  We investigate the effects of Supergiant Shells (SGSs) and their interaction
on dense molecular clumps by observing the Large Magellanic Cloud (LMC) star
forming regions N48 and N49, which are located between two SGSs, LMC 4 and LMC
5. $^{12}$CO ($J$=3-2, 1-0) and $^{13}$CO ($J$=1-0) observations with the ASTE
and Mopra telescopes have been carried out towards these regions. A clumpy
distribution of dense molecular clumps is revealed with 7 pc spatial
resolution. Large velocity gradient analysis shows that the molecular hydrogen
densities ($n({\rm H}_2)$) of the clumps are distributed from low to high
density ($10^3$-$10^5$ cm$^{-3}$) and their kinetic temperatures ($T_{\rm
kin}$) are typically high (greater than $50$ K). These clumps seem to be in the
early stages of star formation, as also indicated from the distribution of
H$\alpha$, young stellar object candidates, and IR emission. We found that the
N48 region is located in the high column density HI envelope at the interface
of the two SGSs and the star formation is relatively evolved, whereas the N49
region is associated with LMC 5 alone and the star formation is quiet. The
clumps in the N48 region typically show high $n({\rm H}_2)$ and $T_{\rm kin}$,
which are as dense and warm as the clumps in LMC massive cluster-forming areas
(30 Dor, N159). These results suggest that the large-scale structure of the
SGSs, especially the interaction of two SGSs, works efficiently on the
formation of dense molecular clumps and stars.


  Given a set $\mathcal{C}=\{C_i\}_{i=1}^m$ of square matrices, the matrix
blind joint block diagonalization problem (BJBDP) is to find a full column rank
matrix $A$ such that $C_i=A\Sigma_iA^\text{T}$ for all $i$, where $\Sigma_i$'s
are all block diagonal matrices with as many diagonal blocks as possible. The
BJBDP plays an important role in independent subspace analysis (ISA). This
paper considers the identification problem for BJBDP, that is, under what
conditions and by what means, we can identify the diagonalizer $A$ and the
block diagonal structure of $\Sigma_i$, especially when there is noise in
$C_i$'s. In this paper, we propose a ``bi-block diagonalization'' method to
solve BJBDP, and establish sufficient conditions under which the method is able
to accomplish the task. Numerical simulations validate our theoretical results.
To the best of the authors' knowledge, existing numerical methods for BJBDP
have no theoretical guarantees for the identification of the exact solution,
whereas our method does.


  We present CHALET, a 3D house simulator with support for navigation and
manipulation. CHALET includes 58 rooms and 10 house configuration, and allows
to easily create new house and room layouts. CHALET supports a range of common
household activities, including moving objects, toggling appliances, and
placing objects inside closeable containers. The environment and actions
available are designed to create a challenging domain to train and evaluate
autonomous agents, including for tasks that combine language, vision, and
planning in a dynamic environment.


  In this paper, we study the fundamental group of a certain class of globally
hyperbolic Lorentzian manifolds with a positive curvature tensor. We prove that
the fundamental group of lightlike geodesically complete parametrized
Lorentzian products is finite under the conditions of a positive curvature
tensor and the fiber compact.


  While in a classification or a regression setting a label or a value is
assigned to each individual document, in a ranking setting we determine the
relevance ordering of the entire input document list. This difference leads to
the notion of relative relevance between documents in ranking. The majority of
the existing learning-to-rank algorithms model such relativity at the loss
level using pairwise or listwise loss functions. However, they are restricted
to univariate scoring functions, i.e., the relevance score of a document is
computed based on the document itself, regardless of other documents in the
list. To overcome this limitation, we propose a new framework for multivariate
scoring functions, in which the relevance score of a document is determined
jointly by multiple documents in the list. We refer to this framework as GSFs
-- groupwise scoring functions. We learn GSFs with a deep neural network
architecture, and demonstrate that several representative learning-to-rank
algorithms can be modeled as special cases in our framework. We conduct
evaluation using click logs from one of the largest commercial email search
engines, as well as a public benchmark dataset. In both cases, GSFs lead to
significant performance improvements, especially in the presence of sparse
textual features.


  Access limitations are restrictions in the way in which the tuples of a
relation can be accessed. Under access limitations, query answering becomes
more complex than in the traditional case, with no guarantee that the answer
tuples that can be extracted (aka maximal answer) are all those that would be
found without access limitations (aka complete answer). The field of query
answering under access limitations has been broadly investigated in the past.
Attention has been devoted to the problem of determining relations that are
relevant for a query, i.e., those (possibly off-query) relations that might
need to be accessed in order to find all tuples in the maximal answer. In this
short paper, we show that relevance is undecidable for Datalog queries.


  The predominant methods currently used to determine nonlinear optical
constants like the nonlinear refractive index n2 or chi3 rely mostly on
experimental, open and closed z-scan techniques and beam deflection methods.
While these methods work well when the linear absorption is relatively small or
negligible, the retrieval process is more complicated for a strongly
scattering, dispersive or absorbing medium. The study of optics at the
nanoscale in the ps or fs regimes demands the development of new theoretical
tools experimental approaches, to extract and verify both linear and nonlinear
optical dispersions exhibited by matter, especially when material constituents
are fashioned into nanostructures of arbitrary shape. We present a practical,
combined experimental and theoretical approach based on a hydrodynamic model
that uses experimental results of harmonic generation conversion efficiencies
to retrieve complex, nonlinear dispersion curves, not necessarily only for
third order processes. We provide examples for materials that are of special
interest to nanophotonics, silicon, gold, and indium tin oxide, which displays
nonlocal effects and a zero-crossing of the real part of the dielectric
constant. The results for silicon and gold compare well with analytical
predictions based on the nonlinear oscillator model. Based on our assessment of
THG conversion efficiencies in silicon, we predict chi3(w) and chi3(3w) are of
order 10^(-17)(m/V)^2, in the visible and IR ranges, with respective peaks of
10^(-14) and 10^(-16)(m/V)^2 in the UV range. Similarly, gold's chi3(w) and
chi3(3w) are of order 10^(-17) and 10^(-16)(m/V)^2, and predict
chi3(w)~10^(-17)(m/V)^2 and chi3(3w)~10^(-18)(m/V)^2 for ITO. These results
suggest that judicious exploitation of the nonlinear dispersion of ordinary
semiconductors can transform device physics in spectral regions that extend
well into the UV range.


  In this paper we give a method for studying global rational points on certain
quotients of Shimura curves by Atkin-Lehner involutions. We obtain explicit
conditions on such quotients for rational points to be ``trivial'' (coming from
CM points only) and exhibit an explicit infinite family of such quotients
satisfying these conditions.


  Context: Observations of transitional disks give us an understanding of the
formation of planets and planetary systems such as our own. But care must be
taken in the identification of such sources: the higher spatial resolution of
the Herschel Space Observatory provides a new view on the origin of the
far-infrared and sub-millimeter excesses observed.
  Aims: We review the nature of previously known transitional disks in the
Chamaeleon I star-forming region with Herschel data.
  Methods: We analyze Herschel PACS and SPIRE images of the young star T54
together with ancillary images. We also analyze its spectral energy
distribution and indications from optical and mid-infrared spectroscopy.
  Results: We detect extended emission in the PACS 70 \mu m image ~6" off
source at a position angle of 196{\deg} from T54. The emission detected at
longer wavelength (PACS 100, 160, SPIRE 250 and 350 \mu m) is also offset from
the position of the star. This suggests that the excess observed in the
far-infrared part of the SED is not fully associated with T54.
  Conclusions: Herschel images show that the far-infrared excess seen in T54 is
not due to a transitional disk but to extended emission south-west of the
source. The object still shows point-like and now downscaled excess at
mid-infrared wavelengths, but its origin cannot be constrained without higher
spatial resolution data. However, different indications point towards an
evolved disk or extended unresolved emission close to the source.


  It is challenging to design an equalizer for the complex time-frequency
doubly-selective channel. In this paper, we employ the deep unfolding approach
to establish an equalizer for the underwater acoustic (UWA) orthogonal
frequency division multiplexing (OFDM) system, namely UDNet. Each layer of
UDNet is designed according to the classical minimum mean square error (MMSE)
equalizer. Moreover, we consider the QPSK equalization as a four-classification
task and adopt minimum Kullback-Leibler (KL) to achieve a smaller symbol error
rate (SER) with the one-hot coding instead of the MMSE criterion. In addition,
we introduce a sliding structure based on the banded approximation of the
channel matrix to reduce the network size and aid UDNet to perform well for
different-length signals without changing the network structure. Furthermore,
we apply the measured at-sea doubly-selective UWA channel and offshore
background noise to evaluate the proposed equalizer. Experimental results show
that the proposed UDNet performs better with low computational complexity.
Concretely, the SER of UDNet is nearly an order of magnitude lower than that of
MMSE.


  Model Predictive Control in buildings can significantly reduce their energy
consumption. The cost and effort necessary for creating and maintaining first
principle models for buildings make data-driven modelling an attractive
alternative in this domain. In MPC the models form the basis for an
optimization problem whose solution provides the control signals to be applied
to the system. The fact that this optimization problem has to be solved
repeatedly in real-time implies restrictions on the learning architectures that
can be used. Here, we adapt Input Convex Neural Networks that are generally
only convex for one-step predictions, for use in building MPC. We introduce
additional constraints to their structure and weights to achieve a convex
input-output relationship for multistep ahead predictions. We assess the
consequences of the additional constraints for the model accuracy and test the
models in a real-life MPC experiment in an apartment in Switzerland. In two
five-day cooling experiments, MPC with Input Convex Neural Networks is able to
keep room temperatures within comfort constraints while minimizing cooling
energy consumption.


  Large Language Models have demonstrated impressive capabilities in various
language tasks but may produce content that misaligns with human expectations,
raising ethical and legal concerns. Therefore, it is important to explore the
limitations and implement restrictions on the models to ensure safety and
compliance, with Reinforcement Learning from Human Feedback (RLHF) being the
primary method. Due to challenges in stability and scalability with the RLHF
stages, researchers are exploring alternative methods to achieve effects
comparable to those of RLHF. However, these methods often depend on large
high-quality datasets and inefficiently utilize generated data. To deal with
this problem, we propose PSLE, i.e., Progressively Selective Label Enhancement
for Language Model Alignment, a framework that fully utilizes all generated
data by guiding the model with principles to align outputs with human
expectations. Using a dynamically updated threshold, our approach ensures
efficient data utilization by incorporating all generated responses and
weighting them based on their corresponding reward scores. Experimental results
on multiple datasets demonstrate the effectiveness of PSLE compared to existing
language model alignment methods.


