title: Number of irreducible mod l rank 2 sheaves on curves over finite fields

abstract:
  Let X be a smooth projective curve of genus g over a finite field F_q of
characteristic p. Consider primes l different from p. We formulate some
questions related to a well known counting formula of Drinfeld.
  Drinfeld counts rank 2, irreducible l-adic sheaves on the base change X_n of
X to F_{q^n} as n varies. We would like to count rank 2, irreducible mod l
sheaves on X_n as n varies. Drinfeld's l-adic count gives an upper bound for
the mod l count. We conjecture that Drinfeld's count is the correct asymptotic
for the count of rank 2, irreducible mod l sheaves on X_n as n varies with
(n,\ell)=1.


title:
Small perturbations leading to large asymmetries in the probability
  density of the ground state

abstract:
  It is shown that in one-particle Schr\"{o}dinger quantum mechanics a small
perturbation of a one-dimensional potential can produce a large change in the
ground state, the effect becoming more pronounced with growing typical length
of the potential, contrary to what standard stationary perturbation theory
could make one believe.The possible consequences of this result for solid state
physics and other fields are also discussed, and examples of such non-standard
perturbations are furnished.


title: Modelling Early Transitions Toward Autonomous Protocells

abstract:
  This thesis broadly concerns the origins of life problem, pursuing a joint
approach that combines general philosophical/conceptual reflection on the
problem along with more detailed and formal scientific modelling work oriented
in the conceptual perspective developed. The central subject matter addressed
is the emergence and maintenance of compartmentalised chemistries as precursors
of more complex systems with a proper cellular organization. Whereas an
evolutionary conception of life dominates prebiotic chemistry research and
overflows into the protocells field, this thesis defends that the 'autonomous
systems perspective' of living phenomena is a suitable - arguably the most
suitable - conceptual framework to serve as a backdrop for protocell research.
The autonomy approach allows a careful and thorough reformulation of the
origins of cellular life problem as the problem of how integrated autopoietic
chemical organisation, present in all full-fledged cells, originated and
developed from more simple far-from-equilibrium chemical aggregate systems.


title: On a function involving generalized complete $(p,q)$- elliptic integrals

abstract:
  Motivated by the work of Alzer and Richards \cite{ar}, here authors study the
monotonicity and convexity properties of the function $$\Delta_{p,q} (r) =
\frac{{E_{p,q}(r) - \left( {r'} \right)^p K_{p,q}(r) }}{{r^p }} -
\frac{{E'_{p,q}(r) - r^p K'_{p,q}(r) }}{{\left( {r'} \right)^p }},$$ where
$K_{p,q}$ and $E_{p,q}$ denote the complete $(p,q)$- elliptic integrals of the
first and the second kind, respectively.


title: Data Recombination for Neural Semantic Parsing

abstract:
  Modeling crisp logical regularities is crucial in semantic parsing, making it
difficult for neural models with no task-specific prior knowledge to achieve
good results. In this paper, we introduce data recombination, a novel framework
for injecting such prior knowledge into a model. From the training data, we
induce a high-precision synchronous context-free grammar, which captures
important conditional independence properties commonly found in semantic
parsing. We then train a sequence-to-sequence recurrent network (RNN) model
with a novel attention-based copying mechanism on datapoints sampled from this
grammar, thereby teaching the model about these structural properties. Data
recombination improves the accuracy of our RNN model on three semantic parsing
datasets, leading to new state-of-the-art performance on the standard GeoQuery
dataset for models with comparable supervision.


