  We carry out a coarse-grained molecular dynamics simulation of phospholipid
vesicles with transmembrane proteins. We measure the mean and Gaussian
curvatures of our protein-embedded vesicles and quantitatively show how protein
clusters change the shapes of their host vesicles. The effects of depletion
force and vesiculation on protein clustering are also investigated. By
increasing the protein concentration, clusters are fragmented to smaller
bundles, which are then redistributed to form more symmetric structures
corresponding to lower bending energies. Big clusters and highly aspherical
vesicles cannot be formed when the fraction of protein to lipid molecules is
large.


  We present rigorous results for the SU($n$) Fermi-Hubbard model on the
railroad-trestle lattice. We first study the model with a flat band at the
bottom of the single-particle spectrum and prove that the ground states exhibit
SU($n$) ferromagnetism when the total fermion number is the same as the number
of unit cells. We then perturb the model by adding extra hopping terms and make
the flat band dispersive. Under the same filling condition, it is proved that
the ground states of the perturbed model remain SU($n$) ferromagnetic when the
bottom band is nearly flat. This is the first rigorous example of the
ferromagnetism in nonsingular SU($n$) Hubbard models in which both the
single-particle density of states and the on-site repulsive interaction are
finite.


  A drawing of a graph in the plane is called a thrackle if every pair of edges
meets precisely once, either at a common vertex or at a proper crossing. Let
t(n) denote the maximum number of edges that a thrackle of n vertices can have.
According to a 40 years old conjecture of Conway, t(n)=n for every n>2. For any
eps>0, we give an algorithm terminating in e^{O((1/eps^2)ln(1/eps))} steps to
decide whether t(n)<(1+eps)n for all n>2. Using this approach, we improve the
best known upper bound, t(n)<=3/2(n-1), due to Cairns and Nikolayevsky, to
167/117n<1.428n.


  We consider the problem of an auctioneer who faces the task of selling a good
(drawn from a known distribution) to a set of buyers, when the auctioneer does
not have the capacity to describe to the buyers the exact identity of the good
that he is selling. Instead, he must come up with a constrained signalling
scheme: a (non injective) mapping from goods to signals, that satisfies the
constraints of his setting. For example, the auctioneer may be able to
communicate only a bounded length message for each good, or he might be legally
constrained in how he can advertise the item being sold. Each candidate
signaling scheme induces an incomplete-information game among the buyers, and
the goal of the auctioneer is to choose the signaling scheme and accompanying
auction format that optimizes welfare. In this paper, we use techniques from
submodular function maximization and no-regret learning to give algorithms for
computing constrained signaling schemes for a variety of constrained signaling
problems.


  Thermoelectric devices convert temperature gradients into electrical power
and vice versa, thus enabling energy scavenging from waste heat, sensing and
cooling. Yet, many of these attractive applications are hindered by the limited
efficiency of thermoelectric materials, especially in the low temperature
regime. This review provides a summary of the recent advances in the design of
new efficient silicon--based thermoelectric materials through nanostructuring,
alloying and chemical optimization, emphasizing the contribution from theory
and atomistic modeling.


  In this paper we update the constraints on the simple decaying cold dark
matter (DCDM) model with dark radiation (DR) as decay product. We consider two
different regimes of the lifetime, i.e. short-lived and long-lived, and use the
most recent CMB data from Planck (2018) to infer new constraints on the decay
parameters with which we compare the constraints inferred by the previous
Planck data (2015). We hereby show that the newest CMB data constrains the
fractional amount of DCDM twice as much as the previous data in the long-lived
regime, leading to our current best 2$\sigma$ upper bound of $f_{\rm
dcdm}<2.44\%$. In the short-lived regime, we get a slightly looser 2$\sigma$
upper bound of $f_{\rm dcdm}<13.1\%$ compared to the previous CMB data. If we
include Baryonic Acoustic Oscillations data from BOSS DR-12, the constraints in
both the long-lived and the short-lived regimes relax to $f_{\rm dcdm}<2.62\%$
and $f_{\rm dcdm}<1.49\%$, respectively. We also investigate how this model
impacts the Hubble and $\sigma_8$ tensions, and we find that each of the decay
regimes can slightly relieve a different one of the tensions. The model can
thus not accommodate both tensions at once, and the improvements on each are
not significant. We furthermore improve on previous work by thoroughly
analysing the impacts of short-lived DCDM on the radiation density and deriving
a mapping between short-lived DCDM and a correction, $\Delta N_{\rm eff}$, to
the effective number of massless neutrino species.


  Emotional contagion in online social networks has been of great interest over
the past years. Previous studies have focused mainly on finding evidence of
affect contagion in homophilic atmospheres. However, these studies have
overlooked users' awareness of the sentiments they share and consume online. In
this paper, we present an experiment with Twitter users that aims to help them
better understand which emotions they experience on this social network. We
introduce Tweet Moodifier (T-Moodifier), a Google Chrome extension that enables
Twitter users to filter and make explicit (through colored visual marks) the
emotional content in their News Feed. We compare behavioral changes between 55
participants and 5089 of their public "friends." The comparison period spans
from two weeks before installing T-Moodifier to one week thereafter. The
results suggest that the use of T-Moodifier might help Twitter users increase
their emotional awareness: T-Moodifier users who had access to emotional
statistics about their posts produced a significantly higher percentage of
neutral content. This behavioral change suggests that people could behave
differently while using real-time mechanisms that increase their affect
reflection. Also, post-experience, those who completed both pre- and
post-surveys could assert more confidently the main emotions they shared and
perceived on Twitter. This shows T-Moodifier's potential to effectively make
users reflect on their News Feed.


  We explore some strategies which tend to perform well in the IPD. We start
off by showing the significance of Tit-For-Tat strategies in evolutionary game
theory. This is followed by a theoretical derivation of zero-determinant
strategies, where we highlight an error on bounds for scale parameters from the
original paper on ZD strategies[6]. We then present examples of such strategies
and create a custom player drawing inspiration from Markov Decision Processes.
At the end we pit them all against each other and see how they perform in an
IPD tournament.


  We derive efficiency factors for the production of gravitational waves
through bubble collisions and plasma-related sources in strong phase
transitions, and find the conditions under which the bubble collisions can
contribute significantly to the signal. We use lattice simulations to clarify
the dependence of the colliding bubbles on their initial state. We illustrate
our findings in two examples, the Standard Model with an extra $|H|^6$
interaction and a classically scale-invariant $U(1)_{\rm B-L}$ extension of the
Standard Model. The contribution to the GW spectrum from bubble collisions is
found to be negligible in the $|H|^6$ model, whereas it can play an important
role in parts of the parameter space in the scale-invariant $U(1)_{\rm B-L}$
model. In both cases the sound-wave period is much shorter than a Hubble time,
suggesting a significant amplification of the turbulence-sourced signal. We
find, however, that the peak of the plasma-sourced spectrum is still produced
by sound waves with the slower-falling turbulence contribution becoming
important off-peak.


  An artinian graded algebra, $A$, is said to have the Weak Lefschetz property
(WLP) if multiplication by a general linear form has maximal rank in every
degree. A vast quantity of work has been done studying and applying this
property, touching on numerous and diverse areas of algebraic geometry,
commutative algebra, and combinatorics. Amazingly, though, much of this work
has a "common ancestor" in a theorem originally due to Stanley, although
subsequently reproved by others. In this expository paper we describe the
different directions in which research has moved starting with this theorem,
and we discuss some of the open questions that continue to motivate current
research.


  We reconsider the nonlinear second order Abel equation of Stewart and Lyth,
which follows from a nonlinear second order slow-roll approximation. We find a
new eigenvalue spectrum in the blue regime. Some of the discrete values of the
spectral index n_s have consistent fits to the cumulative COBE data as well as
to recent ground-base CMB experiments.


  We propose a link prediction algorithm that is based on spring-electrical
models. The idea to study these models came from the fact that
spring-electrical models have been successfully used for networks
visualization. A good network visualization usually implies that nodes similar
in terms of network topology, e.g., connected and/or belonging to one cluster,
tend to be visualized close to each other. Therefore, we assumed that the
Euclidean distance between nodes in the obtained network layout correlates with
a probability of a link between them. We evaluate the proposed method against
several popular baselines and demonstrate its flexibility by applying it to
undirected, directed and bipartite networks.


  We present astrophysical false positive probability calculations for every
Kepler Object of Interest (KOI)---the first large-scale demonstration of a
fully automated transiting planet validation procedure. Out of 7056 KOIs, we
determine that 1935 have probabilities <1% to be astrophysical false positives,
and thus may be considered validated planets. 1284 of these have not yet been
validated or confirmed by other methods. In addition, we identify 428 KOIs
likely to be false positives that have not yet been identified as such, though
some of these may be a result of unidentified transit timing variations. A side
product of these calculations is full stellar property posterior samplings for
every host star, modeled as single, binary, and triple systems. These
calculations use 'vespa', a publicly available Python package able to be easily
applied to any transiting exoplanet candidate.


  Manga, a form of Japanese comics and distinct visual storytelling, has
captivated readers worldwide. Traditionally presented in black and white,
manga's appeal lies in its ability to convey complex narratives and emotions
through intricate line art and shading. Yet, the desire to experience manga in
vibrant colors has sparked the pursuit of manga colorization, a task of
paramount significance for artists. However, existing methods, originally
designed for line art and sketches, face challenges when applied to manga.
These methods often fall short in achieving the desired results, leading to the
need for specialized manga-specific solutions. Existing approaches frequently
rely on a single training step or extensive manual artist intervention, which
can yield less satisfactory outcomes. To address these challenges, we propose a
specialized framework for manga colorization. Leveraging established models for
shading and vibrant coloring, our approach aligns both using a multi-encoder
VAE. This structured workflow ensures clear and colorful results, with the
option to incorporate reference images and manual hints.


  We report experimental studies of spin-wave excitations in individual 22 nm
thick Ni80Fe20 nanotubes with diameters of about 150 nm by means of Brillouin
light-scattering (BLS) spectroscopy. Irradiated by microwaves we resolve sets
of discrete resonances in the center of nanotubes ranging from 2.5 to 12.5 GHz.
Comparing to a recent theoretical work and micromagnetic simulations, we
identify different characteristic eigenmodes depending on the axial, mixed or
vortex configuration. The mixed and vortex states give rise to modes with
helical phase profiles substantiating an unusual nature of modes attributed to
non-reciprocal spin waves. Our findings provide microscopic insight into
tubular spin-wave nanocavities and magnetochiral effects for 3D nanomagnonics.


