  For a finite group $G$, the size of a minimum generating set of $G$ is
denoted by $d(G)$. Given a finite group $G$ and an integer $k$, deciding if
$d(G)\leq k$ is known as the minimum generating set (MIN-GEN) problem.
  A group $G$ of order $n$ has generating set of size $\lceil \log_p n \rceil$
where $p$ is the smallest prime dividing $n=|G|$. This fact is used to design
an $n^{\log_p n+O(1)}$-time algorithm for the group isomorphism problem of
groups specified by their Cayley tables (attributed to Tarjan by Miller, 1978).
The same fact can be used to give an $n^{\log_p n+O(1)}$-time algorithm for the
MIN-GEN problem. We show that the MIN-GEN problem can be solved in time
$n^{(1/4)\log_p n+O(1)}$ for general groups given by their Cayley tables. This
runtime incidentally matches with the runtime of the best known algorithm for
the group isomorphism problem.
  We show that if a group $G$, given by its Cayley table, is the product of
simple groups then a minimum generating set of $G$ can be computed in time
polynomial in $|G|$.
  Given groups $G_i$ along with $d(G_i)$ for $i\in [r]$ the problem of
computing $d(\Pi_{i\in[r]} G_i)$ is nontrivial. As a consequence of our result
for products of simple groups we show that this problem also can be solved in
polynomial time for Cayley table representation.
  For the MIN-GEN problem for permutation groups, to the best of our knowledge,
no significantly better algorithm than the brute force algorithm is known. For
an input group $G\leq S_n$, the brute force algorithm runs in time $|G|^{O(n)}$
which can be $2^{\Omega(n^2)}$. We show that if $G\leq S_n$ is a primitive
permutation group then the MIN-GEN problem can be solved in time
quasi-polynomial in $n$.
  We also design a $\mathrm{DTIME}(2^n)$ algorithm for computing a minimum
generating set of permutation groups all of whose non-abelian chief factors
have bounded orders.


  We classify the cohomology classes of Lagrangian 4-planes $\P^4$ in a smooth
manifold $X$ deformation equivalent to a Hilbert scheme of 4 points on a $K3$
surface, up to the monodromy action. Classically, the cone of effective curves
on a $K3$ surface $S$ is generated by nonegative classes $C$, for which
$(C,C)\geq0$, and nodal classes $C$, for which $(C,C)=-2$; Hassett and
Tschinkel conjecture that the cone of effective curves on a holomorphic
symplectic variety $X$ is similarly controlled by "nodal" classes $C$ such that
$(C,C)=-\gamma$, for $(\cdot,\cdot)$ now the Beauville-Bogomolov form, where
$\gamma$ classifies the geometry of the extremal contraction associated to $C$.
In particular, they conjecture that for $X$ deformation equivalent to a Hilbert
scheme of $n$ points on a $K3$ surface, the class $C=\ell$ of a line in a
smooth Lagrangian $n$-plane $\P^n$ must satisfy $(\ell,\ell)=-\frac{n+3}{2}$.
We prove the conjecture for $n=4$ by computing the ring of monodromy invariants
on $X$, and showing there is a unique monodromy orbit of Lagrangian 4-planes.


  In this thesis, new generalizations of the Bethe approximation and new
understanding of the replica method are proposed. The Bethe approximation is an
efficient approximation for graphical models, which gives an asymptotically
accurate estimate of the partition function for many graphical models. The
Bethe approximation explains the well-known message passing algorithm, belief
propagation, which is exact for tree graphical models. It is also known that
the cluster variational method gives the generalized Bethe approximation,
called the Kikuchi approximation, yielding the generalized belief propagation.
In the thesis, a new series of generalization of the Bethe approximation is
proposed, which is named the asymptotic Bethe approximation. The asymptotic
Bethe approximation is derived from the characterization of the Bethe free
energy using graph covers, which was recently obtained by Vontobel. The
asymptotic Bethe approximation can be expressed in terms of the edge zeta
function by using Watanabe and Fukumizu's result about the Hessian of the Bethe
entropy. The asymptotic Bethe approximation is confirmed to be better than the
conventional Bethe approximation on some conditions. For this purpose, Chertkov
and Chernyak's loop calculus formula is employed, which shows that the error of
the Bethe approximation can be expressed as a sum of weights corresponding to
generalized loops, and generalized for non-binary finite alphabets by using
concepts of information geometry.


  This paper studies the problem of open-domain question answering, with the
aim of answering a diverse range of questions leveraging knowledge resources.
Two types of sources, QA-pair and document corpora, have been actively
leveraged with the following complementary strength. The former is highly
precise when the paraphrase of given question $q$ was seen and answered during
training, often posed as a retrieval problem, while the latter generalizes
better for unseen questions. A natural follow-up is thus leveraging both
models, while a naive pipelining or integration approaches have failed to bring
additional gains over either model alone. Our distinction is interpreting the
problem as calibration, which estimates the confidence of predicted answers as
an indicator to decide when to use a document or QA-pair corpus. The
effectiveness of our method was validated on widely adopted benchmarks such as
Natural Questions and TriviaQA.


  Having analytical instances of the Eigenstate Thermalization Hypothesis (ETH)
is of obvious interest, both for fundamental and applied reasons. This is
generically a hard task, due to the belief that non-linear interactions are
basic ingredients of the thermalization mechanism. In this article we proof
that random gaussian free fermions satisfy ETH in the multiparticle sector, by
analytically computing the correlations and entanglement entropies of the
theory. With the explicit construction at hand, we finally comment on the
differences between fully random Hamiltonians and random Gaussian systems, and
on the connection between chaotic energy spectra and ETH.


  Optical quantum information processing exploits interference of quantum
light. However, when the interferometer is composed of optical fibers,
degradation of interference visibility due to the finite polarization
extinction ratio becomes a problem. Here we propose a method to optimize
interference visibility by controlling the polarizations to a crosspoint of two
circular trajectories on the Poincar\'{e} sphere. Our method maximizes
visibility with low optical loss, which is essential for quantum light, by
using fiber stretchers as polarization controllers. We also experimentally
demonstrate our method, where the visibility was maintained basically above
99.9% for three hours using fiber stretchers with an optical loss of 0.02 dB
(0.5%). Our method makes fiber systems promising for practical fault-tolerant
optical quantum computers.


  Previous determinations of the oxygen isotopic ratios in AGB carbon stars
were at odds with the existing theoretical predictions. We aim to redetermine
the oxygen ratios in these stars using new spectral analysis tools and further
develop discussions on the carbon and nitrogen isotopic ratios in order to
elucidate this problem. Oxygen isotopic ratios were derived from spectra in the
K-band in a sample of galactic AGB carbon stars of different spectral types and
near solar metallicity. Synthetic spectra calculated in LTE with spherical
carbon-rich atmosphere models and updated molecular line lists were used. The
CNO isotope ratios derived in a homogeneous way, were compared with theoretical
predictions for low-mass (1.5-3 M_o) AGB stars computed with the FUNS code
assuming extra mixing both during the RGB and AGB phases. For most of the stars
the 16O/17O/18O ratios derived are in good agreement with theoretical
predictions confirming that, for AGB stars, are established using the values
reached after the FDU according to the initial stellar mass. This fact, as far
as the oxygen isotopic ratios are concerned, leaves little space for the
operation of any extra mixing mechanism during the AGB phase. Nevertheless, for
a few stars with large 16O/17O/18O, the operation of such a mechanism might be
required, although their observed 12C/13C and 14N/15N ratios would be difficult
to reconcile within this scenario. Furthermore, J-type stars tend to have lower
16O/17O ratios than the normal carbon stars, as already indicated in previous
studies. Excluding these peculiar stars, AGB carbon stars occupy the same
region as pre-solar type I oxide grains in a 17O/16O vs. 18O/16O diagram,
showing little spread. This reinforces the idea that these grains were probably
formed in low-mass stars during the previous O-rich phases.


  We study the origin of electroweak symmetry under the assumption that
$SU(4)_{\rm C} \times SU(2)_{\rm L} \times SU(2)_{\rm R}$ is realized on a
five-dimensional space-time. The Pati-Salam type gauge symmetry is reduced to
$SU(3)_{\rm C} \times SU(2)_{\rm L} \times U(1)_{\rm R} \times U(1)_{\rm B-L}$
by orbifold breaking mechanism on the orbifold $S^1/Z_2$. The breakdown of
residual gauge symmetries occurs radiatively via the Coleman-Weinberg
mechanism, such that the $U(1)_{\rm R} \times U(1)_{\rm B-L}$ symmetry is
broken down to $U(1)_{\rm Y}$ by the vacuum expectation value of an $SU(2)_{\rm
L}$ singlet scalar field and the $SU(2)_{\rm L} \times U(1)_{\rm Y}$ symmetry
is broken down to the electric one $U(1)_{\rm EM}$ by the vacuum expectation
value of an $SU(2)_{\rm L}$ doublet scalar field regarded as the Higgs doublet.
The negative Higgs squared mass term is originated from an interaction between
the Higgs doublet and an $SU(2)_{\rm L}$ singlet scalar field as a Higgs
portal. The vacuum stability is recovered due to the contributions from
Kaluza-Klein modes of gauge bosons.


  Mislabeled, duplicated, or biased data in real-world scenarios can lead to
prolonged training and even hinder model convergence. Traditional solutions
prioritizing easy or hard samples lack the flexibility to handle such a variety
simultaneously. Recent work has proposed a more reasonable data selection
principle by examining the data's impact on the model's generalization loss.
However, its practical adoption relies on less principled approximations and
additional holdout data. This work solves these problems by leveraging a
lightweight Bayesian treatment and incorporating off-the-shelf zero-shot
predictors built on large-scale pre-trained models. The resulting algorithm is
efficient and easy to implement. We perform extensive empirical studies on
challenging benchmarks with considerable data noise and imbalance in the online
batch selection scenario, and observe superior training efficiency over
competitive baselines. Notably, on the challenging WebVision benchmark, our
method can achieve similar predictive performance with significantly fewer
training iterations than leading data selection methods.


  We describe a scheme, operating in a manner analogous to a reversed Raman
output coupler, for measuring the phase-sensitive quadrature statistics of an
atom laser beam. This scheme allows for the transferral of the atomic field
statistics to an optical field, for which the quantum statistics may then be
measured using the well-developed technology of optical homodyne measurement.