title:
Drug response prediction by inferring pathway-response associations with
  Kernelized Bayesian Matrix Factorization

abstract:
  A key goal of computational personalized medicine is to systematically
utilize genomic and other molecular features of samples to predict drug
responses for a previously unseen sample. Such predictions are valuable for
developing hypotheses for selecting therapies tailored for individual patients.
This is especially valuable in oncology, where molecular and genetic
heterogeneity of the cells has a major impact on the response. However, the
prediction task is extremely challenging, raising the need for methods that can
effectively model and predict drug responses. In this study, we propose a novel
formulation of multi-task matrix factorization that allows selective data
integration for predicting drug responses. To solve the modeling task, we
extend the state-of-the-art kernelized Bayesian matrix factorization (KBMF)
method with component-wise multiple kernel learning. In addition, our approach
exploits the known pathway information in a novel and biologically meaningful
fashion to learn the drug response associations. Our method quantitatively
outperforms the state of the art on predicting drug responses in two publicly
available cancer data sets as well as on a synthetic data set. In addition, we
validated our model predictions with lab experiments using an in-house cancer
cell line panel. We finally show the practical applicability of the proposed
method by utilizing prior knowledge to infer pathway-drug response
associations, opening up the opportunity for elucidating drug action
mechanisms. We demonstrate that pathway-response associations can be learned by
the proposed model for the well known EGFR and MEK inhibitors.


title: Dust Properties in HII Regions in M33

abstract:
  The conversion of the IR emission into star formation rate can be strongly
dependent on the physical properties of the dust, which are affected by the
environmental conditions where the dust is embedded. We study here the dust
properties of a set of HII regions in the Local Group Galaxy M33 presenting
different spatial configurations between the stars, gas and dust to understand
the dust evolution under different environments. We model the SED of each
region using the DustEM tool and obtain the mass relative to hydrogen for Very
Small Grains (YVSG), Polycyclic Aromatic Hydrocarbons (YPAH) and Big Grains
(YBG). The relative mass of the VSGs (YVSG/YTOT) is a factor of 1.7 higher for
HII regions classified as filled and mixed than for regions presenting a shell
structure. The enhancement of VSGs within NGC 604 and NGC 595 is correlated to
expansive gas structures with velocities greater than 50 km/s. The gas-to-dust
ratio derived for the HII regions in our sample exhibits two regimes related to
the HI-H2 transition of the ISM. Regions corresponding to the HI diffuse regime
present a gas-to-dust ratio compatible with the expected value if we assume
that the gas-to-dust ratio scales linearly with metallicity, while regions
corresponding to a H2 molecular phase present a flatter dust-gas surface
density distribution. The fraction of VSGs can be affected by the conditions of
the interstellar environment: strong shocks of 50-90 km/s existing in the
interior of the most luminous HII regions can lead to fragmentation of BGs into
smaller ones, while the more evolved shell and clear shell objects provide a
more quiescent environment where reformation of dust BG grains might occur. The
gas-to-dust variations found in this analysis might imply that grain
coagulation and/or gas-phase metals incorporation to the dust mass is occurring
in the interior of the HII regions in M33.


title:
From Generalized Langevin Equations to Brownian Dynamics and Embedded
  Brownian Dynamics

abstract:
  We present the reduction of generalized Langevin equations to a
coordinate-only stochastic model, which in its exact form, involves a forcing
term with memory and a general Gaussian noise. It will be shown that a similar
fluctuation-dissipation theorem still holds at this level. We study the
approximation by the typical Brownian dynamics as a first approximation. Our
numerical test indicates how the intrinsic frequency of the kernel function
influences the accuracy of this approximation. In the case when such an
approximate is inadequate, further approximations can be derived by embedding
the nonlocal model into an extended dynamics without memory. By imposing noises
in the auxiliary variables, we show how the second fluctuation-dissipation
theorem is still exactly satisfied.


