  We examine the pattern of star birth in the Orion Nebula Cluster (ONC), with
the goal of discerning the cluster's formation mechanism. Outside of the
Trapezium, the distribution of stellar masses is remarkably uniform, and is not
accurately described by the field-star initial mass function. The deconvolved,
three-dimensional density of cluster members peaks at the Trapezium stars,
which are truly anomalous in mass. Using theoretical pre-main-sequence tracks,
we confirm the earlier finding that star formation has accelerated over the
past 10 Myr. We further show that the rate of acceleration has been the same
for all masses. Thus, there is no correlation between stellar age and mass,
contrary to previous claims. Finally, the acceleration has been spatially
uniform throughout the cluster. Our reconstruction of the parent molecular
cloud spawning the cluster shows that it had a mass of 6700 solar masses prior
to its destruction by the Trapezium. If the cloud was supported against
self-gravity by mildly dissipative turbulence, then it contracted in a
quasi-static, but accelerating manner. We demonstrate this contraction
theoretically through a simple energy argument. The mean turbulent speed
increased to its recent value, which is reflected in the present-day stellar
velocity dispersion. The current ONC will be gravitationally unbound once cloud
destruction is complete, and is destined to become a dispersing OB association.
We hypothesize that similarly crowded groups seen at the centers of distant OB
associations are also unbound, and do not give rise to the Galactic population
of open clusters. Finally, accelerating star formation implies that most clumps
within giant molecular complexes should have relatively low formation activity.
Sensitive infrared surveys could confirm this hypothesis.


  Balanced allocation of online balls-into-bins has long been an active area of
research for efficient load balancing and hashing applications.There exists a
large number of results in this domain for different settings, such as parallel
allocations~\cite{parallel}, multi-dimensional allocations~\cite{multi},
weighted balls~\cite{weight} etc. For sequential multi-choice allocation, where
$m$ balls are thrown into $n$ bins with each ball choosing $d$ (constant) bins
independently uniformly at random, the maximum load of a bin is $O(\log \log n)
+ m/n$ with high probability~\cite{heavily_load}. This offers the current best
known allocation scheme. However, for $d = \Theta(\log n)$, the gap reduces to
$O(1)$~\cite{soda08}.A similar constant gap bound has been established for
parallel allocations with $O(\log ^*n)$ communication rounds~\cite{lenzen}.
  In this paper we propose a novel multi-choice allocation algorithm,
\emph{Improved D-choice with Estimated Average} ($IDEA$) achieving a constant
gap with a high probability for the sequential single-dimensional online
allocation problem with constant $d$. We achieve a maximum load of $\lceil m/n
\rceil$ with high probability for constant $d$ choice scheme with
\emph{expected} constant number of retries or rounds per ball. We also show
that the bound holds even for an arbitrary large number of balls, $m>>n$.
Further, we generalize this result to (i)~the weighted case, where balls have
weights drawn from an arbitrary weight distribution with finite variance,
(ii)~multi-dimensional setting, where balls have $D$ dimensions with $f$
randomly and uniformly chosen filled dimension for $m=n$, and (iii)~the
parallel case, where $n$ balls arrive and are placed parallely in the bins. We
show that the gap in these case is also a constant w.h.p. (independent of $m$)
for constant value of $d$ with expected constant number of retries per ball.


  Combination therapy has shown to improve therapeutic efficacy while reducing
side effects. Importantly, it has become an indispensable strategy to overcome
resistance in antibiotics, anti-microbials, and anti-cancer drugs. Facing
enormous chemical space and unclear design principles for small-molecule
combinations, the computational drug-combination design has not seen generative
models to meet its potential to accelerate resistance-overcoming drug
combination discovery. We have developed the first deep generative model for
drug combination design, by jointly embedding graph-structured domain knowledge
and iteratively training a reinforcement learning-based chemical graph-set
designer. First, we have developed Hierarchical Variational Graph Auto-Encoders
(HVGAE) trained end-to-end to jointly embed gene-gene, gene-disease, and
disease-disease networks. Novel attentional pooling is introduced here for
learning disease-representations from associated genes' representations.
Second, targeting diseases in learned representations, we have recast the
drug-combination design problem as graph-set generation and developed a deep
learning-based model with novel rewards. Specifically, besides chemical
validity rewards, we have introduced a novel generative adversarial award,
being generalized sliced Wasserstein, for chemically diverse molecules with
distributions similar to known drugs. We have also designed a network
principle-based reward for drug combinations. Numerical results indicate that,
compared to graph embedding methods, HVGAE learns more informative and
generalizable disease representations. Case studies on four diseases show that
network-principled drug combinations tend to have low toxicity. The generated
drug combinations collectively cover the disease module similar to FDA-approved
drug combinations and could potentially suggest novel systems-pharmacology
strategies.


  Recently, several authors have adopted new alternative approaches in the
study of some classical notions of modules. Among them, we find the notion of
subprojectivity which was introduced to measure in a way the degree of
projectivity of modules. The study of subprojectivity has recently been
extended to the context of abelian categories, which has brought to light some
interesting new aspects. For instance, in the category of complexes, it gives a
new way to measure, among other things, the exactness of complexes. In this
paper, we prove that the subprojectivity notion provides a new sight of
null-homotopic morphisms in the category of complexes. This will be proven
through two main results. Moreover, various results which emphasize the
importance of subprojectivity in the category of complexes are also given.
Namely, we give some applications by characterizing some classical rings and
establish various examples that allow us to reflect the scope and limits of our
results.


  In this paper, we present a review of the recent work in deep learning
methods for user interface design. The survey encompasses well known deep
learning techniques (deep neural networks, convolutional neural networks,
recurrent neural networks, autoencoders, and generative adversarial networks)
and datasets widely used to design user interface applications. We highlight
important problems and emerging research frontiers in this field. We believe
that the use of deep learning for user interface design automation tasks could
be one of the high potential fields for the advancement of the software
development industry.


  While it is commonly accepted that the disorder induced by magnetic ion
doping in quantum magnets usually generates a rugged free-energy landscape
resulting in slow or glassy spin dynamics, the disorder/distortion effects
associated with non-magnetic ion sites doping are still illusive. Here, using
AC susceptibility measurements, we show that the mixture of Sn/Ti on the
non-magnetic ion sites of pyrochlore $Yb_2(Ti_{1-x}Sn_x)_2O_7$ induces an
antiferromagnetic ground state despite both parent compounds, $Yb_2Ti_2O_7$,
and $Yb_2Sn_2O_7$, order ferromagnetically. Local structure studies through
neutron total scattering reveals the local distortion in the non-magnetic ion
sites and its strong correlation with the magnetic phase switching. Our study,
for the first time, demonstrates the local distortion as induced by the
non-magnetic ion site mixture could be a new path to achieve magnetic phase
switching, which has been traditionally obtained by external stimuli such as
temperature, magnetic field, pressure, strain, light, etc.


  We complete a classification of Cameron-Liebler line classes in ${\rm
PG}(3,5)$, and show in a uniform way all non-existence results for those in
${\rm PG}(3,q)$, $q\leq 5$.


  Simultaneous translation involves translating a sentence before the speaker's
utterance is completed in order to realize real-time understanding in multiple
languages. This task is significantly more challenging than the general full
sentence translation because of the shortage of input information during
decoding. To alleviate this shortage, we propose multimodal simultaneous neural
machine translation (MSNMT), which leverages visual information as an
additional modality. Our experiments with the Multi30k dataset showed that
MSNMT significantly outperforms its text-only counterpart in more timely
translation situations with low latency. Furthermore, we verified the
importance of visual information during decoding by performing an adversarial
evaluation of MSNMT, where we studied how models behaved with incongruent input
modality and analyzed the effect of different word order between source and
target languages.


  Machine Learning (ML) is applicable to scientific problems, i.e. to those
which have a well defined answer, only if this answer can be brought to a
peculiar form ${\cal G}: X\longrightarrow Z$ with ${\cal G}(\vec x)$ expressed
as a combination of iterated Heaviside functions. At present it is far from
obvious, if and when such representations exist, what are the obstacles and, if
they are absent, what are the ways to convert the known formulas into this
form. This gives rise to a program of reformulation of ordinary science in such
terms -- which sounds like a strong enhancement of the constructive mathematics
approach, only this time it concerns all natural sciences. We describe the
first steps on this long way.


  In this paper we calculate the Casimir energy for a dielectric-diamagnetic
cylinder with the speed of light differing on the inside and outside. Although
the result is in general divergent, special cases are meaningful. The
well-known results for a uniform speed of light are reproduced. The self-stress
on a purely dielectric cylinder is shown to vanish through second order in the
deviation of the permittivity from its vacuum value, in agreement with the
result calculated from the sum of van der Waals forces.These results are
unambiguously separated from divergent terms.


  Recent advances in semantic image segmentation have mostly been achieved by
training deep convolutional neural networks (CNNs). We show how to improve
semantic segmentation through the use of contextual information; specifically,
we explore `patch-patch' context between image regions, and `patch-background'
context. For learning from the patch-patch context, we formulate Conditional
Random Fields (CRFs) with CNN-based pairwise potential functions to capture
semantic correlations between neighboring patches. Efficient piecewise training
of the proposed deep structured model is then applied to avoid repeated
expensive CRF inference for back propagation. For capturing the
patch-background context, we show that a network design with traditional
multi-scale image input and sliding pyramid pooling is effective for improving
performance. Our experimental results set new state-of-the-art performance on a
number of popular semantic segmentation datasets, including NYUDv2, PASCAL VOC
2012, PASCAL-Context, and SIFT-flow. In particular, we achieve an
intersection-over-union score of 78.0 on the challenging PASCAL VOC 2012
dataset.


  New sequences of orthogonal polynomials with ultra-exponential weight
functions are discovered. In particular, it gives an explicit solution to the
Ditkin-Prudnikov problem (1966). The 3-term recurrence relations, explicit
representations, generating functions and Rodrigues type formulae are derived.
The method is based on differential properties of the involved special
functions and their representations in terms of the Mellin-Barnes and Laplace
integrals. A notion of the composition polynomial orthogonality is introduced.
The corresponding advantages of this orthogonality to discover new sequences of
polynomials and their relations to the corresponding multiple orthogonal
polynomial ensembles are shown.