  We consider 36 candidates for close (within 1 pc) encounters with the Solar
system. These stars have been selected in accordance with the results of an
analysis of their motion obtained by various authors. For most stars from this
list the kinematic characteristics have been taken from the Gaia DR2 catalogue.
The parameters of the encounters of these stars with the Solar system have been
calculated using three methods: (1) the linear one, (2) the epicyclic one, and
(3) by integrating the orbits in an axisymmetric potential. We have concluded
that the epicyclic method works quite well only on a time interval no longer
than $\pm$1 Myr. Based on the third method, in good agreement with the first
method, for the first time we have obtained the following estimates of the
encounter parameters for the star Gaia DR2 3130033734235815424:
$t_{min}=-0.62\pm0.12$ Myr and $d_{min}=0.30\pm0.10$ pc.


  Double hybrid density functional theory arguably sits on the seamline between
wavefunction methods and DFT: it represents a special case of Rung 5 on the
"Jacobs Ladder" of John P. Perdew. For large and chemically diverse benchmarks
such as GMTKN55, empirical double hybrid functionals with dispersion
corrections can achieve accuracies approaching wavefunction methods at a cost
not greatly dissimilar to hybrid DFT approaches, provided RI-MP2 and/or another
MP2 acceleration techniques are available in the electronic structure code.
Only a half-dozen or fewer empirical parameters are required. For vibrational
frequencies, accuracies intermediate between CCSD and CCSD(T) can be achieved,
and performance for other properties is encouraging as well. Organometallic
reactions can likewise be treated well, provided static correlation is not too
strong. Further prospects are discussed, including range-separated and
RPA-based approaches.


  Excited states in 212Po have been populated by alpha transfer using the
208Pb(18O,14C) reaction at 85MeV beam energy and studied with the EUROBALL IV
gamma multidetector array. The level scheme has been extended up to ~ 3.2 MeV
excitation energy from the triple gamma coincidence data. Spin and parity
values of most of the observed states have been assigned from the gamma angular
distributions and gamma -gamma angular correlations. Several gamma lines with
E(gamma) < 1 MeV have been found to be shifted by the Doppler effect, allowing
for the measurements of the associated lifetimes by the DSAM method. The
values, found in the range [0.1-0.6] ps, lead to very enhanced E1 transitions.
All the emitting states, which have non-natural parity values, are discussed in
terms of alpha-208Pb structure. They are in the same excitation-energy range as
the states issued from shell-model configurations.


  We report the results of an unsupervised decomposition of the local stellar
halo in the chemo-dynamical space spanned by the abundance measurements from
APOGEE DR17 and GALAH DR3. In our Gaussian Mixture Model, only four independent
components dominate the halo in the Solar neighborhood, three previously known
$Aurora$, $Splash$ and Gaia-Sausage/Enceladus (GS/E) and one new, $Eos$. Only
one of these four is of accreted origin, namely the GS/E, thus supporting the
earlier claims that the GS/E is the main progenitor of the Galactic stellar
halo. We show that $Aurora$ is entirely consistent with the chemical properties
of the so-called Heracles merger. In our analysis in which no predefined
chemical selection cuts are applied, $Aurora$ spans a wide range of [Al/Fe]
with a metallicity correlation indicative of a fast chemical enrichment in a
massive galaxy, the young Milky Way. The new halo component dubbed $Eos$ is
classified as $in situ$ given its high mean [Al/Fe]. $Eos$ shows strong
evolution as a function of [Fe/H], where it changes from being the closest to
GS/E at its lowest [Fe/H] to being indistinguishable from the Galactic
low-$\alpha$ population at its highest [Fe/H]. We surmise that at least some of
the outer thin disk of the Galaxy started its evolution in the gas polluted by
the GS/E, and $Eos$ is an evidence of this process.


  There is a natural one-to-one correspondence between squarefree monomial
ideals and finite simple hypergraphs via the cover ideal construction. Let H be
a finite simple hypergraph, and let J = J(H) be its cover ideal in a polynomial
ring R. We give an explicit description of all associated primes of R/J^s, for
any power J^s of J, in terms of the coloring properties of hypergraphs arising
from H. We also give an algebraic method for determining the chromatic number
of H, proving that it is equivalent to a monomial ideal membership problem
involving powers of J. Our work yields two new purely algebraic
characterizations of perfect graphs, independent of the Strong Perfect Graph
Theorem; the first characterization is in terms of the sets Ass(R/J^s), while
the second characterization is in terms of the saturated chain condition for
associated primes.


  Out-of-distribution generalization (OODG) is a longstanding challenge for
neural networks. This challenge is quite apparent in tasks with well-defined
variables and rules, where explicit use of the rules could solve problems
independently of the particular values of the variables, but networks tend to
be tied to the range of values sampled in their training data. Large
transformer-based language models have pushed the boundaries on how well neural
networks can solve previously unseen problems, but their complexity and lack of
clarity about the relevant content in their training data obfuscates how they
achieve such robustness. As a step toward understanding how transformer-based
systems generalize, we explore the question of OODG in small scale transformers
trained with examples from a known distribution. Using a reasoning task based
on the puzzle Sudoku, we show that OODG can occur on a complex problem if the
training set includes examples sampled from the whole distribution of simpler
component tasks. Successful generalization depends on carefully managing
positional alignment when absolute position encoding is used, but we find that
suppressing sensitivity to absolute positions overcomes this limitation. Taken
together our results represent a small step toward understanding and promoting
systematic generalization in transformers.


  This paper appears as the confluence of hyperbolic dynamics, symplectic
topology and low dimensional topology, etc. We show that composite symplectic
Dehn twists have certain form of nonuniform hyperbolicity: it has positive
topological entropy as well as two families of local stable and unstable
Lagrangian manifolds, which are analogous to signatures of pseudo Anosov
mapping classes. Moreover, we show that the rank of the Floer cohomology group
of these compositions grows exponentially under iterations, which partially
answers a question of Smith concerning the classification of symplectic mapping
class group in higher dimensions. Finally, we propose a conjecture on the
positive metric entropy of our model and point out its relationship with the
standard map.


  Dictionary training for sparse representations involves dealing with large
chunks of data and complex algorithms that determine time consuming
implementations. SBO is an iterative dictionary learning algorithm based on
constructing unions of orthonormal bases via singular value decomposition, that
represents each data item through a single best fit orthobase. In this paper we
present a GPGPU approach of implementing SBO in OpenCL. We provide a lock-free
solution that ensures full-occupancy of the GPU by following the map-reduce
model for the sparse-coding stage and by making use of the Partitioned Global
Address Space (PGAS) model for developing parallel dictionary updates. The
resulting implementation achieves a favourable trade-off between algorithm
complexity and data representation quality compared to PAK-SVD which is the
standard overcomplete dictionary learning approach. We present and discuss
numerical results showing a significant acceleration of the execution time for
the dictionary learning process.


  In planar turbulence modelled as an isotropic and homogeneous collection of
2-D non-interacting compact vortices, the structure functions S_p(r) of a
statistically stationary passive scalar field have the following scaling
behaviour in the limit where the P\'eclet number Pe -> \infty S_p(r) ~
constant+\ln({\frac{r}{LPe^{-1/3}}}) for LPe^{-1/3} << L, S_p(r) ~
({\frac{r}{LPe^{-1/3}}})^{6(1-D)} for LPe^{-1/2} << LPe^{-1/3}, where L is a
large scale and D is the fractal co-dimension of the spiral scalar structures
generated by the vortices (1/2 <= D < 2/3). Note that LPe^{-1/2} is the scalar
Taylor microscale which stems naturally from our analytical treatment of the
advection-diffusion equation. The essential ingredients of our theory are the
locality of inter-scale transfer and Lundgren's time average assumption. A
phenomenological theory explicitly based only on these two ingredients
reproduces our results and a generalisation of this phenomenology to spatially
smooth chaotic flows yields (k\ln k)^{-1} generalised power spectra for the
advected scalar fields.


  We propose S=+1 baryon interpolating operators, which are based on an exotic
description of the antidecuplet baryon like diquark-diqaurk-antiquark. By using
one of the new operators, the mass spectrum of the spin-1/2 pentaquark states
is calculated in quenched lattice QCD at $\beta=6/g^2=6.2$ on a $32^3\times48$
lattice. It is found that the $J^P$ assignment of the lowest $\Theta(uudd \bar
s)$ state is most likely $(1/2)^-$. We also calculate the mass of the charm
analog of the $\Theta$ and find that the $\Theta_c(uudd \bar c)$ state lies
much higher than the $DN$ threshold, in contrast to several model predictions.


  In this paper we study the global regularity of the following 2D
(two-dimensional) generalized magnetohydrodynamic equations \begin{eqnarray*}
  \left\{\begin{array}{llll} u_t + u \cdot \nabla u & = & - \nabla p + b \cdot
\nabla b - \nu (-\triangle)^{\alpha} u b_t + u \cdot \nabla b & = & b \cdot
\nabla u - \kappa (-\triangle)^{\beta} b
  \end{array}\right. \end{eqnarray*} and get global regular solutions when $
0\leqslant\alpha < 1 / 2,\,\, \beta \geqslant 1, \,\,3\alpha + 2\beta >3 $,
which improves the results in \cite{TYZ2013}. In particular, we obtain the
global regularity of the 2D generalized MHD when $\alpha=0$ and $\beta>\frac
32$.


  This paper deploys bibliometric indices and semantic techniques for
understanding to what extent research grants are likely to impact publications,
research direction, and co-authorship rate of principal investigators. The
novelty of this paper lies within the fact that it includes semantic analysis
in the research funding evaluation process in order to effectively study
short-term and long-term funding impact in terms of publication outputs. Our
dataset consists of researchers that receive research grants from the National
ICT Research and Development funding program of Pakistan. We show a number of
interesting case studies to conclude that bibliometric-based quantitative
assessment combined with semantics can lead to building better sustainable
pathways to deploy evaluation frameworks for research funding effectively.


  We compare the optical science & engineering educational environments at two
universities in two countries: Utsunomiya University in Japan, and the
University of Arizona in the United States. Because the university education
systems in the two countries are so different, we also explain how financial
arrangements drive many of these differences and discuss how these impact
student and faculty life.