title: On Matching and Thickness in Heterogeneous Dynamic Markets

abstract:
  We study dynamic matching in an infinite-horizon stochastic market. While all
agents are potentially compatible with each other, some are hard-to-match and
others are easy-to-match. Agents prefer to be matched as soon as possible and
matches are formed either bilaterally or indirectly through chains. We adopt an
asymptotic approach and compute tight bounds on the limit of waiting time of
agents under myopic policies that differ in matching technology and
prioritization.
  We find that the market composition is a key factor in the desired matching
technology and prioritization level. When hard-to-match agents arrive less
frequently than easy-to-match ones (i) bilateral matching is almost as
efficient as chains (waiting times scale similarly under both, though chains
always outperform bilateral matching by a constant factor), and (ii) assigning
priorities to hard-to-match agents improves their waiting times. When
hard-to-match agents arrive more frequently, chains are much more efficient
than bilateral matching and prioritization has no impact.
  We further conduct comparative statics on arrival rates. Somewhat
surprisingly, we find that in a heterogeneous market and under bilateral
matching, increasing arrival rate has a non-monotone effect on waiting times,
due to the fact that, under some market compositions, there is an adverse
effect of competition. Our comparative statics shed light on the impact of
merging markets and attracting altruistic agents (that initiate chains) or
easy-to-match agents.
  This work uncovers fundamental differences between heterogeneous and
homogeneous dynamic markets, and potentially helps policy makers to generate
insights on the operations of matching markets such as kidney exchange
programs.


title:
Experimental Signatures of the Inverted Phase in InAs/GaSb Coupled
  Quantum Wells

abstract:
  Transport measurements are performed on InAs/GaSb double quantum wells at
zero and finite magnetic fields applied parallel and perpendicular to the
quantum wells. We investigate a sample in the inverted regime where electrons
and holes coexist, and compare it with another sample in the non-inverted
semiconducting regime. Activated behavior in conjunction with a strong
suppression of the resistance peak at the charge neutrality point in a parallel
magnetic field attest to the topological hybridization gap between electron and
hole bands in the inverted sample. We observe an unconventional Landau level
spectrum with energy gaps modulated by the magnetic field applied perpendicular
to the quantum wells. This is caused by strong spin-orbit interaction provided
jointly by the InAs and the GaSb quantum wells.


title: metricDTW: local distance metric learning in Dynamic Time Warping

abstract:
  We propose to learn multiple local Mahalanobis distance metrics to perform
k-nearest neighbor (kNN) classification of temporal sequences. Temporal
sequences are first aligned by dynamic time warping (DTW); given the alignment
path, similarity between two sequences is measured by the DTW distance, which
is computed as the accumulated distance between matched temporal point pairs
along the alignment path. Traditionally, Euclidean metric is used for distance
computation between matched pairs, which ignores the data regularities and
might not be optimal for applications at hand. Here we propose to learn
multiple Mahalanobis metrics, such that DTW distance becomes the sum of
Mahalanobis distances. We adapt the large margin nearest neighbor (LMNN)
framework to our case, and formulate multiple metric learning as a linear
programming problem. Extensive sequence classification results show that our
proposed multiple metrics learning approach is effective, insensitive to the
preceding alignment qualities, and reaches the state-of-the-art performances on
UCR time series datasets.


title:
Sensitivity of a proposed space-based Cerenkov astrophysical-neutrino
  telescope (CHANT)

abstract:
  Neutrinos with energies in the PeV to EeV range produce upgoing extensive air
showers when they interact underground close enough to the surface of the
Earth. We study the possibility for detection of such showers with a system of
very wide field-of-view imaging atmospheric Cherenkov telescopes, named CHANT
for CHerenkov from Astrophysical Neutrinos Telescope, pointing down to a strip
below the Earth's horizon from space. We find that CHANT provides sufficient
sensitivity for the study of the astrophysical neutrino flux in a wide energy
range, from 10 PeV to 10 EeV. A space-based CHANT system can discover and study
in detail the cosmogenic neutrino flux originating from interactions of
ultra-high-energy cosmic rays in the intergalactic medium.


title:
The Structures, Functions, and Evolution of Sm-like Archaeal Proteins
  (SmAPs)

abstract:
  Sm proteins were discovered nearly 20 years ago as a group of small antigenic
proteins ($\approx$ 90-120 residues). Since then, an extensive amount of
biochemical and genetic data have illuminated the crucial roles of these
proteins in forming ribonucleoprotein (RNP) complexes that are used in RNA
processing, e.g., spliceosomal removal of introns from pre-mRNAs. Spliceosomes
are large macromolecular machines that are comparable to ribosomes in size and
complexity, and are composed of uridine-rich small nuclear RNPs (U snRNPs).
Various sets of seven different Sm proteins form the cores of most snRNPs.
Despite their importance, very little is known about the atomic-resolution
structure of snRNPs or their Sm cores. As a first step towards a
high-resolution image of snRNPs and their hierarchic assembly, we have
determined the crystal structures of archaeal homologs of Sm proteins, which we
term Sm-like archaeal proteins (SmAPs).