  We assemble 3524 quasars from Sloan Digital Sky Survey (SDSS) with repeated
observations to search for variations of narrow C IV1548,1551 and Mg
II2796,2803 absorption doublets in spectral regions shortward of 7000 Ang at
the observed frame, which corresponds to time-scales of about 150 ~ 2643 days
at quasar rest frame. In these quasar spectra, we detect 3580 C IV absorption
systems with z_{abs} = 1.5188 ~ 3.5212, and 1809 Mg II absorption systems with
z_{abs} = 0.3948 ~ 1.7167. In term of the absorber velocity (beta) distribution
at quasar rest frame, we find a substantial number of C IV absorbers with
beta<0.06, which might be connected to the absorptions of quasar outflows. The
outflow absorptions peak at v~2000 km/s and drop rapidly below the peak value.
Among 3580 C IV absorption systems, 52 systems (~ 1.5%) show obvious variations
in equivalent widths at the absorber rest frame (Wr): 16 enhanced, 16 emerged,
12 weaken, and 8 disappeared systems, respectively. We find that changes in
Wr548 are neither related to time-scales of the two SDSS observations, nor to
absorber velocities at the quasar rest frame. Variable absorptions in
low-ionization species are important to constraint the physical conditions of
absorbing gas. There are two variable Mg II absorption systems measured from
SDSS spectra detected by Hacker et al. However, in our Mg II$ absorption
sample, we find that neither shows variable absorption with confident levels of
>4sigma for lambda2796 lines and >3sigma for lambda2803 lines.


  In this paper, we propose a method for user Finger Vein Authentication (FVA)
as a biometric system. Using the discriminative features for classifying theses
finger veins is one of the main tips that make difference in related works,
Thus we propose to learn a set of representative features, based on
autoencoders. We model the user finger vein using a Gaussian distribution.
Experimental results show that our algorithm perform like a state-of-the-art on
SDUMLA-HMT benchmark.


  Mobile telepresence robots (MTRs) allow people to navigate and interact with
a remote environment that is in a place other than the person's true location.
Thanks to the recent advances in 360 degree vision, many MTRs are now equipped
with an all-degree visual perception capability. However, people's visual field
horizontally spans only about 120 degree of the visual field captured by the
robot. To bridge this observability gap toward human-MTR shared autonomy, we
have developed a framework, called GHAL360, to enable the MTR to learn a
goal-oriented policy from reinforcements for guiding human attention using
visual indicators. Three telepresence environments were constructed using
datasets that are extracted from Matterport3D and collected from a real robot
respectively. Experimental results show that GHAL360 outperformed the baselines
from the literature in the efficiency of a human-MTR team completing target
search tasks.


  This article provides an original understanding of the behavior of a class of
graph-oriented semi-supervised learning algorithms in the limit of large and
numerous data. It is demonstrated that the intuition at the root of these
methods collapses in this limit and that, as a result, most of them become
inconsistent. Corrective measures and a new data-driven parametrization scheme
are proposed along with a theoretical analysis of the asymptotic performances
of the resulting approach. A surprisingly close behavior between theoretical
performances on Gaussian mixture models and on real datasets is also
illustrated throughout the article, thereby suggesting the importance of the
proposed analysis for dealing with practical data. As a result, significant
performance gains are observed on practical data classification using the
proposed parametrization.


  An essential part of cyber-physical systems is the online evaluation of
real-time data streams. Especially in systems that are intrinsically
safety-critical, a dedicated monitoring component inspecting data streams to
detect problems at runtime greatly increases the confidence in a safe
execution. Such a monitor needs to be based on a specification language capable
of expressing complex, high-level properties using only the accessible
low-level signals. Moreover, tight constraints on computational resources
exacerbate the requirements on the monitor. Thus, several existing approaches
to monitoring are not applicable due to their dependence on an operating
system. We present an FPGA-based monitoring approach by compiling an RTLola
specification into synthesizable VHDL code. RTLola is a stream-based
specification language capable of expressing complex real-time properties while
providing an upper bound on the execution time and memory requirements. The
statically determined memory bound allows for a compilation to an FPGA with a
fixed size. An advantage of FPGAs is a simple integration process in existing
systems and superb executing time. The compilation results in a highly parallel
implementation thanks to the modular nature of RTLola specifications. This
further increases the maximal event rate the monitor can handle.


  In 2018, we reported our discovery of a dozen quiescent X-ray binaries in the
central parsec (pc) of the Galaxy (Hailey et al. 2018). In a recent follow-up
paper (Mori et al. 2021), we published an extended analysis of these sources
and other X-ray binaries (XRBs) in the central pc and beyond, showing that most
if not all of the 12 non-thermal sources are likely black hole low-mass X-ray
binary (BH-LMXB) candidates. In response, Maccarone et al. 2022 (TM22
hereafter) argued, primarily on the claim that neutron star low-mass X-ray
binaries (NS-LMXBs) often do not have short outburst recurrence times (<~ 10
yr), that they cannot be excluded as a designation for the 12 quiescent X-ray
binary sources. TM22 cites three main factors in their study: (1) X-ray
outburst data of NS transients detected by RXTE and MAXI, (2) the Galactic
population of NS-LMXBs, and (3) (persistently) quiescent NS-LMXBs in globular
clusters. We address these arguments of TM22 and correct their
misunderstandings of our work and the literature, even though most of these
points have already been thoroughly addressed by Mori et al. 2021. We also
correct TM22's assertion that our arguments are based solely on NS transients'
recurrence times.


  The advent of cost effective cloud computing over the past decade and
ever-growing accumulation of high-fidelity clinical data in a modern hospital
setting is leading to new opportunities for translational medicine. Machine
learning is driving the appetite of the research community for various types of
signal data such as patient vitals. Health care systems, however, are ill
suited for massive processing of large volumes of data. In addition, due to the
sheer magnitude of the data being collected, it is not feasible to retain all
of the data in health care systems in perpetuity. This gold mine of information
gets purged periodically thereby losing invaluable future research
opportunities. We have developed a highly scalable solution that: a) siphons
off patient vital data on a nightly basis from on-premises bio-medical systems
to a cloud storage location as a permanent archive, b) reconstructs the
database in the cloud, c) generates waveforms, alarms and numeric data in a
research-ready format, and d) uploads the processed data to a storage location
in the cloud ready for research.
  The data is de-identified and catalogued such that it can be joined with
Electronic Medical Records (EMR) and other ancillary data types such as
electroencephalogram (EEG), radiology, video monitoring etc. This technique
eliminates the research burden from health care systems. This highly scalable
solution is used to process high density patient monitoring data aggregated by
the Philips Patient Information Center iX (PIC iX) hospital surveillance system
for archival storage in the Philips Data Warehouse Connect enterprise-level
database. The solution is part of a broader platform that supports a secure
high performance clinical data science platform.


  Discrete-time quantum walks, quantum generalizations of classical random
walks, provide a framework for quantum information processing, quantum
algorithms and quantum simulation of condensed matter systems. The key property
of quantum walks, which lies at the heart of their quantum information
applications, is the possibility for a parametric quantum speed-up in
propagation compared to classical random walks. In this work we study
propagation of quantum walks on percolation-generated two-dimensional random
lattices. In large-scale simulations of topological and trivial split-step
walks, we identify distinct pre-diffusive and diffusive behaviors at different
time scales. Importantly, we show that even arbitrarily weak concentrations of
randomly removed lattice sites give rise to a complete breakdown of the
superdiffusive quantum speed-up, reducing the motion to ordinary diffusion. By
increasing the randomness, quantum walks eventually stop spreading due to
Anderson localization. Near the localization threshold, we find that the
quantum walks become subdiffusive. The fragility of quantum speed-up implies
dramatic limitations for quantum information applications of quantum walks on
random geometries and graphs.


  Inelastic x-ray scattering (IXS) with very high (meV) energy resolution has
become a valuable spectroscopic tool, complementing the well established
coherent inelastic neutron scattering (INS) technique for phonon dispersion
investigations. In the study of crystalline systems IXS is a viable alternative
to INS, especially in cases where only small samples are available. Using IXS,
we have measured the phonon dispersion of Nd_{1.86}Ce_{0.14}CuO_{4+\delta}
along the [x,0,0] and [x,x,0] in-plane directions. Compared to the undoped
parent compound, the two highest longitudinal optical (LO) phonon branches are
shifted to lower energies because of Coulomb-screening effects brought about by
the doped charge carriers. An additional anomalous softening of the highest
branch is observed around q=(0.2,0,0). This anomalous softening, akin to what
has been observed in other compounds, provides evidence for a strong
electron-phonon coupling in the electron-doped high-temperature
superconductors.


  Following "Boundary Value Problems" by Gakhov, we present basic details of
the Cauchy Type Integral and its Jump Decomposition. We also contextualize its
place and importance in Geometric Function Theory, and efforts to define these
integrals over Quasicircles. Finally, we briefly touch on Faber series in
relation to the Jump Problem following the work of H. Tietz.


  We investigate the structure of geometrically ruled surfaces whose
anti-canonical class is big. As an application we show that the Picard group of
a normal projective surface whose anti-canonical class is nef and big is a free
abelian group of finite rank.


  We locate a supersymmetry breaking hidden sector and supersymmetric standard
model on different lattice points of an orbifold moose. The hidden sector is
encoded in a set of current correlators and the effects of the current
correlators are mediated by the lattice site gauge groups with "lattice
hopping" functions and through the bifundamental matter that links the lattice
sites together. We show how the gaugino mass, scalar mass and Casimir energy of
the lattice can be computed for a general set of current correlators and then
give specific formulas when the hidden sector is specified to be a generalised
messenger sector coupled to a supersymmetry breaking spurion. The results
reproduce the effect of five dimensional gauge mediation from a purely four
dimensional construction.


  Two possible physical mechanisms of superconductivity suppression at
superconductor/antiferromagnet (S/AF) interfaces, which work even for
interfaces with compensated antiferromagnets, were reported. One of them
suggests that the Neel order of the AF induces rapidly oscillating spin-triplet
correlations in the S layer. They are called Neel triplets, and they suppress
singlet superconductivity. Nonmagnetic disorder destroys this type of triplet
correlations. As a result, the critical temperature of the S/AF bilayer grows
with impurity strength. The second mechanism, on the contrary, suggests that
nonmagnetic impurity scattering suppresses superconductivity in S/AF hybrids.
The predictions were made in the framework of two different quasiclassical
approaches [G. A. Bobkov et al. Phys. Rev. B 106, 144512 (2022) and E. H. Fyhn
et al. arXiv:2210.09325]. Here we suggest the unified theory of the proximity
effect in thin-film S/AF structures, which incorporates both pictures as
limiting cases, and we study the proximity effect at S/AF interfaces for
arbitrary impurity strength, chemical potential, and the value of the Neel
exchange field.