  We study the interaction between polarized terahertz (THz) radiation and
micro-structured large-area graphene in transmission geometry. In order to
efficiently couple the radiation into the two-dimensional material, a lateral
periodic patterning of a closed graphene sheet by intercalation doping into
stripes is chosen, yielding unequal transmittance of the radiation polarized
parallel and perpendicular to the stripes. Indeed, a polarization contrast up
to 20% is observed. The effect even increases up to 50% when removing graphene
stripes in analogy to a wire grid polarizer. The polarization dependence is
analyzed in a large frequency range from < 80 GHz to 3 THz, including the
plasmon-polariton resonance. The results are in excellent agreement with
theoretical calculations based on the electronic energy spectrum of graphene
and the electrodynamics of the patterned structure.


  From the partition function for two classes of classically non-local actions
containing the fractional Laplacian, we show that as long as there exists a
suitable (non-local) Hilbert-space transform the underlying action can be
mapped onto a purely local theory. In all such cases the partition function is
equivalent to that of a local theory and an area law for the entanglement
entropy obtains. When such a reduction fails, the entanglement entropy deviates
strongly from an area law and can in some cases scale as the volume. As these
two criteria are coincident, we conjecture that they are equivalent and provide
the ultimate test for locality of Gaussian theories rather than a simple
inspection of the explicit operator content.


  The composited cosmological defects in superfluids of nafen-disorded 3He are
discussed in this review article. In spite of the existence of the half quantum
vortices (Alice strings), more novel composited cosmological defects such as
Nambu monople, Kibble-Lazarides-Shafi (KLS) string wall, and nexus objects
appear in this system in the two-step successive symmetry breaking phase
transition. To reveal them, we analyzed symmetry breaking patterns in detail by
using the algebraic topology and group theory. It turns out that the fibrations
of the degenerate parameter spaces of symmetry breaking patterns dominate the
existence of composite cosmological defects in the successive symmetry breaking
of nafen-distorted 3He. To compare our model with ROTA's experiment of string
wall, we demonstrate how the KLS string wall extend to 1D nexus in equilibrium
states. The equilibrium free energies, which determines the configurations 1D
nexus, are evaluated by non-linear numerical optimization algorithm. Based on
these equilibrium configurations, we calculated the spectrum of spin dynamical
response of system under weak magnetic driving. The results exactly coincide
with the experimental observations.


  Network visualization is essential for many scientific, societal,
technological and artistic domains. The primary goal is to highlight patterns
out of nodes interconnected by edges that are easy to understand, facilitate
communication and support decision-making. This is typically achieved by
rearranging the nodes to minimize the edge crossings responsible of
unintelligible and often unaesthetic trends. But when the nodes cannot be
moved, as in spatial and physical networks, this procedure is not viable. Here,
we overcome this situation by turning the edge crossing problem into a graph
filtering optimization. We demonstrate that the presence of longer connections
prompt the optimal solution to yield sparser networks, thereby limiting the
number of intersections and getting more readable layouts. This theoretical
result matches human behavior and provides an ecologically-inspired criterion
to visualize and model real-world interconnected systems.


  Recently, generative adversarial networks (GANs) can generate photo-realistic
fake facial images which are perceptually indistinguishable from real face
photos, promoting research on fake face detection. Though fake face forensics
can achieve high detection accuracy, their anti-forensic counterparts are less
investigated. Here we explore more \textit{imperceptible} and
\textit{transferable} anti-forensics for fake face imagery detection based on
adversarial attacks. Since facial and background regions are often smooth, even
small perturbation could cause noticeable perceptual impairment in fake face
images. Therefore it makes existing adversarial attacks ineffective as an
anti-forensic method. Our perturbation analysis reveals the intuitive reason of
the perceptual degradation issue when directly applying existing attacks. We
then propose a novel adversarial attack method, better suitable for image
anti-forensics, in the transformed color domain by considering visual
perception. Simple yet effective, the proposed method can fool both deep
learning and non-deep learning based forensic detectors, achieving higher
attack success rate and significantly improved visual quality. Specially, when
adversaries consider imperceptibility as a constraint, the proposed
anti-forensic method can improve the average attack success rate by around 30\%
on fake face images over two baseline attacks. \textit{More imperceptible} and
\textit{more transferable}, the proposed method raises new security concerns to
fake face imagery detection. We have released our code for public use, and
hopefully the proposed method can be further explored in related forensic
applications as an anti-forensic benchmark.


  Learning causal structures from interventional data is a fundamental problem
with broad applications across various fields. While many previous works have
focused on recovering the entire causal graph, in practice, there are scenarios
where learning only part of the causal graph suffices. This is called
$targeted$ causal discovery. In our work, we focus on two such well-motivated
problems: subset search and causal matching. We aim to minimize the number of
interventions in both cases.
  Towards this, we introduce the $Meek~separator$, which is a subset of
vertices that, when intervened, decomposes the remaining unoriented edges into
smaller connected components. We then present an efficient algorithm to find
Meek separators that are of small sizes. Such a procedure is helpful in
designing various divide-and-conquer-based approaches. In particular, we
propose two randomized algorithms that achieve logarithmic approximation for
subset search and causal matching, respectively. Our results provide the first
known average-case provable guarantees for both problems. We believe that this
opens up possibilities to design near-optimal methods for many other targeted
causal structure learning problems arising from various applications.


  We consider T-systems and Y-systems arising from cluster mutations applied to
quivers that have the property of being periodic under a sequence of mutations.
The corresponding nonlinear recurrences for cluster variables (coefficient-free
T-systems) were described in the work of Fordy and Marsh, who completely
classified all such quivers in the case of period 1, and characterized them in
terms of the skew-symmetric exchange matrix B that defines the quiver. A
broader notion of periodicity in general cluster algebras was introduced by
Nakanishi, who also described the corresponding Y-systems, and T-systems with
coefficients.
  A result of Fomin and Zelevinsky says that the coefficient-free T-system
provides a solution of the Y-system. In this paper, we show that in general
there is a discrepancy between these two systems, in the sense that the
solution of the former does not correspond to the general solution of the
latter. This discrepancy is removed by introducing additional non-autonomous
coefficients into the T-system. In particular, we focus on the period 1 case
and show that, when the exchange matrix B is degenerate, discrete Painlev\'e
equations can arise from this construction.


  It is shown that the Gel'fand-Tsetlin realization of irreducible
representations of the $A_n$ algebra is directly connected with a linear
exactly integrable system in the n-dimensional space. General solution for this
system is explicitly given.


  Foundation models are trained on vast amounts of data at scale using
self-supervised learning, enabling adaptation to a wide range of downstream
tasks. At test time, these models exhibit zero-shot capabilities through which
they can classify previously unseen (user-specified) categories. In this paper,
we address the problem of quantifying uncertainty in these zero-shot
predictions. We propose a heuristic approach for uncertainty estimation in
zero-shot settings using conformal prediction with web data. Given a set of
classes at test time, we conduct zero-shot classification with CLIP-style
models using a prompt template, e.g., "an image of a <category>", and use the
same template as a search query to source calibration data from the open web.
Given a web-based calibration set, we apply conformal prediction with a novel
conformity score that accounts for potential errors in retrieved web data. We
evaluate the utility of our proposed method in Biomedical foundation models;
our preliminary results show that web-based conformal prediction sets achieve
the target coverage with satisfactory efficiency on a variety of biomedical
datasets.


  All-sky and wide parameter space searches for continuous gravitational waves
are generally template-matching schemes which test a bank of signal waveforms
against data from a gravitational wave detector. Such searches can offer
optimal sensitivity for a given computing cost and signal model, but are
highly-tuned to specific signal types and are computationally expensive, even
for semi-coherent searches. We have developed a search method based on the
well-known Viterbi algorithm which is model-agnostic and has a computational
cost several orders of magnitude lower than template methods, with a modest
reduction in sensitivity. In particular, this method can search for signals
which have an unknown frequency evolution. We test the algorithm on three
simulated and real data sets: gapless Gaussian noise, Gaussian noise with gaps
and real data from the final run of initial LIGO (S6). We show that at 95%
efficiency, with a 1% false alarm rate, the algorithm has a depth sensitivity
of $\sim 33$, $10$ and $13$ ,Hz$^{-1/2}$ with corresponding SNRs of $\sim 60$,
$72$ and $74$ in these datasets. we discuss the use of this algorithm for
detecting a wide range of quasi-monochromatic gravitational wave signals and
instrumental lines.


  Tomb biographies of the Tang dynasty provide invaluable information about
Chinese history. The original biographies are classical Chinese texts which
contain neither word boundaries nor sentence boundaries. Relying on three
published books of tomb biographies of the Tang dynasty, we investigated the
effectiveness of employing machine-learning methods for algorithmically
identifying the pauses and terminals of sentences in the biographies.
  We consider the segmentation task as a classification problem. Chinese
characters that are and are not followed by a punctuation mark are classified
into two categories. We applied a machine-learning-based mechanism, the
conditional random fields (CRF), to classify the characters (and words) in the
texts, and we studied the contributions of selected types of lexical
information to the resulting quality of the segmentation recommendations.
  This proposal presented at the DH 2018 conference discussed some of the basic
experiments and their evaluations. By considering the contextual information
and employing the heuristics provided by experts of Chinese literature, we
achieved F1 measures that were better than 80%. More complex experiments that
employ deep neural networks helped us further improve the results in recent
work.


  The out-of-equilibrium transport properties of a double quantum dot system in
the Kondo regime are studied theoretically by means of a two-impurity Anderson
Hamiltonian with inter-impurity hopping. The Hamiltonian, formulated in
slave-boson language, is solved by means of a generalization of the
non-crossing approximation (NCA) to the present problem. We provide benchmark
calculations of the predictions of the NCA for the linear and nonlinear
transport properties of coupled quantum dots in the Kondo regime. We give a
series of predictions that can be observed experimentally in linear and
nonlinear transport measurements through coupled quantum dots. Importantly, it
is demonstrated that measurements of the differential conductance ${\cal
G}=dI/dV$, for the appropriate values of voltages and inter-dot tunneling
couplings, can give a direct observation of the coherent superposition between
the many-body Kondo states of each dot. This coherence can be also detected in
the linear transport through the system: the curve linear conductance vs
temperature is non-monotonic, with a maximum at a temperature $T^*$
characterizing quantum coherence between both Kondo states.


  We report the Fermi Large Area Telescope (LAT) detections of high-energy
(>100 MeV) gamma-ray emission from two recent optically bright classical novae,
V1369 Centauri 2013 and V5668 Sagittarii 2015. At early times, Fermi
target-of-opportunity observations prompted by their optical discoveries
provided enhanced LAT exposure that enabled the detections of gamma-ray onsets
beginning ~2 days after their first optical peaks. Significant gamma-ray
emission was found extending to 39-55 days after their initial LAT detections,
with systematically fainter and longer duration emission compared to previous
gamma-ray detected classical novae. These novae were distinguished by multiple
bright optical peaks that encompassed the timespans of the observed gamma rays.
The gamma-ray light curves and spectra of the two novae are presented along
with representative hadronic and leptonic models, and comparisons to other
novae detected by the LAT are discussed.