title:
A Stern-Gerlach-like approach to electron orbital angular momentum
  measurement

abstract:
  Many methods now exist to prepare free electrons into orbital angular
momentum states, and the predicted applications of these electron states as
probes of materials and scattering processes are numerous. The development of
electron orbital angular momentum measurement techniques has lagged behind. We
show that coupling between electron orbital angular momentum and a spatially
varying magnetic field produces an angular momentum-dependent focusing effect.
We propose a design for an orbital angular momentum measurement device built on
this principle. As the method of measurement is non-interferometric, the device
works equally well for mixed, superposed and pure final orbital angular
momentum states. The energy and orbital angular momentum distributions of
inelastically scattered electrons may be simultaneously measurable with this
technique.


title:
Natural Language Generation in Dialogue using Lexicalized and
  Delexicalized Data

abstract:
  Natural language generation plays a critical role in spoken dialogue systems.
We present a new approach to natural language generation for task-oriented
dialogue using recurrent neural networks in an encoder-decoder framework. In
contrast to previous work, our model uses both lexicalized and delexicalized
components i.e. slot-value pairs for dialogue acts, with slots and
corresponding values aligned together. This allows our model to learn from all
available data including the slot-value pairing, rather than being restricted
to delexicalized slots. We show that this helps our model generate more natural
sentences with better grammar. We further improve our model's performance by
transferring weights learnt from a pretrained sentence auto-encoder. Human
evaluation of our best-performing model indicates that it generates sentences
which users find more appealing.


title: Efficient exploration of cosmology dependence in the EFT of LSS

abstract:
  The most effective use of data from current and upcoming large scale
structure~(LSS) and CMB observations requires the ability to predict the
clustering of LSS with very high precision. The Effective Field Theory of Large
Scale Structure (EFTofLSS) provides an instrument for performing analytical
computations of LSS observables with the required precision in the mildly
nonlinear regime. In this paper, we develop efficient implementations of these
computations that allow for an exploration of their dependence on cosmological
parameters. They are based on two ideas. First, once an observable has been
computed with high precision for a reference cosmology, for a new cosmology the
same can be easily obtained with comparable precision just by adding the
difference in that observable, evaluated with much less precision. Second, most
cosmologies of interest are sufficiently close to the Planck best-fit cosmology
that observables can be obtained from a Taylor expansion around the reference
cosmology. These ideas are implemented for the matter power spectrum at two
loops and are released as public codes. When applied to cosmologies that are
within 3$\sigma$ of the Planck best-fit model, the first method evaluates the
power spectrum in a few minutes on a laptop, with results that have 1\% or
better precision, while with the Taylor expansion the same quantity is
instantly generated with similar precision. The ideas and codes we present may
easily be extended for other applications or higher-precision results.


title: The Opacity of Backbones

abstract:
  This paper approaches, using structural complexity theory, the question of
whether there is a chasm between knowing an object exists and getting one's
hands on the object or its properties. In particular, we study the
nontransparency of so-called backbones. A backbone of a boolean formula $F$ is
a collection $S$ of its variables for which there is a unique partial
assignment $a_S$ such that $F[a_S]$ is satisfiable [MZK+99,WGS03]. We show
that, under the widely believed assumption that integer factoring is hard,
there exist sets of boolean formulas that have obvious, nontrivial backbones
yet finding the values, $a_S$, of those backbones is intractable. We also show
that, under the same assumption, there exist sets of boolean formulas that
obviously have large backbones yet producing such a backbone $S$ is
intractable. Furthermore, we show that if integer factoring is not merely
worst-case hard but is frequently hard, as is widely believed, then the
frequency of hardness in our two results is not too much less than that
frequency. These results hold more generally, namely, in the settings where,
respectively, one's assumption is that P $\neq$ NP $\cap$ coNP or that some
problem in NP $\cap$ coNP is frequently hard.