  Tidal Downsizing is the modern version of the Kuiper (1951) scenario of
planet formation. Detailed simulations of self-gravitating discs, gas
fragments, dust grain dynamics, and planet evolutionary calculations are
summarised here and used to build a predictive planet formation model and
population synthesis. A new interpretation of exoplanetary and debris disc
data, the Solar System's origins, and the links between planets and brown
dwarfs is offered. This interpretation is contrasted with the current
observations and the predictions of the Core Accretion theory. Observations
that can distinguish the two scenarios are pointed out. In particular, Tidal
Downsizing predicts that presence of debris discs, sub-Neptune mass planets,
planets more massive than $\sim 5$~Jupiter masses and brown dwarfs should not
correlate strongly with the metallicity of the host. For gas giants of $\sim$
Saturn to a few Jupiter mass, a strong host star metallicity correlation is
predicted only inwards of a few AU from the host. Composition of massive cores
is predicted to be dominated by rock rather than ices. Debris discs made by
Tidal Downsizing are distinct from those made by Core Accretion at birth: they
have an innermost edge always larger than about 1 au, have smaller total masses
and are usually in a dynamically excited state. It is argued that planet
formation in surprisingly young or very dynamic systems such as HL Tau and
Kepler-444 may be a signature of Tidal Downsizing. Open questions and potential
weaknesses of the hypothesis are pointed out.


  The modeling of supercooled liquids approaching dynamic arrest has a long
tradition, which is documented through a plethora of competing theoretical
approaches. Here, we review the modeling of supercooled liquids in terms of
dynamic "defects", also called excitations or soft spots, that are able to
sustain motion. To this end, we consider a minimal statistical mechanics
description in terms of active regions with the order parameter related to
their typical size. This is the basis for both Adam-Gibbs and dynamical
facilitation theory, which differ in their relaxation mechanism as the liquid
is cooled: collective motion of more and more particles vs. concerted
hierarchical motion over larger and larger length scales. For the latter,
dynamic arrest is possible without a growing static correlation length, and we
sketch the derivation of a key result: the parabolic law for the structural
relaxation time. We critically discuss claims in favor of a growing static
length and argue that the resulting scenarios for pinning and dielectric
relaxation are in fact compatible with dynamic facilitation.


  Observables of cosmic structures are usually not the underlying matter field
but biased tracers of matter, such as galaxies or halos. We show how the bias
found in Newtonian N-body simulations can be interpreted in terms of the
weak-field limit of General Relativity (GR). For this we employ standard
Newtonian simulations of cold dark matter and incorporate GR/radiation via a
weak-field dictionary that we have recently developed. We find that even when a
simple local biasing scheme is employed in the Newtonian simulation, the
relativistic bias becomes inherently scale-dependent due to the presence of
radiation and GR corrections. This scale-dependence could be in principle
observed on large scales in upcoming surveys. As a working example, we apply
our methodology to Newtonian simulations for the spherical collapse and recover
permille-level agreement between the approaches for extracting the relativistic
bias on all considered scales.


  Effects of lattice distortion on the magnetic ground state of YTiO$_3$ and
LaiO$_3$ are investigated on the basis accurate tight-binding parametrization
of the $t_{2g}$ electronic structure extracted from the local-density
approximation. The complexity of these compounds is related with the fact that
the $t_{2g}$-level splitting, caused by lattice distortions, is comparable with
the energies of superexchange and spin-orbit interactions. Therefore, all these
interactions are equally important and should be treated on an equal footing.
The Hartree-Fock approximation fails to provide a coherent description
simultaneously for YTiO$_3$ and LaTiO$_3$, and it is essential to go beyond.


  In this paper we revisit the notion of the "minus logarithm of stationary
probability" as a generalized potential in nonequilibrium systems and attempt
to illustrate its central role in an axiomatic approach to stochastic
nonequilibrium thermodynamics of complex systems. It is demonstrated that this
quantity arises naturally through both monotonicity results of Markov processes
and as the rate function when a stochastic process approaches a deterministic
limit. We then undertake a more detailed mathematical analysis of the
consequences of this quantity, culminating in a necessary and sufficient
condition for the criticality of stochastic systems. This condition is then
discussed in the context of recent results about criticality in biological
systems


  The star 55 Cancri was one of the first known exoplanet hosts and each of the
planets in this system is remarkable. Planets b and c are in a near 1:3
resonance. Planet d has a 14.5 year orbit, and is one of the longest known
orbital periods for a gas giant planet. Planet e has a mass of 8 M? and
transits this bright star, providing a unique case for modeling of the interior
structure and atmospheric composition of an exoplanet. Planet f resides in the
habitable zone of the star. If the planets are approximately co-planar, then by
virtue of having one transiting planet, this is a system where the Doppler
technique has essentially measured the true mass of the planets, rather than
just Msin i. The unfolding history of planet discovery for this system provides
a good example of the challenges and importance of understanding the star to
understand the planets.


  Nasal adhesions are a known postoperative complication following surgical
procedures for nasal airway obstruction (NAO); and are a common cause of
surgical failure, with patients often reporting significant NAO, despite
relatively minor adhesion size. Division of such nasal adhesions often provides
much greater relief than anticipated, based on the minimal reduction in
cross-sectional area associated with the adhesion. The available literature
regarding nasal adhesions provides little evidence examining their quantitative
and qualitative effects on nasal airflow using objective measures. This study
examined the impact of nasal adhesions at various anatomical sites on nasal
airflow and mucosal cooling using computational fluid dynamics (CFD). A
high-resolution CT scan of the paranasal sinuses of a 25-year-old, healthy
female patient was segmented to create a three-dimensional nasal airway model.
Virtual nasal adhesions of 2.5~mm diameter were added to various locations
within the nasal cavity, representing common sites seen following NAO surgery.
A series of models with single adhesions were created. CFD analysis was
performed on each model and compared with a baseline no-adhesion model,
comparing airflow and heat and mass transfer. The nasal adhesions resulted in
no significant change in bulk airflow patterns through the nasal cavity.
However, significant changes were observed in local airflow and mucosal cooling
around and immediately downstream to the nasal adhesions. These were most
evident with anterior nasal adhesions at the internal valve and anterior
inferior turbinate.


  At present, whenever we work in newtonian mechanics we consider momentum to
be a three-dimensional vector or a 4-dimensional one when we work in
relativistic mechanics. However, this mathematical vector model has barely 200
years and its complete installation in the physics is hardly more than 100
years old. In classical mechanics, based on point particles with mass, momentum
is considered as a vector because the speed of the particle is applied on a
single point and the mass is defined only at that point. When the extension to
classical rigid solids is made, that is, to solids that keep their shape, being
so that the speed is defined to be the same for each and every point where the
solid is defined, then the viewpoint of momentum as a vector is the simplest
and more convenient approach. Relativity (restricted and general) was developed
in the decades of 1900 and 1910 and quantum mechanics in the decades of 1920
and 1930. Relativity then impedes the existence of classical rigid solids. The
maximum speed for the transmission of signals prevents a body from varying its
status without being deformed. On the other hand, quantum physics breaks up
with the concept of point particles with mass, substituting this model for the
one of the wave function defined in an extensive region of the space
(space-time in the quantum relativity). Can we really define, the moment of a
relativistic solid or of a wave function with a simple vector?


  By patterning mesoscopic crystals of Bi2Sr2CaCu2O8 (BSCCO) into
electromagnetic resonators the oscillations of a large number of intrinsic
Josephson junctions can be synchronized into a macroscopic coherent state
accompanied by the emission of strong continuous wave THz-radiation. The
temperature dependence of the emission is governed by the interplay of
self-heating in the resonator and by re-trapping of intrinsic Josephson
junctions which can yield a strongly nonmonotonic temperature dependence of the
emission power. Furthermore, proper shaping of the resonators yields THzsources
with voltage-tunable emission frequencies.


  In this paper, we propose a method for measuring the similarity low sample
tabular data with synthetically generated data with a larger number of samples
than original. This process is also known as data augmentation. But
significance levels obtained from non-parametric tests are suspect when sample
size is small. Our method uses a combination of geometry, topology and robust
statistics for hypothesis testing in order to compare the validity of generated
data. We also compare the results with common global metric methods available
in the literature for large sample size data.


  Convolutional Neural Networks are widely used in various machine learning
domains. In image processing, the features can be obtained by applying 2D
convolution to all spatial dimensions of the input. However, in the audio case,
frequency domain input like Mel-Spectrogram has different and unique
characteristics in the frequency dimension. Thus, there is a need for a method
that allows the 2D convolution layer to handle the frequency dimension
differently. In this work, we introduce SubSpectral Normalization (SSN), which
splits the input frequency dimension into several groups (sub-bands) and
performs a different normalization for each group. SSN also includes an affine
transformation that can be applied to each group. Our method removes the
inter-frequency deflection while the network learns a frequency-aware
characteristic. In the experiments with audio data, we observed that SSN can
efficiently improve the network's performance.


  We compare the higher twist (HT) contribution to the unpolarized structure
function $F_3$ with that one to the nonsinglet combination g_1^p - g_1^n of the
polarized proton and neutron structure functions using the assumption that the
HT contributions to the Gross-Llewellyn Smith and the Bjorken sum rules are
similar. We have found, that the relation 1/3x h^{xF_3}(x) ~ 6/g_A h^{g_1^p -
g_1^n}(x) is valid for x > 0.1 and for x > 0.2 in the case of LO and NLO QCD
approximations, respectively.


  Partial MDS (PMDS) codes are erasure codes combining local (row) correction
with global additional correction of entries, while Sector-Disk (SD) codes are
erasure codes that address the mixed failure mode of current RAID systems. It
has been an open problem to construct general codes that have the PMDS and the
SD properties, and previous work has relied on Monte-Carlo searches. In this
paper, we present a general construction that addresses the case of any number
of failed disks and in addition, two erased sectors. The construction requires
a modest field size. This result generalizes previous constructions extending
RAID~5 and RAID~6.