  The exponential growth of scientific production makes secondary literature
abridgements increasingly demanding. We introduce a new open-source framework
for systematic reviews that significantly reduces time and workload for
collecting and screening scientific literature. The framework provides three
main tools: 1) an automatic citation search engine and manager that collects
records from multiple online sources with a unified query syntax, 2) a
Bayesian, active machine learning, citation screening tool based on iterative
human-machine interaction to increase predictive accuracy and, 3) a
semi-automatic, data-driven query generator to create new search queries from
existing citation data sets. To evaluate the automatic screener's performance,
we estimated the median posterior sensitivity and efficiency [90% Credible
Intervals] using Bayesian simulation to predict the distribution of undetected
potentially relevant records. Tested on an example topic, the framework
collected 17,755 unique records through the citation manager; 766 records
required human evaluation while the rest were excluded by the automatic
classifier; the theoretical efficiency was 95.6% [95.3%, 95.7%] with a
sensitivity of 100% [93.5%, 100%]. A new search query was generated from the
labelled dataset, and 82,579 additional records were collected; only 567
records required human review after automatic screening, and six additional
positive matches were found. The overall expected sensitivity decreased to
97.3% [73.8%, 100%] while the efficiency increased to 98.6% [98.2%, 98.7%]. The
framework can significantly reduce the workload required to conduct large
literature reviews by simplifying citation collection and screening while
demonstrating exceptional sensitivity. Such a tool can improve the
standardization and repeatability of systematic reviews.


  A Hamiltonian is presented, which can be used to convert any asymmetric state
$|\varphi \rangle_{a}|\phi \rangle_{b}$ of two oscillators $a$ and $b$ into an
entangled state. Furthermore, with this Hamiltonian and local operations only,
two oscillators, initially in any asymmetric initial states, can be entangled
with a third oscillator. The prepared entangled states can be engineered with
an arbitrary degree of entanglement. A discussion on the realization of this
Hamiltonian is given. Numerical simulations show that, with current circuit QED
technology, it is feasible to generate high-fidelity entangled states of two
microwave optical fields, such as entangled coherent states, entangled squeezed
states, entangled coherent-squeezed states, and entangled cat states. Our
finding opens a new avenue for creating not only two-color or three-color
entanglement of light but also wave-like or particle-like entanglement or novel
wave-like and particle-like hybrid entanglement.


  The PHENIX collaboration has measured low momentum direct photon radiation in
Au+Au collisions at 200 GeV, 62.4 GeV and 39 GeV, in Cu+Cu at 200 GeV as well
as in p+p, p+Au and d+Au at $\sqrt{s_{NN}} =$ 200 GeV. In these measurements
PHENIX has discovered a large excess over the scaled p+p yield of direct
photons in A+A collisions, and a non-zero excess, observed within systematic
uncertainties, over the scaled p+p yield in central p+A collisions. Another
finding is that at low-$p_{T}$ the integrated yield of direct photons,
$dN_{\gamma}/dy$, from large systems shows a behavior of universal scaling as a
function of the charged-particle multiplicity, $(dN_{ch}/d\eta)^{\alpha}$, with
$\alpha = 1.25$, which means that the photon production yield increases faster
than the charged-particle multiplicity.


  A temperature-dependent EXAFS investigation of La{1-x}Ca{x}MnO3 is presented
for the concentration range that spans the ferromagnetic-insulator (FMI) to
ferromagnetic-metal (FMM) transition region, x = 0.16-0.22. The samples are
insulating for x = 0.16-0.2 and show a metal/insulator transition for x = 0.22.
All samples are ferromagnetic although the saturation magnetization for the 16%
Ca sample is only ~ 70% of the expected value at 0.4T. We find that the FMI
samples have similar correlations between changes in the local Mn-O distortions
and the magnetization as observed previously for the colossal magnetoresistance
(CMR) samples (0.2 < x < 0.5) - except that the FMI samples never become fully
magnetized. The data show that there are at least two distinct types of
distortions. The initial distortions removed as the insulating sample becomes
magnetized are small and provides direct evidence that roughly 50% of the Mn
sites have a small distortion/site and are magnetized first. The large
remaining Mn-O distortions at low T are attributed to a small fraction of
Jahn-Teller-distorted Mn sites that are either antiferromagnetically ordered or
unmagnetized. Thus the insulating samples are very similar to the behavior of
the CMR samples up to the point at which the M/I transition occurs for the CMR
materials. The lack of metallic conductivity for x <= 0.2, when 50% or more of
the sample is magnetic, implies that there must be preferred magnetized Mn
sites and that such sites do not percolate at these concentrations.


  The radio source Hercules A resides at the center of a cooling flow cluster
of galaxies at redshift z = 0.154. A Chandra X-ray image reveals a shock front
in the intracluster medium (ICM) surrounding the radio source, about 160 kpc
from the active galactic nucleus (AGN) that hosts it. The shock has a Mach
number of 1.65, making it the strongest of the cluster-scale shocks driven by
an AGN outburst found so far. The age of the outburst ~5.9e7 y, its energy
about 3e61 erg and its mean power ~1.6e46 erg/s. As for the other large AGN
outbursts in cooling flow clusters, this outburst overwhelms radiative losses
from the ICM of the Hercules A cluster by a factor of ~100. It adds to the case
that AGN outbursts are a significant source of preheating for the ICM. Unless
the mechanical efficiency of the AGN in Hercules A exceeds 10%, the central
black hole must have grown by more than 1.7e8 Msun to power this one outburst.


  We assess the prospects for indirect detection of Weakly Interacting Massive
Particles using a large Liquid Argon TPC detector. Signal events will consist
of energetic electron (anti)neutrinos coming from the decay of $\tau$ leptons
and $b$ quarks produced in WIMP annihilation in the core of the Sun. Background
contamination from atmospheric neutrinos is expected to be low, thanks to the
superb angular resolution and particle identification capabilities provided by
the considered experimental set-up. We evaluate the event rates predicted for
an annihilating WIMP as a function of its elastic scattering cross section with
nucleons. This technique is particularly useful for WIMPs lighter than
$\sim100$ GeV


  Frontier AI systems are making transformative impacts across society, but
such benefits are not without costs: models trained on web-scale datasets
containing personal and private data raise profound concerns about data privacy
and security. Language models are trained on extensive corpora including
potentially sensitive or proprietary information, and the risk of data leakage
- where the model response reveals pieces of such information - remains
inadequately understood. Prior work has investigated what factors drive
memorization and have identified that sequence complexity and the number of
repetitions drive memorization. Here, we focus on the evolution of memorization
over training. We begin by reproducing findings that the probability of
memorizing a sequence scales logarithmically with the number of times it is
present in the data. We next show that sequences which are apparently not
memorized after the first encounter can be "uncovered" throughout the course of
training even without subsequent encounters, a phenomenon we term "latent
memorization". The presence of latent memorization presents a challenge for
data privacy as memorized sequences may be hidden at the final checkpoint of
the model but remain easily recoverable. To this end, we develop a diagnostic
test relying on the cross entropy loss to uncover latent memorized sequences
with high accuracy.


  This paper explores a simple question: How would we provide a high-quality
search experience on Mars, where the fundamental physical limit is
speed-of-light propagation delays on the order of tens of minutes? On Earth,
users are accustomed to nearly instantaneous response times from search
engines. Is it possible to overcome orders-of-magnitude longer latency to
provide a tolerable user experience on Mars? In this paper, we formulate the
searching from Mars problem as a tradeoff between "effort" (waiting for
responses from Earth) and "data transfer" (pre-fetching or caching data on
Mars). The contribution of our work is articulating this design space and
presenting two case studies that explore the effectiveness of baseline
techniques, using publicly available data from the TREC Total Recall and
Sessions Tracks. We intend for this research problem to be aspirational and
inspirational - even if one is not convinced by the premise of Mars
colonization, there are Earth-based scenarios such as searching from a rural
village in India that share similar constraints, thus making the problem worthy
of exploration and attention from researchers.


  RP-Rewriter (Retain-Property) is a verified clause processor that can use
some of the existing ACL2 rewrite rules to prove conjectures through term
rewriting. Optimized for conjectures that can expand into large terms, the
rewriter tries to mimic some of the ACL2 rewriting heuristics but also adds
some extra features. It can attach side-conditions to terms that help the
rewriter retain properties about them and prevent possibly some very expensive
backchaining. The rewriter supports user-defined complex meta rules that can
return a special structure to prevent redundant rewriting. Additionally, it can
store fast alists even when values are not quoted. RP-Rewriter is utilized for
two applications, multiplier design proofs and SVEX simplification, which
involve very large terms.


  Let $U$ be a unitary operator acting on the Hilbert space $\ch$, and
$\a:\{1,..., 2k\}\mapsto\{1,..., k\}$ a pair partition. Then the ergodic
average $$ \frac{1}{N^{k}}\sum_{n_{1},...,n_{k}=0}^{N-1}
U^{n_{\a(1)}}A_{1}U^{n_{\a(2)}}... U^{n_{\a(2k-1)}}A_{2k-1}U^{n_{\a(2k)}} $$
converges in the strong operator topology provided $U$ is almost periodic, that
is when $\ch$ is generated by the eigenvalues of $U$. We apply the present
result to obtain the convergence of the Cesaro mean of several multiple
correlations.


  We consider the analogue of the Andr\'e-Oort conjecture for Drinfeld modular
varieties which was formulated by Breuer. We prove this analogue for special
points with separable reflex field over the base field by adapting methods
which were used by Klingler and Yafaev to prove the Andr\'e-Oort conjecture
under the generalized Riemann hypothesis in the classical case. Our result
extends results of Breuer showing the correctness of the analogue for special
points lying in a curve and for special points having a certain behaviour at a
fixed set of primes.


  The spacetime solution for a black hole, surrounded by an exotic matter
field, in Rastall gravity, is calculated in an arbitrary d-dimensional
spacetime. After this, we calculate the scalar quasinormal modes of such
solution, and study the shift on the modes caused by the modification of the
theory of gravity, i.e., by the introduction of a new term due to Rastall. We
conclude that the shift strongly depends on the kind of exotic field one is
studying, but for a low density matter that supposedly pervades the universe,
it is unlikely that Rastall gravity will cause any instability on the probe
field.


