions and up to a multiplicity of $2S+1 = 11$. A proof of
concept implementation utilizing the generated operators in a coupled cluster
(CC) calculation was successfully applied to the high spin states of the Boron
atom. The results show pure spin states as well as small effects on the
correlation energy compared to spinorbital CC. A comparison to spin-adapted but
spin-incomplete CC shows a significant spin incompleteness error.


  Concurrent observation technologies have made high-precision real-time data
available in large quantities. Data assimilation (DA) is concerned with how to
combine this data with physical models to produce accurate predictions. For
spatial-temporal models, the Ensemble Kalman Filter with proper localization
techniques is considered to be a state-of-the-art DA methodology. This article
proposes and investigates a localized Ensemble Kalman Bucy Filter (l-EnKBF) for
nonlinear models with short-range interactions. We derive dimension-independent
and component-wise error bounds and show the long time path-wise error only has
logarithmic dependence on the time range. The theoretical results are verified
through some simple numerical tests.


  We introduce the notion of a cylindrical bialgebra, which is a
quasitriangular bialgebra $H$ endowed with a universal K-matrix, i.e., a
universal solution of a generalized reflection equation, yielding an action of
cylindrical braid groups on tensor products of its representations. We prove
that new examples of such universal K-matrices arise from quantum symmetric
pairs of Kac-Moody type and depend upon the choice of a pair of generalized
Satake diagrams. In finite type, this yields a refinement of a result obtained
by Balagovi\'c and Kolb, producing a family of non-equivalent solutions
interpolating between the quasi-K-matrix originally due to Bao and Wang and the
full universal K-matrix. Finally, we prove that this construction yields formal
solutions of the generalized reflection equation with a spectral parameter in
the case of finite-dimensional representations over the quantum affine algebra
$U_qL\mathfrak{sl}_2$.


  Extensive cooperation among unrelated individuals is unique to humans, who
often sacrifice personal benefits for the common good and work together to
achieve what they are unable to execute alone. The evolutionary success of our
species is indeed due, to a large degree, to our unparalleled other-regarding
abilities. Yet, a comprehensive understanding of human cooperation remains a
formidable challenge. Recent research in social science indicates that it is
important to focus on the collective behavior that emerges as the result of the
interactions among individuals, groups, and even societies. Non-equilibrium
statistical physics, in particular Monte Carlo methods and the theory of
collective behavior of interacting particles near phase transition points, has
proven to be very valuable for understanding counterintuitive evolutionary
outcomes. By studying models of human cooperation as classical spin models, a
physicist can draw on familiar settings from statistical physics. However,
unlike pairwise interactions among particles that typically govern solid-state
physics systems, interactions among humans often involve group interactions,
and they also involve a larger number of possible states even for the most
simplified description of reality. The complexity of solutions therefore often
surpasses that observed in physical systems. Here we review experimental and
theoretical research that advances our understanding of human cooperation,
focusing on spatial pattern formation, on the spatiotemporal dynamics of
observed solutions, and on self-organization that may either promote or hinder
socially favorable states.


  X-Ray diffraction, ac susceptibility and electrical resistivity measurements
were performed in order to investigate the effect of Ag substitution for Pr in
the polycrystalline Pr$_{1-x}$Ag$_x$MnO$_3$ (0.0$\leq$x$\leq$0.25) manganites.
The XRD results show that the samples crystallize in the O'-orthorhombic
structure with a cooperative Jahn-Teller deformation. We give evidence that the
Ag$^{+}$ is not substituted at the Pr$^{3+}$ site in the PrMnO$_{3}$ structure.
The increase of $T_{C}$ up to 130 K is suggested to be due to further oxidation
of manganite grains by the oxygen released from the metallic silver at high
temperatures. No metal-insulator transition was observed in the samples.


  Three-loop corrections to the scalar and pseudo-scalar current correlator are
calculated. By applying the large momentum expansion mass terms up to order
(m^2/q^2)^4 are evaluated analytically. As an application O(\alpha_s^2)
corrections to the decay of a scalar and pseudo-scalar Higgs boson into top
quarks are considered. It is shown that for a Higgs mass not far above the
$t\bar{t}$ threshold these higher order mass corrections are necessary to get
reliable results.


  A method to perform bosonization of a fermionic theory in (1+1) dimensions in
a path integral framework is developed. The method relies exclusively on the
path integral property of allowing variable shifts, and does not depend on the
explicit form of Greens functions. Two examples, the Schwinger model and the
massless Thirring model, are worked out.


  In this work we show that a magnon spin capacitor can be realized at a
junction between two exchange coupled ferromagnets. In this junction, the
buildup of magnon spin over the junction is coupled to the difference in magnon
chemical potential, realizing the magnon spin analogue of an electrical
capacitor. The relation between magnon spin and magnon chemical potential
difference directly follows from considering the magnon density-density
interaction between the two ferromagnets. We analyse the junction in detail by
considering spin injection and detection from normal metal leads, the tunneling
current across the junction and magnon decay within the ferromagnet, showing
that such a structure realizes a magnon spin capacitor in series with a spin
resistor. Choosing yttrium iron garnet as the ferromagnet, we numerically
calculate the capacitance, which ranges from picofarad to microfarad, depending
on the area of the junction. We therefore conclude that the magnon spin
capacitor could directly be of use in applications.