  We provide a complete motivic decomposition of a twisted form of a smooth
hyperplane section of Gr(3,6). This variety is a norm variety corresponding to
a (3,3)-symbol.


  In this paper, a subgroup least squares and a convex clustering are
introduced for inferring a partially heterogenous linear regression that has
potential application in the areas of precision marketing and precision
medicine. The homogenous parameter and the subgroup-average of the heterogenous
parameters can be consistently estimated by the subgroup least squares, without
need of the sparsity assumption on the heterogenous parameters. The
heterogenous parameters can be consistently clustered via the convex
clustering. Unlike the existing methods for regression clustering, our
clustering procedure is a standard mean clustering, although the model under
study is a type of regression, and the corresponding algorithm only involves
low dimensional parameters. Thus, it is simple and stable even if the sample
size is large. The advantage of the method is further illustrated via
simulation studies and the analysis of car sales data.


  The interaction of strong near-infrared (NIR) laser pulses with wide-bandgap
dielectrics produces high harmonics in the extreme ultraviolet (XUV) wavelength
range. These observations have opened up the possibility of attosecond
metrology in solids, which would benefit from a precise measurement of the
emission times of individual harmonics with respect to the NIR laser field.
Here we show that, when high-harmonics are detected from the input surface of a
magnesium oxide crystal, a bichromatic probing of the XUV emission shows a
clear synchronization largely consistent with a semiclassical model of
electron-hole recollisions in bulk solids. On the other hand, the bichromatic
spectrogram of harmonics originating from the exit surface of the 200
$\mu$m-thick crystal is strongly modified, indicating the influence of laser
field distortions during propagation. Our tracking of sub-cycle electron and
hole re-collisions at XUV energies is relevant to the development of
solid-state sources of attosecond pulses.


  This short paper introduces a new way by which to design production system
rules. An indirect encoding scheme is presented which views such rules as
protein complexes produced by the temporal behaviour of an artificial genetic
regulatory network. This initial study begins by using a simple Boolean
regulatory network to produce traditional ternary-encoded rules before moving
to a fuzzy variant to produce real-valued rules. Competitive performance is
shown with related genetic regulatory networks and rule-based systems on
benchmark problems.


  We investigate recent transformer networks pre-trained for automatic speech
recognition for their ability to detect speaker and language changes in speech.
We do this by simply adding speaker (change) or language targets to the labels.
For Wav2vec2 pre-trained networks, we also investigate if the representation
for the speaker change symbol can be conditioned to capture speaker identity
characteristics. Using a number of constructed data sets we show that these
capabilities are definitely there, with speaker recognition equal error rates
of the order of 10% and language detection error rates of a few percent. We
will publish the code for reproducibility.


  For high speed ultra-wideband (UWB) communication systems, the multipath
interference exhibits a primary obstacle to improve transmission performance.
In order to enhance the signal to interference plus noise ratio (SINR) in the
receiver, a partial template receiver is proposed in this paper. Instead of
using the conventional template, the model in this paper adopts a partial
template to demodulate signals. To analyze the performance of the proposed
receiver, bit error rate (BER) formulation of IR-UWB systems in the presence of
multipath interference, multiuser interference and addictive white Gaussian
noise (AWGN) is derived in IEEE 802.15.4a channel models. Simulation results
show that, compared with the conventional correlation receiver, the proposed
receiver can achieve a better BER performance for high Eb/N0, which falls in
the conventional used Eb/N0 range.


  The 3D matter power spectrum, $P_{\delta}(k,z)$ is a fundamental quantity in
the analysis of cosmological data such as large-scale structure, 21cm
observations, and weak lensing. Existing computer models (Boltzmann codes) such
as CLASS can provide it at the expense of immoderate computational cost. In
this paper, we propose a fast Bayesian method to generate the 3D matter power
spectrum, for a given set of wavenumbers, $k$ and redshifts, $z$. Our code
allows one to calculate the following quantities: the linear matter power
spectrum at a given redshift (the default is set to 0); the non-linear 3D
matter power spectrum with/without baryon feedback; the weak lensing power
spectrum. The gradient of the 3D matter power spectrum with respect to the
input cosmological parameters is also returned and this is useful for
Hamiltonian Monte Carlo samplers. The derivatives are also useful for Fisher
matrix calculations. In our application, the emulator is accurate when
evaluated at a set of cosmological parameters, drawn from the prior, with the
fractional uncertainty, $\Delta P_{\delta}/P_{\delta}$ centred on 0. It is also
$\sim 300$ times faster compared to CLASS, hence making the emulator amenable
to sampling cosmological and nuisance parameters in a Monte Carlo routine. In
addition, once the 3D matter power spectrum is calculated, it can be used with
a specific redshift distribution, $n(z)$ to calculate the weak lensing and
intrinsic alignment power spectra, which can then be used to derive constraints
on cosmological parameters in a weak lensing data analysis problem. The
software ($\texttt{emuPK}$) can be trained with any set of points and is
distributed on Github, and comes with a pre-trained set of Gaussian Process
(GP) models, based on 1000 Latin Hypercube (LH) samples, which follow roughly
the current priors for current weak lensing analyses.


  We studied the weekly number and the growth decline rates of COVID-19 deaths
of the period from October 31, 2022 to February 9, 2023, in Italy, finding that
that COVID 19 winter wave reached its peak during the three holiday weeks from
December 16, 2002 to January 5, 2023, and it was definitely trending downward,
returning to the same number of deaths of the end of October 2022, in the first
week February 2023. During this period of 15 weeks, that wave caused a number
of deaths as large as 8,526. Its percentage average growth rate was +7.89
deaths per week (10 weeks), while the percentage average weekly decline rate
was -12.32 (5 weeks). At the time of writing of this paper, Italy was
experiencing a new COVID 19 wave, with the latest 7 weekly bulletins available
at that date (from October 26, 2023 to December 14, 2023) showing that the
deaths had climbed from 148 to 322. The percentage weekly growth rate had risen
by +14.08 deaths, on average. In the hypothesis that this 2023-2024 wave will
have a total duration similar to that of 2022 2023, with a comparable extension
of both the growing period and of the decline period and similar growth decline
rates, a prediction can be cast regarding the number of COVID 19 deaths of the
period from the end of October 2023 to the beginning of February 2024. They
should be in the neighbourhood of 4100,4200. A preliminary assessment of this
forecast, based on 11 of the 15 weeks of the period, has confirmed the accuracy
of the approach.


  The various ionization mechanisms at play in active galactic nuclei (AGN) and
quasars have been well studied, but relatively little has been done to
separately investigate the contributions of these ionization mechanisms within
the host galaxy and outflowing components. Using Gemini integral field
spectroscopy (IFS) data, we study the ionization properties of these two
components in four nearby ($z \lesssim$ 0.2) radio-quiet Type 1 quasars.
Emission line ratios and widths are employed to identify the dominant
ionization mechanisms for the host and outflow components in each object. We
find that photoionization by the AGN often dominates the ionization of both
gaseous components in these systems. In three cases, the outflowing gas is more
highly ionized than the gas in the host, indicating that it is more strongly
exposed to the ionizing radiation field of the AGN. In two objects, a positive
correlation between the line widths and line ratios in the outflowing gas
component indicates that shocks with velocities of order 100 $-$ 500 km
s$^{-1}$ may also be contributing to the ionization and heating of the
outflowing gas component. The line ratios in the outflowing gas of one of these
two objects also suggest a significant contribution from photoionization by
hot, young stars in the portion of the outflow that is closest to star-forming
regions in the host galaxy component. The data thus favor photoionization by
hot stars in the host galaxy rather than stars formed in the outflow itself.


  Holographic duality implies that the geometric properties of the
gravitational bulk theory should be encoded in the dual field theory. These
naturally include the metric on dimensions that become compact near the
conformal boundary, as is the case for any asymptotically locally
$\text{AdS}_n\times\mathbb{S}^k$ spacetime. Almost all previous work on metric
reconstruction ignores these dimensions and would thus at most apply to
dimensionally-reduced metrics. In this work, we generalize the approach to bulk
reconstruction using light-cone cuts and propose a prescription to obtain the
full higher-dimensional metric of generic spacetimes up to an overall conformal
factor. We first extend the definition of light-cone cuts to include
information about the asymptotic compact dimensions, and show that the full
conformal metric can be recovered from these extended cuts. We then give a
prescription for obtaining these extended cuts from the dual field theory. The
location of the usual cuts can still be obtained from bulk-point singularities
of correlators, and the new information in the extended cut can be extracted by
using appropriate combinations of operators dual to Kaluza-Klein modes of the
higher-dimensional bulk fields.


  In Chordal/Interval Vertex Deletion we ask how many vertices one needs to
remove from a graph to make it chordal (respectively: interval). We study these
problems under the parameterization by treewidth $tw$ of the input graph $G$.
On the one hand, we present an algorithm for Chordal Vertex Deletion with
running time $2^{O(tw)} \cdot |V(G)|$, improving upon the running time
$2^{O(tw^2)} \cdot |V(G)|^{O(1)}$ by Jansen, de Kroon, and Wlodarczyk
(STOC'21). When a tree decomposition of width $tw$ is given, then the base of
the exponent equals $2^{\omega-1}\cdot 3 + 1$. Our algorithm is based on a
novel link between chordal graphs and graphic matroids, which allows us to
employ the framework of representative families. On the other hand, we prove
that the known $2^{O(tw \log tw)} \cdot |V(G)|$-time algorithm for Interval
Vertex Deletion cannot be improved assuming Exponential Time Hypothesis.


  We develop assistive agents based on Large Language Models (LLMs) that aid
interlocutors in business negotiations. Specifically, we simulate business
negotiations by letting two LLM-based agents engage in role play. A third LLM
acts as a remediator agent to rewrite utterances violating norms for improving
negotiation outcomes. We introduce a simple tuning-free and label-free
In-Context Learning (ICL) method to identify high-quality ICL exemplars for the
remediator, where we propose a novel select criteria, called value impact, to
measure the quality of the negotiation outcomes. We provide rich empirical
evidence to demonstrate its effectiveness in negotiations across three
different negotiation topics. The source code and the generated dataset will be
publicly available upon acceptance.