  We present a novel traffic trajectory editing method which uses
spatio-temporal keyframes to control vehicles during the simulation to generate
desired traffic trajectories. By taking self-motivation, path following and
collision avoidance into account, the proposed force-based traffic simulation
framework updates vehicle's motions in both the Frenet coordinates and the
Cartesian coordinates. With the way-points from users, lane-level navigation
can be generated by reference path planning. With a given keyframe, the
coarse-to-fine optimization is proposed to efficiently generate the plausible
trajectory which can satisfy the spatio-temporal constraints. At first, a
directed state-time graph constructed along the reference path is used to
search for a coarse-grained trajectory by mapping the keyframe as the goal.
Then, using the information extracted from the coarse trajectory as
initialization, adjoint-based optimization is applied to generate a finer
trajectory with smooth motions based on our force-based simulation. We validate
our method with extensive experiments.


  We have investigated the properties of interfaces between LaAlO3 films grown
on SrTiO3 substrates singly terminated by TiO2. We used RF sputtering in a
high-pressure oxygen atmosphere. The films are smooth, with flat surfaces.
Transmission Electron Microscopy shows atomically sharp and continuous
interfaces while EELS measurements show some slight intermixing. The elemental
ratio of La to Al measured by EDX is found to be 1.07. Importantly, we find
these interfaces to be non-conducting, indicating that the sputtered interface
is not electronically reconstructed in the way reported for films grown by
Pulsed Laser Deposition because of the different interplay between
stoichiometry, mixing and oxygen vacancies.


  In this paper, we analyze the dynamics of spreading processes taking place
over time-varying networks. A common approach to model time-varying networks is
via Markovian random graph processes. This modeling approach presents the
following limitation: Markovian random graphs can only replicate switching
patterns with exponential inter-switching times, while in real applications
these times are usually far from exponential. In this paper, we introduce a
flexible and tractable extended family of processes able to replicate, with
arbitrary accuracy, any distribution of inter-switching times. We then study
the stability of spreading processes in this extended family. We first show
that a direct analysis based on It\^o's formula provides stability conditions
in terms of the eigenvalues of a matrix whose size grows exponentially with the
number of edges. To overcome this limitation, we derive alternative stability
conditions involving the eigenvalues of a matrix whose size grows linearly with
the number of nodes. Based on our results, we also show that heuristics based
on aggregated static networks approximate the epidemic threshold more
accurately as the number of nodes grows, or the temporal volatility of the
random graph process is reduced. Finally, we illustrate our findings via
numerical simulations.


  We present a detailed study of the properties of unitary Bose polarons, i.e.,
impurities strongly interacting with a bath of dilute bosons via a short-range
potential with infinite scattering length. Through a comparison with Diffusion
Monte-Carlo calculations, we demonstrate that the Gross-Pitaevskii theory
accurately describes the properties of a heavy impurity if the gas parameter
remains small everywhere in the system, including the vicinity of the impurity.
Furthermore, we investigate the effects of the finite range of the bath-bath
interaction potential by means of a non-local extension of the Gross-Pitaevskii
equation and we discuss the applicability of the Born approximation.


  Optomechanical systems are promising platforms for controlled light-matter
interactions. They are capable of providing several fundamental and practical
novel features when the mechanical oscillator is cooled down to nearly reach
its ground state. In this framework, measuring the effective temperature of the
oscillator is perhaps the most relevant step in the characterization of those
systems. In conventional schemes, the cavity is driven strongly, and the
overall system is well-described by a linear (Gaussian preserving) Hamiltonian.
Here, we depart from this regime by considering an undriven optomechanical
system via non-Gaussian radiation-pressure interaction. To measure the
temperature of the mechanical oscillator, initially in a thermal state, we use
light as a probe to coherently interact with it and create an entangled state.
We show that the optical probe gets a nonlinear phase, resulting from the
non-Gaussian interaction, and undergoes an incoherent phase diffusion process.
To efficiently infer the temperature from the entangled light-matter state, we
propose using a nonlinear Kerr medium before a homodyne detector. Remarkably,
placing the Kerr medium enhances the precision to nearly saturate the ultimate
quantum bound given by the quantum Fisher information. Furthermore, it also
simplifies the thermometry procedure as it makes the choice of the homodyne
local phase independent of the temperature, which avoids the need for adaptive
sensing protocols.


  Let ${\mathcal Q}_n^d$ be the vector space of forms of degree $d\ge 3$ on
${\mathbb C}^n$, with $n\ge 2$. The object of our study is the map $\Phi$,
introduced in papers [EI], [AI1], that assigns every nondegenerate form in
${\mathcal Q}_n^d$ the so-called associated form, which is an element of
${\mathcal Q}_n^{n(d-2)*}$. We focus on two cases: those of binary quartics
($n=2$, $d=4$) and ternary cubics ($n=3$, $d=3$). In these situations the map
$\Phi$ induces a rational equivariant involution on the projectivized space
${\mathbb P}({\mathcal Q}_n^d)$, which is in fact the only nontrivial rational
equivariant involution on ${\mathbb P}({\mathcal Q}_n^d)$. In particular, there
exists an equivariant involution on the space of elliptic curves with
nonvanishing $j$-invariant. In the present paper, we give a simple
interpretation of this involution in terms of projective duality. Furthermore,
we express it via classical contravariants.


  Software design smells are design attributes which violate the fundamental
design principles. Design smells are a key cause of design debt. Although the
activities of design smell identification and measurement are predominantly
considered in current literature, those which identify and communicate which
design smells occur more frequently in newly developing software and which ones
are more dominant in established software have been studied to a limited
extent. This research describes a mechanism for identifying the design smells
that are more prevalent in developing and established software respectively. A
tool is provided which is used for design smell detection by analyzing large
volumes of source code. More specifically, 164,609 Lines of Code (LoC) and
5,712 class files of six developing and 244,930 LoC and 12,048 class files of
five established open-source Java software are analyzed. Obtained results show
that out of the 4,020 occurrences of smells that were made for nine preselected
types of design smells, 1,643 design smells were detected for developing
software, which mainly consisted of four specific types of smells. For
established software, 2,397 design smells were observed which predominantly
consisted of four other types of smells. The remaining design smell was equally
prevalent in both developing and established software. Desirable precision
values ranging from 72.9% to 84.1% were obtained for the tool.


  We investigate optical, infrared, and radio active galactic nucleus (AGN)
signs in the merger remnant Arp 187, which hosts luminous jets launched in the
order of $10^5$ yr ago but whose present-day AGN activity is still unknown. We
find AGN signs from the optical BPT diagram and infrared [OIV]25.89 $\mu$m
line, originating from the narrow line regions of AGN. On the other hand,
Spitzer/IRS show the host galaxy dominated spectra, suggesting that the thermal
emission from the AGN torus is considerably small or already diminished.
Combining the black hole mass, the upper limit of radio luminosity of the core,
and the fundamental plane of the black hole enable us to estimate X-ray
luminosity, which gives $<10^{40}$ erg s$^{-1}$. Those results suggest that the
AGN activity of Arp 187 has already been quenched, but the narrow line region
is still alive owing to the time delay of emission from the past AGN activity.


  Trying a global fit of the experimental branching ratios and CP-asymmetries
of the charmless $B\to PV$ decays according to QCD factorization, we find it
impossible to reach a satisfactory agreement, the confidence level (CL) of the
best fit is smaller than .1 %. This failure reflects the difficulty to
accommodate several large experimental branching ratios of the strange
channels. Furthermore, experiment was not able to exclude a large direct CP
asymmetry in $\bar {B}^0\to\rho^+ \pi^-$, which is predicted very small by QCD
factorization. Proposing a fit with QCD factorization complemented by a
charming-penguin inspired model we reach a best fit which is not excluded by
experiment (CL of about 8 %) but is not fully convincing.
  These negative results must be tempered by the remark that some of the
experimental data used are recent and might still evolve significantly.


  Language agents that interact with the world on their own have great
potential for automating digital tasks. While large language model (LLM) agents
have made progress in understanding and executing tasks such as textual games
and webpage control, many real-world tasks also require collaboration with
humans or other LLMs in equal roles, which involves intent understanding, task
coordination, and communication. To test LLM's ability to collaborate, we
design a blocks-world environment, where two agents, each having unique goals
and skills, build a target structure together. To complete the goals, they can
act in the world and communicate in natural language. Under this environment,
we design increasingly challenging settings to evaluate different collaboration
perspectives, from independent to more complex, dependent tasks. We further
adopt chain-of-thought prompts that include intermediate reasoning steps to
model the partner's state and identify and correct execution errors. Both
human-machine and machine-machine experiments show that LLM agents have strong
grounding capacities, and our approach significantly improves the evaluation
metric.


  Creating meaningful art is often viewed as a uniquely human endeavor. A human
artist needs a combination of unique skills, understanding, and genuine
intention to create artworks that evoke deep feelings and emotions. In this
paper, we introduce a multi-conditional Generative Adversarial Network (GAN)
approach trained on large amounts of human paintings to synthesize
realistic-looking paintings that emulate human art. Our approach is based on
the StyleGAN neural network architecture, but incorporates a custom
multi-conditional control mechanism that provides fine-granular control over
characteristics of the generated paintings, e.g., with regard to the perceived
emotion evoked in a spectator. For better control, we introduce the conditional
truncation trick, which adapts the standard truncation trick for the
conditional setting and diverse datasets. Finally, we develop a diverse set of
evaluation techniques tailored to multi-conditional generation.