  In this paper we present the first deterministic polynomial time algorithm
for determining the existence of a Hamiltonian cycle and finding a Hamiltonian
cycle in general graphs. Our algorithm can also solve the Hamiltonian path
problem in the traceable graphs. The space complexity of our algorithm is
O(n^4). The time complexity are theoretically O(n^5*d^2) on average and
O(n^6*d^2) in the worst case respectively, where d is the maximum degree of
vertex. With parallel computing, the space complexity can be improved to O(n^3)
and the time complexity to O(n^3*d^2) on average and O(n^4*d^2) in the worst
case. We construct the corresponding path hologram transformed from the
original graph and compute the path set, which is a collection of segment sets
consisting of all the vertices located on the same segment level among all the
longest basic paths, of every vertex with greedy strategy. The path hologram is
a multi-segment graph with the vertex <u, k> where u is a vertex and k is the
segment level of u in the path hologram. To ensure that each valid path
fragments can be visited and invalid path fragments cannot be visited, the key
strategy of our method is the "consecutive" deleting-replenishing operations
recursively on the left/right action field of a vertex, respectively. In fact,
our algorithm can be directly applied to the original graph. Besides, our
algorithm can deal with the finite general graphs including undirected,
directed, and mixed. As a result, the well-known problem HCP in NPC can be now
solved practically in deterministic polynomial time for general graphs in the
worst case.


  This paper discusses a scenario approach to robust optimization of a blackbox
function in a bandit setting. We assume that the blackbox function can be
modeled as a Gaussian Process (GP) for every realization of the uncertain
parameter. We adopt a scenario approach in which we draw fixed independent
samples of the uncertain parameter. For a given policy, i.e., a sequence of
query points and uncertain parameters in the sampled set, we introduce a notion
of regret defined with respect to additional draws of the uncertain parameter,
termed as scenario regret under re-draw. We present a scenario-based iterative
algorithm using the upper confidence bound (UCB) of the fixed independent
scenarios to compute a policy for the blackbox optimization. For this
algorithm, we characterize a high probability upper bound on the regret under
re-draw for any finite number of iterations of the algorithm. We further
characterize parameter regimes in which the regret tends to zero asymptotically
with the number of iterations with high probability. Finally, we supplement our
analysis with numerical results.


  Let CABA be the category of complete and atomic boolean algebras and complete
boolean homomorphisms, and let CSL be the category of complete
meet-semilattices and complete meet-homomorphisms. We show that the forgetful
functor from CABA to CSL has a left adjoint. This allows us to describe an
endofunctor H on CABA such that the category Alg(H) of algebras for H is dually
equivalent to the category Coalg(P) of coalgebras for the powerset endofunctor
P on Set. As a consequence, we derive Thomason duality from Tarski duality,
thus paralleling how J\'onsson-Tarski duality is derived from Stone duality.


  Locating planets in circumstellar Habitable Zones is a priority for many
exoplanet surveys. Space-based and ground-based surveys alike require robust
toolsets to aid in target selection and mission planning. We present the
Catalog of Earth-Like Exoplanet Survey Targets (CELESTA), a database of
Habitable Zones around 36,000 nearby stars. We calculated stellar parameters,
including effective temperatures, masses, and radii, and we quantified the
orbital distances and periods corresponding to the circumstellar Habitable
Zones. We gauged the accuracy of our predictions by contrasting CELESTA's
computed parameters to observational data. We ascertain a potential return on
investment by computing the number of Habitable Zones probed for a given survey
duration. A versatile framework for extending the functionality of CELESTA into
the future enables ongoing comparisons to new observations, and recalculations
when updates to Habitable Zone models, stellar temperatures, or parallax data
become available. We expect to upgrade and expand CELESTA using data from the
Gaia mission as the data becomes available.


  We consider the version of the Zee model where both Higgs doublets couple to
leptons. Within this framework we study charged Higgs decays. We focus on a
model with minimal number of parameters consistent with experimental neutrino
data. Using constraints from neutrino physics we (i) discuss the reconstruction
of the parameter space of the model using the leptonic decay patterns of both
of the two charged Higgses, $h_{1,2}^{+}\to \ell_{j}^{+}\nu_{i}$, and the decay
of the heavier charged Higgs, $h_{2}^{+}\to h^{+}_{1}h^{0}$; (ii) show that the
decay rate $\Gamma(h_{1}^{+}\to \mu^{+}\nu_{i})$ in general is enhanced in
comparision to the standard two Higgs doublet model while in some regions of
parameter space $\Gamma(h_{1}^{+}\to \mu^{+}\nu_{i})$ even dominates over
$\Gamma(h_{1}^{+}\to \tau^{+}\nu_{i})$.


  SU(2)-flavor-symmetry breaking in antiquark distributions of the nucleon was
suggested by the New Muon Collaboration (NMC) in deep inelastic muon
scattering. As an independent test, Drell-Yan data for the tungsten target have
been used for examining the asymmetry. We investigate whether there exists
significant modification of the $\bar u -\bar d$ distribution in nuclei in a
parton recombination model. It should be noted that a finite $\bar u-\bar d$
distribution is theoretically possible in nuclei even if the sea is $SU(2)_f$
symmetric in the nucleon. In neutron-excess nuclei such as the tungsten, there
exist more $d$-valence quarks than $u$-valence quarks, so that more $\bar
d$-quarks are lost than $\bar u$-quarks are due to parton recombinations in the
small $x$ region. Our results suggest that the nuclear modification in the
tungsten is a 2--10 % effect on the $\bar u-\bar d$ distribution suggested by
the NMC data. Nuclear effects on the flavor asymmetric distribution could be an
interesting topic for future theoretical and experimental investigations.


  Let $(X_A,\sigma_A)$ be the right one-sided topological Markov shift for an
irreducible matrix with entries in $\{0,1\}$, and $\Gamma_A$ the continuous
full group of $(X_A,\sigma_A)$. For two irreducible matrices $A$ and $B$ with
entries in $\{0,1\}$, it will be proved that the continuous full groups
$\Gamma_A$ and $\Gamma_B$ are isomorphic as abstract groups if and only if
their one-sided topological Markov shifts $(X_A,\sigma_A)$ and $(X_B,\sigma_B)$
are continuously orbit equivalent.


  Magnetically-levitated superconducting microparticles have been recently
proposed as a promising platform for performing quantum experiments with
particles in the picogram regime. Here, we demonstrate the superconducting
technology to achieve chip-based magnetic levitation of superconducting
microparticles. We simulate and fabricate a chip-based magnetic trap capable of
levitating superconducting particles with diameters from 0.5$\,\mu$m to
200$\,\mu$m. The trap consists of two stacked silicon chips, each patterned
with a planar multi-winding superconducting coil made of niobium. The two coils
generate a magnetic field resembling a quadrupole near the trap center, in
which we demonstrate trapping of a spherical 50\,$\mu$m diameter SnPb
microparticle at temperatures of 4\,K and 40\,mK.


  The group $\text{Aut}_{\text{hol}}(\mathbb C^n)$ of self-biholomorphisms of
$\mathbb C^n$ consists of affine maps if $n=1$, but in higher dimensions it is
a large object that has not been described explicitly. Despite the intricacies
involved when $n>1$, surprisingly every $F\in \text{Aut}_{\text{hol}}(\mathbb
C^n)$ is uniquely determined inside the group by only two data, of
infinitesimal and global nature: the $1$-jet of $F$ at $0$, and the complex
Hessian of a certain plurisubharmonic function associated to $F$. If $n=1$ this
global datum is zero for all $F$, which is then determined solely by its
$1$-jet at $0$, and one recovers $\text{Aut}_{\text{hol}}(\mathbb C)=
\text{Aff}(\mathbb C)\cong \mathbb C \times \mathbb C^{*}$. Our main result,
formulated as the existence of a canonical embedding of $
\text{Aut}_{\text{hol}} ( \mathbb C^n)$, also singles out a natural candidate
for moduli space of $ \text{Aut}_{\text{hol}} ( \mathbb C^n)$, for all $n>1$.


  We give two improved explicit versions of the prime number theorem for primes
in arithmetic progression: the first isolating the contribution of the Siegel
zero and the second completely explicit, where the improvement is for
medium-sized values. This will give an improved explicit Bombieri-Vinogradov
like result for non-exceptional moduli.


  Humans have entered the age of algorithms. Each minute, algorithms shape
countless preferences from suggesting a product to a potential life partner. In
the marketplace algorithms are trained to learn consumer preferences from
customer reviews because user-generated reviews are considered the voice of
customers and a valuable source of information to firms. Insights mined from
reviews play an indispensable role in several business activities ranging from
product recommendation, targeted advertising, promotions, segmentation etc. In
this research, we question whether reviews might hold stereotypic gender bias
that algorithms learn and propagate Utilizing data from millions of
observations and a word embedding approach, GloVe, we show that algorithms
designed to learn from human language output also learn gender bias. We also
examine why such biases occur: whether the bias is caused because of a negative
bias against females or a positive bias for males. We examine the impact of
gender bias in reviews on choice and conclude with policy implications for
female consumers, especially when they are unaware of the bias, and the ethical
implications for firms.


  A special customer must complete service from two servers in series, in
either order, each with an M/M/1 queueing system. It is assumed that the two
queueing system lengths are independent with initial numbers of customers a and
b at the instant when the special customer arrives. We find the expected total
time (ETT) for the special customer to complete service. We show that even if
the interarrival and service time parameters of two queues are identical, there
exist examples (specific values of the parameters and initial lengths) for
which the special customer surprisingly has a lower expected total time to
completion by joining the longer queue first rather than the shorter one.


  The use of sparse precision (inverse covariance) matrices has become popular
because they allow for efficient algorithms for joint inference in
high-dimensional models. Many applications require the computation of certain
elements of the covariance matrix, such as the marginal variances, which may be
non-trivial to obtain when the dimension is large. This paper introduces a fast
Rao-Blackwellized Monte Carlo sampling based method for efficiently
approximating selected elements of the covariance matrix. The variance and
confidence bounds of the approximations can be precisely estimated without
additional computational costs. Furthermore, a method that iterates over
subdomains is introduced, and is shown to additionally reduce the approximation
errors to practically negligible levels in an application on functional
magnetic resonance imaging data. Both methods have low memory requirements,
which is typically the bottleneck for competing direct methods.