  This paper introduces DCC Sidekick, a web-based conversational AI tool that
enhances an existing LLM-powered C/C++ compiler by generating educational
programming error explanations. The tool seamlessly combines code display,
compile- and run-time error messages, and stack frame read-outs alongside an AI
interface, leveraging compiler error context for improved explanations. We
analyse usage data from a large Australian CS1 course, where 959 students
engaged in 11,222 DCC Sidekick sessions, resulting in 17,982 error explanations
over seven weeks. Notably, over 50% of interactions occurred outside business
hours, underscoring the tool's value as an always-available resource. Our
findings reveal strong adoption of AI-assisted debugging tools, demonstrating
their scalability in supporting extensive CS1 courses. We provide
implementation insights and recommendations for educators seeking to
incorporate AI tools with appropriate pedagogical safeguards.


  We present the description and initial results of the TRAPUM (TRAnsients And
PUlsars with MeerKAT) search for pulsars associated with supernova remnants
(SNRs), pulsar wind nebulae and unidentified TeV emission. The list of sources
to be targeted includes a large number of well-known candidate pulsar locations
but also new candidate SNRs identified using a range of criteria. Using the
64-dish MeerKAT radio telescope, we use an interferometric beamforming
technique to tile the potential pulsar locations with coherent beams which we
search for radio pulsations, above a signal-to-noise of 9, down to an average
flux density upper limit of 30 $\mu$Jy. This limit is target-dependent due to
the contribution of the sky and nebula to the system temperature. Coherent
beams are arranged to overlap at their 50 per cent power radius, so the
sensitivity to pulsars is not degraded by more than this amount, though
realistically averages around 65 per cent if every location in the beam is
considered. We report the discovery of two new pulsars; PSR J1831$-$0941 is an
adolescent pulsar likely to be the plerionic engine of the candidate PWN
G20.0+0.0, and PSR J1818$-$1502 appears to be an old and faint pulsar that we
serendipitously discovered near the centre of a SNR already hosting a compact
central object. The survey holds importance for better understanding of neutron
star birth rates and the energetics of young pulsars.


  GQA~\citep{hudson2019gqa} is a dataset for real-world visual reasoning and
compositional question answering. We found that many answers predicted by the
best vision-language models on the GQA dataset do not match the ground-truth
answer but still are semantically meaningful and correct in the given context.
In fact, this is the case with most existing visual question answering (VQA)
datasets where they assume only one ground-truth answer for each question. We
propose Alternative Answer Sets (AAS) of ground-truth answers to address this
limitation, which is created automatically using off-the-shelf NLP tools. We
introduce a semantic metric based on AAS and modify top VQA solvers to support
multiple plausible answers for a question. We implement this approach on the
GQA dataset and show the performance improvements. Code and data are available
in this link \url{https://github.com/luomancs/alternative_answer_set.git}.


  We prove that, for closed exact embedded Lagrangian submanifolds of cotangent
bundles, the homomorphism of homotopy groups induced by the stable Lagrangian
Gauss map vanishes. In particular, we prove that this map is null-homotopic for
all spheres. The key tool that we introduce in order to prove this is the
notion of twisted generating function and we show that every closed exact
Lagrangian can be described using such an object, by extending a doubling
argument developed in the setting of sheaf theory. Floer theory and sheaf
theory constrain the type of twisted generating functions that can appear to a
class which is closely related to Waldhausen's tube space, and our main result
follows by a theorem of B\"okstedt which computes the rational homotopy type of
the tube space.


  Previous radio observations of the galaxy cluster A3560 in the Shapley
Concentration showed complex radio emission associated with the brightest
cluster member.To understand its origin we observed it with the GMRT, the VLA
and ATCA at 240 and 610 MHz, 1.28,1.4, 2.3,4.8 and 8.4 GHz, and performed a
detailed morphological and spectral study of the radio emission associated with
the BCG. We also observed the cluster with XMM-Newton and Chandra to derive the
properties of the ICM. The radio emission of the N-E nucleus of the dumb-bell
BCG shows an active radio galaxy, plus aged diffuse emission, which is not
refurbished at present. Our Chandra data show that the radio active nucleus of
the BCG has extended X-ray emission, which we classify as a low-luminosity
corona. A residual image of the XMM-Newton brightness shows the presence of a
spiral-like feature, which we interpret as the signature of gas sloshing. The
presence of a subgroup is clear in the surface brightness residual map, and in
the XMM-Newton temperature analysis. The optical 2D analysis shows substructure
in A3560. A galaxy clump was found at the location of the X-ray subgroup, and
another group is present south of the cluster core, close to the spiral-like
feature. The aged part of the radio emission closely follows the spiral pattern
of the X-ray residual brightness distribution, while the two active radio lobes
are bent in a completely different direction. We conclude that the complex
radio emission associated with the cluster BCG is the result of a minor merger
event in A3560. The aged diffuse emission is strongly affected by the sloshing
motion in the ICM. On the other hand, the bent jets and lobes of the current
radio AGN activity may reflect a complex gas velocity field in the innermost
cluster regions and/or sloshing-induced oscillations in the motion of the cD
galaxy.


  We extend the classical Ambarzumyan's theorem to the quasi-periodic boundary
value problems by using only a part knowledge of one spectrum. We also weaken
slightly the Yurko's conditions on the first eigenvalue.


  An exact analytic form for the second virial coefficient, valid for the
entire range of temperature, is presented for the Lennard-Jones fluid in this
paper. It is derived by making variable transformation that gives rise to the
Hamiltonian mimicking a harmonic oscillator-like dynamics for negative energy.
It is given in terms of parabolic cylinder functions or confluent
hypergeometric functions. Exact limiting laws for the second virial coefficient
in the limits of low and high temperatuers are also deduced for the
Lennard-Jones fluid.


  Media streaming has been adopted for a variety of applications such as
entertainment, visualization, and design. Unlike video/audio streaming where
the content is usually consumed sequentially, 3D applications such as gaming
require streaming 3D assets to facilitate client-side interactions such as
object manipulation and viewpoint movement. Compared to audio and video
streaming, 3D streaming often requires larger data sizes and yet lower latency
to ensure sufficient rendering quality, resolution, and latency for perceptual
comfort. Thus, streaming 3D assets can be even more challenging than streaming
audios/videos, and existing solutions often suffer from long loading time or
limited quality.
  To address this critical and timely issue, we propose a
perceptually-optimized progressive 3D streaming method for spatial quality and
temporal consistency in immersive interactions. Based on the human visual
mechanisms in the frequency domain, our model selects and schedules the
streaming dataset for optimal spatial-temporal quality. We also train a neural
network for our model to accelerate this decision process for real-time
client-server applications. We evaluate our method via subjective studies and
objective analysis under varying network conditions (from 3G to 5G) and client
devices (HMD and traditional displays), and demonstrate better visual quality
and temporal consistency than alternative solutions.


  The traceless Ricci tensor $C_{ab}$ in 4-dimensional pseudo-Riemannian spaces
equipped with the metric of the neutral signature is analyzed. Its algebraic
classification is given. This classification uses the properties of $C_{ab}$
treated as a matrix. The Petrov-Penrose types of Pleba\'nski spinors associated
with the traceless Ricci tensor are given. Finally, the classification is
compared with a similar classification in the complex case.


  We have performed a multi-wavelength analysis of a mid-infrared (MIR) bubble
N37 and its surrounding environment. The selected 15$' \times$15$'$ area around
the bubble contains two molecular clouds (N37 cloud; V$_{lsr}\sim$37-43 km
s$^{-1}$, and C25.29+0.31; V$_{lsr}\sim$43-48 km s$^{-1}$) along the line of
sight. A total of seven OB stars are identified towards the bubble N37 using
photometric criteria, and two of them are spectroscopically confirmed as O9V
and B0V stars. Spectro-photometric distances of these two sources confirm their
physical association with the bubble. The O9V star is appeared to be the
primary ionizing source of the region, which is also in agreement with the
desired Lyman continuum flux analysis estimated from the 20 cm data. The
presence of the expanding HII region is revealed in the N37 cloud which could
be responsible for the MIR bubble. Using the $^{13}$CO line data and
photometric data, several cold molecular condensations as well as clusters of
young stellar objects (YSOs) are identified in the N37 cloud, revealing ongoing
star formation (SF) activities. However, the analysis of ages of YSOs and the
dynamical age of the HII region do not support the origin of SF due to the
influence of OB stars. The position-velocity analysis of $^{13}$CO data reveals
that two molecular clouds are inter-connected by a bridge-like structure,
favoring the onset of a cloud-cloud collision process. The SF activities (i.e.
the formation of YSOs clusters and OB stars) in the N37 cloud are possibly
influenced by the cloud-cloud collision.


  In this paper we introduce a new, simple and efficient numerical scheme for
the implementation of the freezing method for capturing similarity solutions in
partial differential equations. The scheme is based on an IMEX-Runge-Kutta
approach for a method of lines (semi-)discretization of the freezing partial
differential algebraic equation (PDAE). We prove second order convergence for
the time discretization at smooth solutions in the ODE-sense and we present
numerical experiments that show second order convergence for the full
discretization of the PDAE. As an example serves the multi-dimensional Burgers'
equation. By considering very different sizes of viscosity, Burgers' equation
can be considered as a prototypical example of general coupled
hyperbolic-parabolic PDEs. Numerical experiments show that our method works
perfectly well for all sizes of viscosity, suggesting that the scheme is indeed
suitable for capturing similarity solutions in general hyperbolic-parabolic
PDEs by direct forward simulation with the freezing method.


  Radio frequency interference (RFI) is a significant challenge faced by
today's radio astronomers. While most past efforts were devoted to cleaning the
RFI from the data, we develop a novel method for categorizing and cataloguing
RFI for forensic purpose. We present a classifier that categorizes RFI into
different types based on features extracted using Principal Component Analysis
(PCA) and Fourier analysis. The classifier can identify narrowband non-periodic
RFI above 2 sigma, narrowband periodic RFI above 3 sigma, and wideband
impulsive RFI above 5 sigma with F1 scores between 0.87 and 0.91 in simulation.
This classifier could be used to identify the sources of RFI as well as to
clean RFI contamination (particularly in pulsar search). In the long-term
analysis of the categorized RFI, we found a special type of drifting periodic
RFI that is detrimental to pulsar search. We also found evidences of an
increased rate of impulsive RFI when the telescope is pointing toward the
cities. These results demonstrate this classifier's potential as a forensic
tool for RFI environment monitoring of radio telescopes.