title: Integer complexity: algorithms and computational results

abstract:
  Define $\|n\|$ to be the complexity of $n$, the smallest number of ones
needed to write $n$ using an arbitrary combination of addition and
multiplication. Define $n$ to be stable if for all $k\ge 0$, we have $\|3^k
n\|=\|n\|+3k$. In [7], this author and Zelinsky showed that for any $n$, there
exists some $K=K(n)$ such that $3^K n$ is stable; however, the proof there
provided no upper bound on $K(n)$ or any way of computing it. In this paper, we
describe an algorithm for computing $K(n)$, and thereby also show that the set
of stable numbers is a computable set. The algorithm is based on considering
the defect of a number, defined by $\delta(n):=\|n\|-3\log_3 n$, building on
the methods presented in [3]. As a side benefit, this algorithm also happens to
allow fast evaluation of the complexities of powers of $2$; we use it to verify
that $\|2^k 3^\ell\|=2k+3\ell$ for $k\le48$ and arbitrary $\ell$ (excluding the
case $k=\ell=0$), providing more evidence for the conjecture that $\|2^k
3^\ell\|=2k+3\ell$ whenever $k$ and $\ell$ are not both zero. An implementation
of these algorithms in Haskell is available.


title:
BigEAR: Inferring the Ambient and Emotional Correlates from
  Smartphone-based Acoustic Big Data

abstract:
  This paper presents a novel BigEAR big data framework that employs
psychological audio processing chain (PAPC) to process smartphone-based
acoustic big data collected when the user performs social conversations in
naturalistic scenarios. The overarching goal of BigEAR is to identify moods of
the wearer from various activities such as laughing, singing, crying, arguing,
and sighing. These annotations are based on ground truth relevant for
psychologists who intend to monitor/infer the social context of individuals
coping with breast cancer. We pursued a case study on couples coping with
breast cancer to know how the conversations affect emotional and social well
being. In the state-of-the-art methods, psychologists and their team have to
hear the audio recordings for making these inferences by subjective evaluations
that not only are time-consuming and costly, but also demand manual data coding
for thousands of audio files. The BigEAR framework automates the audio
analysis. We computed the accuracy of BigEAR with respect to the ground truth
obtained from a human rater. Our approach yielded overall average accuracy of
88.76% on real-world data from couples coping with breast cancer.


title: LISA pathfinder appreciably constrains collapse models

abstract:
  LISA Pathfinder's measurement of a relative acceleration noise between two
free-falling test masses with a square root of the power spectral density of
$5.2 \pm 0.1 \mbox{ fm s}^{-2}/\sqrt{\rm{Hz}}$ appreciably constrains collapse
models. In particular, we bound the localization rate parameter, $\lambda_{\rm
CSL}$, in the continuous spontaneous localization model (CSL) to be at most
$\left( 2.96 \pm 0.12 \right) \times 10^{-8} \mbox{ s}^{-1}$. Moreover, we
bound the regularization scale, $\sigma_{\rm DP}$, used in the Diosi-Penrose
(DP) model to be at least $40.1 \pm 0.5 \mbox{ fm}$. These bounds significantly
constrain the validity of these models. In particular: (i) a lower bound of
$2.2 \times 10^{-8\pm2} \mbox{s}^{-1}$ for $\lambda_{\rm CSL}$ has been
proposed in (although a lower bound of about $10^{-17} \mbox{s}^{-1}$ is
sometimes used), in order for the collapse noise to be substantial enough to
explain the phenomenology of quantum measurement, and (ii) 40 fm is larger than
the size of any nucleus, while the regularization scale has been proposed to be
the size of the nucleus.


title: Probabilistic Cellular Automata for low temperature Ising model

abstract:
  We construct a parallel stochastic dynamics with invariant measure converging
to the Gibbs measure of the low temperature Ising model. The proof of such
convergence requires a polymer expansion based on suitably defined Peierls-type
contours.