  The 19/20-parameter p(henomenological)MSSM with either a neutralino or
gravitino LSP offers a flexible framework for the study of a wide variety of
R-parity conserving MSSM SUSY phenomena at the 7, 8 and 14 TeV LHC. Here we
present the results of a study of SUSY signatures at these facilities obtained
via a fast Monte Carlo 'replication' of the ATLAS SUSY analysis suite. In
particular, we show the ranges of the sparticle masses that are either
disfavored or remain viable after all of the various searches at the 7 and 8
TeV runs are combined. We then extrapolate to 14 TeV with both 300 fb^-1 and 3
ab^-1 of integrated luminosity and determine the sensitivity of a jets + MET
search to the pMSSM parameter space. We find that the high-luminosity LHC
performs extremely well in probing natural SUSY models.


  We propose a quantum-like description of markets and economics. The approach
has roots in the recently developed quantum game theory.


  We provide compelling evidence that all Fermat primes were already known to
Fermat.


  We consider Bernoulli distributions and their regularizations, which are
measures on the $p$-adic integers $\mathbb{Z}_p$. It is well known that their
Mellin transform can be used to define $p$-adic $L$-functions. We show that for
$p>2$ one of the regularized Bernoulli distributions is particularly simple and
equal to a measure on $\mathbb{Z}_p$ that takes the values $\pm \frac{1}{2}$ on
clopen balls. We apply this to $p$-adic $L$-functions for Dirichlet characters
of $p$-power conductor and obtain Dirichlet series expansions similar to the
complex case. Such expansions were studied by D. Delbourgo, and this
contribution provides an approach via $p$-adic measures.


  We present an efficient computational approach to perform real-space
electronic structure calculations using an adaptive higher-order finite-element
discretization of Kohn-Sham density-functional theory (DFT). To this end, we
develop an a-priori mesh adaption technique to construct a close to optimal
finite-element discretization of the problem. We further propose an efficient
solution strategy for solving the discrete eigenvalue problem by using spectral
finite-elements in conjunction with Gauss-Lobatto quadrature, and a Chebyshev
acceleration technique for computing the occupied eigenspace. The proposed
approach has been observed to provide a staggering 100-200 fold computational
advantage over the solution of a generalized eigenvalue problem. Using the
proposed solution procedure, we investigate the computational efficiency
afforded by higher-order finite-element discretization of the Kohn-Sham DFT
problem. Our studies suggest that staggering computational savings of the order
of 1000 fold relative to linear finite-elements can be realized, for both
all-electron and local pseudopotential calculations. On all the benchmark
systems studied, we observe diminishing returns in computational savings beyond
the sixth-order for accuracies commensurate with chemical accuracy. A
comparative study of the computational efficiency of the proposed higher-order
finite-element discretizations suggests that the performance of finite-element
basis is competing with the plane-wave discretization for non-periodic local
pseudopotential calculations, and compares to the Gaussian basis for
all-electron calculations within an order of magnitude. Further, we demonstrate
the capability of the proposed approach to compute the electronic structure of
a metallic system containing 1688 atoms using modest computational resources,
and good scalability of the present implementation up to 192 processors.


  Binary neutron star mergers are the first confirmed site of element
nucleosynthesis by the rapid neutron-capture process (r-process). The kilonova
AT2017gfo is the only electromagnetic counterpart of a neutron star merger
spectroscopically observed. We analyse the entire spectral sequence of
AT2017gfo (from merger to +10.4 days) and identify seven emission-like
features. We confirm that the prominent 1.08 um feature can be explained by the
Sr II near-infrared triplet evolving from a P-Cygni profile through to pure
emission. We calculate the expected strength of the [Sr II] doublet and show
that its absence requires highly clumped ejecta. Near-infrared features at 1.58
and 2.07 um emerge after three days and become more prominent as the spectra
evolve. We model these as optically thick P-Cygni profiles and alternatively as
pure emission features (with FWHM = 35600 +/- 6600 km/s), and favour the latter
interpretation. The profile of the strong 2.07 um emission feature is best
reproduced with two lines, centred at 2.059 and 2.135 um. We search for
candidate ions for all prominent features in the spectra. Strong, permitted
transitions of La III, Ce III, Gd III, Ra II and Ac I are plausible candidates
for the emission features. If any of these features are produced by
intrinsically weak, forbidden transitions, we highlight candidate ions spanning
the three r-process peaks. The second r-process peak elements Te and I have
plausible matches to multiple features. We highlight the need for more detailed
and quantitative atomic line transition data.