  The Vehicle Routing Problem (VRP) is a widely studied combinatorial
optimization problem and has been applied to various practical problems. While
the explainability for VRP is significant for improving the reliability and
interactivity in practical VRP applications, it remains unexplored. In this
paper, we propose RouteExplainer, a post-hoc explanation framework that
explains the influence of each edge in a generated route. Our framework
realizes this by rethinking a route as the sequence of actions and extending
counterfactual explanations based on the action influence model to VRP. To
enhance the explanation, we additionally propose an edge classifier that infers
the intentions of each edge, a loss function to train the edge classifier, and
explanation-text generation by Large Language Models (LLMs). We quantitatively
evaluate our edge classifier on four different VRPs. The results demonstrate
its rapid computation while maintaining reasonable accuracy, thereby
highlighting its potential for deployment in practical applications. Moreover,
on the subject of a tourist route, we qualitatively evaluate explanations
generated by our framework. This evaluation not only validates our framework
but also shows the synergy between explanation frameworks and LLMs. See
https://ntt-dkiku.github.io/xai-vrp for our code, datasets, models, and demo.


  In this paper, we present a notion of differential privacy (DP) for data that
comes from different classes. Here, the class-membership is private information
that needs to be protected. The proposed method is an output perturbation
mechanism that adds noise to the release of query response such that the
analyst is unable to infer the underlying class-label. The proposed DP method
is capable of not only protecting the privacy of class-based data but also
meets quality metrics of accuracy and is computationally efficient and
practical. We illustrate the efficacy of the proposed method empirically while
outperforming the baseline additive Gaussian noise mechanism. We also examine a
real-world application and apply the proposed DP method to the autoregression
and moving average (ARMA) forecasting method, protecting the privacy of the
underlying data source. Case studies on the real-world advanced metering
infrastructure (AMI) measurements of household power consumption validate the
excellent performance of the proposed DP method while also satisfying the
accuracy of forecasted power consumption measurements.


  We consider the inflation model of a singlet scalar field (sigma field) with
both quadratic and linear non-minimal couplings where unitarity is ensured up
to the Planck scale. We assume that a $Z_2$ symmetry for the sigma field is
respected by the scalar potential in Jordan frame but it is broken explicitly
by the linear non-minimal coupling due to quantum gravity. We discuss the
impacts of the linear non-minimal coupling on various dynamics from inflation
to low energy, such as a sizable tensor-to-scalar ratio, a novel reheating
process with quartic potential dominance, and suppressed physical parameters in
the low energy, etc. In particular, the linear non-minimal coupling leads to
the linear couplings of the sigma field to the Standard Model through the trace
of the energy-momentum tensor in Einstein frame. Thus, regarding the sigma
field as a decaying dark matter, we consider the non-thermal production
mechanisms for dark matter from the decays of Higgs and inflaton condensate and
show the parameter space that is compatible with the correct relic density and
cosmological constraints.


  For each dimension $d$, $d$-dimensional integral simplices with exactly one
interior integral point have bounded volume. This was first shown by Hensley.
Explicit volume bounds were determined by Hensley, Lagarias and Ziegler,
Pikhurko, and Averkov. In this paper we determine the exact upper volume bound
for such simplices and characterize the volume-maximizing simplices. We also
determine the sharp upper bound on the coefficient of asymmetry of an integral
polytope with a single interior integral point. This result confirms a
conjecture of Hensley from 1983. Moreover, for an integral simplex with
precisely one interior integral point, we give bounds on the volumes of its
faces, the barycentric coordinates of the interior integral point and its
number of integral points. Furthermore, we prove a bound on the lattice
diameter of integral polytopes with a fixed number of interior integral points.
The presented results have applications in toric geometry and in integer
optimization.


  The performance of a lossy data compression scheme for uniformly biased
Boolean messages is investigated via methods of statistical mechanics. Inspired
by a formal similarity to the storage capacity problem in the research of
neural networks, we utilize a perceptron of which the transfer function is
appropriately designed in order to compress and decode the messages. Employing
the replica method, we analytically show that our scheme can achieve the
optimal performance known in the framework of lossy compression in most cases
when the code length becomes infinity. The validity of the obtained results is
numerically confirmed.


  Following the emergence of many novel two-dimensional (2-D) materials beyond
graphene, interest has grown in exploring implications for fundamental physics
and practical applications, ranging from electronics, photonics, phononics, to
thermal management and energy storage. In this Colloquium, we first summarize
and compare the phonon properties, such as phonon dispersion and relaxation
time, of pristine 2-D materials with single layer graphene to understand the
role of crystal structure and dimension on thermal conductivity. We then
compare the phonon properties, contrasting idealized 2-D crystals, realistic
2-D crystals, and 3-D crystals, and synthesizing this to develop a physical
picture of how the sample size of 2-D materials affects their thermal
conductivity. The effects of geometry, such as number of layers, and nanoribbon
width, together with the presence of defects, mechanical strain, and substrate
interactions, on the thermal properties of 2-D materials are discussed.
Intercalation affects both the group velocities and phonon relaxation times of
layered crystals and thus tunes the thermal conductivity along both the
through-plane and basal-plane directions. We conclude with a discussion of the
challenges in theoretical and experimental studies of thermal transport in 2-D
materials. The rich and special phonon physics in 2-D materials make them
promising candidates for exploring novel phenomena such as topological phonon
effects and applications such as phononic quantum devices.


  We propose a design method for a robust safety filter based on Input
Constrained Control Barrier Functions (ICCBF) for car-like robots moving in
complex environments. A robust ICCBF that can be efficiently implemented is
obtained by learning a smooth function of the environment using Support Vector
Machine regression. The method takes into account steering constraints and is
validated in simulation and a real experiment.


  We show by numerical simulations that the correlation function of the random
field Ising model (RFIM) in the critical region in three dimensions has very
strong fluctuations and that in a finite volume the correlation length is not
self-averaging. This is due to the formation of a bound state in the underlying
field theory. We argue that this non perturbative phenomenon is not particular
to the RFIM in 3-d. It is generic for disordered systems in two dimensions and
may also happen in other three dimensional disordered systems.


  Physical qubits in experimental quantum information processors are inevitably
exposed to different sources of noise and imperfections, which lead to errors
that typically accumulate hindering our ability to perform long computations
reliably. Progress towards scalable and robust quantum computation relies on
exploiting quantum error correction (QEC) to actively battle these undesired
effects. In this work, we present a comprehensive study of crosstalk errors in
a quantum-computing architecture based on a single string of ions confined by a
radio-frequency trap, and manipulated by individually-addressed laser beams.
This type of errors affects spectator qubits that, ideally, should remain
unaltered during the application of single- and two-qubit quantum gates
addressed at a different set of active qubits. We microscopically model
crosstalk errors from first principles and present a detailed study showing the
importance of using a coherent vs incoherent error modelling and, moreover,
discuss strategies to actively suppress this crosstalk at the gate level.
Finally, we study the impact of residual crosstalk errors on the performance of
fault-tolerant QEC numerically, identifying the experimental target values that
need to be achieved in near-term trapped-ion experiments to reach the
break-even point for beneficial QEC with low-distance topological codes.


  Self-supervised learning of graph neural networks (GNN) is in great need
because of the widespread label scarcity issue in real-world graph/network
data. Graph contrastive learning (GCL), by training GNNs to maximize the
correspondence between the representations of the same graph in its different
augmented forms, may yield robust and transferable GNNs even without using
labels. However, GNNs trained by traditional GCL often risk capturing redundant
graph features and thus may be brittle and provide sub-par performance in
downstream tasks. Here, we propose a novel principle, termed adversarial-GCL
(AD-GCL), which enables GNNs to avoid capturing redundant information during
the training by optimizing adversarial graph augmentation strategies used in
GCL. We pair AD-GCL with theoretical explanations and design a practical
instantiation based on trainable edge-dropping graph augmentation. We
experimentally validate AD-GCL by comparing with the state-of-the-art GCL
methods and achieve performance gains of up-to $14\%$ in unsupervised, $6\%$ in
transfer, and $3\%$ in semi-supervised learning settings overall with 18
different benchmark datasets for the tasks of molecule property regression and
classification, and social network classification.


  The standard, scale-invariant, inflationary perturbation spectrum will be
modified if the effects of trans-Planckian physics are incorporated into the
dynamics of the matter field in a phenomenological manner, say, by the
modification of the dispersion relation. The spectrum also changes if we retain
the standard dynamics but modify the initial quantum state of the matter field.
We show that, given {\it any} spectrum of perturbations, it is possible to
choose a class of initial quantum states which can exactly reproduce this
spectrum with the standard dynamics. We provide an explicit construction of the
quantum state which will produce the given spectrum. We find that the various
modified spectra that have been recently obtained from `trans-Planckian
considerations' can be constructed from suitable squeezed states above the
Bunch-Davies vacuum in the standard theory. Hence, the CMB observations can, at
most, be useful in determining the initial state of the matter field in the
standard theory, but it can {\it not} help us to discriminate between the
various Planck scale models of matter fields. We study the problem in the
Schrodinger picture, clarify various conceptual issues and determine the
criterion for negligible back reaction due to modified initial conditions.


  This article provides, through theoretical analysis, an in-depth
understanding of the classification performance of the empirical risk
minimization framework, in both ridge-regularized and unregularized cases, when
high dimensional data are considered. Focusing on the fundamental problem of
separating a two-class Gaussian mixture, the proposed analysis allows for a
precise prediction of the classification error for a set of numerous data
vectors $\mathbf{x} \in \mathbb R^p$ of sufficiently large dimension $p$. This
precise error depends on the loss function, the number of training samples, and
the statistics of the mixture data model. It is shown to hold beyond Gaussian
distribution under some additional non-sparsity condition of the data
statistics. Building upon this quantitative error analysis, we identify the
simple square loss as the optimal choice for high dimensional classification in
both ridge-regularized and unregularized cases, regardless of the number of
training samples.