title:
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks
  via Binary Consensus Algorithms

abstract:
  Compressive Sensing has been utilized in Cognitive Radio Networks (CRNs) to
exploit the sparse nature of the occupation of the primary users. Also,
distributed spectrum sensing has been proposed to tackle the wireless channel
problems, like node or link failures, rather than the common (centralized
approach) for spectrum sensing. In this paper, we propose a distributed
spectrum sensing framework based on consensus algorithms where SU nodes
exchange their binary decisions to take global decisions without a fusion
center to coordinate the sensing process. Each SU will share its decision with
its neighbors, and at every new iteration each SU will take a new decision
based on its current decision and the decisions it receives from its neighbors;
in the next iteration, each SU will share its new decision with its neighbors.
We show via simulations that the detection performance can tend to the
performance of majority rule Fusion Center based CRNs.


title: TASI lectures on cosmological observables and string theory

abstract:
  These lectures provide an updated pedagogical treatment of the theoretical
structure and phenomenology of some basic mechanisms for inflation, along with
an overview of the structure of cosmological uplifts of holographic duality. A
full treatment of the problem requires `ultraviolet completion' because of the
sensitivity of inflation to quantum gravity effects, including back reaction
and non-adiabatic production of heavy degrees of freedom. Cosmological
observations imply accelerated expansion of the late universe, and provide
increasingly precise constraints and discovery potential on the amplitude and
shape of primordial tensor and scalar perturbations, and some of their
correlation functions. Most backgrounds of string theory have positive
potential energy, with a rich but still highly constrained landscape of
solutions. The theory contains novel mechanisms for inflation, some subject to
significant observational tests. Although the detailed ultraviolet completion
is not accessible experimentally, some of these mechanisms directly stimulate a
more systematic analysis of the space of low energy theories and signatures
relevant for analysis of data, which is sensitive to physics orders of
magnitude above the energy scale of inflation as a result of long time
evolution (dangerous irrelevance) and the substantial amount of data. Portions
of these lectures appeared previously in Les Houches 2013, "Post-Planck
Cosmology" .


title: Role of Iso-connectivity Topologies in Multi-agent Interactions

abstract:
  In this paper, we present the benefits of exploring different topologies with
equal connectivity measure, or iso-connectivity topologies, in relation to the
multiagent system dynamics. The level of global information sharing ability
among agents in a multi-agent network can be quantified by a connectivity
measure, called as the Algebraic Connectivity of the associated graph
consisting of point-mass agents as nodes and inter-connection links between
them as edges. Distinct topologies with the same connectivity play profound
role in multi-agent dynamics as they offer various ways to reorganize agents
locations according to the requirement during a cooperative mission, without
sacrificing the information exchange capability of the entire network.
Determination of the distinct multi-agent graphs with identical connectivity is
a multimodal problem, in other words, there exist multiple graphs that share
the same connectivity. We present analytical solutions of determining distinct
graphs with identical connectivity. A family of isospectral graphs are found
out by utilizing an appropriate similarity transformation. Moreover, a zone of
no connectivity change in a dense graph is unraveled where an agent can move
freely without causing any change in the global connectivity. The proposed
solutions are validated with the help of sufficient examples.


title: On the Runtime of Universal Coating for Programmable Matter

abstract:
  Imagine coating buildings and bridges with smart particles (also coined smart
paint) that monitor structural integrity and sense and report on traffic and
wind loads, leading to technology that could do such inspection jobs faster and
cheaper and increase safety at the same time. In this paper, we study the
problem of uniformly coating objects of arbitrary shape in the context of
self-organizing programmable matter, i.e., programmable matter which consists
of simple computational elements called particles that can establish and
release bonds and can actively move in a self-organized way. Particles are
anonymous, have constant-size memory, and utilize only local interactions in
order to coat an object. We continue the study of our Universal Coating
algorithm by focusing on its runtime analysis, showing that our algorithm
terminates within a linear number of rounds with high probability. We also
present a matching linear lower bound that holds with high probability. We use
this lower bound to show a linear lower bound on the competitive gap between
fully local coating algorithms and coating algorithms that rely on global
information, which implies that our algorithm is also optimal in a competitive
sense. Simulation results show that the competitive ratio of our algorithm may
be better than linear in practice.