  Visual grounding (VG) aims to establish fine-grained alignment between vision
and language. Ideally, it can be a testbed for vision-and-language models to
evaluate their understanding of the images and texts and their reasoning
abilities over their joint space. However, most existing VG datasets are
constructed using simple description texts, which do not require sufficient
reasoning over the images and texts. This has been demonstrated in a recent
study~\cite{luo2022goes}, where a simple LSTM-based text encoder without
pretraining can achieve state-of-the-art performance on mainstream VG datasets.
Therefore, in this paper, we propose a novel benchmark of \underline{S}cene
\underline{K}nowledge-guided \underline{V}isual \underline{G}rounding (SK-VG),
where the image content and referring expressions are not sufficient to ground
the target objects, forcing the models to have a reasoning ability on the
long-form scene knowledge. To perform this task, we propose two approaches to
accept the triple-type input, where the former embeds knowledge into the image
features before the image-query interaction; the latter leverages linguistic
structure to assist in computing the image-text matching. We conduct extensive
experiments to analyze the above methods and show that the proposed approaches
achieve promising results but still leave room for improvement, including
performance and interpretability. The dataset and code are available at
\url{https://github.com/zhjohnchan/SK-VG}.


  Recent results and future plans of kaon physics are reviewed. Topics include
CP violation, rare decays, light neutral-boson search, lepton flavor
universality, and CPT and QM tests.


  Following a baryogenesis scenario proposed by Lazarides, Panagiotakopoulos
and Shafi, we show how the observed baryon asymmetry can be explained via
resonant leptogenesis in a class of supersymmetric models with an intermediate
mass scale M_I<~10^9 GeV. It involves the out of equilibrium decay of heavy
(<~M_I) right handed neutrinos at a temperature close to the TeV supersymmetry
breaking scale. Such models can also resolve the MSSM mu problem.


  Direct imaging suggests that there is a Jovian exoplanet around the primary
A-star in the triple-star system HD131399. We investigate a high-quality
spectrum of the primary component HD131399A obtained with FEROS on the ESO/MPG
2.2m telescope, aiming to characterise the star's atmospheric and fundamental
parameters, and to determine elemental abundances at high precision and
accuracy. The aim is to constrain the chemical composition of the birth cloud
of the system and therefore the bulk composition of the putative planet. A
hybrid non-local thermal equilibrium (non-LTE) model atmosphere technique is
adopted for the quantitative spectral analysis. Comparison with the most recent
stellar evolution models yields the fundamental parameters. The atmospheric and
fundamental stellar parameters of HD131399A are constrained to Teff=9200+-100
K, log g=4.37+-0.10, M=1.95+0.08-0.06 Msun, R=1.51+0.13-0.10 Rsun, and log
L/Lsun=1.17+-0.07, locating the star on the zero-age main sequence. Non-LTE
effects on the derived metal abundances are often smaller than 0.1dex, but can
reach up to ~0.8dex for individual lines. The observed lighter elements up to
calcium are overall consistent with present-day cosmic abundances, with a C/O
ratio of 0.45$\pm$0.07 by number, while the heavier elements show mild
overabundances. We conclude that the birth cloud of the system had a standard
chemical composition, but we witness the onset of the Am phenomenon in the
slowly rotating star. We furthermore show that non-LTE analyses have the
potential to solve the remaining discrepancies between observed abundances and
predictions by diffusion models for Am stars. Moreover, the present case allows
mass loss, not turbulent mixing, to be identified as the main transport process
competing with diffusion in very young Am stars.


  We use the planar circular restricted three-body problem in order to
numerically investigate the orbital dynamics of orbits of a spacecraft, or a
comet, or an asteroid in the Saturn-Titan system in a scattering region around
the Titan. The orbits can escape through the necks around the Lagrangian points
$L_1$ and $L_2$ or collide with the surface of the Titan. We explore all the
four possible Hill's regions depending on the value of the Jacobi constant. We
conduct a thorough numerical analysis on the phase space mixing by classifying
initial conditions of orbits and distinguishing between three types of motion:
(i) bounded, (ii) escaping and (iii) collisional. In particular, we locate the
different basins and we relate them with the corresponding spatial
distributions of the escape and crash times. Our results reveal the high
complexity of this planetary system. Furthermore, the numerical analysis shows
a strong dependence of the properties of the considered basins with the total
orbital energy, with a remarkable presence of fractal basin boundaries along
all the regimes. We hope our contribution to be useful in both space mission
design and study of planetary systems.


  Learning at the edge is a challenging task from several perspectives, since
data must be collected by end devices (e.g. sensors), possibly pre-processed
(e.g. data compression), and finally processed remotely to output the result of
training and/or inference phases. This involves heterogeneous resources, such
as radio, computing and learning related parameters. In this context, we
propose an algorithm that dynamically selects data encoding scheme, local
computing resources, uplink radio parameters, and remote computing resources,
to perform a classification task with the minimum average end devices' energy
consumption, under E2E delay and inference reliability constraints. Our method
does not assume any prior knowledge of the statistics of time varying context
parameters, while it only requires the solution of low complexity per-slot
deterministic optimization problems, based on instantaneous observations of
these parameters and that of properly defined state variables. Numerical
results on convolutional neural network based image classification illustrate
the effectiveness of our method in striking the best trade-off between energy,
delay and inference reliability.


  In this paper we study the category of finitely generated modules over a
right $3$-Nakayama artin algebra. First we give a characterization of right
$3$-Nakayama artin algebras and then we give a complete list of non-isomorphic
finitely generated indecomposable modules over any right $3$-Nakayama artin
algebra. Also we compute all almost split sequences for the class of right
$3$-Nakayama artin algebras. Finally, we classify finite dimensional right
$3$-Nakayama algebras in terms of their quivers with relations.


  For two $s$-uniform hypergraphs $H$ and $F$, the Tur\'{a}n number $ex_s(H,F)$
is the maximum number of edges in an $F$-free subgraph of $H$. Let $s, r, k,
n_1, \ldots, n_r$ be integers satisfying $2\leq s\leq r$ and $n_1\leq n_2\leq
\cdots\leq n_r$. De Silva, Heysse and Young determined $ex_2(K_{n_1, \ldots,
n_r}, kK_2)$ and De Silva, Heysse, Kapilow, Schenfisch and Young determined
$ex_2(K_{n_1, \ldots, n_r},kK_r)$. In this paper, as a generalization of these
results, we consider three Tur\'{a}n-type problems for $k$ disjoint cliques in
$r$-partite $s$-uniform hypergraphs. First, we consider a multi-partite version
of the Erd\H{o}s matching conjecture and determine $ex_s(K_{n_1, \ldots,
n_r}^{(s)},kK_s^{(s)})$ for $n_1\geq s^3k^2+sr$. Then, using a probabilistic
argument, we determine $ex_s(K_{n_1, \ldots, n_r}^{(s)},kK_r^{(s)})$ for all
$n_1\geq k$. Recently, Alon and Shikhelman determined asymptotically, for all
$F$, the generalized Tur\'{a}n number $ex_2(K_n,K_s,F)$, which is the maximum
number of copies of $K_s$ in an $F$-free graph on $n$ vertices. Here we
determine $ex_2(K_{n_1, \ldots, n_r}, K_s, kK_r)$ with $n_1\geq k$ and
$n_3=\cdots=n_r$. Utilizing a result on rainbow matchings due to Glebov,
Sudakov and Szab\'{o}, we determine $ex_2(K_{n_1, \ldots, n_r}, K_s, kK_r)$ for
all $n_1, \ldots, n_r$ with $n_4\geq r^r(k-1)k^{2r-2}$.


  This review represents a detailed and comprehensive discussion of the Thermal
Field Theory (TFT) concepts and key results in Yukawa-type theories. We start
with a general pedagogical introduction into the TFT in the imaginary- and
real-time formulation. As phenomenologically relevant implications, we present
a compendium of thermal decay rates for several typical reactions calculated
within the framework of the real-time formalism and compared to the
imaginary-time results found in the literature. Processes considered here are
those of a neutral (pseudo)scalar decaying into two distinct (pseudo)scalars or
into a fermion-antifermion pair. These processes are extended from earlier
works to include chemical potentials and distinct species in the final state.
In addition, a (pseudo)scalar emission off a fermion line is also discussed.
These results demonstrate the importance of thermal effects in particle decay
observables relevant in many phenomenological applications in systems at high
temperatures and densities.


  This vision paper focuses on the mental health crisis impacting healthcare
workers (HCWs), which exacerbated by the COVID-19 pandemic, leads to increased
stress and psychological issues like burnout. Peer Support Programs (PSP) are a
recognized intervention for mitigating these issues. These programs are
increasingly being delivered virtually through Peer Support Systems (PSS) for
increased convenience and accessibility. However, HCWs perception of these
systems results in fear of information sharing, perceived lack of safety, and
low participation rate, which challenges these systems ability to achieve their
goals. In line with the rich body of research on the requirements and
properties of trustworthy systems, we posit that increasing HCWs trust in PSS
could address these challenges. However, extant research focuses on objectively
defined trustworthiness rather than perceptual trust because trustworthy
requirements are viewed as more controllable and easier to operationalize. This
study proposes a novel approach to elicit perceptual trust requirements by
proposing a trust framework anchored in recognized trust theories from
different disciplines that unpacks trust into its recognized types and their
antecedents. This approach allows the identification of trust requirements
beyond those already proposed for trustworthy systems, providing a strong
foundation for improving the effectiveness of PSS for HCWs. Keywords: Trust
Requirements, Requirements elicitation, Peer support systems, Healthcare
workers


  Initially, the acoustic field forced the droplets into an oblate spheroid
shape, though the counteracting force of the cooling stream caused them to
circularize. Droplet geometry was thus the net result of streaming forces and
surface tension at the acoustic boundary layer/air-liquid interface. Nucleation
was determined to be neither homogeneous nor heterogeneous but secondary, and
thus dependent on the cooling rate and not on the degree of supercooling. It
was likely initiated by aerosolized ice particles from the air or from droplets
that had already nucleated and broken up. The latter secondary ice production
process resulted in multi-drop systems with statistically identical nucleation
times. Notably, this meant that the presence of interfacial rupture at an
adjacent droplet could influence the crystallization behaviour of another.
After the formation of an initial ice shell around the individual droplets,
dendritic protrusions grew from the droplet surface, likely seeded by the same
ice particles that caused nucleation, but at a quasi-liquid layer. When
freezing was complete, it was determined that the frozen core had undergone a
volumetric expansion of 30.75%, compared to 9% for pure, sessile water
expansion. This significantly greater expansion may have resulted from
entrained air bubbles at the inner solid-liquid interface and oscillations at
the moving phase boundary caused by changes in local acoustic forces. Soon
after melting began, acoustic streaming, the buoyancy of the remaining ice, and
convective currents caused by an inner thermal gradient and thermocapillary
effects along the air-liquid interface, all contributed to the droplet spinning
about the horizontal axis.