  Neural networks promise to bring robust, quantitative analysis to medical
fields, but adoption is limited by the technicalities of training these
networks. To address this translation gap between medical researchers and
neural networks in the field of pathology, we have created an intuitive
interface which utilizes the commonly used whole slide image (WSI) viewer,
Aperio ImageScope (Leica Biosystems Imaging, Inc.), for the annotation and
display of neural network predictions on WSIs. Leveraging this, we propose the
use of a human-in-the-loop strategy to reduce the burden of WSI annotation. We
track network performance improvements as a function of iteration and quantify
the use of this pipeline for the segmentation of renal histologic findings on
WSIs. More specifically, we present network performance when applied to
segmentation of renal micro compartments, and demonstrate multi-class
segmentation in human and mouse renal tissue slides. Finally, to show the
adaptability of this technique to other medical imaging fields, we demonstrate
its ability to iteratively segment human prostate glands from radiology imaging
data.


  A method for achieving the continuous-space electromagnetic channel capacity
bound using loaded N-port structures is described. It is relevant for the
design of compact multiple-input multiple-output (MIMO) antennas that can
achieve channel capacity bounds when constrained by size. The method is not
restricted to a specific antenna configuration and a closed-form expression for
the channel capacity limits are provided with various constraints. Furthermore,
using loaded N-port structures to represent arbitrary antenna geometries, an
efficient optimization approach is proposed for finding the optimum MIMO
antenna design that achieves the channel capacity bounds. Simulation results of
the channel capacity bounds achieved using our MIMO antenna design with one
square wavelength size are provided. These show that at least 18 ports can be
supported in one square wavelength and achieve the continuous-space
electromagnetic channel capacity bound. The results demonstrate that our method
can link continuous-space electromagnetic channel capacity bounds to MIMO
antenna design.


  We show that, under certain combinations of the parameters governing the
interaction of a harmonically trapped ion with a laser beam, it is possible to
find one or more exact eigenstates of the Hamiltonian, with no approximations
except the optical rotating-wave approximation. These are related via a unitary
equivalence to exact eigenstates of the full Jaynes-Cummings model (including
counter-rotating terms) supplemented by a static driving term.


  The existence of intrinsic spin of matter requires the metric-affine
formulation of gravity, in which the affine connection is not constrained to be
symmetric and its antisymmetric part (torsion tensor) is a dynamical variable.
We show that the cyclic identity for the curvature tensor in the metric-affine
formulation forbids fermions represented by Dirac spinors to form point or
string configurations. Consequently, fermionic strings contradict the
gravitational field equations in the presence of spin. Superstring theory is
therefore incorrect.


  In 2013 P. Habegger proved the Bogomolov property for the field generated
over Q by the torsion points of a rational elliptic curve. We explore the
possibility of applying the same strategy of proof to the case of field
extensions fixed by the kernel of some modular Galois representations.


  The Palatini $f(R,T)$ gravity theory is considered. The standard
Einstein-Hilbert action is replaced by an arbitrary function of the Ricci
scalar $R$ and of the trace $T$ of the energy-momentum tensor. In the Palatini
approach, the Ricci scalar is a function of the metric and the connection.
These two quantities, metric and connection, are taken as independents
variables. Then, it is examined whether Palatini $f(R,T)$ gravity theory allows
solutions in which lead to violation of causality. The G\"{o}del and
G\"{o}del-type space-times are considered. In addition, a critical radius,
which permits to examine limits for violation of causality, is calculated. It
is shown that, for different matter contents, non-causal solutions can be
avoided in this Palatini gravitational theory.


  A conversion of thermal energy into electricity is considered in the
electrically polarized graphene stripes with zigzag edges where the heavy
chiral fermion (HCF) states are formed. The stripes are characterized by a high
electric conductance Ge and by a significant Seebeck coefficient S. The
electric current in the stripes is induced due to a non-equilibrium thermal
injection of "hot" electrons. This thermoelectric generation process might be
utilized for building of thermoelectric generators with an exceptionally high
figure of merit Z{\delta}T \simeq 100 >> 1 and with an appreciable electric
power densities \sim 1 MW/cm2.


  We establish quantitative estimates on the structure function arising in the
representation of the intertwining wave operators of a Schroedinger operator in
three dimensions. Regularity of zero energy is assumed throughout. This paper
is related to, and corrects some inaccuracies in, the first author's work
https://arxiv.org/abs/1101.0502


  Context. The spatial power spectrum of supergranulation does not fully
characterize the underlying physics of turbulent convection. For example, it
does not describe the non-Gaussianity in the horizontal flow divergence.
  Aims. Our aim is to statistically characterize the spatial pattern of solar
supergranulation beyond the power spectrum. The next-order statistic is the
bispectrum. It measures correlations of three Fourier components and is related
to the nonlinearities in the underlying physics.
  Methods. We estimated the bispectrum of supergranular horizontal surface
divergence maps that were obtained using local correlation tracking (LCT) and
time-distance helioseismology (TD) from one year of data from the Helioseismic
and Magnetic Imager on-board the Solar Dynamics Observatory starting in May
2010.
  Results. We find significantly nonzero and consistent estimates for the
bispectrum. The strongest nonlinearity is present when the three coupling wave
vectors are at the supergranular scale. These are the same wave vectors that
are present in regular hexagons, which were used in analytical studies of solar
convection. At these Fourier components, the bispectrum is positive, consistent
with the positive skewness in the data and with supergranules preferentially
consisting of outflows surrounded by a network of inflows. We use the
bispectrum to generate synthetic divergence maps that are very similar to the
data by a model that consists of a Gaussian term and a weaker quadratic
nonlinear component. Thereby, we estimate the fraction of the variance in the
divergence maps from the nonlinear component to be of the order of 4-6%.
  Conclusions. We propose that bispectral analysis is useful for understanding
solar turbulent convection, for example for comparing observations and
numerical models of supergranular flows. This analysis may also be useful to
generate synthetic flow fields.


  We study the factorization method for the inverse acoustic scattering
problems in the case of limited aperture data. In this case, the factorization
of the far field operator is not symmetric. So, we can not apply the original
factorization method which requires the symmetricity of the factorization. In
this paper, we provide a new functional analytic theorem corresponding to the
case of limited aperture data, and by applying it, we give the
characterizations of the unknown domain.


  We study the duality between the mutations and the initial-seed mutations in
cluster algebras, where the initial-seed mutations are the transformations of
rational expressions of cluster variables in terms of the initial cluster under
the change of the initial cluster. In particular, we define the maximal degree
matrices of the $F$-polynomials called the $F$-matrices and show that the
$F$-matrices have the self-duality which is analogous to the duality between
the $C$- and $G$-matrices.


  Knowledge graphs are an expressive and widely used data structure due to
their ability to integrate data from different domains in a sensible and
machine-readable way. Thus, they can be used to model a variety of systems such
as molecules and social networks. However, it still remains an open question
how symbolic reasoning could be realized in spiking systems and, therefore, how
spiking neural networks could be applied to such graph data. Here, we extend
previous work on spike-based graph algorithms by demonstrating how symbolic and
multi-relational information can be encoded using spiking neurons, allowing
reasoning over symbolic structures like knowledge graphs with spiking neural
networks. The introduced framework is enabled by combining the graph embedding
paradigm and the recent progress in training spiking neural networks using
error backpropagation. The presented methods are applicable to a variety of
spiking neuron models and can be trained end-to-end in combination with other
differentiable network architectures, which we demonstrate by implementing a
spiking relational graph neural network.


  We discuss why the tunneling picture does not necessarily lead to Hawking
radiation from the de Sitter horizon. The experience with the condensed matter
analogs of event horizon suggests that the de Sitter vacuum is stable against
the Hawking radiation. On the other hand the detector immersed into the de
Sitter background will detect the radiation which looks as thermal with the
effective temperature twice larger than the Hawking temperature associated with
the cosmological horizon.


  We prove Juhl type formulas for the curved Ovsienko--Redou operators and
their linear analogues, which indicate the associated formal self-adjointness,
thereby confirming two conjectures of Case, Lin, and Yuan. We also offer an
extension of Juhl's original formula for the GJMS operators.


  We study a simplified scenario in the next-to-minimal supersymmetric standard
model with a split electroweak spectrum, in which only the singlino and
higgsinos are light and other superpartners are decoupled. Serving as a dark
matter candidate, a singlino-dominated neutralino $\tilde{\chi}_1^0$ should
have either resonant annihilation effects or sizable higgsino components to
satisfy the observed relic abundance. The sensitivities of LHC searches and
dark matter detection experiments are investigated. With an integrated
luminosity of $30 (300) \mathrm{fb}^{-1}$, $3l + E_\mathrm{T} \!\!\!\!\!\!\!/
\;\;\;$and $2l + E_\mathrm{T} \!\!\!\!\!\!\!/ \;\;\;$ searches at the 13 (14)
TeV LHC are expected to reach up to $m_{\tilde{\chi}_1^0}\sim 150 (230)
\mathrm{GeV}$ and $m_{\tilde{\chi}_2^0,\tilde{\chi}_1^{\pm}}\sim 320 (480)
\mathrm{GeV}$. Near future dark matter direct and indirect detection
experiments can cover some parameter regions where collider searches lose their
sensitivities.


  The circular Dyson Brownian motion model refers to the stochastic dynamics of
the log-gas on a circle. It also specifies the eigenvalues of certain
parameter-dependent ensembles of unitary random matrices. This model is
considered with the initial condition that the particles are non-interacting
(Poisson statistics). Jack polynomial theory is used to derive a simple exact
expression for the density-density correlation with the position of one
particle specified in the initial state, and the position of one particle
specified at time $\tau$, valid for all $\beta > 0$.
  The same correlation with two particles specified in the initial state is
also derived exactly, and some special cases of the theoretical correlations
are illustrated by comparison with the empirical correlations calculated from
the eigenvalues of certain parameter-dependent Gaussian random matrices.
Application to fluctuation formulas for time displaced linear statistics in
made.