  In this article we compute the Casimir force between two finite-width mirrors
at finite temperature, working in a simplified model in 1+1 dimensions. The
mirrors, considered as dissipative media, are modeled by a continuous set of
harmonic oscillators which in turn are coupled to an external environment at
thermal equilibrium. The calculation of the Casimir force is performed in the
framework of the theory of quantum open systems. It is shown that the Casimir
interaction has two different contributions: the usual radiation pressure from
vacuum, which is obtained for ideal mirrors without dissipation or losses, and
a Langevin force associated with the noise induced by the interaction between
dielectric atoms in the slabs and the thermal bath. Both contributions to the
Casimir force are needed in order to reproduce the analogous of Lifshitz
formula in 1+1 dimensions. We also discuss the relation between the
electromagnetic properties of the mirrors and the spectral density of the
environment


  We consider a fully inhomogeneous stochastic higher spin six vertex model in
a quadrant. For this model we derive concise integral representations for
multi-point q-moments of the height function and for the q-correlation
functions. At least in the case of the step initial condition, our formulas
degenerate in appropriate limits to many known formulas of such type for
integrable probabilistic systems in the (1+1)d KPZ universality class,
including the stochastic six vertex model, ASEP, various q-TASEPs, and
associated zero range processes.
  Our arguments are largely based on properties of a family of symmetric
rational functions which can be defined as partition functions of the
inhomogeneous higher spin six vertex model for suitable domains. In the
homogeneous case, such functions were previously studied in
http://arxiv.org/abs/1410.0976; they also generalize classical Hall-Littlewood
and Schur polynomials. A key role is played by Cauchy-like summation identities
for these functions, which are obtained as a direct corollary of the
Yang-Baxter equation for the higher spin six vertex model.


  Anomaly-based Intrusion Detection System (IDS) has been a hot research topic
because of its ability to detect new threats rather than only memorized
signatures threats of signature-based IDS. Especially after the availability of
advanced technologies that increase the number of hacking tools and increase
the risk impact of an attack. The problem of any anomaly-based model is its
high false-positive rate. The high false-positive rate is the reason why
anomaly IDS is not commonly applied in practice. Because anomaly-based models
classify an unseen pattern as a threat where it may be normal but not included
in the training dataset. This type of problem is called overfitting where the
model is not able to generalize. Optimizing Anomaly-based models by having a
big training dataset that includes all possible normal cases may be an optimal
solution but could not be applied in practice. Although we can increase the
number of training samples to include much more normal cases, still we need a
model that has more ability to generalize. In this research paper, we propose
applying deep model instead of traditional models because it has more ability
to generalize. Thus, we will obtain less false-positive by using big data and
deep model. We made a comparison between machine learning and deep learning
algorithms in the optimization of anomaly-based IDS by decreasing the
false-positive rate. We did an experiment on the NSL-KDD benchmark and compared
our results with one of the best used classifiers in traditional learning in
IDS optimization. The experiment shows 10% lower false-positive by using deep
learning instead of traditional learning.


  Accurate control of light polarization represents a core building block in
polarization metrology, imaging, and optical and quantum communications.
Voltage-controlled liquid crystals offer an efficient way of polarization
transformation. However, common twisted nematic liquid crystals are notorious
for lacking an accurate theoretical model linking control voltages and output
polarization. An inverse model, which would predict control voltages required
to prepare a target polarization, is even more challenging. Here we report both
the direct and inverse models based on deep neural networks, radial basis
functions, and linear interpolation. We present an inverse-direct compound
model solving the problem of control voltages ambiguity. We demonstrate one
order of magnitude improvement in accuracy using deep learning compared to the
radial basis function method and two orders of magnitude improvement compared
to the linear interpolation. Errors of the deep neural network model also
decrease faster than the other methods with an increasing number of training
data. The best direct and inverse models reach the average infidelities of $4
\times 10^{-4}$ and $2 \times 10^{-4}$, respectively, which is the accuracy
level not reported yet. Furthermore, we demonstrate local and remote
preparation of an arbitrary single-photon polarization state using the deep
learning models. The results will impact the application of twisted-nematic
liquid crystals, increasing their control accuracy across the board. The
presented bidirectional learning can be used for optimal classical control of
complex photonic devices and quantum circuits beyond interpolation.


  We generalise theorems of Khodorovskiy and Park-Park-Shin, and give new
topological proofs of those theorems, using embedded surfaces in the 4-ball and
branched double covers. These theorems exhibit smooth codimension-zero
embeddings of certain rational homology balls bounded by lens spaces.


  Winds from the North-West quadrant and lack of precipitation are known to
lead to an increase of PM10 concentrations over a residential neighborhood in
the city of Taranto (Italy). In 2012 the local government prescribed a
reduction of industrial emissions by 10% every time such meteorological
conditions are forecasted 72 hours in advance. Wind forecasting is addressed
using the Weather Research and Forecasting (WRF) atmospheric simulation system
by the Regional Environmental Protection Agency. In the context of
distributions-oriented forecast verification, we propose a comprehensive
model-based inferential approach to investigate the ability of the WRF system
to forecast the local wind speed and direction allowing different performances
for unknown weather regimes. Ground-observed and WRF-forecasted wind speed and
direction at a relevant location are jointly modeled as a 4-dimensional time
series with an unknown finite number of states characterized by homogeneous
distributional behavior. The proposed model relies on a mixture of joint
projected and skew normal distributions with time-dependent states, where the
temporal evolution of the state membership follows a first order Markov
process. Parameter estimates, including the number of states, are obtained by a
Bayesian MCMC-based method. Results provide useful insights on the performance
of WRF forecasts in relation to different combinations of wind speed and
direction.


  The development of virtual agents has enabled human-avatar interactions to
become increasingly rich and varied. Moreover, an expressive virtual agent i.e.
that mimics the natural expression of emotions, enhances social interaction
between a user (human) and an agent (intelligent machine). The set of
non-verbal behaviors of a virtual character is, therefore, an important
component in the context of human-machine interaction. Laughter is not just an
audio signal, but an intrinsic relationship of multimodal non-verbal
communication, in addition to audio, it includes facial expressions and body
movements. Motion analysis often relies on a relevant motion capture dataset,
but the main issue is that the acquisition of such a dataset is expensive and
time-consuming. This work studies the relationship between laughter and body
movements in dyadic conversations. The body movements were extracted from
videos using deep learning based pose estimator model. We found that, in the
explored NDC-ME dataset, a single statistical feature (i.e, the maximum value,
or the maximum of Fourier transform) of a joint movement weakly correlates with
laughter intensity by 30%. However, we did not find a direct correlation
between audio features and body movements. We discuss about the challenges to
use such dataset for the audio-driven co-laughter motion synthesis task.


  (Abridged) The 7-year WMAP data and improved astrophysical data rigorously
test the standard cosmological model and its extensions. By combining WMAP with
the latest distance measurements from BAO and H0 measurement, we determine the
parameters of the simplest LCDM model. The power-law index of the primordial
power spectrum is n_s=0.968+-0.012, a measurement that excludes the
scale-invariant spectrum by 99.5%CL. The other parameters are also improved
from the 5-year results. Notable examples of improved parameters are the total
mass of neutrinos, sum(m_nu)<0.58eV, and the effective number of neutrino
species, N_eff=4.34+0.86-0.88. We detect the effect of primordial helium on the
temperature power spectrum and provide a new test of big bang nucleosynthesis.
We detect, and show on the map for the first time, the tangential and radial
polarization patterns around hot and cold spots of temperature fluctuations, an
important test of physical processes at z=1090 and the dominance of adiabatic
scalar fluctuations. With the 7-year TB power spectrum, the limit on a rotation
of the polarization plane due to potential parity-violating effects has
improved to Delta(alpha)=-1.1+-1.4(stat)+-1.5(syst) degrees. We report
significant detections of the SZ effect at the locations of known clusters of
galaxies. The measured SZ signal agrees well with the expected signal from the
X-ray data. However, it is a factor of 0.5 to 0.7 times the predictions from
"universal profile" of Arnaud et al., analytical models, and hydrodynamical
simulations. We find, for the first time in the SZ effect, a significant
difference between the cooling-flow and non-cooling-flow clusters (or relaxed
and non-relaxed clusters), which can explain some of the discrepancy. This
lower amplitude is consistent with the lower-than-theoretically-expected SZ
power spectrum recently measured by the South Pole Telescope collaboration.


  We apply two methods, namely the Gaussian processes and the non-parametric
smoothing procedure, to reconstruct the Hubble parameter $H(z)$ as a function
of redshift from 15 measurements of the expansion rate obtained from age
estimates of passively evolving galaxies. These reconstructions enable us to
derive the luminosity distance to a certain redshift $z$, calibrate the
light-curve fitting parameters accounting for the (unknown) intrinsic magnitude
of type Ia supernova (SNe Ia) and construct cosmological model-independent
Hubble diagrams of SNe Ia. In order to test the compatibility between the
reconstructed functions of $H(z)$, we perform a statistical analysis
considering the latest SNe Ia sample, the so-called JLA compilation. We find
that, for the Gaussian processes, the reconstructed functions of Hubble
parameter versus redshift, and thus the following analysis on SNe Ia
calibrations and cosmological implications, are sensitive to prior mean
functions. However, for the non-parametric smoothing method, the reconstructed
functions are not dependent on initial guess models, and consistently require
high values of $H_0$, which are in excellent agreement with recent measurements
of this quantity from Cepheids and other local distance indicators.


  Propagation of extremely short unipolar pulses of electromagnetic field
("videopulses") is considered in the framework of a model in which the material
medium is represented by anharmonic oscillators (approximating bound electrons)
with quadratic and cubic nonlinearities. Two families of exact analytical
solutions (with positive or negative polarity) are found for the moving
solitary pulses. Direct simulations demonstrate that the pulses are very robust
against perturbations. Two unipolar pulses collide nearly elastically, while
collisions between pulses with opposite polarities and a small relative
velocity are inelastic, leading to emission of radiation and generation of a
small-amplitude additional pulse.


  Handling faults is a growing concern in HPC. In future exascale systems, it
is projected that silent undetected errors will occur several times a day,
increasing the occurrence of corrupted results. In this article, we propose
SEDAR, which is a methodology that improves system reliability against
transient faults when running parallel message-passing applications. Our
approach, based on process replication for detection, combined with different
levels of checkpointing for automatic recovery, has the goal of helping users
of scientific applications to obtain executions with correct results. SEDAR is
structured in three levels: (1) only detection and safe-stop with notification;
(2) recovery based on multiple system-level checkpoints; and (3) recovery based
on a single valid user-level checkpoint. As each of these variants supplies a
particular coverage but involves limitations and implementation costs, SEDAR
can be adapted to the needs of the system. In this work, a description of the
methodology is presented and the temporal behavior of employing each SEDAR
strategy is mathematically described, both in the absence and presence of
faults. A model that considers all the fault scenarios on a test application is
introduced to show the validity of the detection and recovery mechanisms. An
overhead evaluation of each variant is performed with applications involving
different communication patterns; this is also used to extract guidelines about
when it is beneficial to employ each SEDAR protection level. As a result, we
show its efficacy and viability to tolerate transient faults in target HPC
environments.