  We present high-contrast imaging data on the disk of the classical T Tauri
star TW Hya. The images were obtained through the polarimetric differential
imaging technique with the adaptive optics system NACO. Our commissioning data
show the presence of polarized disk emission between 0.1" and 1.4" from the
star. We derive the first Ks-band radial polarized intensity distribution. We
show that the polarized intensity compares well to shorter wavelengths surface
brightness observations and confirm the previously reported gradual slope
change around 0.8". These results show the potential of the new polarimetric
differential imaging technique at 8m-class telescopes to map the inner regions
of protoplanetary disks.


  The fields of SocialVR, performance capture, and virtual try-on are often
faced with a need to faithfully reproduce real garments in the virtual world.
One critical task is the disentanglement of the intrinsic garment shape from
deformations due to fabric properties, physical forces, and contact with the
body. We propose to use a garment sewing pattern, a realistic and compact
garment descriptor, to facilitate the intrinsic garment shape estimation.
Another major challenge is a high diversity of shapes and designs in the
domain. The most common approach for Deep Learning on 3D garments is to build
specialized models for individual garments or garment types. We argue that
building a unified model for various garment designs has the benefit of
generalization to novel garment types, hence covering a larger design domain
than individual models would. We introduce NeuralTailor, a novel architecture
based on point-level attention for set regression with variable cardinality,
and apply it to the task of reconstructing 2D garment sewing patterns from the
3D point could garment models. Our experiments show that NeuralTailor
successfully reconstructs sewing patterns and generalizes to garment types with
pattern topologies unseen during training.


  We study the complex Monge-Amp\`ere operator in bounded hyperconvex domains
of $\C^n$. We introduce a scale of classes of weakly singular plurisubharmonic
functions : these are functions of finite weighted Monge-Amp\`ere energy. They
generalize the classes introduced by U.Cegrell, and give a stratification of
the space of (almost) all unbounded plurisubharmonic functions. We give an
interpretation of these classes in terms of the speed of decreasing of the
Monge-Amp\`ere capacity of sublevel sets and solve associated complex
Monge-Amp\`ere equations.


  We propose a new algorithm for topic modeling, Vec2Topic, that identifies the
main topics in a corpus using semantic information captured via
high-dimensional distributed word embeddings. Our technique is unsupervised and
generates a list of topics ranked with respect to importance. We find that it
works better than existing topic modeling techniques such as Latent Dirichlet
Allocation for identifying key topics in user-generated content, such as
emails, chats, etc., where topics are diffused across the corpus. We also find
that Vec2Topic works equally well for non-user generated content, such as
papers, reports, etc., and for small corpora such as a single-document.


  We determine the structure modulo p of the de Rham-Witt complex of a smooth
scheme X over a discrete valuation ring of mixed characteristic with log-poles
along the special fiber Y and show that the sub-sheaf fixed by the Frobenius is
isomorphic to the sheaf of p-adic vanishing cycles. We use this together with
results of the second author and Madsen to evaluate the $K$-theory with finite
coefficients of the quotient field K of the henselian local ring of X at a
generic point of Y. The result affirms the Lichtenbaum-Quillen conjecture for
the field K.


  At its core, information access and seeking is an interactive process. In
existing search engines, interactions are limited to a few pre-defined actions,
such as "requery", "click on a document", "scrolling up/down", "going to the
next result page", "leaving the search engine", etc. A major benefit of moving
towards generative IR systems is enabling users with a richer expression of
information need and feedback and free-form interactions in natural language
and beyond. In other words, the actions users take are no longer limited by the
clickable links and buttons available on the search engine result page and
users can express themselves freely through natural language. This can go even
beyond natural language, through images, videos, gestures, and sensors using
multi-modal generative IR systems. This chapter briefly discusses the role of
interaction in generative IR systems. We will first discuss different ways
users can express their information needs by interacting with generative IR
systems. We then explain how users can provide explicit or implicit feedback to
generative IR systems and how they can consume such feedback. Next, we will
cover how users interactively can refine retrieval results. We will expand upon
mixed-initiative interactions and discuss clarification and preference
elicitation in more detail. We then discuss proactive generative IR systems,
including context-aware recommendation, following up past conversations,
contributing to multi-party conversations, and feedback requests. Providing
explanation is another interaction type that we briefly discuss in this
chapter. We will also briefly describe multi-modal interactions in generative
information retrieval. Finally, we describe emerging frameworks and solutions
for user interfaces with generative AI systems.


  Let $\{\xi_1,\xi_2,\ldots\}$ be a sequence of independent random variables,
and $\eta$ be a counting random variable independent of this sequence. In
addition, let $S_0:=0$ and $S_n:=\xi_1+\xi_2+\cdots+\xi_n$ for $n\geqslant1$.
We consider conditions for random variables $\{\xi_1,\xi_2,\ldots\}$ and $\eta$
under which the distribution functions of the random maximum
$\xi_{(\eta)}:=\max\{0,\xi_1,\xi_2,\ldots,\xi_{\eta}\}$ and of the random
maximum of sums $S_{(\eta)}:=\max\{S_0,S_1,S_2,\ldots,S_{\eta}\}$ belong to the
class of consistently varying distributions. In our consideration the random
variables $\{\xi_1,\xi_2,\ldots\}$ are not necessarily identically distributed.


  We prove that there are no nontrivial finite-dimensional Lie representations
of certain Poisson algebras of polynomials on a compact symplectic manifold.
This result is used to establish the existence of a universal obstruction to
quantizing a compact symplectic manifold, regardless of the dimensionality of
the representation.


  This paper presents several types of evolutionary algorithms (EAs) used for
global optimization on real domains. The interest has been focused on
multimodal problems, where the difficulties of a premature convergence usually
occurs. First the standard genetic algorithm (SGA) using binary encoding of
real values and its unsatisfactory behavior with multimodal problems is briefly
reviewed together with some improvements of fighting premature convergence. Two
types of real encoded methods based on differential operators are examined in
detail: the differential evolution (DE), a very modern and effective method
firstly published by R. Storn and K. Price, and the simplified real-coded
differential genetic algorithm SADE proposed by the authors. In addition, an
improvement of the SADE method, called CERAF technology, enabling the
population of solutions to escape from local extremes, is examined. All methods
are tested on an identical set of objective functions and a systematic
comparison based on a reliable methodology is presented. It is confirmed that
real coded methods generally exhibit better behavior on real domains than the
binary algorithms, even when extended by several improvements. Furthermore, the
positive influence of the differential operators due to their possibility of
self-adaptation is demonstrated. From the reliability point of view, it seems
that the real encoded differential algorithm, improved by the technology
described in this paper, is a universal and reliable method capable of solving
all proposed test problems.


  Group-V elemental monolayers including phosphorene are emerging as promising
2D materials with semiconducting electronic properties. Here, we present the
results of first principles calculations on stability, mechanical and
electronic properties of 2D antimony (Sb), antimonene. Our calculations show
that free-standing {\alpha} and \b{eta} allotropes of antimonene are stable and
semiconducting. The {\alpha}-Sb has a puckered structure with two atomic
sub-layers and \b{eta}-Sb has a buckled hexagonal lattice. The calculated Raman
spectra and STM images have distinct features thus facilitating
characterization of both allotropes. The \b{eta}-Sb has nearly isotropic
mechanical properties while {\alpha}-Sb shows strongly anisotropic
characteristics. An indirect-direct band gap transition is expected with
moderate tensile strains applied to the monolayers, which opens up the
possibility of their applications in optoelectronics.


  We present an analysis of stationary discrete shock profiles for a
discontinuous Galerkin method approximating scalar nonlinear hyperbolic
conservation laws with a convex flux. Using the Godunov method for the
numerical flux, we characterize the steady state solutions for arbitrary
approximation orders and show that they are oscillatory only in one mesh cell
and are parametrized by the shock strength and its relative position in the
cell. In the particular case of the inviscid Burgers equation, we derive
analytical solutions of the numerical scheme and predict their oscillations up
to fourth-order of accuracy. Moreover, a linear stability analysis shows that
these profiles may become unstable at points where the Godunov flux is not
differentiable. Theoretical and numerical investigations show that these
results can be extended to other numerical fluxes. In particular, shock
profiles are found to vanish exponentially fast from the shock position for
some class of monotone numerical fluxes and the oscillatory and unstable
characters of their solutions present strong similarities with that of the
Godunov method.


  A compound with metallic electrical conductivity usually has a considerable
total thermal conductivity because both electrons and photons contribute to
thermal transport. Here, we show an exceptional example of iridium oxide,
Bi3Ir3O11, that concurrently displays metallic electrical conductivity and
ultralow thermal conductivity approaching 0.61 W m-1 K-1 at 300 K. The compound
crystallizes into a cubic structural framework with space group Pn-3. The edge-
and corner-sharing IrO6 octahedra with a mixed Ir4.33+ charge state favor
metallic electrical transport. Bi3Ir3O11 exhibits an extremely low lattice
thermal conductivity close to the minimum limit in theory owing to its
tunnel-like structure with filled heavy atoms Bi rattling inside. Theoretical
calculations reveal the underlying mechanisms for the extraordinary
compatibility between metallic electrical conductivity and ultralow thermal
conductivity. This study may establish a new avenue for designing and
developing unprecedented heat-insulation metals.


  We employ a homogenization technique based on the Lorentz electronic theory
to show that planar chiral structures (PCSs) can be described by an effective
dielectric tensor similar to that of biaxial elliptically dichroic crystals.
Such a crystal is shown to behave like a PCS insofar as it exhibits its
characteristic optical properties, namely, co-rotating elliptical polarization
eigenstates and asymmetric, direction-dependent transmission for
left/right-handed incident wave polarization.


  In this paper an investigation of flow noise in sonar applications is
presented. Based on a careful identification of the dominant coupling effects,
the acoustic noise at the sensor position resulting from the turbulent wall
pressure fluctuations is modelled with a system of hydrodynamic, bending and
acoustic waves. We describe for the first time an analytical solution of the
problem which is based on a biorthogonal system which can be solved in the
spectral domain without the usual simplifying assumptions. Finally, it is
demonstrated that the analytical solution describes the flow noise generation
and propagation mechanisms of the considered sea trials.