  This paper introduces the system submitted by dun_oscar team for the ICPR MSR
Challenge. Three subsystems for task1-task3 are descripted respectively. In
task1, we develop a visual system which includes a OCR model, a text tracker,
and a NLP classifier for distinguishing subtitles and non-subtitles. In task2,
we employ an ASR system which includes an AM with 18 layers and a 4-gram LM.
Semi-supervised learning on unlabeled data is also vital. In task3, we employ
the ASR system to improve the visual system, some false subtitles can be
corrected by a fusion module.


  We present an update of the results for pion mass and pion decay constant as
obtained by the ETM collaboration in large scale simulations with maximally
twisted mass fermions and two mass degenerate flavours of light quarks. We
discuss the continuum, chiral and infinite volume extrapolation of these
quantities as well as the extraction of low energy constants, and investigate
possible systematic uncertainties.


  The time evolution of the quark gluon plasma created in gold-gold collisions
of the Relativistic Heavy Ion Collider (RHIC) can be described by
hydrodynamical models. Distribution of hadrons reflects the freeze-out state of
the matter. To investigate the time evolution one needs to analyze penetrating
probes, such as direct photon spectra. Distributions of low energy photons was
published in 2010 by PHENIX. In this paper we analyze a 3+1 dimensional
solution of relativistic hydrodynamics and calculate momentum distribution of
direct photons. Using earlier fits of this model to hadronic spectra, we
compare photon calculations to measurements and find that the initial
temperature of the center of the fireball is at least 519+-12 MeV, while for
the equation of state we get c_s= 0.36+-0.02.


  The pair correlations of primitive inflation rules are analysed via their
exact renormalisation relations. We introduce the inflation displacement
algebra that is generated by the Fourier matrix of the inflation and deduce
various consequences of its structure. Moreover, we derive a sufficient
criterion for the absence of absolutely continuous diffraction components, as
well as a necessary criterion for its presence. This is achieved via estimates
for the Lyapunov exponents of the Fourier matrix cocycle of the inflation rule.
We also discuss some consequences for the spectral measures of such systems.
While we develop the theory first for the classic setting in one dimension, we
also present its extension to primitive inflation rules in higher dimensions
with finitely many prototiles up to translations.


  I consider a theoretical description of recent experiments on doping the
spin-Peierls compound CuGeO$_3$ and the Haldane gap compounds
PbNi$_2$V$_2$O$_8$ and Y$_2$BaNiO$_5$. The effective theory is the one of
randomly distributed spin-$1/2$ moments interacting with an exchange decaying
exponentially with distance. The model has two phases in the (doping,
interchain coupling) plane: (i) A N\'eel ordered phase at small doping; (ii) A
quantum disordered phase at larger doping and small interchain interactions.
The spin-Peierls compound CuGeO$_3$ and the Haldane gap Nickel oxides
PbNi$_2$V$_2$O$_8$ and Y$_2$BaNiO$_5$ fit well into this phase diagram. At
small temperature, the N\'eel phase is found to be reentrant into the quantum
disordered region. The N\'eel transition relevant for CuGeO$_3$ and
PbNi$_2$V$_2$O$_8$ can be described in terms of a classical disordered model. A
simplified version of this model is introduced, and is solved on a hierarchical
lattice structure, which allows to discuss the renormalization group flow of
the model. It is found that the system looks non disordered at large scale,
which is not against available susceptibility experiments. In the quantum
disordered regime relevant for Y$_2$BaNiO$_5$, the two spin model and the
cluster RG in the 1D regime show a power law susceptibility, in agreement with
recent experiments on Y$_2$BaNiO$_5$. It is found that there is a succession of
two distinct quantum disordered phases as the temperature is decreased. The
classical disordered model of the doped spin-$1$ chain contains already a
physics relevant to the quantum disordered phase.


  In cellular automata with multiple speeds for each cell $i$ there is a
positive integer $p_i$ such that this cell updates its state still periodically
but only at times which are a multiple of $p_i$. Additionally there is a finite
upper bound on all $p_i$. Manzoni and Umeo have described an algorithm for
these (one-dimensional) cellular automata which solves the Firing Squad
Synchronization Problem. This algorithm needs linear time (in the number of
cells to be synchronized) but for many problem instances it is slower than the
optimum time by some positive constant factor. In the present paper we derive
lower bounds on possible synchronization times and describe an algorithm which
is never slower and in some cases faster than the one by Manzoni and Umeo and
which is close to a lower bound (up to a constant summand) in more cases.


  We analyze the possibility of delensing CMB polarization maps using
foreground weak lensing (WL) information. We build an estimator of the CMB
lensing potential out of optimally combined projected potential estimators to
different source redshift bins. Our estimator is most sensitive to the redshift
depth of the WL survey, less so to the shape noise level. Estimators built
using galaxy surveys like LSST and SNAP yield a 30-50% reduction in the lensing
B-mode power. We illustrate the potential advantages of a 21-cm survey by
considering a fiducial WL survey for which we take the redshift depth zmax and
the effective angular concentration of sources n as free parameters. For a
noise level of 1 muK arcmin in the polarization map itself, as projected for a
CMBPol experiment, and a beam with FWHM=10 arcmin, we find that going to
zmax=20 at n=100 gal/sqarcmin yields a delensing performance similar to that of
a quadratic lensing potential estimator applied to small-scale CMB maps: the
lensing B-mode contamination is reduced by almost an order of magnitude. In
this case, there is also a reduction by a factor of ~4 in the detectability
threshold of the tensor B-mode power. At this CMB noise level, there is little
gain from sources with zmax>20. The delensing gains are lost if the CMB beam
exceeds ~20 arcmin. The delensing efficiency and useful zmax depend acutely on
the CMB map noise level, but beam sizes below 10 arcmin do not help. Delensing
via foreground sources does not require arcminute-resolution CMB observations,
a substantial practical advantage over the use of CMB observables for
delensing.


  The study of socioeconomic status has been reformed by the availability of
digital records containing data on real estate, points of interest, traffic and
social media trends such as micro-blogging. In this paper, we describe a
heterogeneous, multi-source, multi-modal, multi-view and multi-distributional
dataset named "H4M". The mixed dataset contains data on real estate
transactions, points of interest, traffic patterns and micro-blogging trends
from Beijing, China. The unique composition of H4M makes it an ideal test bed
for methodologies and approaches aimed at studying and solving problems related
to real estate, traffic, urban mobility planning, social sentiment analysis
etc. The dataset is available at: https://indigopurple.github.io/H4M/index.html


  We introduce new multilevel methods for solving large-scale unconstrained
optimization problems. Specifically, the philosophy of multilevel methods is
applied to Newton-type methods that regularize the Newton sub-problem using
second order information from a coarse (low dimensional) sub-problem. The new
\emph{regularized multilevel methods} provably converge from any initialization
point and enjoy faster convergence rates than Gradient Descent. In particular,
for arbitrary functions with Lipschitz continuous Hessians, we show that their
convergence rate interpolates between the rate of Gradient Descent and that of
the cubic Newton method. If, additionally, the objective function is assumed to
be convex, then the proposed method converges with the fast
$\mathcal{O}(k^{-2})$ rate. Hence, since the updates are generated using a
\emph{coarse} model in low dimensions, the theoretical results of this paper
significantly speed-up the convergence of Newton-type or preconditioned
gradient methods in practical applications. Preliminary numerical results
suggest that the proposed multilevel algorithms are significantly faster than
current state-of-the-art methods.


  We use HST/ACS imaging of 100 early-type galaxies in the ACS Virgo Cluster
Survey to investigate the nature of diffuse star clusters (DSCs). Compared to
globular clusters (GCs), these star clusters have moderately low luminosities
(M_V > -8) and a broad distribution of sizes (3 < r_h < 30 pc), but they are
principally characterized by their low mean surface brightnesses which can be
more than three magnitudes fainter than a typical GC (mu_g > 20 mag arcsec^-2).
The median colors of diffuse star cluster systems are red, 1.1 < g-z < 1.6,
which is redder than metal-rich GCs and often as red as the galaxy itself. Most
DSC systems thus have mean ages older than 5 Gyr or else have super-solar
metallicities. We find that 12 galaxies in our sample contain a significant
excess of diffuse star cluster candidates -- nine are lenticulars (S0s), and
five visibly contain dust. We also find a substantial population of DSCs in the
halo of the giant elliptical M49, associated with the companion galaxy VCC
1199. Most DSC systems appear to be both aligned with the galaxy light and
associated with galactic disks, but at the same time many lenticulars do not
host substantial DSC populations. Diffuse star clusters in our sample share
similar characteristics to those identified in other nearby lenticular, spiral,
and dwarf galaxies, and we suggest that DSCs preferentially form, survive, and
coevolve with galactic disks. Their properties are broadly consistent with
those of merged star cluster complexes, and we note that despite being 3 - 5
magnitudes brighter than DSCs, ultra-compact dwarfs have similar surface
brightnesses. The closest Galactic analogs to the DSCs are the old open
clusters. We suggest that if a diffuse star cluster population did exist in the
disk of the Milky Way, it would be very difficult to find. (Abridged)


  Efforts to make machine learning more widely accessible have led to a rapid
increase in Auto-ML tools that aim to automate the process of training and
deploying machine learning. To understand how Auto-ML tools are used in
practice today, we performed a qualitative study with participants ranging from
novice hobbyists to industry researchers who use Auto-ML tools. We present
insights into the benefits and deficiencies of existing tools, as well as the
respective roles of the human and automation in ML workflows. Finally, we
discuss design implications for the future of Auto-ML tool development. We
argue that instead of full automation being the ultimate goal of Auto-ML,
designers of these tools should focus on supporting a partnership between the
user and the Auto-ML tool. This means that a range of Auto-ML tools will need
to be developed to support varying user goals such as simplicity,
reproducibility, and reliability.


  Let X be a smooth projective variety. Starting with a finite set of cycles on
powers X^m of X, we consider the Q-vector subspaces of the Q-linear Chow groups
of the X^m obtained by iterating the algebraic operations and pullback and push
forward along those morphisms X^l -> X^m for which each component X^l -> X is a
projection. It is shown that these Q-vector subspaces are all
finite-dimensional, provided that the Q-linear Chow motive of X is a direct
summand of that of an abelian variety.


  We improve known estimates for the number of points of bounded height in
semigroup orbits of polarized dynamical systems. In particular, we give exact
asymptotics for generic semigroups acting on the projective line. The main new
ingredient is the Wiener-Ikehara Tauberian theorem, which we use to count
functions in semigroups of bounded degree.