  Transformer-based approaches have been successfully proposed for 3D human
pose estimation (HPE) from 2D pose sequence and achieved state-of-the-art
(SOTA) performance. However, current SOTAs have difficulties in modeling
spatial-temporal correlations of joints at different levels simultaneously.
This is due to the poses' spatial-temporal complexity. Poses move at various
speeds temporarily with various joints and body-parts movement spatially.
Hence, a cookie-cutter transformer is non-adaptable and can hardly meet the
"in-the-wild" requirement. To mitigate this issue, we propose Hierarchical
Spatial-Temporal transFormers (HSTFormer) to capture multi-level joints'
spatial-temporal correlations from local to global gradually for accurate 3D
HPE. HSTFormer consists of four transformer encoders (TEs) and a fusion module.
To the best of our knowledge, HSTFormer is the first to study hierarchical TEs
with multi-level fusion. Extensive experiments on three datasets (i.e.,
Human3.6M, MPI-INF-3DHP, and HumanEva) demonstrate that HSTFormer achieves
competitive and consistent performance on benchmarks with various scales and
difficulties. Specifically, it surpasses recent SOTAs on the challenging
MPI-INF-3DHP dataset and small-scale HumanEva dataset, with a highly
generalized systematic approach. The code is available at:
https://github.com/qianxiaoye825/HSTFormer.


  The problem of answering queries posed to a peer who is a member of a
peer-to-peer data exchange system is studied. The answers have to be consistent
wrt to both the local semantic constraints and the data exchange constraints
with other peers; and must also respect certain trust relationships between
peers. A semantics for peer consistent answers under exchange constraints and
trust relationships is introduced and some techniques for obtaining those
answers are presented.


  In this paper, the development of the dual mirror Small Size Telescopes (SST)
for the Cherenkov Telescope Array (CTA) is reviewed. Up to 70 SST, with a
primary mirror diameter of 4 m, will be produced and installed at the CTA
southern site. These will allow investigation of the gamma-ray sky at the
highest energies accessible to CTA, in the range from about 1 TeV to 300 TeV.
The telescope presented in this contribution is characterized by two major
innovations: the use of a dual mirror Schwarzschild-Couder configuration and of
an innovative camera using as sensors either multi-anode photomultipliers
(MAPM) or silicon photomultipliers (SiPM). The reduced plate-scale of the
telescope, achieved with the dual-mirror optics, allows the camera to be
compact (40 cm in diameter), and low-cost. The camera, which has about 2000
pixels of size 6x6 mm^2, covers a field of view of 10{\deg}. The dual mirror
telescopes and their cameras are being developed by three consortia, ASTRI
(Astrofisica con Specchi a Tecnologia Replicante Italiana, Italy/INAF), GATE
(Gamma-ray Telescope Elements, France/Paris Observ.) and CHEC (Compact High
Energy Camera, universities in UK, US and Japan) which are merging their
efforts in order to finalize an end-to-end design that will be constructed for
CTA. A number of prototype structures and cameras are being developed in order
to investigate various alternative designs. In this contribution, these designs
are presented, along with the technological solutions under study.


  This book is devoted to the problem of sequential probability forecasting,
that is, predicting the probabilities of the next outcome of a growing sequence
of observations given the past. This problem is considered in a very general
setting that unifies commonly used probabilistic and non-probabilistic
settings, trying to make as few as possible assumptions on the mechanism
generating the observations. A common form that arises in various formulations
of this problem is that of mixture predictors, which are formed as a
combination of a finite or infinite set of other predictors attempting to
combine their predictive powers. The main subject of this book are such mixture
predictors, and the main results demonstrate the universality of this method in
a very general probabilistic setting, but also show some of its limitations.
While the problems considered are motivated by practical applications,
involving, for example, financial, biological or behavioural data, this
motivation is left implicit and all the results exposed are theoretical.
  The book targets graduate students and researchers interested in the problem
of sequential prediction, and, more generally, in theoretical analysis of
problems in machine learning and non-parametric statistics, as well as
mathematical and philosophical foundations of these fields.
  The material in this volume is presented in a way that presumes familiarity
with basic concepts of probability and statistics, up to and including
probability distributions over spaces of infinite sequences. Familiarity with
the literature on learning or stochastic processes is not required.


  Recently, Marin and Gonz\'alez-Meneses introduced a class of ``parabolic''
subgroups for generalized braid groups associated to arbitrary complex
reflection groups. Using notably Garside group structures on these generalized
braid groups, they proved general results on parabolic subgroups for all cases
but one. This last case is that of the complex braid group B(G31), which has no
Garside group structure known so far, but instead a Garside groupoid structure.
Using this Garside groupoid structure, we complete the results of Marin and
Gonz\'alez-Meneses by proving their main theorems for the parabolic subgroups
of the complex braid group B(G31).


  Reflections in videos are obstructions that often occur when videos are taken
behind reflective surfaces like glass. These reflections reduce the quality of
such videos, lead to information loss and degrade the accuracy of many computer
vision algorithms. A video containing reflections is a combination of
background and reflection layers. Thus, reflection removal is equivalent to
decomposing the video into two layers. This, however, is a challenging and
ill-posed problem as there is an infinite number of valid decompositions. To
address this problem, we propose a user-assisted method for video reflection
removal. We rely on both spatial and temporal information and utilize sparse
user hints to help improve separation. The key idea of the proposed method is
to use motion cues to separate the background layer from the reflection layer
with minimal user assistance. We show that user-assistance significantly
improves the layer separation results. We implement and evaluate the proposed
method through quantitative and qualitative results on real and synthetic
videos. Our experiments show that the proposed method successfully removes
reflection from video sequences, does not introduce visual distortions, and
significantly outperforms the state-of-the-art reflection removal methods in
the literature.


  We have performed a first-principles study on the deformation effect of the
electronic structures of graphite nanoribbon arrays with zigzag edges on both
sides, and the edge atoms are terminated with hydrogen atoms. A uniaxial strain
is considered to have deformation effect on the graphite nanoribbons. We found
that the antiferromagnetic arrangement of the spin polarizing edges of graphite
nanoribbon is still more favorable than that with the ferromagnetic arrangement
under deformation. We also learned that a tensile strain increases the
magnetization of the graphite ribbon while a compressive strain decreases it. A
positive pressure derivative of the band gap of antiferromagnetic state is
observed for the graphite nanoribbon under uniaxial strains. The strain changes
the shape of band structure and the band gap; in here, the edge atoms play a
crucial role. The deformations are also found to influence the contribution of
the edge atoms to the bands near the Fermi level. The deformation effect for
the graphite nanoribbon under transverse electric field is also studied.


  We introduce a new family of multivariate wavelets which are obtained by
"polyharmonic subdivision". They generalize directly the original compactly
supported Daubechies wavelets.


  Nuclear stellar clusters are a common phenomenon in spirals and in starbursts
galaxies, and they may be a natural consequence of the star formation processes
in the central regions of galaxies. HST UV imaging of a few Seyfert 2 galaxies
have resolved nuclear starbursts in Seyfert 2 revealing stellar clusters as the
main building blocks of the extended emission. However, we do not know whether
stellar clusters are always associated with all types of nuclear activity. We
present NUV and optical images provided by HST to find out the role that
stellar clusters play in different types of AGNs (Seyferts and LLAGNs). Also
with these images, we study the circumnuclear dust morphology as a probe of the
circumnuclear environment of AGNs. Here we present a summary of the the first
results obtained for the sample of Seyferts and LLAGN galaxies.


  Nonlinear gravitational effects of large-scale inhomogeneities on Cosmic
Microwave Background (CMB) anisotropies are studied. based on the relativistic
second-order theory of perturbations in nonzero-Lambda flat cosmological
models, which has been analytically derived by the present author, and on the
second-order formula of CMB anisotropies derived by Mollerach and Matarrese. In
this paper we derive the components of the CMB anisotropy power spectra in the
range of l = 1 -22 which are caused by asymmetric local inhomogeneities on
scales of 300 Mpc. Using our results it is found that there is a possibility to
explain the small north-south asymmetry of CMB anisotropies which has recently
been observed.


  We introduce the first very large detection dataset for event cameras. The
dataset is composed of more than 39 hours of automotive recordings acquired
with a 304x240 ATIS sensor. It contains open roads and very diverse driving
scenarios, ranging from urban, highway, suburbs and countryside scenes, as well
as different weather and illumination conditions. Manual bounding box
annotations of cars and pedestrians contained in the recordings are also
provided at a frequency between 1 and 4Hz, yielding more than 255,000 labels in
total. We believe that the availability of a labeled dataset of this size will
contribute to major advances in event-based vision tasks such as object
detection and classification. We also expect benefits in other tasks such as
optical flow, structure from motion and tracking, where for example, the large
amount of data can be leveraged by self-supervised learning methods.


  We calculate $K^-$-nucleus elastic differential, reaction and total cross
sections for different nuclei ($^{12}$C,$^{40}$Ca and $^{208}$Pb) at several
laboratory antikaon momenta, ranging from 127 MeV to 800 MeV. We use different
antikaon-nucleus optical potentials, some of them fitted to kaonic atom data,
and study the sensitivity of the cross sections to the considered
antikaon-nucleus dynamics.


  We propose a new parametrization of the impact parameter u0 and impact angle
{\alpha} for microlensing systems composed by an Earth-like Exoplanet around a
Solar mass Star at 1 AU. We present the caustic topology of such system, as
well as the related light curves generated by using such a new parametrization.
Based on the same density of points and accuracy of regular methods, we obtain
results 5 times faster for discovering Earth-like exoplanet. In this big data
revolution of photometric astronomy, our method will impact future missions
like WFIRST (NASA) and Euclid (ESA) and they data pipelines, providing a rapid
and deep detection of exoplanets for this specific class of microlensing event
that might otherwise be lost.