  Social media has been on the vanguard of political information diffusion in
the 21st century. Most studies that look into disinformation, political
influence and fake-news focus on mainstream social media platforms. This has
inevitably made English an important factor in our current understanding of
political activity on social media. As a result, there has only been a limited
number of studies into a large portion of the world, including the largest,
multilingual and multi-cultural democracy: India. In this paper we present our
characterisation of a multilingual social network in India called ShareChat. We
collect an exhaustive dataset across 72 weeks before and during the Indian
general elections of 2019, across 14 languages. We investigate the cross
lingual dynamics by clustering visually similar images together, and exploring
how they move across language barriers. We find that Telugu, Malayalam, Tamil
and Kannada languages tend to be dominant in soliciting political images (often
referred to as memes), and posts from Hindi have the largest cross-lingual
diffusion across ShareChat (as well as images containing text in English). In
the case of images containing text that cross language barriers, we see that
language translation is used to widen the accessibility. That said, we find
cases where the same image is associated with very different text (and
therefore meanings). This initial characterisation paves the way for more
advanced pipelines to understand the dynamics of fake and political content in
a multi-lingual and non-textual setting.


  We investigate the radio properties of a sample of 53 sources selected at 850
$\mu$m from the SCUBA-2 Cosmology Legacy Survey using new deep, low-frequency
radio imaging of the Lockman Hole field from the Low Frequency Array. Combining
these data with additional radio observations from the GMRT and the JVLA, we
find a variety of radio spectral shapes and luminosities within our sample
despite their similarly bright submillimetre flux densities. We characterise
their spectral shapes in terms of multi-band radio spectral indices. Finding
strong spectral flattening at low frequencies in ~20% of sources, we
investigate the differences between sources with extremely flat low-frequency
spectra and those with `normal' radio spectral indices. As there are no other
statistically significant differences between the two subgroups of our sample
as split by the radio spectral index, we suggest that any differences are
undetectable in galaxy-averaged properties that we can observe with our
unresolved images, and likely relate to galaxy properties that we cannot
resolve, on scales $\lesssim$ 1 kpc. We attribute the observed spectral
flattening in the radio to free-free absorption, proposing that those sources
with significant low-frequency spectral flattening have a clumpy distribution
of star-forming gas. We estimate an average spatial extent of absorbing
material of at most several hundred parsecs to produce the levels of absorption
observed in the radio spectra. This estimate is consistent with the
highest-resolution observations of submillimetre galaxies in the literature,
which find examples of non-uniform dust distributions on scales of ~100 pc,
with evidence for clumps and knots in the interstellar medium. Additionally, we
find two bright (> 6 mJy) submm sources undetected at all other wavelengths. We
speculate that these objects may be very high redshift sources, likely residing
at z > 4.


  A nanocrystalline metal's strength increases significantly as its grain size
decreases, a phenomenon known as the Hall-Petch relation. Such relation,
however, breaks down when the grains become too small. Experimental studies
have circumvented this problem in a set of Ni-Mo alloys by stabilizing the
grain boundaries (GB). Here, using atomistic simulations with a machine
learning-based interatomic potential (ML-IAP), we demonstrate that the inverse
Hall-Petch relation can be correctly reproduced due to a change in the dominant
deformation mechanism as the grain becomes small in the Ni-Mo polycrystals. It
is found that the atomic von Mises strain can be significantly reduced by
either solute doping and/or annealing for small-grain-size polycrystals,
leading to the increased strength of the polycrystals. On the other hand, for
large-grain-size polycrystals, annealing weakens the material due to the large
atomic movements in GB regions. Over a broad range of grain size, the
superposition of the solute and annealing effects on polycrystals enhances the
strength of those with small grain size more than those with large ones, giving
rise to the continuous strengthening at extremely small grain sizes. Overall,
this study not only demonstrates the reliability of the ML-IAP, but also
provides a comprehensive atomistic view of complex strengthening mechanisms in
nanocrystals, opening up a new avenue to tailor their mechanical properties.


  In this paper, we theoretically investigate a particular experimental setup
which coalesces the concepts of the double slit and single slit diffraction. In
Thomas Young's classic double slit experiment, monochromatic plane light wave
impinges on an opaque screen with two parallel, long and narrow slits and the
resulting intensity pattern is observed on a screen placed at a great distance
placed from the slits. We show, via theoretical calculations that the if
another opaque screen with a single long narrow slit is placed at a distance
from the two slits, the resulting intensity pattern on the observation screen
is nontrivial and interesting.


  A rapidly increasing amount of human conversation occurs online. But
divisiveness and conflict can fester in text-based interactions on social media
platforms, in messaging apps, and on other digital forums. Such toxicity
increases polarization and, importantly, corrodes the capacity of diverse
societies to develop efficient solutions to complex social problems that impact
everyone. Scholars and civil society groups promote interventions that can make
interpersonal conversations less divisive or more productive in offline
settings, but scaling these efforts to the amount of discourse that occurs
online is extremely challenging. We present results of a large-scale experiment
that demonstrates how online conversations about divisive topics can be
improved with artificial intelligence tools. Specifically, we employ a large
language model to make real-time, evidence-based recommendations intended to
improve participants' perception of feeling understood in conversations. We
find that these interventions improve the reported quality of the conversation,
reduce political divisiveness, and improve the tone, without systematically
changing the content of the conversation or moving people's policy attitudes.
These findings have important implications for future research on social media,
political deliberation, and the growing community of scholars interested in the
place of artificial intelligence within computational social science.


  The valence band maxima of most group-VI transition metal dichalcogenide thin
films remain at the $\Gamma$-point all the way from bulk to bilayer. In this
paper we develop a continuum theory of the moir\`e minibands that are formed in
the valence bands of $\Gamma$-valley homobilayers by a small relative twist.
Our effective theory is benchmarked against large-scale ab initio electronic
structure calculations that account for lattice relaxation. As a consequence of
an emergent $D_6$ symmetry we find that low-energy $\Gamma$-valley moir\`e
holes differ qualitatively from their K-valley counterparts addressed
previously; in energetic order the first three bands realize i) a
single-orbital model on a honeycomb lattice, ii) a two-orbital model on a
honeycomb lattice, and iii) a single-orbital model on a kagome lattice.


  The applications of geometric control theory methods on Lie groups and
homogeneous spaces to the theory of quantum computations are investigated.
These methods are shown to be very useful for the problem of constructing an
universal set of gates for quantum computations: the well-known result that the
set of all one-bit gates together with almost any one two-bit gate is universal
is considered from the control theory viewpoint.


  Beam power is a fundamental parameter that describes, in part, the state of a
supermassive black hole system. Determining the beam powers of powerful
classical double radio sources requires substantial observing time, so it would
be useful to determine the relationship between beam power and radio power so
that radio power could be used as a proxy for beam power. A sample of 31
powerful classical double radio sources with previously determined beam and
radio powers are studied; the sources have redshifts between about 0.056 and
1.8. It is found that the relationship between beam power, Lj, and radio power,
P, is well described by Log(Lj) = 0.84 Log(P) + 2.15, where both L_j and P are
in units of 10^(44) erg/s. This indicates that beam power is converted to radio
power with an efficiency of about 0.7%. The ratio of beam power to radio power
is studied as a function of redshift; there is no significant evidence for
redshift evolution of this ratio over the redshift range studied. The
relationship is consistent with empirical results obtained by Cavagnolo et al.
(2010) for radio sources in gas rich environments, which are primarily FRI
sources, and with the theoretical predictions of Willott et al. (1999).


  Unsupervised image-to-image translation (UNIT) aims at learning a mapping
between several visual domains by using unpaired training images. Recent
studies have shown remarkable success for multiple domains but they suffer from
two main limitations: they are either built from several two-domain mappings
that are required to be learned independently, or they generate low-diversity
results, a problem known as mode collapse. To overcome these limitations, we
propose a method named GMM-UNIT, which is based on a content-attribute
disentangled representation where the attribute space is fitted with a GMM.
Each GMM component represents a domain, and this simple assumption has two
prominent advantages. First, it can be easily extended to most multi-domain and
multi-modal image-to-image translation tasks. Second, the continuous domain
encoding allows for interpolation between domains and for extrapolation to
unseen domains and translations. Additionally, we show how GMM-UNIT can be
constrained down to different methods in the literature, meaning that GMM-UNIT
is a unifying framework for unsupervised image-to-image translation.


  While a large body of work has scrutinized the meaning of conditional
sentences, considerably less attention has been paid to formal models of their
pragmatic use and interpretation. Here, we take a probabilistic approach to
pragmatic reasoning about indicative conditionals which flexibly integrates
gradient beliefs about richly structured world states. We model listeners'
update of their prior beliefs about the causal structure of the world and the
joint probabilities of the consequent and antecedent based on assumptions about
the speaker's utterance production protocol. We show that, when supplied with
natural contextual assumptions, our model uniformly explains a number of
inferences attested in the literature, including epistemic inferences,
conditional perfection and the dependency between antecedent and consequent of
a conditional. We argue that this approach also helps explain three puzzles
introduced by Douven (2012) about updating with conditionals: depending on the
utterance context, the listener's belief in the antecedent may increase,
decrease or remain unchanged.


  Although the Perseus cluster has often been regarded as an archetypical
relaxed galaxy cluster, several lines of evidence including ancient,
large-scale cold fronts, asymmetric plasma morphology, filamentary galaxy
distribution, etc., provide a conflicting view of its dynamical state,
suggesting that the cluster might have experienced a major merger. However, the
absence of a clear merging companion identified to date hampers our
understanding of the evolutionary track of the Perseus cluster consistent with
these observational features. In this paper, through careful weak lensing
analysis, we successfully identified the missing subcluster halo
($M_{200}=1.70^{+0.73}_{-0.59}\times10^{14}~M_{\odot}$) at the >5$\sigma$ level
centered on NGC1264, which is located ~430 kpc west of the Perseus main cluster
core. Moreover, a significant ($>3\sigma$) mass bridge, which is also traced by
the cluster member galaxies, is detected between the Perseus main and sub
clusters, which serves as direct evidence of gravitational interaction. With
idealized numerical simulations, we demonstrate that a ~3:1 off-axis major
merger can create the cold front observed ~700 kpc east of the main cluster
core and also generate the observed mass bridge through multiple core
crossings.


  Lyman break galaxies (LBGs) at $z \sim 3$ exhibit number densities and
clustering similar to local $L_*$ galaxies; however, their star formation rates
(SFRs) are much higher. We explore the scenario in which LBGs are starburst
galaxies triggered by collisions, and thus provide an explanation for these key
properties. The relative paucity of starburst galaxies at low redshift can be
attributed to a much higher collision rate in the past. We use high-resolution
cosmological $N$-body simulations and a hierarchical halo finder to estimate
the galaxy collision rate as a function of time in the popular $\Lambda$CDM
cosmological model. We find that bright collisional starbursts are frequent
enough to account for most of the high-$z$ ($2.5-4.5$) LBGs. Moreover, many of
the objects are of relatively small mass, but they cluster about large-mass
halos. They therefore exhibit strong clustering, similar to that observed and
stronger than that of the relevant massive halos.