  The study of canonically complete attribute-based access control (ABAC)
languages is relatively new. A canonically complete language is useful as it is
functionally complete and provides a "normal form" for policies. However,
previous work on canonically complete ABAC languages requires that the set of
authorization decisions is totally ordered, which does not accurately reflect
the intuition behind the use of the allow, deny and not-applicable decisions in
access control. A number of recent ABAC languages use a fourth value and the
set of authorization decisions is partially ordered. In this paper, we show how
canonical completeness in multi-valued logics can be extended to the case where
the set of truth values forms a lattice. This enables us to investigate the
canonical completeness of logics having a partially ordered set of truth
values, such as Belnap logic, and show that ABAC languages based on Belnap
logic, such as PBel, are not canonically complete. We then construct a
canonically complete four-valued logic using connections between the generators
of the symmetric group (defined over the set of decisions) and unary operators
in a canonically suitable logic. Finally, we propose a new authorization
language $\text{PTaCL}_{\sf 4}^{\leqslant}$, an extension of PTaCL, which
incorporates a lattice-ordered decision set and is canonically complete. We
then discuss how the advantages of $\text{PTaCL}_{\sf 4}^{\leqslant}$ can be
leveraged within the framework of XACML.


  We investigate the question which Q-valued characters and characters of
Q-representations of finite groups are Z-linear combinations of permutation
characters. This question is known to reduce to that for quasi-elementary
groups, and we give a solution in that case. As one of the applications, we
exhibit a family of simple groups with rational representations whose smallest
multiple that is a permutation representation can be arbitrarily large.


  The temperature structure of a giant planet was traditionally thought to be
an adiabat assuming convective mixing homogenizes entropy. The only in-situ
measurement made by the Galileo Probe detected a near-adiabatic temperature
structure within one of Jupiter's 5$\mu$m hot spots with small but definite
local departures from adiabaticity. We analyze Juno's microwave observations
near Jupiter's equator (0 ~ 5$^o$N) and find that the equatorial temperature
structure is best characterized by a stable super-adiabatic temperature profile
rather than an adiabatic one. Water is the only substance with sufficient
abundance to alter the atmosphere's mean molecular weight and prevent dynamic
instability if a super-adiabatic temperature gradient exists. Thus, from the
super-adiabaticity, our results indicate a water concentration (or the oxygen
to hydrogen ratio) of about 4.9 times solar with a possible range of 1.5 ~ 8.3
times solar in Jupiter's equatorial region.


  The goal of this work is to study an infectious disease spreading in a medium
size population occupying a confined environment. For this purpose, we consider
a kinetic theory approach to model crowd dynamics in bounded domains and couple
it to a kinetic equation to model contagion. The interactions of a person with
other pedestrians and the environment are modeled by using tools of game
theory. The pedestrian dynamics model allows to weight between two competing
behaviors: the search for less congested areas and the tendency to follow the
stream unconsciously in a panic situation. Each person in the system has a
contagion level that is affected by their neighborhood. For the numerical
solution of the coupled problem, we propose a numerical algorithm that at every
time step solves one crowd dynamics problem and one contagion problem, i.e.
with no subiterations between the two. We test our coupled model on a problem
involving a small crowd walking through a corridor.


  The widespread use of smartphones gives rise to new security and privacy
concerns. Smartphone thefts account for the largest percentage of thefts in
recent crime statistics. Using a victim's smartphone, the attacker can launch
impersonation attacks, which threaten the security of the victim and other
users in the network. Our threat model includes the attacker taking over the
phone after the user has logged on with his password or pin. Our goal is to
design a mechanism for smartphones to better authenticate the current user,
continuously and implicitly, and raise alerts when necessary. In this paper, we
propose a multi-sensors-based system to achieve continuous and implicit
authentication for smartphone users. The system continuously learns the owner's
behavior patterns and environment characteristics, and then authenticates the
current user without interrupting user-smartphone interactions. Our method can
adaptively update a user's model considering the temporal change of user's
patterns. Experimental results show that our method is efficient, requiring
less than 10 seconds to train the model and 20 seconds to detect the abnormal
user, while achieving high accuracy (more than 90%). Also the combination of
more sensors provide better accuracy. Furthermore, our method enables adjusting
the security level by changing the sampling rate.


  Opher et al. (2015), Drake et al. (2015) have shown that the heliospheric
magnetic field results in formation of two-jet structure of the solar wind flow
in the inner heliosheath, i.e. in the subsonic region between the heliospheric
termination shock and the heliopause. In this scenario the heliopause has a
tube-like topology as compared with a sheet-like topology in the most models of
the global heliosphere (e.g. Izmodenov and Alexashov, 2015). In this paper we
explore the two-jet scenario for a simplified astrosphere in which 1) the star
is at rest with respect to the circumstellar medium, 2) radial magnetic field
is neglected as compared with azimuthal component, 3) the stellar wind outflow
is assumed to be hypersonic (both the Mach number and the Alfv\'enic Mach
number are much greater than unity at the inflow boundary). We have shown that
the problem can be formulated in dimensionless form, in which the solution
depends only on one dimensionless parameter epsilon that is reciprocal of the
Alfv\'enic Mach number at the inflow boundary. This parameter is proportional
to stellar magnetic field. We present the numerical solution of the problem for
various values of epsilon. Three first integrals of the governing ideal MHD
equations are presented, and we make use of them in order to get the plasma
distribution in the jets. Simple relations between distances to the termination
shock, astropause and the size of the jet are established. These relations
allow us to determine the stellar magnetic field from the geometrical pattern
of the jet-like astrosphere.


  In this paper we generalize to higher dimensions several types of fermion
actions on the hyperdiamond lattice including a two-parameter class of
minimal-doubling fermions "Creutz fermion" and a simple fermion with sufficient
discrete symmetry "BBTW fermion". Then it is shown that they possess some
properties in common with the four-dimensional case: BBTW fermions in higher
even dimensions inevitably yield unphysical degrees of freedom. Creutz fermions
are defined on the distorted lattices, and they lose the high discrete symmetry
of the original lattices. We also find properties specific to the
higher-dimensional cases. The parameter range for Creutz action to yield
minimal-doubling and physical fermions becomes narrower with the dimension
getting higher, thus it becomes more and more difficult to realize
minimal-doubling. In addition, we generalize the subspecies of Creutz and BBTW
actions including a new class of minimal-doubling actions "Appended Creutz
action".


  In this note we provide a negative answer to a question raised by M. Kreisel
concerning a condition on the short-time Fourier transform that would imply the
HRT conjecture. In particular we provide a new type of uncertainty principle
for the short-time Fourier transform which forbids the arrangement of an
arbitrary "bump with fat tail" profile.


  We study the ground state energy E_G(n) of N classical n-vector spins with
the hamiltonian H = - \sum_{i>j} J_ij S_i.S_j where S_i and S_j are n-vectors
and the coupling constants J_ij are arbitrary. We prove that E_G(n) is
independent of n for all n > n_{max}(N) = floor((sqrt(8N+1)-1) / 2) . We show
that this bound is the best possible. We also derive an upper bound for E_G(m)
in terms of E_G(n), for m<n. We obtain an upper bound on the frustration in the
system, as measured by F(n), which is defined to be (\sum_{i>j} |J_ij| +
E_G(n)) / (\sum_{i>j} |J_ij|). We describe a procedure for constructing a set
of J_ij's such that an arbitrary given state, {S_i}, is the ground state.


  Multiple-Input Multiple-Output (MIMO) systems are essential for wireless
communications. Sinceclassical algorithms for symbol detection in MIMO setups
require large computational resourcesor provide poor results, data-driven
algorithms are becoming more popular. Most of the proposedalgorithms, however,
introduce approximations leading to degraded performance for realistic
MIMOsystems. In this paper, we introduce a neural-enhanced hybrid model,
augmenting the analyticbackbone algorithm with state-of-the-art neural network
components. In particular, we introduce aself-attention model for the
enhancement of the iterative Orthogonal Approximate Message Passing(OAMP)-based
decoding algorithm. In our experiments, we show that the proposed model
canoutperform existing data-driven approaches for OAMP while having improved
generalization to otherSNR values at limited computational overhead.


  We have performed a high resolution specific heat measurement on 4He
completely filling the pores of Vycor glass. Within 10mK of the superfluid
transition we have found a peak in the heat capacity which is only 0.02% the
size of the background. The peak can be fit with a rounded version of the
``logarithmic singularity'' observed in bulk 4He. Along with the previously
observed ``2/3'' power law dependence of the superfluid density on temperature,
this strongly suggests that the disorder imposed by the Vycor is irrelevant to
the 3DXY superfluid phase transition. The critical amplitude of the peak, while
in agreement with that found in other experiments in dilute superfluid 4He, is
considerably larger than that predicted by the theory of hyperuniversality.


  Lookahead search has been a critical component of recent AI successes, such
as in the games of chess, go, and poker. However, the search methods used in
these games, and in many other settings, are tabular. Tabular search methods do
not scale well with the size of the search space, and this problem is
exacerbated by stochasticity and partial observability. In this work we replace
tabular search with online model-based fine-tuning of a policy neural network
via reinforcement learning, and show that this approach outperforms
state-of-the-art search algorithms in benchmark settings. In particular, we use
our search algorithm to achieve a new state-of-the-art result in self-play
Hanabi, and show the generality of our algorithm by also showing that it
outperforms tabular search in the Atari game Ms. Pacman.


  Using the cumulative catalog of planets detected by the NASA Kepler mission,
we reconstruct the intrinsic occurrence of Earth- to Neptune-size (1 -
4$R_{\oplus}$) planets and their distributions with radius and orbital period.
We analyze 76,711 solar-type ($0.8<R_*/R_{\odot}<1.2 $) stars with 430 planets
on 20-200~d orbits, excluding close-in planets that may have been affected by
the proximity to the host star. Our analysis considers errors in planet radii
and includes an "iterative simulation" technique that does not bin the data. We
find a radius distribution that peaks at 2-2.8 Earth radii, with lower numbers
of smaller and larger planets. These planets are uniformly distributed with
logarithmic period, and the mean number of such planets per star is $0.46 \pm
0.03$. The occurrence is $\sim 0.66$ if planets interior to 20~d are included.
We estimate the occurrence of Earth-size planets in the "habitable zone"
(defined as 1-2$R_{\oplus}$, 0.99-1.7 AU for solar-like stars) as
$6.4^{+3.4}_{-1.1} \%$. Our results largely agree with those of Petigura et al.
(2013), although we find a higher occurrence of 2.8-4 Earth-radii planets. The
reasons for this excess are the inclusion of errors in planet radius, updated
Huber et al. (2014) stellar parameters, and also the exclusion of planets which
may have been affected by proximity to the host star.