  Wolf-Rayet (WR) stars are luminous massive blue stars thought to be immediate
precursors to the supernova terminating their brief lives. The existence of
dust shells around such stars has been enigmatic since their discovery some 30
years ago; the intense radiation field from the star should be inimical to dust
survival. Although dust-creation models, including those involving interacting
stellar winds from a companion star, have been put forward, high-resolution
observations are required to understand this phenomena. Here we present
resolved images of the dust outflow around Wolf-Rayet WR 104, obtained with
novel imaging techniques, revealing detail on scales corresponding to about 40
AU at the star. Our maps show that the dust forms a spatially confined stream
following precisely a linear (or Archimedian) spiral trajectory. Images taken
at two separate epochs show a clear rotation with a period of 220 +/- 30 days.
Taken together, these findings prove that a binary star is responsible for the
creation of the circumstellar dust, while the spiral plume makes WR 104 the
prototype of a new class of circumstellar nebulae unique to interacting wind
systems.


  A general procedure to describe the coupling $U_A (1) \times U_B (1)$ between
antisymmetric gauge fields is proposed. For vector gauge theories the inclusion
of magnetic mixing in the hidden sector induces millicharges -- in principle --
observable. We extend the analysis to antisymmetric fields and the extension to
higher order monopoles is discussed. A modification of the model discussed in
\cite{Ibarra} with massless antisymmetric fields as dark matter is also
considered and the total cross section ratio are found and discussed.


  We show that every Finsler module over a $C^*$-algebra has a
quasi-representation into the Banach space
$\mathbb{B}(\mathscr{H},\mathscr{K})$ of all bounded linear operators between
some Hilbert spaces $\mathscr{H}$ and $\mathscr{K}$. We define the notion of
completely positive $\varphi$-morphism and establish a Stinespring type theorem
in the framework of Finsler modules over $C^*$-algebras. We also investigate
the nondegeneracy and the irreducibility of quasi-representations.


  Recently, the application of Contrastive Representation Learning (CRL) in
learning with noisy labels (LNL) has shown promising advancements due to its
remarkable ability to learn well-distributed representations for better
distinguishing noisy labels. However, CRL is mainly used as a pre-training
technique, leading to a complicated multi-stage training pipeline. We also
observed that trivially combining CRL with supervised LNL methods decreases
performance. Using different images from the same class as negative pairs in
CRL creates optimization conflicts between CRL and the supervised loss. To
address these two issues, we propose an end-to-end PLReMix framework that
avoids the complicated pipeline by introducing a Pseudo-Label Relaxed (PLR)
contrastive loss to alleviate the conflicts between losses. This PLR loss
constructs a reliable negative set of each sample by filtering out its
inappropriate negative pairs that overlap at the top k indices of prediction
probabilities, leading to more compact semantic clusters than vanilla CRL.
Furthermore, a two-dimensional Gaussian Mixture Model (GMM) is adopted to
distinguish clean and noisy samples by leveraging semantic information and
model outputs simultaneously, which is expanded on the previously widely used
one-dimensional form. The PLR loss and a semi-supervised loss are
simultaneously applied to train on the GMM divided clean and noisy samples.
Experiments on multiple benchmark datasets demonstrate the effectiveness of the
proposed method. Our proposed PLR loss is scalable, which can be easily
integrated into other LNL methods and boost their performance. Codes will be
available.


  We present a three-photon based fluorescence readout method where the
strength of the fluorescence scales with the strength of the radio-frequency
(RF) field being applied. We compare this method to conventional three-photon
electromagnetically-induced transparency (EIT) and electromagnetically-induced
absorption (EIA). Our demonstrated EIA/EIT sensitivity in the collinear
three-photon Cesium system is the best reported to date at roughly 30
uVm^{-1}Hz^{-1/2}. The fluorescence is nearly 4 fold better in sensitivity
compared to EIA/EIT readout.


  The IRAC shallow survey covers 8.5 square degrees in the NOAO Deep Wide-Field
Survey in Bootes with 3 or more 30 second exposures per position. An overview
of the survey design, reduction, calibration, star-galaxy separation, and
initial results is provided. The survey includes approximately 370,000,
280,000, 38,000, and 34,000 sources brighter than the 5 sigma limits of 6.4,
8.8, 51, and 50 microJy at 3.6, 4.5, 5.8, and 8 microns respectively, including
some with unusual spectral energy distributions.


  Let $G$, $H$ be groups. We denote by $\eta(G,H)$ a certain extension of the
non-abelian tensor product $G \otimes H$ by $G \times H$. We prove that if $G$
and $H$ are groups that act compatibly on each other and such that the set of
all tensors $T_{\otimes}(G,H)=\{g\otimes h \, : \, g \in G, \, h\in H\}$ is
finite, then the non-abelian tensor product $G \otimes H$ is finite. In the
opposite direction we examine certain finiteness conditions of $G$ in terms of
similar conditions for the tensor square $G \otimes G$.


  A new method to construct algebro-geometric solutions of rank two Schlesinger
systems is presented. For an elliptic curve represented as a ramified double
covering of CP^1, a meromorphic differential is constructed with the following
property: the common projection of its two zeros on the base of the covering,
regarded as a function of the only moving branch point of the covering, is a
solution of a Painleve VI equation. This differential provides an invariant
formulation of a classical Okamoto transformation for the Painleve VI
equations. A generalization of this differential to hyperelliptic curves is
also constructed. In this case, positions of zeros of the differential provide
part of a solution of the multidimensional Garnier system. The corresponding
solutions of the rank two Schlesinger systems associated with elliptic and
hyperelliptic curves are constructed in terms of this differential. The initial
data for construction of the meromorphic differential include a point in the
Jacobian of the curve, under the assumption that this point has nonvariable
coordinates with respect to the lattice of the Jacobian while the branch points
vary. It appears that the cases where the coordinates of the point are rational
correspond to periodic trajectories of the billiard ordered games associated
with g confocal quadrics in (g+1)-dimensional space. This is a generalization
of a situation studied by Hitchin, who related algebraic solutions of a
Painleve VI equation with the Poncelet polygons.


  Symbiotic binaries are long period interacting binaries consisting of a white
dwarf (WD) accreting material from a cool evolved giant star via stellar winds.
In this paper we study the symbiotic binary LIN 358 located in the SMC. We have
observed LIN 358 with the integral field spectrograph WiFeS and obtained its
line emission spectrum. With the help of the plasma simulation and spectral
synthesis code Cloudy, we have constructed a 2D photo-ionisation model of LIN
358. From comparison with the observations, we have determined the colour
temperature of the WD in LIN 358 to be 19 eV, its bolometric luminosity $L =
(1.02 \pm 0.15) \times 10^{38}$ erg s$^{-1}$, and the mass-loss rate from the
donor star to be $ 1.2 \times 10^{-6}$ M$_{\odot}$ yr$^{-1}$. Assuming a solar
H to He ratio in the wind material, a lower limit to the accreted mass fraction
in LIN 358 is 0.31. The high mass-accretion efficiency of a wind Roche lobe
overflow implies that the WD is accreting above the upper boundary of stable
hydrogen fusion and thus growing in mass with the maximal rate of $\approx 4
\times 10^{-7}$ M$_{\odot}$ yr$^{-1}$. This causes the WD photosphere to
expand, which explains its low colour temperature. Our calculations show that
the circumstellar material in LIN 358 is nearly completely ionized except for a
narrow cone around the donor star, and that the WD emission is freely escaping
the system. However, due to its low colour temperature, this emission can be
easily attenuated by even moderate amounts of neutral ISM. We speculate that
other symbiotic systems may be operating in a similar regime, thus explaining
the paucity of observed systems.


  Let $H \in C^2(\mathbb{R}^{N \times n})$, $H\geq 0$. The PDE system \[
\label{1} A_\infty u \, :=\, \Big(H_P \otimes H_P + H [H_P]^\bot H_{PP}
\Big)(Du) : D^2 u\, = \, 0 \tag{1} \] arises as the ``Euler-Lagrange PDE" of
vectorial variational problems for the functional $E_{\infty}(u,\Omega) = \|
H(Du) \|_{L^\infty(\Omega)}$ defined on maps $u : \Omega \subseteq \mathbb{R}^n
\longrightarrow \mathbb{R}^N$. \eqref{1} first appeared in the author's recent
work \cite{K3}. The scalar case though has a long history initiated by Aronsson
in \cite{A1}. Herein we study the solutions of \eqref{1} with emphasis on the
case of $n=2\leq N$ with $H$ the Euclidean norm on $\mathbb{R}^{N \times n}$,
which we call the ``$\infty$-Laplacian". By establishing a rigidity theorem for
rank-one maps of independent interest, we analyse a phenomenon of separation of
the solutions to phases with qualitatively different behaviour. As a corollary,
we extend to $N \geq 2$ the Aronsson-Evans-Yu theorem regarding non-existence
of zeros of $|Du|$ and prove a Maximum Principle. We further characterise all
$H$ for which \eqref{1} is elliptic and also study the initial value problem
for the ODE system arising for $n=1$ but with $H(\cdot,u,u')$ depending on all
the arguments.


  This paper is a continuation of the systematic study of the distributions of
quadrant marked mesh patterns initiated in [6]. Given a permutation $\sg =
\sg_1 ... \sg_n$ in the symmetric group $S_n$, we say that $\sg_i$ matches the
quadrant marked mesh pattern $MMP(a,b,c,d)$ if there are at least $a$ elements
to the right of $\sg_i$ in $\sg$ that are greater than $\sg_i$, at least $b$
elements to left of $\sg_i$ in $\sg$ that are greater than $\sg_i$, at least
$c$ elements to left of $\sg_i$ in $\sg$ that are less than $\sg_i$, and at
least $d$ elements to the right of $\sg_i$ in $\sg$ that are less than $\sg_i$.
We study the distribution of $MMP(a,b,c,d)$ in 132-avoiding permutations. In
particular, we study the distribution of $MMP(a,b,c,d)$, where only one of the
parameters $a,b,c,d$ are non-zero. In a subsequent paper [7], we will study the
the distribution of $MMP(a,b,c,d)$ in 132-avoiding permutations where at least
two of the parameters $a,b,c,d$ are non-zero.