  The quenched approximation for QCD is, at present and in the foreseeable
future, unavoidable in lattice calculations with realistic choices of the
lattice spacing, volume and quark masses. In this talk, I review an analytic
study of the effects of quenching based on chiral perturbation theory. Quenched
chiral perturbation theory leads to quantitative insight on the difference
between quenched and unquenched QCD, and reveals clearly some of the diseases
which are expected to plague quenched QCD. Uses jnl.tex and epsf.tex for figure
3. Figures 1 and 2 not included, sorry. Available as hardcopy on request.


  The shape of crystalline nanoparticles (NP) can often be described by
polyhedra with flat facet surfaces. Thus, structural studies of polyhedral
bodies can help to describe geometric details of NPs. Here we consider compact
polyhedra of cubic point symmetry Oh.as simple models. Their surfaces are
described by facets with normal vectors along selected directions (a, b, c)
together with their symmetry equivalents forming a direction family {abc}. For
given {abc} this yields generic polyhedra with up to 48 facets where we focus
on polyhedra with facets of {abc} = {100}, {110}, and {111}, suggested for
metal NPs with cubic lattices. The resulting generic polyhedra, cubic,
rhombohedral, and octahedral, can serve for the description of non-generic
polyhedra as intersections of corresponding generic species. Their structural
properties are shown to be fully determined by only three structure parameters,
facet distances R100, R110, and R111 of three types of facets. This provides a
phase diagram to completely classify the corresponding Oh symmetry polyhedra.
Structural properties of all polyhedra, such as shape, size, and facet
geometries, are discussed in analytical and numerical detail with visualization
of characteristic examples. The results may be used for respective nanoparticle
simulations but also as a repository assisting the interpretation of structures
of real compact nanoparticles observed by experiment.


  We present a new calculation for the production of isolated photon pairs at
the LHC with NNLL$'_{\mathcal{T}_0}$+NNLO accuracy. This is the first
implementation within the GENEVA Monte Carlo framework of a process with a
nontrivial Born-level definition which suffers from QED singularities.
Throughout the computation we use a smooth-cone isolation algorithm to remove
such divergences. The higher-order resummation of the 0-jettiness resolution
variable $\mathcal{T}_0$ is based on a factorisation formula derived within
Soft-Collinear Effective Theory which predicts all of the singular, virtual and
real NNLO corrections. Starting from this precise parton-level prediction and
by employing the GENEVA method, we provide fully showered and hadronised events
using PYTHIA8, while retaining the NNLO QCD accuracy for observables which are
inclusive over the additional radiation. We compare our final predictions to
LHC data at 7 TeV and find good agreement.


  We report on the latest version of our GNU-Linux port of the ATLAS - SYNTHE -
WIDTH suite of codes for the stellar atmosphere modeling. The latest version
(8.1 at the time of the workshop) of the Intel Fortran Compiler allowed for a
significantly better backward compatibility with the VMS version of the code,
thus allowing us to remove almost all the modifications we initially introduced
in order to compile the code under IFC. We now provide ported versions both of
ATLAS 9 (the ODF version of ATLAS) and ATLAS 12 (the opacity sampling version).
A comprehensive website has been created to host the ported codes, along with a
growing body of documentation and resources. Also, three mailing lists have
been created at the university of Ljubljana in order to cover general usage,
code development and ATLAS-related announcements.


  In this paper we address the problem of tracking non-rigid objects whose
local appearance and motion changes as a function of time. This class of
objects includes dynamic textures such as steam, fire, smoke, water, etc., as
well as articulated objects such as humans performing various actions. We model
the temporal evolution of the object's appearance/motion using a Linear
Dynamical System (LDS). We learn such models from sample videos and use them as
dynamic templates for tracking objects in novel videos. We pose the problem of
tracking a dynamic non-rigid object in the current frame as a maximum
a-posteriori estimate of the location of the object and the latent state of the
dynamical system, given the current image features and the best estimate of the
state in the previous frame. The advantage of our approach is that we can
specify a-priori the type of texture to be tracked in the scene by using
previously trained models for the dynamics of these textures. Our framework
naturally generalizes common tracking methods such as SSD and kernel-based
tracking from static templates to dynamic templates. We test our algorithm on
synthetic as well as real examples of dynamic textures and show that our simple
dynamics-based trackers perform at par if not better than the state-of-the-art.
Since our approach is general and applicable to any image feature, we also
apply it to the problem of human action tracking and build action-specific
optical flow trackers that perform better than the state-of-the-art when
tracking a human performing a particular action. Finally, since our approach is
generative, we can use a-priori trained trackers for different texture or
action classes to simultaneously track and recognize the texture or action in
the video.


  We consider a three-mode system and show how steering correlations can be
created between two modes of the system using the fast dissipation of the third
mode. These correlations result in a directional form of entanglement, called
quantum or EPR steering. We illustrate this on examples of the interactions
among damped radiation modes in an optomechanical three-mode system. By
assuming that one of the modes undergoes fast dissipation, we show that the
coupling of that mode to one or two other modes of the system may result in
one- or two-way quantum steering. Explicit analytical results are given for the
steering parameters. We find that two modes coupled by the parametric-type
interaction and damped with the same rates can be entangled but cannot exhibit
quantum steering. When, in addition, one of the modes is coupled to a fast
damped mode, steering correlations are created and the modes then exhibit
one-way steering. The creation of the steering correlations is interpreted in
the context of the variances of the quadrature components of the modes that the
steering correlations result from an asymmetry in the variances of the
quadrature components of the modes induced by the auxiliary mode. It is found
that the fluctuations act directionally that quantum steering may occur only
when the variance of the steering mode is larger that the variance of the
steered mode. The scheme is shown to be quite robust against the thermal
excitation of the modes if the fluctuations of the steering mode are larger
than the fluctuations of the steered mode.


  The main observation of this paper is that some sequential weak compactness
arguments in Hilbert space theory can be replaced by Heine/Borel compactness
arguments (for the strong topology). Even though the latter form of compactness
fails in (infinite-dimensional) Hilbert spaces, it nevertheless trivializes
under the so-called bounded functional interpretation. As a consequence, the
proof mining programme of extracting computational bounds from ordinary proofs
of mathematics can be applied to {\em modified proofs} which use these false
Heine/Borel compactness arguments. Additionally, the bounded functional
interpretation provides good logical guidance in formulating quantitative
versions of analytical statements. We illustrate these claims with three
minings. The bounded functional interpretation is here used for the first time
in proof mining.


  The Jeans criterion sets the foundation of our understanding of gravitational
collapse. Jog studied the fragmentation of gas under external tides and derived
a dispersion relation $$
  l' = l_{\rm Jeans} \frac{1} {(1 + \lambda_0' / 4 \pi G \rho_0)^{1/2}} \;. $$
She further concludes that the Jeans mass is $m_{\rm incorrect}'=m_{\rm Jeans}
( 1/(1 + \lambda_0' / 4 \pi G \rho_0)^{3/2})$. We clarify that due to the
inhomogeneous nature of tides, this characteristic mass is incorrect. Under
weak tides, the mass is $m \approx \rho\, l_1 l_2 l_3$, where the modifications
to Jeans lengths along all three dimensions need to be considered; when the
tide is strong enough, collapse can only occur once 1 or 2 dimensions. In the
latter case, tides can stretch the gas, leading to the formation of filaments.


  A jet of heavy fluid is injected upwards, at time $t=0$, into a lighter fluid
and reaches a maximum height at time $t=t_i$ and then flows back around the
upward flow. A similar flow situation occurs for a light fluid injected
downward into a heavy one. In this paper an exact analytical expression for
$t_i$ is derived. The expression remains valid for laminar and turbulent
buoyant jets with or without swirl.


  The study of finite J effects for the light-cone AdS superstring by means of
the Thermodynamic Bethe Ansatz requires an understanding of a companion 2d
theory which we call the mirror model. It is obtained from the original string
model by the double Wick rotation. The S-matrices describing the scattering of
physical excitations in the string and mirror models are related to each other
by an analytic continuation. We show that the unitarity requirement for the
mirror S-matrix fixes the S-matrices of both theories essentially uniquely. The
resulting string S-matrix S(z_1,z_2) satisfies the generalized unitarity
condition and, up to a scalar factor, is a meromorphic function on the elliptic
curve associated to each variable z. The double Wick rotation is then
accomplished by shifting the variables z by quarter of the imaginary period of
the torus. We discuss the apparent bound states of the string and mirror
models, and show that depending on a choice of the physical region there are
one, two or 2^{M-1} solutions of the M-particle bound state equations sharing
the same conserved charges. For very large but finite values of J, most of
these solutions, however, exhibit various signs of pathological behavior. In
particular, they might receive a finite J correction to their energy which is
complex, or the energy correction might exceed corrections arising due to
finite J modifications of the Bethe equations thus making the asymptotic Bethe
ansatz inapplicable.


  We propose a novel spin density wave (SDW) state as a possible mechanism of
the anomalous antiferromagnetism, so-called the micromagnetism, in URu_2Si_2
below 17.5[K]. In this new SDW, the electron-hole pair amplitude changes its
sign in the momentum space as in the case of the unconventional
superconductivity. It is shown that this state can be realized in an extended
Hubbard model within the mean field theory. We also examine some characteristic
properties of this SDW to compare with the experimental results. All these
properties well explain the unsolved problem of the micromagnetism.


  Higher-order topological phases are gapped phases of matter that host gapless
corner or hinge modes. For the case of superconductors, corner or hinge modes
are gapless Majorana modes or Majorana zero modes. To construct 3d higher-order
topological superconductors, we consider a topological-insulator/superconductor
multilayer under in-plane Zeeman coupling. We found three different types of
higher-order topological superconductor phases, a second-order topological
superconductor phase with Majorana hinge flat bands, a second-order Dirac
superconductor phase with surface Majorana cones and Majorana hinge arcs, and
nodal-line superconductor phases with drumhead surface states and Majorana
hinge arcs.