  In this talk, after reviewing the dynamical gluon mass generation mechanism
within the pinch technique framework and its phenomenological predictions, we
will introduce the modern formulation of the pinch technique which makes
extensive use of the Batalin-Vilkovisky quantization formalism. In this
framework a certain auxiliary function \Lambda_{\mu\nu}(q) - and its associated
form factors G(q^2) and L(q^2) -play a prominent role. After showing that in
the (background) Landau gauge \Lambda_{\mu\nu}(q) fully constrains the QCD
ghost sector, we show that G(q^2) coincides with the Kugo-Ojima function
u(q^2), whose infrared behavior has traditionally served as the standard
criterion for the realization of the Kugo-Ojima confinement mechanism. The
determination of the behavior of G(q^2) (and therefore of the Kugo-Ojima
function) for all momenta through a combination of the available lattice data
on the gluon and ghost propagators as well as the dynamical equation G(q^2)
satisfies, will be then discussed. In particular we will show that (i) in the
deep infrared the function deviates considerably from the value associated with
the realization of the Kugo-Ojima confinement scenario, and (ii) establish the
dependence on the renormalization point of u(q^2), and especially of its value
at q^2=0.


  The comparison of the free precession of co-located 3He-129Xe spins (clock
comparison) enables us to search for very tiny nonmagnetic spin interactions.
With our setup we could establish new limits for Lorentz invariance violating
interactions of spins with a relic background field which permeates the
Universe and points in a preferred direction in space.


  We investigate the computational complexity of the discrete logarithm, the
computational Diffie-Hellman and the decisional Diffie-Hellman problems in some
identity black-box groups G_{p,t}, where p is a prime number and t is a
positive integer. These are defined as quotient groups of vector space
Z_p^{t+1} by a hyperplane H given through an identity oracle. While in general
black-box groups with unique encoding these computational problems are
classically all hard and quantumly all easy, we find that in the groups G_{p,t}
the situation is more contrasted. We prove that while there is a polynomial
time probabilistic algorithm to solve the decisional Diffie-Hellman problem in
$G_{p,1}$, the probabilistic query complexity of all the other problems is
Omega(p), and their quantum query complexity is Omega(sqrt(p)). Our results
therefore provide a new example of a group where the computational and the
decisional Diffie-Hellman problems have widely different complexity.


  Supercontinuum generation in Kerr media has become a staple of nonlinear
optics. It has been celebrated for advancing the understanding of soliton
propagation as well as its many applications in a broad range of fields.
Coherent spectral broadening of laser light is now commonly performed in
laboratories and used in commercial white light sources. The prospect of
miniaturizing the technology is currently driving experiments in different
integrated platforms such as semiconductor on insulator waveguides. Central to
the spectral broadening is the concept of higher-order soliton fission. While
widely accepted in silica fibers, the dynamics of soliton decay in
semiconductor waveguides is yet poorly understood. In particular, the role of
nonlinear loss and free carriers, absent in silica, remains an open question.
Here, through experiments and simulations, we show that nonlinear loss is the
dominant perturbations in wire waveguides, while free-carrier dispersion is
dominant in photonic crystal waveguides.


  Keyword spotting accuracy degrades when neural networks are exposed to noisy
environments. On-site adaptation to previously unseen noise is crucial to
recovering accuracy loss, and on-device learning is required to ensure that the
adaptation process happens entirely on the edge device. In this work, we
propose a fully on-device domain adaptation system achieving up to 14% accuracy
gains over already-robust keyword spotting models. We enable on-device learning
with less than 10 kB of memory, using only 100 labeled utterances to recover 5%
accuracy after adapting to the complex speech noise. We demonstrate that domain
adaptation can be achieved on ultra-low-power microcontrollers with as little
as 806 mJ in only 14 s on always-on, battery-operated devices.


  The relationship between solar activity and the global climate is not only an
academically interesting issue, but also an important problem for human beings.
Lean and Rind have analyzed a considerable amount of climate data from around
the world from 1889 to 2006. According to their analysis, the global effect was
estimated to be 0.17 plus-minus 0.01K between the solar maximum and minimum.
However, they noticed that the effect strongly appeared in the zones between
70N and 30N, and between 25S and 50S. At its peak latitude (near 40), the
effect was estimated to be 0.5 K. Therefore, we analyzed a tree that survived
at the Murooji temple in Nara prefecture (34d32mN, 136d2mE) Japan, for 391
years.
  Quite surprisingly, Fourier analysis of the annual growth rate identified two
cycles with periodicities of 12 and 25 years during the Maunder minimum. We
have continued the analysis, using different samples from the Nagusa shrine in
Hyogo Prefecture, Itayanagi City, Aomori Prefecture, Japan, and from Yaku
Island in Kyusyu, Japan. An evidence of solar activity was found in all of the
samples. In particular, we have discovered a correlation between Swiss glacier
fluctuation and the growth rate of the Yaku tree ring.


  Shrinkage estimates of small domain parameters typically utilize a
combination of a noisy "direct" estimate that only uses data from a specific
small domain and a more stable regression estimate. When the regression model
is misspecified, estimation performance for the noisier domains can suffer due
to substantial shrinkage towards a poorly estimated regression surface. In this
paper, we introduce a new class of robust, empirically-driven regression
weights that target estimation of the small domain means under potential
misspecification of the global regression model. Our regression weights are a
convex combination of the model-based weights associated with the best linear
unbiased predictor (BLUP) and those associated with the observed best predictor
(OBP). The compromise parameter in this convex combination is found by
minimizing a novel, unbiased estimate of the mean-squared prediction error for
the small domain means, and we label the associated small domain estimates the
"compromise best predictor", or CBP. Using a data-adaptive mixture for the
regression weights enables the CBP to possess the robustness of the OBP while
retaining the main advantages of the EBLUP whenever the regression model is
correct. We demonstrate the use of the CBP in an application estimating gait
speed in older adults.


  We have made a precise measurement of the absolute branching fractions of
Bs->Ds(*)+ Ds(*)- decays using 121.4 fb-1 of data recorded by the Belle
experiment running on the Upsilon(5S) resonance. The results are B(Bs0->Ds+
Ds-) = (0.58 +0.11 -0.09 +-0.13)%, B(Bs0->Ds*+- Ds-+) = (1.8 +-0.2 +-0.4)%, and
B(Bs0->Ds*+ Ds*-) = (2.0 +-0.3 +-0.5)%; the sum is B(Bs0->Ds(*)+ Ds(*)- = (4.3
+-0.4 +-1.0)%. Assuming Bs->Ds(*)+ Ds(*)- saturates decays to CP-even final
states, the branching fraction constrains the ratio Delta(Gamma_s)/cos(phi_s),
where Delta(Gamma_s) is the difference in widths between the two Bs0-Bs0bar
mass eigenstates, and phi_s is the CP-violating phase in Bs0-Bs0bar mixing. For
phi_s=0 we obtain Delta(Gamma_s)/Gamma_s = 0.090 +-0.009 (stat.) +-0.023
(syst.), where Gamma_s is the mean decay width. We also measure for the first
time the longitudinal polarization fraction of Bs->Ds*+ Ds*-; the result is
0.12 +0.30 -0.28 +0.04 -0.05.


  We have shown theoretically that efficient multiple exciton generation (MEG)
by a single photon can be observed in small nanocrystals (NCs). Our quantum
simulations that include hundreds of thousands of exciton and multi-exciton
states demonstrate that the complex time-dependent dynamics of these states in
a closed electronic system yields a saturated MEG effect on a picosecond
timescale. Including phonon relaxation confirms that efficient MEG requires the
exciton--biexciton coupling time to be faster than exciton relaxation time.


  The R\'{e}nyi and von Neumann entropies of the thermal state in the
generalized uncertainty principle (GUP)-corrected single harmonic oscillator
system are explicitly computed within the first order of the GUP parameter
$\alpha$. While the von Neumann entropy with $\alpha = 0$ exhibits a
monotonically increasing behavior in external temperature, the nonzero GUP
parameter makes the decreasing behavior of the von Neumann entropy at the large
temperature region. As a result, the von Neumann entropy is maximized at the
finite temperature if $\alpha \neq 0$. The R\'{e}nyi entropy $S_{\gamma}$ with
nonzero $\alpha$ also exhibits similar behavior at the large temperature
region. In this region the R\'{e}nyi entropy exhibit decreasing behavior with
increasing the temperature. The decreasing rate becomes larger when the order
of the R\'{e}nyi entropy $\gamma$ is smaller.


  GNU Data Language (GDL) is an open-source interpreted language aimed at
numerical data analysis and visualisation. It is a free implementation of the
Interactive Data Language (IDL) widely used in Astronomy. GDL has a full syntax
compatibility with IDL, and includes a large set of library routines targeting
advanced matrix manipulation, plotting, time-series and image analysis,
mapping, and data input/output including numerous scientific data formats. We
will present the current status of the project, the key accomplishments, and
the weaknesses - areas where contributions are welcome !


  We develop a consistent stochastic thermodynamics for environments composed
of thermodynamic reservoirs in an external conservative force field, that is
environments described by the Generalized or Gibbs canonical ensemble. We
demonstrate that small systems weakly coupled to such reservoirs exchange both
heat and work by verifying a local detailed balance relation for the induced
stochastic dynamics. Based on this analysis, we help to rationalize the
observation that nonthermal reservoirs can increase the efficiency of
thermodynamic heat engines.


  The two dimensional gravity water wave problem concerns the motion of an
incompressible fluid occupying half the 2D space and flowing under its own
gravity. In this paper we study long-term regularity of solutions evolving from
small but non-localized initial data.
  Our main result is that if the $H^s$ norm of the initial data is $\epsilon$,
where $s \gg 1$ and $\epsilon \ll 1$, then the equation is wellposed at least
for a time proportional to $\epsilon^{-4}$, improving on the $\epsilon^{-3}$
lifespan obtained in \cite{BeFePu,Wu2DL}. We also study period water waves and
show a lifespan bridging the gap between non-periodic waves and waves with a
period of 1.


  High-fidelity and rapid readout of a qubit state is key to quantum computing
and communication, and it is a prerequisite for quantum error correction. We
present a readout scheme for superconducting qubits that combines two microwave
techniques: applying a shelving technique to the qubit that effectively
increases the energy-relaxation time, and a two-tone excitation of the readout
resonator to distinguish among qubit populations in higher energy levels. Using
a machine-learning algorithm to post-process the two-tone measurement results
further improves the qubit-state assignment fidelity. We perform single-shot
frequency-multiplexed qubit readout, with a 140ns readout time, and demonstrate
99.5% assignment fidelity for two-state readout and 96.9% for three-state
readout - without using a quantum-limited amplifier.