  We present a structure associated to the class of linear codes. The
properties of that structure are similar to some structures in the linear
algebra techniques into the framework of the Gr\"obner bases tools. It allows
to get some insight in the problem of determining whether two codes are
permutation equivalent or not. Also an application to the decoding problem is
presented, with particular emphasis on the binary case.


  A new topological vertex finder is presented which combines ideas of the
well-established ZVTOP algorithm with a novel minimum spanning tree approach. A
preliminary performance study with simulated e+e- --> qqbar events at a centre
of mass energy of 92 GeV shows that the new approach is competitive with
existing vertex finder algorithms.


  Let the process Y(t) be a Skorohod integral process with respect to Brownian
motion. We use a recent result by Tudor (2004), to prove that Y(t) can be
represented as the limit of linear combinations of processes that are products
of forward and backward Brownian martingales. Such a result is a further step
towards the connection between the theory of continuous-time (semi)martingales,
and that of anticipating stochastic integration. We establish an explicit link
between our results and the classic characterization, due to Duc and Nualart
(1990), of the chaotic decomposition of Skorohod integral processes. We also
explore the case of Skorohod integral processes that are time-reversed Brownian
martingales, and provide an "anticipating" counterpart to the classic Optional
Sampling Theorem for It\^{o} stochastic integrals.


  We argue that all fermionic states characterized by the (four) velocity not
momentum of the fermion are always occupied by a single fermion. So there is no
fermion number violation whatsoever. The mass of an elementary particle is not
determined by field equations and is quite arbitrary. Though constant in the
free state, interaction may cause a fermion to go from finite to zeromass
state, interpreted as vacuum, and vice versa. We argue that a massless fermion
can't be detected. Since occupation number is not fixed the arguments leading
to the above conclusions cannot be extended to bosons. We calculate scattering
amplitude for a typical process like Rutherford scattering in the light of the
present discussion to show that there is no contradiction with the experimental
results.


  We select a sample of 34 gamma-ray bursts (GRBs) whose $\Gamma_0$ values are
derived with the onset peaks observed in the afterglow lightcurves (except for
GRB 060218 whose $\Gamma_0$ is estimated with its radio data), and investigate
the correlations among $\Gamma_0$, the isotropic peak luminosity ($L_{\rm
iso}$), and the peak energy ($E_{\rm p,z}$) of the $\nu f_\nu$ spectrum in the
cosmological rest frame. An analysis of pair correlations among these
observables well confirms the results reported by the previous papers. More
interestingly, a tight correlation among $L_{\rm iso}$, $E_{\rm p,z}$, and
$\Gamma_0$ is found from a multiple regression analysis, which takes the form
of $L_{\rm iso} \propto E_{\rm p,z}^{1.34\pm 0.14} \Gamma_0^{1.32\pm 0.19}$ or
$E_{\rm p,z} \propto L_{\rm iso}^{0.55\pm 0.06}\Gamma_0^{-0.50\pm 0.17}$. Nine
other GRBs whose $\Gamma_0$ are derived via the pair production opacity
constraint also follow such a correlation. Excluding GRB 060218, the $L_{\rm
iso}-E_{\rm p,z}-\Gamma_0$ correlation is valid, and it even holds in the jet
co-moving frame. However, GRB 060218 deviates the $L^{'}_{\rm iso}-E^{'}_{\rm
p}$ relation of typical GRBs in the jet co-moving frame with $3\sigma$. We
argue that the $L_{\rm iso} - E_{\rm p, z} - \Gamma_0$ correlation may be more
physical than the $L_{\rm iso} - E_{\rm p,z}$ correlation, since physically the
relationship between the observed $L_{\rm iso}$ and $E_{\rm p,z}$ not only
depends on radiation physics, but also depends on the bulk motion of the jet.
We explore the possible origins of this correlation and discuss its physical
implications for understanding GRB jet composition and radiation mechanism.


  Dynamical electro-weak symmetry breaking is an appealing, strongly-coupled
alternative to the weakly-coupled models based on an elementary scalar field
developing a vacuum expectation value. In the first two sections of this set of
lectures, I summarize the arguments, based on low-energy phenomenology,
supporting walking technicolor as a realistic realization of this idea. This
pedagogical introduction to walking technicolor, and more generally to the
physics of extensions of the standard model, makes extensive use of effective
field theory arguments, symmetries and counting rules. The strongly-coupled
nature of the underlying interactions, and the peculiar quasi-conformal
behavior of the theory, require to use non-perturbative methods in order to
address many fundamental questions within this framework. The recent
development of gauge/gravity dualities provides an ideal set of such
non-perturbative instruments. The remaining two sections illustrate the
potential of these techniques with two technical examples, one within the
bottom-up phenomenological approach to holography in five-dimensions, the other
within a more systematic top-down construction derived from ten-dimensional
type-IIB supergravity.


  The most successful methods such as ReLU transfer functions, batch
normalization, Xavier initialization, dropout, learning rate decay, or dynamic
optimizers, have become standards in the field due, particularly, to their
ability to increase the performance of Neural Networks (NNs) significantly and
in almost all situations. Here we present a new method to calculate the
gradients while training NNs, and show that it significantly improves final
performance across architectures, data-sets, hyper-parameter values, training
length, and model sizes, including when it is being combined with other common
performance-improving methods (such as the ones mentioned above). Besides being
effective in the wide array situations that we have tested, the increase in
performance (e.g. F1) it provides is as high or higher than this one of all the
other widespread performance-improving methods that we have compared against.
We call our method Population Gradients (PG), and it consists on using a
population of NNs to calculate a non-local estimation of the gradient, which is
closer to the theoretical exact gradient (i.e. this one obtainable only with an
infinitely big data-set) of the error function than the empirical gradient
(i.e. this one obtained with the real finite data-set).


  The emission from a nonlinear photonic mode coupled weakly to a gain medium
operating below threshold is predicted to exhibit antibunching. In the steady
state regime, analytical solutions for the relevant observable quantities are
found in accurate agreement with exact numerical results. Under pulsed
excitation, the unequal time second order correlation function demonstrates the
triggered probabilistic generation of single photons well separated in time.


  Identifying and managing high-emitters among heavy-duty diesel vehicles is a
key to mitigating urban air pollution, as a small number of such vehicles could
contribute a significant amount of total transport emissions. On-board
monitoring (OBM) systems can directly monitor the real-time emission
performance of heavy-duty vehicles on road and have become part of the future
emissions compliance framework. The challenge, however, lies in the frequent
unavailability of OBM data, affecting the effective screening of high-emitting
vehicles. This work proposes to bridge the gap by integrating OBM data with
remote sensing data to create a comprehensive monitoring system. OBM data is
used to characterize the detailed real-world NOx emission performance of both
normally-behaving vehicles and high-emitters at various vehicle operating
conditions. Remote sensing data is employed to screen out candidate
high-emitting vehicles based on thresholds determined by OBM data. Finally, the
dynamic NOx emission reduction potential across all roads is mapped by
combining the trajectory data for each vehicle with the emission data. A case
study in Chengdu, China, utilizing emission and traffic data from heavy-duty
vehicles for transporting construction waste (a.k.a. slag trucks), reveals the
national threshold for identifying high-emitters via remote sensing might be
too lenient, particularly in the medium speed range. An emission reduction of
18.8% in the China V slag truck fleet could be achieved by implementing this
novel method in practice in Chengdu. This approach establishes a reliable and
ongoing scheme for pinpointing high-emitters through multi-source data, which
allows local authorities to develop more robust and targeted strategies to
mitigate urban air pollution from heavy-duty diesel vehicles.


  In the present paper we have constructed a gauge invariant extension of a
generic Horava Gravity (HG) model (with quadratic curvature terms) in
linearized version in a systematic procedure. No additional fields are
introduced. The linearized HG model is explicitly shown to be a gauge fixed
version of the Einstein Gravity (EG) thus proving the Bellorin-Restuccia
conjecture in a robust way. In the process we have explicitly computed the
correct Hamiltonian dynamics using Dirac Brackets appearing from the Second
Class Constraints present in the HG model. We comment on applying this scheme
to the full non-linear HG.


  Sulphur is a volatile alpha-element which is not locked into dust grains in
the interstellar medium (ISM). Hence, its abundance does not need to be
corrected for dust depletion when comparing the ISM to the stellar atmospheres.
The abundance of sulphur in the photosphere of metal-poor stars is a matter of
debate: according to some authors, [S/Fe] versus [Fe/H] forms a plateau at low
metallicity, while, according to other studies, there is a large scatter or
perhaps a bimodal distribution. In metal-poor stars sulphur is detectable by
its lines of Mult.1 at 920 nm, but this range is heavily contaminated by
telluric absorptions, and one line of the multiplet is blended by the hydrogen
Paschen zeta line. We study the possibility of using Mult. 3 (at 1045 nm) for
deriving the sulphur abundance because this range, now observable at the VLT
with the infra-red spectrograph CRIRES, is little contaminated by telluric
absorption and not affected by blends at least in metal-poor stars. We compare
the abundances derived from Multiplets 1 and 3, taking into account NLTE
corrections and 3D effects. Here we present the results for a sample of four
stars, although the scatter is less pronounced than in previous analysis, we
cannot find a plateau in [S/Fe], and confirm the scatter of the sulphur
abundance at low metallicity.


  We have used a coupled dynamical and chemical model to examine the chemical
changes induced by the passage of an interstellar shock in well shielded
regions. Using this model we demonstrate that the formation of water in a shock
will be followed in the post--shock phase by depletion of the water molecules
onto the grain surfaces. To attempt to discriminate between the creation of
ices behind shocks and their production by means of grain surface chemistry, we
examine the deuterium chemistry of water before, during, and after a shock. We
show that chemical evolution in the post--shock gas can account for both the
deuterium fractionation and the abundance of carbon dioxide relative to water
observed in interstellar and cometary ices. Given the pervasiveness of shocks
and turbulent motions within molecular clouds, the model presented here offers
an alternate theory to grain surface chemistry for the creation of ices in the
interstellar medium, ices that may ultimately be incorporated into comets.


  We show that the requirement of the relativistic invariance for any
self-interacting, abelian p-form theory uniquely determines the form of the
corresponding quantization condition.