  This technical report shows and discusses in detail how Sparse Audio
Declipper (SPADE) algorithms are derived from the signal model using the ADMM
approach. The analysis version (A-SPADE) of Kiti\'c et. al. (LVA/ICA 2015) is
derived and justified. The synthesis version (S-SPADE) of the same research
team is shown to solve a different optimization task than intended. This issue
is corrected in this report, leading to the new S-SPADE algorithm which is in
line to A-SPADE.


  The general aim of this paper is to supply a method to decide whether a
discrete system decoheres or not, and under what conditions decoherence occurs,
with no need of appealing to computer simulations to obtain the time evolution
of the reduced state. In particular, a lemma is presented as the core of the
method.


  This paper explores the task of detecting images generated by text-to-image
diffusion models. To evaluate this, we consider images generated from captions
in the MSCOCO and Wikimedia datasets using two state-of-the-art models: Stable
Diffusion and GLIDE. Our experiments show that it is possible to detect the
generated images using simple Multi-Layer Perceptrons (MLPs), starting from
features extracted by CLIP, or traditional Convolutional Neural Networks
(CNNs). We also observe that models trained on images generated by Stable
Diffusion can detect images generated by GLIDE relatively well, however, the
reverse is not true. Lastly, we find that incorporating the associated textual
information with the images rarely leads to significant improvement in
detection results but that the type of subject depicted in the image can have a
significant impact on performance. This work provides insights into the
feasibility of detecting generated images, and has implications for security
and privacy concerns in real-world applications. The code to reproduce our
results is available at:
https://github.com/davide-coccomini/Detecting-Images-Generated-by-Diffusers


  We survey diverse approaches to the notion of information: from Shannon
entropy to Kolmogorov complexity. Two of the main applications of Kolmogorov
complexity are presented: randomness and classification. The survey is divided
in two parts in the same volume. Part I is dedicated to information theory and
the mathematical formalization of randomness based on Kolmogorov complexity.
This last application goes back to the 60's and 70's with the work of
Martin-L\"of, Schnorr, Chaitin, Levin, and has gained new impetus in the last
years.


  Tidal Dwarf Galaxies (TDGs), produced from material expelled in galactic
interactions, are well--suited to test the laws of star formation (SF) due to
their simple structure, high metallicity -- making CO a reliable tracer of the
molecular gas content -- and recent SF. Here, we study the conditions for the
onset of SF and for the rate at which SF proceeds once above a threshold in a
small sample of TDGs. We use data for the gas (atomic and molecular) surface
density and SF rate per area to test the laws of SF found for spiral and dwarf
galaxies in this more extreme environment. We find in general a good agreement
with the Schmidt law found for the total gas and for the molecular gas but note
that higher resolution CO observations are necessary to clarify some possible
discrepancies. We find, down to a scale of $\sim$1 kpc, in general a good
agreement between the peaks of SF and of the molecular gas, but also find in
some objects surprisingly large quantities of molecular gas at places where no
SF is occuring. A high column density of molecular gas is therefore not a
sufficient condition for the onset of SF. We find that the kinematical
properties of the gas are also relevant: in two objects our observations showed
that SF only occured in regions with a narrow line width.


  We propose a new scenario of the electroweak baryogenesis based on the
properties of electroweak domain walls surrounding black holes in the Higgs
phase vacuum of the extended Standard Model with 2-Higgs doublets. It is shown
that there exists the electroweak domain walls and the symmetric regions
surrounding black holes and that the Hawking radiation of the black holes can
produce a non-zero baryon number density. The scenario can explain the origin
of the baryon number in our universe even without the assumption of the
existence of the first order electroweak phase transition.


  We designed and experimentally demonstrated a silicon photonics-integrated
time-domain balanced homodyne detector (TBHD), whose optical part has
dimensions of 1.5 mm * 0.4 mm. To automatically and accurately balance the
detector, new variable optical attenuators were used, and a common mode
rejection ratio of 86.9 dB could be achieved. In the quantum tomography
experiment, the density matrix and Wigner function of a coherent state were
reconstructed with 99.97 % fidelity. The feasibility of this TBHD in a
continuous-variable quantum key distribution (CVQKD) system was also
demonstrated. This facilitates the integration of the optical circuits of the
CVQKD system based on the GG02 protocol on the silicon photonics chip using
TBHD.


  In this paper, a backward Euler method combined with finite element
discretization in spatial direction is discussed for the equations of motion
arising in the $2D$ Oldroyd model of viscoelastic fluids of order one with the
forcing term independent of time or in $L^{\infty}$ in time. It is shown that
the estimates of the discrete solution in Dirichlet norm is bounded uniformly
in time. Optimal {\it a priori} error estimate in $\textbf{L}^2$-norm is
derived for the discrete problem with non-smooth initial data. This estimate is
shown to be uniform in time, under the assumption of uniqueness condition.
Finally, we present some numerical results to validate our theoretical results.


  We re-express gravitational wave results in terms of post-Newtonian
parameters. Using these expressions, and some simplifying assumptions, we
compute that in a favorable case, i.e. a ten solar-mass black hole spiraling in
to a 10^6 solar-mass black hole, LISA observations will be able to constrain at
the 10% level or better a single combination of post-post-Newtonian parameters
one order higher than those already constrained by solar system evidence. This
significant constraint will be possible even if the signal-to-noise level is so
low that the signal can only be found by matched filtering, and hence only
deviations between alternate signal interpretations of order one half cycle or
more can be detected.


  We report novel interference effects in wave packet scattering of identical
particles incident on the same side of a resonant barrier, different from those
observed in Hong-Ou-Mandel experiments. These include significant changes in
the mean number of transmissions and full counting statistics, as well as
"bunching" and "anti-bunching" effects in the all-particles transmission
channel. With several resonances involved, pseudo-resonant driving of the
two-level system in the barrier, may result in sharp enhancement of scattering
probabilities for certain values of temporal delay between the particles.


  Appliance-level load forecasting plays a critical role in residential energy
management, besides having significant importance for ancillary services
performed by the utilities. In this paper, we propose to use an LSTM-based
sequence-to-sequence (seq2seq) learning model that can capture the load
profiles of appliances. We use a real dataset collected fromfour residential
buildings and compare our proposed schemewith three other techniques, namely
VARMA, Dilated One Dimensional Convolutional Neural Network, and an LSTM
model.The results show that the proposed LSTM-based seq2seq model outperforms
other techniques in terms of prediction error in most cases.


  We report an unambiguous observation of third-order nonlinear optical effect,
spatial self-phase modulation (SSPM), in a MoTe2 dispersion. The values of the
third order nonlinear optical coefficients effectively for one layer MoTe2,
\c{hi}(3)one-layer, are obtained through the SSPM method at excitation
wavelengths 473, 532, 750 and 801 nm, respectively. The wind-chime model is
used to explain the ring formation time. The wavelength dependence of
\c{hi}(3)one-layer compares well with the photo-absorption spectra.
Significantly, we find a correlation between \c{hi}(3) and the carrier mobility
{\mu} or effective mass m*, which again further supports the laser-induced ac
electron coherence in 2D materials.


  We study the cross section for the photoproduction reaction gamma N -> h X in
fixed-target scattering at COMPASS, where the hadron h is produced at large
transverse momentum. We investigate the role played by higher-order QCD
corrections to the cross section. In particular we address large logarithmic
"threshold" corrections to the rapidity dependent partonic cross sections,
which we resum to all orders at next-to-leading accuracy. In our comparison to
the experimental data we find that the threshold contributions are large and
improve the agreement between data and theoretical predictions significantly.


  The giant molecular cloud Orion A is the closest massive star-forming region
to earth ($d\sim400$ pc). It contains the rich Orion Nebula Cluster (ONC) in
the North, and low-mass star-forming regions (L1641, L1647) to the South. To
get a better understanding of the differences in star formation activity, we
perform an analysis of the gas mass distribution and star formation rate across
the cloud. We find that the gas is roughly uniformly distributed, while, oddly,
the ONC region produced about a factor of ten more stars compared to the rest
of the cloud. For a better interpretation of this phenomenon, we use Gaia DR2
parallaxes, to analyse distances of young stellar objects, using them as proxy
for cloud distances. We find that the ONC region indeed lies at about 400 pc
while the low-mass star-forming parts are inclined about 70$^\circ$ from the
plane of the sky reaching until $\sim$470 pc. With this we estimate that Orion
A is an about 90 pc long filamentary cloud (about twice as long as previously
assumed), with its "Head" (the ONC region) being "bent" and oriented toward the
galactic mid-plane. This striking new view allows us to perform a more robust
analysis of this important star-forming region in the future.


  Building on the detection of J=7-6 SiO maser emission in both the v=1 and v=2
vibrational states towards the symbiotic Mira, R Aquarii, we have used the
James Clerk Maxwell Telescope to study the changes in the SiO maser features
from R Aqr over a stellar pulsational period. The observations, complemented by
contemporaneous data taken at 86 GHz, represent a test of the popular
thermal-instability clump models of SiO masers. The `clump' model of SiO maser
emission considers the SiO masers to be discrete emitting regions which differ
from their surroundings in the values of one or more physical variables (SiO
abundance, for example). We find that our observational data are consistent
with a clump model in which the appearance of maser emission in the J=7-6
transitions coincides with an outward-moving shock impinging on the inner edge
of the maser zone.


  We study balanced solutions for network bargaining games with general
capacities, where agents can participate in a fixed but arbitrary number of
contracts. We provide the first polynomial time algorithm for computing
balanced solutions for these games. In addition, we prove that an instance has
a balanced solution if and only if it has a stable one. Our methods use a new
idea of reducing an instance with general capacities to a network bargaining
game with unit capacities defined on an auxiliary graph. This represents a
departure from previous approaches, which rely on computing an allocation in
the intersection of the core and prekernel of a corresponding cooperative game,
and then proving that the solution corresponding to this allocation is
balanced. In fact, we show that such cooperative game methods do not extend to
general capacity games, since contrary to the case of unit capacities, there
exist allocations in the intersection of the core and prekernel with no
corresponding balanced solution. Finally, we identify two sufficient conditions
under which the set of balanced solutions corresponds to the intersection of
the core and prekernel, thereby extending the class of games for which this
result was previously known.