  Planetary rotation rate has a significant effect on atmospheric circulation,
where the strength of the Coriolis effect in part determines the efficiency of
latitudinal heat transport, altering cloud distributions, surface temperatures,
and precipitation patterns. In this study we use the ROCKE-3D dynamic-ocean
general circulation model to study the effects of slow rotations and increased
insolations on the 'fractional habitability' and silicate weathering rate of an
Earth-like world. Defining the fractional habitability f_h to be the percentage
of a planet's surface which falls in the 0 <= T <= 100 C temperature regime, we
find a moderate increase in f_h with a 10% and 20% increase in insolation and a
possible maximum in f_h at sidereal day-lengths between 8 and 32 times that of
the modern Earth. By tracking precipitation and run-off we further determine
that there is a rotational regime centered on a 4-day period in which the
silicate weathering rate is maximized and is particularly strongly peaked at
higher overall insolations. Because of weathering's integral role in the
long-term carbonate-silicate cycle, we suggest that climate stability may be
strongly affected by the anticipated rotational evolution of temperate
terrestrial-type worlds, and should be considered a major factor in their
study. In light of our results we argue that planetary rotation period is an
important factor to consider when determining the habitability of terrestrial
worlds.


  The road networks, vehicle users could enjoy numerous kind of services such
as location based service in vehicle users can connected to Internet and
communication of different users. Therefore, in order to acquire adequate
privacy level and quality of service, one must have to wisely place mix zones
to connect vehicle users to internet or some other internetwork. According to
this research, we have analyzed the problem of optimal placement mix zones over
road network. To enhance the coverage capacity of vehicles, in order to reduce
the cost and communication delay. Further, it has also been discovered to
minimize the cost of mix zone placement. Moreover, it has also been shown that,
as the best deployment mix zones get minimized cost while at the same time the
average capacity of mix zone can be maximized also privacy level increased
because of optimal placement and high traffic environment.


  In photon upconverting core-shell nanoparticles, structure strongly dictates
performance. Conventional imaging in scanning transmission electron microscopy
has sufficient resolution to probe the atomic structure of these nanoparticles,
but contrast, dose, and projection limitations make conventional imaging modes
insufficient for fully characterizing these structures. Phase retrieval methods
provide a promising alternative imaging mode, and in particular, multislice
electron ptychography can recover depth-dependent information. Here, we study
beam-sensitive photon upconverting core-shell nanoparticles with a multislice
ptychography approach using a low electron dose to avoid damage. Large strain
fields arise in these heterostructures due to the mismatch in lattice parameter
between the core and the shell. We reconstruct both a nanoparticle that appears
defect-free and one that has a large break in the side and map the distribution
of strain in 3D by computing distortion fields from high-resolution potential
images of each slice. In the defect-free nanoparticle, we observe twisting of
the shell, while in the broken nanoparticle we measure the 3D position of the
crack, the core, and dislocations. These results highlight the advantage of
multislice electron ptychography to recover 3D information from a single scan,
even under strict electron dose requirements from beam-sensitive samples.


  We describe our recent lattice study of SU(4) gauge theory with fermions in
the fundamental and sextet representations. In this theory, a new type of
baryon consists of quarks in both representations. The spectrum of these
"chimera baryons" has a straightforward interpretation in terms of a
non-relativistic quark model based on SU(4). Our results are particularly
relevant to composite Higgs models in which the top quark is partially
composite.


  Traditional learning-based approaches for run-time Hardware Trojan detection
require complex and expensive on-chip data acquisition frameworks and thus
incur high area and power overhead. To address these challenges, we propose to
leverage the power correlation between the executing instructions of a
microprocessor to establish a machine learning-based run-time Hardware Trojan
(HT) detection framework, called MacLeR. To reduce the overhead of data
acquisition, we propose a single power-port current acquisition block using
current sensors in time-division multiplexing, which increases accuracy while
incurring reduced area overhead. We have implemented a practical solution by
analyzing multiple HT benchmarks inserted in the RTL of a system-on-chip (SoC)
consisting of four LEON3 processors integrated with other IPs like vga_lcd,
RSA, AES, Ethernet, and memory controllers. Our experimental results show that
compared to state-of-the-art HT detection techniques, MacLeR achieves 10\%
better HT detection accuracy (i.e., 96.256%) while incurring a 7x reduction in
area and power overhead (i.e., 0.025% of the area of the SoC and <0.07% of the
power of the SoC). In addition, we also analyze the impact of process variation
and aging on the extracted power profiles and the HT detection accuracy of
MacLeR. Our analysis shows that variations in fine-grained power profiles due
to the HTs are significantly higher compared to the variations in fine-grained
power profiles caused by the process variations (PV) and aging effects.
Moreover, our analysis demonstrates that, on average, the HT detection accuracy
drop in MacLeR is less than 1% and 9% when considering only PV and PV with
worst-case aging, respectively, which is ~10x less than in the case of the
state-of-the-art ML-based HT detection technique.