  We consider the Gibbs sampler, or heat bath dynamics associated to
log-concave measures on $\mathbb{R}^N$ describing $\nabla\varphi$ interfaces
with convex potentials. Under minimal assumptions on the potential, we find
that the spectral gap of the process is always given by
$\mathrm{gap}_N=1-\cos(\pi/N)$, and that for all $\epsilon\in(0,1)$, its
$\epsilon$-mixing time satisfies $T_N(\epsilon)\sim \frac{\log
N}{2\mathrm{gap}_N}$ as $N\to\infty$, thus establishing the cutoff phenomenon.
The results reveal a universal behavior in that they do not depend on the
choice of the potential.


  We fix three natural numbers $k, n, N$, such that $n+k+1=N$, and introduce
the notion of two dual arrangements of hyperplanes. One of the arrangements is
an arrangement of $N$ hyperplanes in a $k$-dimensional affine space, the other
is an arrangement of $N$ hyperplanes in an $n$-dimensional affine space. We
assign weights $\al_1, ..., \al_N$ to the hyperplanes of the arrangements and
for each of the arrangements consider the associated period matrices. The first
is a matrix of $k$-dimensional hypergeometric integrals and the second is a
matrix of $n$-dimensional hypergeometric integrals. The size of each matrix is
equal to the number of bounded domains of the corresponding arrangement. We
show that the dual arrangements have the same number of bounded domains and the
product of the determinants of the period matrices is equal to an alternating
product of certain values of Euler's gamma function multiplied by a product of
exponentials of the weights.


  One of the fundamental challenges in causal inference is to estimate the
causal effect of a treatment on its outcome of interest from observational
data. However, causal effect estimation often suffers from the impacts of
confounding bias caused by unmeasured confounders that affect both the
treatment and the outcome. The instrumental variable (IV) approach is a
powerful way to eliminate the confounding bias from latent confounders.
However, the existing IV-based estimators require a nominated IV, and for a
conditional IV (CIV) the corresponding conditioning set too, for causal effect
estimation. This limits the application of IV-based estimators. In this paper,
by leveraging the advantage of disentangled representation learning, we propose
a novel method, named DVAE.CIV, for learning and disentangling the
representations of CIV and the representations of its conditioning set for
causal effect estimations from data with latent confounders. Extensive
experimental results on both synthetic and real-world datasets demonstrate the
superiority of the proposed DVAE.CIV method against the existing causal effect
estimators.


  We review some recent results obtained by our group on models of neutrino
masses and mixings in the general context of SUSY GUT's with an associated
flavour symmetry.


  A large branch of explainable machine learning is grounded in cooperative
game theory. However, research indicates that game-theoretic explanations may
mislead or be hard to interpret. We argue that often there is a critical
mismatch between what one wishes to explain (e.g. the output of a classifier)
and what current methods such as SHAP explain (e.g. the scalar probability of a
class). This paper addresses such gap for probabilistic models by generalising
cooperative games and value operators. We introduce the distributional values,
random variables that track changes in the model output (e.g. flipping of the
predicted class) and derive their analytic expressions for games with Gaussian,
Bernoulli and Categorical payoffs. We further establish several characterising
properties, and show that our framework provides fine-grained and insightful
explanations with case studies on vision and language models.


  We consider the Sobolev type spaces $H^\sigma(K)$ with $\sigma\geq 0$, where
$K$ is a post-critically finite self-similar set with the natural boundary.
Firstly, we compare different classes of Sobolev spaces
$H^\sigma_N(K),H^\sigma_D(K)$ and $H^\sigma(K)$, {and observe} a sequence of
critical orders of $\sigma$ in our comparison theorem. Secondly, We present a
general atomic decomposition theorem of Sobolev spaces $H^\sigma(K)$, where the
same critical orders play an important role. At the same time, we provide
purely analytic approaches for various Besov type characterizations of Sobolev
spaces $H^\sigma(K)$.


  The change in the arrangement of magnetic dipole moments in a magnetic
metasurface, due to the influence of an external static magnetic field, is
discussed. Each meta-atom of the metasurface is composed of three identical
disk-shaped resonators (trimer) made of magnetically saturated ferrite. To
provide physical insight, full-wave numerical simulations of the near-fields
and transmission characteristics of the metasurface are complemented by the
theoretical description based on the multipole decomposition method. With these
methods, the study of eigenmodes and scattering conditions of a single magnetic
resonator, trimer, and their array forming the metasurface is performed. It is
found that the magnetic dipole-based collective hybrid mode of the trimer can
be gradually transformed from the radial (pseudomonopole) to azimuthal
(toroidal) order and vice versa by varying the bias magnetic field strength.
This is because the magnetic dipole moment of each individual disk constituting
the trimer undergoes rotation as the bias magnetic field strength changes. This
transition between two orders is accompanied by various patterns of
localization of the electric field inside the meta-atoms. Due to the unique
field configuration of these modes, the proposed metasurface can be considered
for designing magnetic field sensors and nonreciprocal devices.


  We measure the impact of nonvanishing boundary localised terms on $\Delta
B=2$ transitions in five-dimensional Universal Extra Dimensional scenario where
masses and coupling strengths of several interactions of Kaluza-Klein modes are
significantly modified with respect to the minimal counterpart. In such
scenario we estimate the Kaluza-Klein contributions of quarks, gauge bosons and
charged Higgs by evaluating the one-loop box diagrams that are responsible for
the $\Delta B=2$ transitions. Using the loop function (obtained from one-loop
box diagrams) we determine several important elements that are involved in
Wolfenstein parametrisation. Moreover, with these elements we also study the
geometrical shape of unitarity triangle. Besides, we compute the quantity
$\Delta M_s$ scaled by the corresponding Standard Model value. Outcomes of our
theoretical predictions have been compared to the allowed ranges of the
corresponding observables simultaneously. Our current analysis shows that,
depending on the parameters in this scenario the lower limit on the inverse of
the radius of compactification can reach to an appreciable large value
($\approx 1.48$ TeV or even higher).


  We show how center vortices and Abelian monopoles both appear as local gauge
ambiguities in the Laplacian Center gauge. Numerical results, for SU(2) and
SU(3), support the view that the string tension obtained in the
center-projected theory matches the full string tension when the continuum
limit is taken.


  A recent version of statistical associating fluid theory (SAFT), namely
SAFT2, is coupled with the van der Waals and Platteeuw theory to study the
alkane hydrate phase equilibrium conditions. The model is found to provide an
accurate representation of the alkane hydrate dissociation conditions with and
without inhibitors, such as salts, alcohols, as well as mixed salts and
alcohol. Based on SAFT2, a heterosegmented SAFT equation of state is developed
to model the thermodynamic properties of aqueous ionic liquid (IL) solutions,
which is recently discovered as dual function gas hydrate inhibitors. With
transferrable model parameters, the heterosegmented SAFT generally well
represents the liquid density, activity coefficient, and osmotic coefficient of
aqueous imidazolium IL solutions. The inhibition effects of imidazolium IL on
methane hydrate is also studied by the heterosegmented SAFT and the van der
Waals and Platteeuw theory.
  The heterosegmented SAFT is then modified to better represent the
thermodynamic properties of aqueous IL solutions with the help of Monte Carlo
simulation and the solutions of Ornstein-Zernike integral equation. A simple
modification of MSA, referred to as KMSA, is proposed to accurately predict the
excess energies of electrolyte system in mixture with neutral component. Monte
Carlo simulations are also conducted on flexible charged hard-sphere chain
molecules, and a SAFT model which implements either a dimer or a dimer-monomer
approach to account for the charged chain connectivity is proposed.


  Extending the works arXiv:1504.05991 and arXiv:1510.02142, we study three
dimensional Euclidean higher spin gravity with negative cosmological constant.
This theory can be formulated in terms of SL(N,C) Chern-Simons theory. By
introducing auxiliary fields, we rewrite it in a supersymmetric way and compute
its partition function exactly by using the localization method. We obtain a
good expression for the partition function in terms of characters for the
vacuum and primaries in 2D unitary CFT with W_N symmetry. We also check that
the coefficients of the character expansion are positive integers and exhibit
Cardy formula in the large central charge limit.


  We study the effect of cooling on the spatial distribution of the topological
charge density in quenched SU(2) lattice gauge theory with overlap fermions. We
show that as the gauge field configurations are cooled, the Hausdorff dimension
of regions where the topological charge is localized gradually changes from d =
2..3 towards the total space dimension. Therefore, the cooling procedure
destroys some of the essential properties of the topological charge
distribution.


  (Ga,Mn)As in wurtzite crystal structure, is coherently grown by molecular
beam epitaxy on the {1100} side facets of wurtizte (Ga,In)As nanowires and
further encapsulated by (Ga,Al)As and low temperature GaAs. For the first time
a true long-range ferromagnetic magnetic order is observed in non-planar
(Ga,Mn)As, which is attributed to a more effective hole confinement in the
shell containing Mn by a proper selection/choice of both the core and outer
shell materials.


  We aim to determine the distribution of basaltic asteroids (classified as
V-types) based on the spectrophotometric data reported in the MOVIS-C
catalogue. A total of 782 asteroids were identified. The observations with all
four filters (Y, J, H, Ks), available for 297 of these candidates, allow a
reliable comparison with the laboratory data of howardite, eucrite, and
diogenite meteorites.
  We found that the majority of the basaltic candidates ($\approx$ 95$\%$) are
located in the inner main belt, while only 29 ($\approx$ 4$\%$) and 8
($\approx$ 1$\%$) are located in the middle and outer main belt, respectively.
A fraction of $\approx$ 33$\%$ from the V-type candidates is associated with
the Vesta family (with respect to AstDyS). We also identified four middle main
belt V-type candidates belonging to (15) Eunomia family, and another four low
inclination ones corresponding to (135) Hertha.
  We report differences between the color indices and albedo distributions of
the V-type candidates located in the inner main belt compared to those from the
middle and outer main belt. These results support the hypothesis of a different
origin for the basaltic asteroids with a semi-major axis beyond 2.5 A.U.
Furthermore, lithological differences are present between the vestoids and the
inner low inclination basaltic asteroids.
  The data allow us to estimate the unbiased distribution of basaltic asteroids
across the main asteroid belt. We highlight that at least 80$\%$ of the ejected
basaltic material from (4) Vesta is missing or is not yet detected because it
is fragmented in sizes smaller than 1 km.