  The damage personal attacks cause to online discourse motivates many
platforms to try to curb the phenomenon. However, understanding the prevalence
and impact of personal attacks in online platforms at scale remains
surprisingly difficult. The contribution of this paper is to develop and
illustrate a method that combines crowdsourcing and machine learning to analyze
personal attacks at scale. We show an evaluation method for a classifier in
terms of the aggregated number of crowd-workers it can approximate. We apply
our methodology to English Wikipedia, generating a corpus of over 100k high
quality human-labeled comments and 63M machine-labeled ones from a classifier
that is as good as the aggregate of 3 crowd-workers, as measured by the area
under the ROC curve and Spearman correlation. Using this corpus of
machine-labeled scores, our methodology allows us to explore some of the open
questions about the nature of online personal attacks. This reveals that the
majority of personal attacks on Wikipedia are not the result of a few malicious
users, nor primarily the consequence of allowing anonymous contributions from
unregistered users.


  Depth is a very important modality in computer vision, typically used as
complementary information to RGB, provided by RGB-D cameras. In this work, we
show that it is possible to obtain the same level of accuracy as RGB-D cameras
on a semantic segmentation task using infrared (IR) and depth images from a
single Time-of-Flight (ToF) camera. In order to fuse the IR and depth
modalities of the ToF camera, we introduce a method utilizing depth-specific
convolutions in a multi-task learning framework. In our evaluation on an in-car
segmentation dataset, we demonstrate the competitiveness of our method against
the more costly RGB-D approaches.


  Heavy-light mesons can be studied using the 1/M expansion of NRQCD, provided
the heavy quark mass is sufficiently large. Calculations of the S-wave charmed
meson masses from a classically and tadpole-improved action are presented. A
comparison of O(1/M), O(1/M^2) and O(1/M^3) results allows convergence of the
expansion to be discussed. It is shown that the form of discretized heavy quark
propagation must be chosen carefully.


  In network science, identifying optimal partitions of a signed network into
internally cohesive and mutually divisive clusters based on generalized balance
theory is computationally challenging. We reformulate and generalize two binary
linear programming models that tackle this challenge, demonstrating their
practicality by applying them them to partition networks of collaboration in
the US House of Representatives. These models guarantee a globally optimal
network partition and can be practically applied to signed networks containing
up to 30,000 edges. In the US House context, we find that a three-cluster
partition is better than a conventional two-cluster partition, where the
otherwise hidden third coalition is composed of highly effective legislators
who are ideologically aligned with the majority party.


  We demonstrate that the large scatter in the ultra-violet (UV) colours of
intermediate-mass early-type galaxies in the local Universe and the inferred
low-level recent star formation in these objects can be reproduced by minor
mergers in the standard LCDM cosmology. Numerical simulations of mergers with
mass ratios less than or equal to 1:4, with reasonable assumptions for the
ages, metallicities and dust properties of the merger progenitors, produce good
agreement to the observed UV colours of the early-type population, if the
infalling satellites are assumed to have (cold) gas fractions greater than 20%.
Early-types that satisfy (NUV-r) < 3.8 are likely to have experienced mergers
with mass ratios between 1:4 and 1:6 within the last ~1.5 Gyrs, while those
that satisfy 3.8<(NUV-r)<5.5 are consistent with either recent mergers with
mass ratios < 1:6 or mergers with higher mass ratios that occurred more than
~1.5 Gyrs in the past. We demonstrate that the early-type colour-magnitude
relations and colour distributions in both the UV and optical spectral ranges
are consistent with the expected frequency of minor merging activity in the
standard LCDM cosmology at low redshift. We present a strong plausibility
argument for minor mergers to be the principal mechanism behind the large UV
scatter and associated low-level recent star formation observed in early-type
galaxies in the nearby Universe.


  Ares is a modular framework, designed to implement dynamic, reconfigurable,
fault-tolerant, read/write and strongly consistent distributed shared memory
objects. Recent enhancements of the framework have realized the efficient
implementation of large objects, by introducing versioning and data striping
techniques. In this work, we identify performance bottlenecks of the Ares's
variants by utilizing distributed tracing, a popular technique for monitoring
and profiling distributed systems. We then propose optimizations across all
versions of Ares, aiming in overcoming the identified flaws, while preserving
correctness. We refer to the optimized version of Ares as Ares II, which now
features a piggyback mechanism, a garbage collection mechanism, and a batching
reconfiguration technique for improving the performance and storage efficiency
of the original Ares. We rigorously prove the correctness of Ares II, and we
demonstrate the performance improvements by an experimental comparison (via
distributed tracing) of the Ares II variants with their original counterparts.


  Bitcoin and its decentralized computing paradigm for digital currency trading
are one of the most disruptive technology in the 21st century. This paper
presents a novel approach to developing a Bitcoin transaction forecast model,
DLForecast, by leveraging deep neural networks for learning Bitcoin transaction
network representations. DLForecast makes three original contributions. First,
we explore three interesting properties between Bitcoin transaction accounts:
topological connectivity pattern of Bitcoin accounts, transaction amount
pattern, and transaction dynamics. Second, we construct a time-decaying
reachability graph and a time-decaying transaction pattern graph, aiming at
capturing different types of spatial-temporal Bitcoin transaction patterns.
Third, we employ node embedding on both graphs and develop a Bitcoin
transaction forecasting system between user accounts based on historical
transactions with built-in time-decaying factor. To maintain an effective
transaction forecasting performance, we leverage the multiplicative model
update (MMU) ensemble to combine prediction models built on different
transaction features extracted from each corresponding Bitcoin transaction
graph. Evaluated on real-world Bitcoin transaction data, we show that our
spatial-temporal forecasting model is efficient with fast runtime and effective
with forecasting accuracy over 60\% and improves the prediction performance by
50\% when compared to forecasting model built on the static graph baseline.


  In this work we study the structure of finitely generated groups for which a
space of harmonic functions with fixed polynomial growth is finite dimensional.
It is conjectured that such groups must be virtually nilpotent (the converse
direction to Kleiner's theorem). We prove that this is indeed the case for
solvable groups. The investigation is partly motivated by Kleiner's proof for
Gromov's theorem on groups of polynomial growth.


  We study the runtime verification of hyperproperties, expressed in the
temporal logic HyperLTL, as a means to inspect a system with respect to
security polices. Runtime monitors for hyperproperties analyze trace logs that
are organized by common prefixes in the form of a tree-shaped Kripke structure,
or are organized both by common prefixes and by common suffixes in the form of
an acyclic Kripke structure. Unlike runtime verification techniques for trace
properties, where the monitor tracks the state of the specification but usually
does not need to store traces, a monitor for hyperproperties repeatedly model
checks the growing Kripke structure. This calls for a rigorous complexity
analysis of the model checking problem over tree-shaped and acyclic Kripke
structures. We show that for trees, the complexity in the size of the Kripke
structure is L-complete independently of the number of quantifier alternations
in the HyperLTL formula. For acyclic Kripke structures, the complexity is
PSPACE-complete (in the level of the polynomial hierarchy that corresponds to
the number of quantifier alternations). The combined complexity in the size of
the Kripke structure and the length of the HyperLTL formula is PSPACE-complete
for both trees and acyclic Kripke structures, and is as low as NC for the
relevant case of trees and alternation-free HyperLTL formulas. Thus, the size
and shape of both the Kripke structure and the formula have significant impact
on the complexity of the model checking problem.


  One major drawback of state of the art Neural Networks (NN)-based approaches
for document classification purposes is the large number of training samples
required to obtain an efficient classification. The minimum required number is
around one thousand annotated documents for each class. In many cases it is
very difficult, if not impossible, to gather this number of samples in real
industrial processes. In this paper, we analyse the efficiency of NN-based
document classification systems in a sub-optimal training case, based on the
situation of a company document stream. We evaluated three different
approaches, one based on image content and two on textual content. The
evaluation was divided into four parts: a reference case, to assess the
performance of the system in the lab; two cases that each simulate a specific
difficulty linked to document stream processing; and a realistic case that
combined all of these difficulties. The realistic case highlighted the fact
that there is a significant drop in the efficiency of NN-Based document
classification systems. Although they remain efficient for well represented
classes (with an over-fitting of the system for those classes), it is
impossible for them to handle appropriately less well represented classes.
NN-Based document classification systems need to be adapted to resolve these
two problems before they can be considered for use in a company document
stream.


  We study linearized gravity in a six-dimensional Einstein-Maxwell model of
warped braneworlds, where the extra dimensions are compactified by a magnetic
flux. It is difficult to construct a strict codimension two braneworld with
matter sources other than pure tension. To overcome this problem we replace the
codimension two defect by an extended brane, with one spatial dimension
compactified on a Kaluza-Klein circle. Our background is composed of a warped,
axisymmetric bulk and one or two branes. We find that weak gravity sourced by
arbitrary matter on the brane(s) is described by a four-dimensional
scalar-tensor theory. We show, however, that the scalar mode is suppressed at
long distances and hence four-dimensional Einstein gravity is reproduced on the
brane.


  The Quantum Approximate Optimization Algorithm can naturally be applied to
combinatorial search problems on graphs. The quantum circuit has p applications
of a unitary operator that respects the locality of the graph. On a graph with
bounded degree, with p small enough, measurements of distant qubits in the
state output by the QAOA give uncorrelated results. We focus on finding big
independent sets in random graphs with dn/2 edges keeping d fixed and n large.
Using the Overlap Gap Property of almost optimal independent sets in random
graphs, and the locality of the QAOA, we are able to show that if p is less
than a d-dependent constant times log n, the QAOA cannot do better than finding
an independent set of size .854 times the optimal for d large. Because the
logarithm is slowly growing, even at one million qubits we can only show that
the algorithm is blocked if p is in single digits. At higher p the algorithm
"sees" the whole graph and we have no indication that performance is limited.