  Line intensity mapping (LIM) is a promising approach to study star formation
and the interstellar medium (ISM) in galaxies by measuring the aggregate line
emission from the entire galaxy population. In this work, we develop a simple
yet physically-motivated framework for modeling the line emission as would be
observed in LIM experiments, which is done by building on analytic models of
the Cosmic Infrared Background that connect total infrared luminosity of
galaxies to their host dark matter halos. We present models of the HI 21cm,
CO(1-0), [C II] 158$\mu$m, and [N II] 122 and 205$\mu$m lines consistent with
current observational constraints. With four case studies of various
combinations of these lines that probe different ISM phases, we demonstrate the
potential for reliably extracting physical properties of the ISM, and the
evolution of these properties with cosmic time, from auto and cross-correlation
analysis of these lines as measured by future LIM experiments.


  The non-equilibrium early time evolution of an ultra-relativistic heavy ion
collision is often described by classical lattice Yang-Mills theory, starting
from the colour glass condensate (CGC) effective theory with an anisotropic
energy momentum tensor as initial condition. In this work we investigate the
systematics associated with such studies and their dependence on various model
parameters (IR, UV cutoffs and the amplitude of quantum fluctuations) which are
not yet fixed by experiment. We perform calculations for SU(2) and SU(3), both
in a static box and in an expanding geometry. Generally, the dependence on
model parameters is found to be much larger than that on technical parameters
like the number of colours, boundary conditions or the lattice spacing. In a
static box, all setups lead to isotropisation through chromo-Weibel
instabilities, which is illustrated by the accompanying filamentation of the
energy density. However, the associated time scale depends strongly on the
model parameters and in all cases is longer than the phenomenologically
expected one. In the expanding system, no isotropisation is observed for any
parameter choice. We show how investigations at fixed initial energy density
can be used to better constrain some of the model parameters.


  The molecular simulations solve the equation of motion of molecular systems,
making 3D shapes of molecules four-dimensional by adding the time coordinate.
These methods have a great potential in drug discovery because they can
realistically model the structures of protein molecules targeted by drugs as
well as the process of binding of a potential drug to its molecular target.
However, routine application of biomolecular simulations is hampered by the
very high computational costs of this method. Several methods have been
developed to address this problem. One of them, metadynamics, disfavors states
of the simulated system that have been already visited and thus forces the
system to explore new and new states. Here we present the package metadynminer
and metadynminer3d to analyze and visualize results from metadynamics, in
particular those produced by a popular metadynamics package Plumed.


  This paper reviews the highlights of a 4-years-long research project
supported by the Japanese Government to explore new superconducting materials
and relevant functional materials. The project found several tens of new
superconductors by examining ~1000 materials, each of which was chosen by
Japanese team member experts with a background in solid state chemistry. This
review summarizes the major achievements of the project in newly found
superconducting materials, and the wire and tape fabrication of iron-based
superconductors. It is a unique feature of this review to incorporate a list of
~700 unsuccessful materials examined for superconductivity in the project. In
addition, described are new functional materials and functionalities discovered
during the project.


  In a previous paper, we derived a recursive formula determining the weight
distributions of the [n=(q^m-1)/(q-1)] Hamming code H(m,q), when (m,q-1)=1.
Here q is a prime power. We note here that the formula actually holds for any
positive integer m and any prime power q, without the restriction (m, q-1)=1.


  There has been great interest in two-dimensional materials, beyond graphene,
for both fundamental sciences and technological applications. Silicene, a
silicon counterpart of graphene, has been shown to possess some better
electronic properties than graphene. However, its thermal transport properties
have not been fully studied. In this paper, we apply the first-principles-based
phonon Boltzmann transport equation to investigate the thermal conductivity of
silicene as well as the phonon scattering mechanisms. Although both graphene
and silicene are two-dimensional crystals with similar crystal structure, we
find that phonon transport in silicene is quite different from that in
graphene. The thermal conductivity of silicene shows a logarithmic increase
with respect to the sample size due to the small scattering rates of acoustic
in-plane phonon modes, while that of graphene is finite. Detailed analysis of
phonon scattering channels shows that the linear dispersion of the acoustic
out-of-plane (ZA) phonon modes, which is induced by the buckled structure,
makes the long-wavelength longitudinal acoustic (LA) phonon modes in silicene
not as efficiently scattered as that in graphene. Compared with graphene, where
most of the heat is carried by the acoustic out-of-plane (ZA) phonon modes, the
ZA phonon modes in silicene only have ~10% contribution to the total thermal
conductivity, which can also be attributed to the buckled structure. This
systematic comparison of phonon transport and thermal conductivity of silicene
and graphene using the first-principle-based calculations shed some light on
other two-dimensional materials, such as two-dimensional transition metal
dichalcogenides.


  With the ubiquity of IoT devices there is a growing demand for
confidentiality and integrity of data. Solutions based on reconfigurable logic
(CPLD or FPGA) have certain advantages over ASIC and MCU/SoC alternatives.
Programmable logic devices are ideal for both confidentiality and upgradability
purposes. In this context the hardware security aspects of CPLD/FPGA devices
are paramount. This paper shows preliminary evaluation of hardware security in
Intel MAX 10 devices. These FPGAs are one of the most suitable candidates for
applications demanding extensive features and high level of security. Their
strong and week security aspects are revealed and some recommendations are
suggested to counter possible security vulnerabilities in real designs. This is
a feasibility study paper. Its purpose is to highlight the most vulnerable
areas to attacks aimed at data extraction and reverse engineering. That way
further investigations could be performed on specific areas of concern.


  The primary ingredients of reality are the universal quantum fields, which
fluctuate persistently, spontaneously, and randomly. The general perception of
the scientific community is that these quantum fluctuations are due to the
uncertainty principle. Here, we present cogent arguments to show that the
uncertainty principle is a consequence of the quantum fluctuations, but not
their cause. This poses a conspicuous enigma as to how the universal fields
remain immutable with an expectation value so accurate that it leads to
experimental results, which are precise to one part in a trillion. We discuss
some reasonable possibilities in the absence of a satisfactory solution to this
enigma.


  Microlensing is the tool of choice for the search and the analysis of compact
halo objects ("MACHOs"), a still viable class of dark matter candidates at the
galactic scale. Different analyses point towards an agreement in excluding dark
matter MACHOs of less than about 0.1 solar mass; it remains however an ongoing
debate for values in the mass range (0.1-1) solar mass. The more robust
constraints, though not all in agreement, come from the observational campaigns
towards the Magellanic Clouds (the LMC and the SMC). The analyses towards the
nearby galaxy of M31, in the so called "pixel lensing" regime, have expanded
the perspectives in this field of research. In this contribution first we draw
a critical view on recent results and then we focus on the pixel lensing
analysis towards M31 of the PLAN collaboration.


  A common consensus in the literature is that the citation profile of
published articles in general follows a universal pattern - an initial growth
in the number of citations within the first two to three years after
publication followed by a steady peak of one to two years and then a final
decline over the rest of the lifetime of the article. This observation has long
been the underlying heuristic in determining major bibliometric factors such as
the quality of a publication, the growth of scientific communities, impact
factor of publication venues etc. In this paper, we gather and analyze a
massive dataset of scientific papers from the computer science domain and
notice that the citation count of the articles over the years follows a
remarkably diverse set of patterns - a profile with an initial peak (PeakInit),
with distinct multiple peaks (PeakMul), with a peak late in time (PeakLate),
that is monotonically decreasing (MonDec), that is monotonically increasing
(MonIncr) and that can not be categorized into any of the above (Oth). We
conduct a thorough experiment to investigate several important characteristics
of these categories such as how individual categories attract citations, how
the categorization is influenced by the year and the venue of publication of
papers, how each category is affected by self-citations, the stability of the
categories over time, and how much each of these categories contribute to the
core of the network. Further, we show that the traditional preferential
attachment models fail to explain these citation profiles. Therefore, we
propose a novel dynamic growth model that takes both the preferential
attachment and the aging factor into account in order to replicate the
real-world behavior of various citation profiles. We believe that this paper
opens the scope for a serious re-investigation of the existing bibliometric
indices for scientific research.


  Models with extra dimensions and the fundamental scale at the TeV could imply
sign als in large neutrino telescopes due to gravitational scattering of
cosmogenic neu trinos in the detection volume. Apart from the production of
microscopic black hol es, extensively studied in the literature, we present
gravity-mediated interactions at larger distances, that can be calculated in
the e ikonal approximation. In these elastic processes the neutrino loses a
small fracti on of energy to a hadronic shower and keeps going. The event rate
of these events is higher than that of black hole formation and the signal is
distinct: no charged leptons and possibly multiple-bang events.


  Current trends in scientific imaging are challenged by the emerging need of
integrating sophisticated machine learning with Big Data analytics platforms.
This work proposes an in-memory distributed learning architecture for enabling
sophisticated learning and optimization techniques on scientific imaging
problems, which are characterized by the combination of variant information
from different origins. We apply the resulting, Spark-compliant, architecture
on two emerging use cases from the scientific imaging domain, namely: (a) the
space variant deconvolution of galaxy imaging surveys (astrophysics), (b) the
super-resolution based on coupled dictionary training (remote sensing). We
conduct evaluation studies considering relevant datasets, and the results
report at least 60\% improvement in time response against the conventional
computing solutions. Ultimately, the offered discussion provides useful
practical insights on the impact of key Spark tuning parameters on the speedup
achieved, and the memory/disk footprint.


  We report the results of a series of three-dimensional (3-D) simulations of
the deflagration phase of the gravitationally confined detonation mechanism for
Type Ia supernovae. In this mechanism, ignition occurs at one or several
off-center points, resulting in a burning bubble of hot ash that rises rapidly,
breaks through the surface of the star, and collides at a point opposite
breakout on the stellar surface. We find that detonation conditions are
robustly reached in our 3-D simulations for a range of initial conditions and
resolutions. Detonation conditions are achieved as the result of an
inwardly-directed jet that is produced by the compression of unburnt surface
material when the surface flow collides with itself. A high-velocity
outwardly-directed jet is also produced. The initial conditions explored in
this paper lead to conditions at detonation that can be expected to produce
large amounts of $^{56}$Ni and small amounts of intermediate mass elements.
These particular simulations are therefore relevant only to high luminosity
Type Ia supernovae. Recent observations of Type Ia supernovae imply a
compositional structure that is qualitatively consistent with that expected
from these simulations.


  In this paper, we give positive answer to the open question raised in [E.
Zuazua, Exponential decay for the semilinear wave equation with localized
damping in unbounded domains. J. Math. Pures Appl., 70 (1991) 513--529] on the
exponential decay of solutions for the semilinear plate equation with localized
damping.