  Cross-graph Relational Learning (CGRL) refers to the problem of predicting
the strengths or labels of multi-relational tuples of heterogeneous object
types, through the joint inference over multiple graphs which specify the
internal connections among each type of objects. CGRL is an open challenge in
machine learning due to the daunting number of all possible tuples to deal with
when the numbers of nodes in multiple graphs are large, and because the labeled
training instances are extremely sparse as typical. Existing methods such as
tensor factorization or tensor-kernel machines do not work well because of the
lack of convex formulation for the optimization of CGRL models, the poor
scalability of the algorithms in handling combinatorial numbers of tuples,
and/or the non-transductive nature of the learning methods which limits their
ability to leverage unlabeled data in training. This paper proposes a novel
framework which formulates CGRL as a convex optimization problem, enables
transductive learning using both labeled and unlabeled tuples, and offers a
scalable algorithm that guarantees the optimal solution and enjoys a linear
time complexity with respect to the sizes of input graphs. In our experiments
with a subset of DBLP publication records and an Enzyme multi-source dataset,
the proposed method successfully scaled to the large cross-graph inference
problem, and outperformed other representative approaches significantly.


  Accurate detection of the myocardial infarction (MI) area is crucial for
early diagnosis planning and follow-up management. In this study, we propose an
end-to-end deep-learning algorithm framework (OF-RNN ) to accurately detect the
MI area at the pixel level. Our OF-RNN consists of three different function
layers: the heart localization layers, which can accurately and automatically
crop the region-of-interest (ROI) sequences, including the left ventricle,
using the whole cardiac magnetic resonance image sequences; the motion
statistical layers, which are used to build a time-series architecture to
capture two types of motion features (at the pixel-level) by integrating the
local motion features generated by long short-term memory-recurrent neural
networks and the global motion features generated by deep optical flows from
the whole ROI sequence, which can effectively characterize myocardial
physiologic function; and the fully connected discriminate layers, which use
stacked auto-encoders to further learn these features, and they use a softmax
classifier to build the correspondences from the motion features to the tissue
identities (infarction or not) for each pixel. Through the seamless connection
of each layer, our OF-RNN can obtain the area, position, and shape of the MI
for each patient. Our proposed framework yielded an overall classification
accuracy of 94.35% at the pixel level, from 114 clinical subjects. These
results indicate the potential of our proposed method in aiding standardized MI
assessments.


  The objective of this study is to compare several change detection methods
for a mono static camera and identify the best method for different complex
environments and backgrounds in indoor and outdoor scenes. To this end, we used
the CDnet video dataset as a benchmark that consists of many challenging
problems, ranging from basic simple scenes to complex scenes affected by bad
weather and dynamic backgrounds. Twelve change detection methods, ranging from
simple temporal differencing to more sophisticated methods, were tested and
several performance metrics were used to precisely evaluate the results.
Because most of the considered methods have not previously been evaluated on
this recent large scale dataset, this work compares these methods to fill a
lack in the literature, and thus this evaluation joins as complementary
compared with the previous comparative evaluations. Our experimental results
show that there is no perfect method for all challenging cases, each method
performs well in certain cases and fails in others. However, this study enables
the user to identify the most suitable method for his or her needs.


  Demographic data and recent experiments verify earlier predictions that
mortality has short (few percent of the life span) memory of the previous life
history, may be significantly decreased, reset to its value at a much younger
age, and (until certain age) eliminated. Such mortality dynamics is
demonstrated to be characteristic only of evolutionary unprecedented protected
populations. When conditions improve, their mortality decreases stepwise. At
crossovers the rate of decrease rapidly changes. The crossovers manifest the
edges of the stairs in the universal ladder of rapid mortality adjustment to
changing conditions. Mortality is dominated by the established universal law
which reduces it to few biologically explicit parameters and which is verified
with human and fly mortality data. Specific experiments to test universality of
the law for other animals, and to unravel the mechanism of stepwise life
extension, are suggested.


  Fragment-size distributions have been studied experimentally in masticated
viscoelastic food (fish sausage).The mastication experiment in seven subjects
was examined. We classified the obtained results into two groups, namely, a
single lognormal distribution group and a lognormal distribution with
exponential tail group. The facts suggest that the individual variability might
affect the fragmentation pattern when the food sample has a much more
complicated physical property. In particular, the latter result (lognormal
distribution with exponential tail) indicates that the fragmentation pattern by
human mastication for fish sausage is different from the fragmentation pattern
for raw carrot shown in our previous study. The excellent data fitting by the
lognormal distribution with exponential tail implies that the fragmentation
process has a size-segregation-structure between large and small parts.In order
to explain this structure, we propose a mastication model for fish sausage
based on stochastic processes.


  Arising from the extreme/saddle point in electronic bands, Van Hove
singularity (VHS) manifests divergent density of states (DOS) and induces
various new states of matter such as unconventional superconductivity. VHS is
believed to exist in one and two dimensions, but rarely found in three
dimension (3D). Here, we report the discovery of 3D VHS in a topological magnet
EuCd2As2 by magneto-infrared spectroscopy. External magnetic fields effectively
control the exchange interaction in EuCd2As2, and shift 3D Weyl bands
continuously, leading to the modification of Fermi velocity and energy
dispersion. Above the critical field, the 3D VHS forms and is evidenced by the
abrupt emergence of inter-band transitions, which can be quantitatively
described by the minimal model of Weyl semimetals. Three additional optical
transitions are further predicted theoretically and verified in
magneto-near-infrared spectra. Our results pave the way to exploring VHS in 3D
systems and uncovering the coordination between electronic correlation and the
topological phase.


  With the broad use of face recognition, its weakness gradually emerges that
it is able to be attacked. So, it is important to study how face recognition
networks are subject to attacks. In this paper, we focus on a novel way to do
attacks against face recognition network that misleads the network to identify
someone as the target person not misclassify inconspicuously. Simultaneously,
for this purpose, we introduce a specific attentional adversarial attack
generative network to generate fake face images. For capturing the semantic
information of the target person, this work adds a conditional variational
autoencoder and attention modules to learn the instance-level correspondences
between faces. Unlike traditional two-player GAN, this work introduces face
recognition networks as the third player to participate in the competition
between generator and discriminator which allows the attacker to impersonate
the target person better. The generated faces which are hard to arouse the
notice of onlookers can evade recognition by state-of-the-art networks and most
of them are recognized as the target person.


  We develop a novel multi-configurational Symmetrized Projector Quantum Monte
Carlo (MSPQMC) method to calculate excited state properties of Hubbard models.
We compare the MSPQMC results for finite Hubbard chains with exact results
(where available) or with Density Matrix Renormalization Group (DMRG) results
for longer chains. The energies and correlation functions of the excited states
are in good agreement with those obtained from the alternate techniques.


  Class-imbalanced data, in which some classes contain far more samples than
others, is ubiquitous in real-world applications. Standard techniques for
handling class-imbalance usually work by training on a re-weighted loss or on
re-balanced data. Unfortunately, training overparameterized neural networks on
such objectives causes rapid memorization of minority class data. To avoid this
trap, we harness meta-learning, which uses both an ''outer-loop'' and an
''inner-loop'' loss, each of which may be balanced using different strategies.
We evaluate our method, MetaBalance, on image classification, credit-card fraud
detection, loan default prediction, and facial recognition tasks with severely
imbalanced data, and we find that MetaBalance outperforms a wide array of
popular re-sampling strategies.


  One of the latest proposed applications of ultra-intense laser pulses is
their possible use to gauge extreme high vacuum by measuring the photon
radiation resulting from nonlinear Thomson scattering within a vacuum tube.
Here, we provide a complete analysis of the process, computing the expected
rates and spectra, both for linear and circular polarizations of the laser
pulses, taking into account the effect of the time envelope in a slowly varying
envelope approximation. We also design a realistic experimental configuration
allowing for the implementation of the idea and compute the corresponding
geometric efficiencies. Finally, we develop an optimization procedure for this
photonic gauge of extreme high vacuum at high repetition rate Petawatt and
multi-Petawatt laser facilities, such as VEGA, JuSPARC and ELI.


  Taking into account information across the temporal domain helps to improve
environment perception in autonomous driving. However, it has not been studied
so far whether temporally fused neural networks are vulnerable to deliberately
generated perturbations, i.e. adversarial attacks, or whether temporal history
is an inherent defense against them. In this work, we study whether temporal
feature networks for object detection are vulnerable to universal adversarial
attacks. We evaluate attacks of two types: imperceptible noise for the whole
image and locally-bound adversarial patch. In both cases, perturbations are
generated in a white-box manner using PGD. Our experiments confirm, that
attacking even a portion of a temporal input suffices to fool the network. We
visually assess generated perturbations to gain insights into the functioning
of attacks. To enhance the robustness, we apply adversarial training using
5-PGD. Our experiments on KITTI and nuScenes datasets demonstrate, that a model
robustified via K-PGD is able to withstand the studied attacks while keeping
the mAP-based performance comparable to that of an unattacked model.


  Autocorrelation is a common method to estimate the duration of ultra-short
laser pulses. In the ultra-violet (UV) regime it is increasingly challenging to
employ the standard process of second harmonic generation, most prominently due
to absorption in nonlinear crystals at very short wavelengths. Here we show how
to utilize spontaneous parametric down-conversion (SPDC) to generate an
autocorrelation signal for UV pulses in the infrared. Our method utilizes the
$n^{\text{th}}$ order emission of the SPDC process, which occurs for low
pumping powers proportional to the $n^{\text{th}}$ power of the UV intensity.
Thus, counting $2n$ down-converted photons directly yields the
$n^{\text{th}}$-order autocorrelation. The method, now with detection of near
infrared photons with high efficiency, is applied here to the first direct
measurement of ultra-short UV-pulses (approximately $176\, \text{fs}$, center
wavelength $390\, \text{nm}$) inside a UV enhancement cavity.


  It is shown, by means of Monte Carlo simulation and Finite Size Scaling
analysis, that the Heisenberg spin glass undergoes a finite-temperature phase
transition in three dimensions. There is a single critical temperature, at
which both a spin glass and a chiral glass orderings develop. The Monte Carlo
algorithm, adapted from lattice gauge theory simulations, makes possible to
thermalize lattices of size L=32, larger than in any previous spin glass
simulation in three dimensions. High accuracy is reached thanks to the use of
the Marenostrum supercomputer. The large range of system sizes studied allow us
to consider scaling corrections.


  A variant of the Bourgain-Gamburd machine without using any girth bounds is
obtained. Also, we find series of applications of %our result the
Bourgain-Gamburd machine to problems of Additive Combinatorics, Number Theory
and Probability.