  We develop the shape derivative analysis of solutions to the problem of
scattering of time-harmonic electromagnetic waves by a bounded penetrable
obstacle. Since boundary integral equations are a classical tool to solve
electromagnetic scattering problems, we study the shape differentiability
properties of the standard electromagnetic boundary integral operators. Using
Helmholtz decomposition, we can base their analysis on the study of scalar
integral operators in standard Sobolev spaces, but we then have to study the
G\^ateaux differentiability of surface differential operators. We prove that
the electromagnetic boundary integral operators are infinitely differentiable
without loss of regularity and that the solutions of the scattering problem are
infinitely shape differentiable away from the boundary of the obstacle, whereas
their derivatives lose regularity on the boundary. We also give a
characterization of the first shape derivative as a solution of a new
electromagnetic scattering problem.


  Properties of the Kondo effect in quantum dots depend sensitively on the
coupling parameters and so on the realization of the quantum dot -- the Kondo
temperature itself becomes a mesoscopic quantity. Assuming chaotic dynamics in
the dot, we use random matrix theory to calculate the distribution of both the
Kondo temperature and the conductance in the Coulomb blockade regime. We study
two experimentally relevant cases: leads with single channels and leads with
many channels. In the single-channel case, the distribution of the conductance
is very wide as $T_K$ fluctuates on a logarithmic scale. As the number of
channels increases, there is a slow crossover to a self-averaging regime.


  Let $V$ be a complete discrete valuation ring with residue field $k$ and with
fraction field $K$ of characteristic 0. We clarify the analysis behind the
Monsky--Washnitzer completion of a commutative $V$-algebra using spectral
radius estimates for bounded subsets in complete bornological $V$-algebras.
This leads us to a functorial chain complex for commutative $k$-algebras that
computes Berthelot's rigid cohomology. This chain complex is related to the
periodic cyclic homology of certain complete bornological $V$-algebras.


  Suppose that $a_1(n),a_2(n),...,a_s(n),m(n)$ are integer-valued polynomials
in $n$ with positive leading coefficients. This paper presents Popoviciu type
formulas for the generalized restricted partition function
$$p_{A(n)}(m(n)):=#\{(x_1,...,x_s)\in \mathbb{Z}^{s}: all x_j\geqslant 0,
x_1a_1(n)+...+x_sa_s(n)=m(n) \}$$ when $s=2$ or 3. In either case, the formula
implies that the function is an integer-valued quasi-polynomial. The main
result is proved by a reciprocity law for a class of fractional part sums and
the theory of generalized Euclidean division.


  Estimating frequencies of certain items among a population is a basic step in
data analytics, which enables more advanced data analytics (e.g., heavy hitter
identification, frequent pattern mining), client software optimization, and
detecting unwanted or malicious hijacking of user settings in browsers.
Frequency estimation and heavy hitter identification with local differential
privacy (LDP) protect user privacy as well as the data collector. Existing LDP
algorithms cannot leverage 1) prior knowledge about the noise in the estimated
item frequencies and 2) prior knowledge about the true item frequencies. As a
result, they achieve suboptimal performance in practice.
  In this work, we aim to design LDP algorithms that can leverage such prior
knowledge. Specifically, we design ${Calibrate}$ to incorporate the prior
knowledge via statistical inference. ${Calibrate}$ can be appended to an
existing LDP algorithm to reduce its estimation errors. We model the prior
knowledge about the noise and the true item frequencies as two probability
distributions, respectively. Given the two probability distributions and an
estimated frequency of an item produced by an existing LDP algorithm, our
${Calibrate}$ computes the conditional probability distribution of the item's
frequency and uses the mean of the conditional probability distribution as the
calibrated frequency for the item. It is challenging to estimate the two
probability distributions due to data sparsity. We address the challenge via
integrating techniques from statistics and machine learning. Our empirical
results on two real-world datasets show that ${Calibrate}$ significantly
outperforms state-of-the-art LDP algorithms for frequency estimation and heavy
hitter identification.


  A study of the yields of strange particles produced in heavy-ion and
elementary collisions is presented using preliminary results from the STAR
experiment at RHIC. The strange particle production rates, relative to those of
p+p, have been proposed as a means of determining an enhancement of strangeness
production in heavy-ion collisions. Analysis of results from STAR show that
this enhancement measure is reduced, or comparable, when contrasted to that at
top CERN SPS energies. A smaller suppression in the p+p yields at RHIC energies
due to finite volume effects could be the cause of such a result. By studying
the yields as a function of centrality we hope to establish how these effects
vary with the volume of the source created.


  The electron-doped cuprate Pr(2-x)Ce(x)CuO(4) is investigated using infrared
magneto-optical measurements. The optical Hall conductivity sigma_{xy} shows a
strong doping, frequency and temperature dependence consistent with the
presence of a temperature and doping-dependent coherent backscattering
amplitude which doubles the electronic unit cell. The data suggest that the
coherent backscattering vanishes at a quantum critical point inside the
superconducting dome and is associated with the commensurate antiferromagnetic
order observed by other workers. Using a spectral weight analysis we have
further investigated the Fermi-liquid like behavior of the overdoped sample.
The observed Hall-conductance spectral weight is about 10 times less than that
predicted by band theory, raising the fundamental question concerning the
effect of Mott and antiferromagnetic correlations on the Hall conductance of
strongly correlated materials.


  I study some aspects of common envelope evolution, where a compact star
enters the envelope of a giant star. I show that in some binary systems under a
narrow range of parameters, a substantial fraction of the giant stellar
envelope is lost before the onset of the common envelope. The reduced envelope
mass at the onset of the common envelope implies that the binary system emerges
from the common envelope with a relatively large orbital separation. I
therefore caution against a simple treatment, which omits this process, in the
study of systems that evolved through a common envelope phase and ended with a
relatively large orbital separation, e.g., PG1115+166. A fraction of the
envelope that is lost while the companion is still outside the giant stellar
envelope is accreted by the companion. The companion may form an accretion disk
and blow two jets. I propose this scenario for the formation of the bipolar
planetary nebula NGC 2346, which has a binary nucleus with an orbital period
longer than that of any other known binary system in planetary nebulae. Because
this scenario, of a late common envelope phase, occurs for a narrow range of
parameters, I expect it to be applicable to a small, but non-negligible, number
of systems.


  In this review, we first discuss the perspectives concerning a better
determination of the proton structure in terms of quarks and gluons at LHC
after describing the results coming from HERA and Tevatron. In a second part of
the review, we describe the diffractive phenomena at HERA and Tevatron and the
consequences for LHC.


  We argue that the conclusion of Bell theorem, namely, that there must be
spatial non-local correlations in certain experimental situations, does not
apply to typical individual measurements performed on entangled EPR pairs. Our
claim is based on three points, (i) on the notion of quantum {\it complete
measurements}; (ii) on Bell results on local yet distant measurements; and
(iii) on the fact that perfect simultaneity is banned by the quantum mechanics.
We show that quantum mechanics indicates that, while the measurements of the
pair members are indeed space-like separated, the pair measurement is actually
a sequence of two complete measurements, the first one terminating the
entanglement and, therefore, the second one becoming unrelated to the initial
preparation of the entangled pair. The outstanding feature of these
measurements is that neither of them violates the principle of locality. We
discuss that the present measurement viewpoint appears to run contrary to the
usual interpretation of "superposition" of states with its concomitant
"collapse" on measurement.


  X-ray computed tomography (CT) reconstructs cross-sectional images from
projection data. However, ionizing X-ray radiation associated with CT scanning
might induce cancer and genetic damage. Therefore, the reduction of radiation
dose has attracted major attention. Few-view CT image reconstruction is an
important topic to reduce the radiation dose. Recently, data-driven algorithms
have shown great potential to solve the few-view CT problem. In this paper, we
develop a dual network architecture (DNA) for reconstructing images directly
from sinograms. In the proposed DNA method, a point-based fully-connected layer
learns the backprojection process requesting significantly less memory than the
prior arts do. Proposed method uses O(C*N*N_c) parameters where N and N_c
denote the dimension of reconstructed images and number of projections
respectively. C is an adjustable parameter that can be set as low as 1. Our
experimental results demonstrate that DNA produces a competitive performance
over the other state-of-the-art methods. Interestingly, natural images can be
used to pre-train DNA to avoid overfitting when the amount of real patient
images is limited.


  We experimentally study an unequally-spaced optical comb (USOC), which is
generated by a unidirectional fiber loop laser operated at low temperatures.
The underlying mechanism responsible for USOC formation is explored using both
close and open loop measurements. The role played by dispersion is investigated
using radio frequency spectrum measurements. By integrating a saturable
absorber into the loop, a lasing state is revealed, in which mode locking
coexists with the USOC.


  A wealth of data from the Herschel and Planck satellites and now from ALMA,
revealing cold dust thermal emission, is available for astronomical
environments ranging from interstellar clouds, cold clumps, circumstellar
envelops, and protoplanetary disks. The interpretation of these observations
relies on the understanding and modeling of cold dust emission and on the
knowledge of the dust optical properties. The aim of this work is to provide
astronomers with a set of spectroscopic data of realistic interstellar dust
analogues that can be used to interpret the observations. Glassy silicates of
mean composition (1-x)MgO - xSiO2 with x = 0.35, 0.40 and 0.50 were
synthesized. The mass absorption coefficient (MAC) of the samples was measured
in the spectral domain 30 - 1000 $\mu$m for grain temperature in the range 300
K - 10 K and at room temperature in the 5 - 40 $\mu$m domain. We find that the
MAC of all samples varies with the grains temperature. In the FIR/submm, and
above 30K, the MAC value at a given wavelength increases with the temperature
as thermally activated absorption processes appear. The studied materials
exhibit different and complex behaviors at long wavelengths (lambda $\geq$ 200
to 700 $\mu$m depending on the samples) and the MAC cannot be approximated by a
single power law in ${\lambda}^{-\beta}$. These behaviors are attributed to the
amorphous nature of dust and to the amount and nature of the defects within
this amorphous structure. Above 20 $\mu$m, the measured MAC are much higher
than the MAC calculated from interstellar silicate dust models indicating that
the analogues measured in this study are more emissive than the silicates in
cosmic dust models. This has important astrophysical implications because
masses are overestimated by the models. Moreover, constraints on elemental
abundance of heavy elements in cosmic dust models are relaxed