title:
Canards, folded nodes and mixed-mode oscillations in piecewise-linear
  slow-fast systems

abstract:
  Canard-induced phenomena have been extensively studied in the last three
decades, both from the mathematical and from the application viewpoints.
Canards in slow-fast systems with (at least) two slow variables, especially
near folded-node singularities, give an essential generating mechanism for
Mixed-Mode oscillations (MMOs) in the framework of smooth multiple timescale
systems. There is a wealth of literature on such slow-fast dynamical systems
and many models displaying canard-induced MMOs, in particular in neuroscience.
In parallel, since the late 1990s several papers have shown that the canard
phenomenon can be faithfully reproduced with piecewise-linear (PWL) systems in
two dimensions although very few results are available in the three-dimensional
case. The present paper aims to bridge this gap by analysing canonical PWL
systems that display folded singularities, primary and secondary canards, with
a similar control of the maximal winding number as in the smooth case. We also
show that the singular phase portraits are compatible in both frameworks.
Finally, we show on an example how to construct a (linear) global return and
obtain robust PWL MMOs.


title: Inter-language Collaboration in an Object-oriented Virtual Machine

abstract:
  Multi-language virtual machines have a number of advantages. They allow
software developers to use software libraries that were written for different
programming languages. Furthermore, language implementors do not have to bother
with low-level VM functionality and their implementation can benefit from
optimizations in existing virtual machines. MagLev is an implementation of the
Ruby programming language on top of the GemStone/S virtual machine for the
Smalltalk programming language. In this work, we present how software
components written in both languages can interact. We show how MagLev unifies
the Smalltalk and the Ruby object model, taking into account Smalltalk meta
classes, Ruby singleton classes, and Ruby modules. Besides, we show how we can
call Ruby methods from Smalltalk and vice versa. We also present MagLev's
concept of bridge methods for implementing Ruby method calling conventions.
Finally, we compare our solution to other language implementations and virtual
machines.


title: Martingale property for the Scott correlated stochastic volatility model

abstract:
  In this paper, we study the martingale property for a Scott correlated
stochastic volatility model, when the correlation coefficient between the
Brownian motion driving the volatility and the one driving the asset price
process is arbitrary. For this study we verify the martingale property by using
the necessary and sufficient conditions given by Bernard \emph{et al.}
\cite{Bernard}. Our main results are to prove that the price process is a true
and uniformly integrable martingale if and only if $\rho \in [-1,0]$ for two
transformations of Brownian motion describing the dynamics of the underling
asset.


title: Astromaterial Science and Nuclear Pasta

abstract:
  We define `astromaterial science' as the study of materials in astronomical
objects that are qualitatively denser than materials on earth. Astromaterials
can have unique properties related to their large density, though they may be
organized in ways similar to more conventional materials. By analogy to
terrestrial materials, we divide our study of astromaterials into hard and soft
and discuss one example of each. The hard astromaterial discussed here is a
crystalline lattice, such as the Coulomb crystals in the interior of cold white
dwarfs and in the crust of neutron stars, while the soft astromaterial is
nuclear pasta found in the inner crusts of neutron stars. In particular, we
discuss how molecular dynamics simulations have been used to calculate the
properties of astromaterials to interpret observations of white dwarfs and
neutron stars. Coulomb crystals are studied to understand how compact stars
freeze. Their incredible strength may make crust "mountains" on rotating
neutron stars a source for gravitational waves that the Laser Interferometer
Gravitational-Wave Observatory (LIGO) may detect. Nuclear pasta is expected
near the base of the neutron star crust at densities of $10^{14}$ g/cm$^3$.
Competition between nuclear attraction and Coulomb repulsion rearranges
neutrons and protons into complex non-spherical shapes such as sheets (lasagna)
or tubes (spaghetti). Semi-classical molecular dynamics simulations of nuclear
pasta have been used to study these phases and calculate their transport
properties such as neutrino opacity, thermal conductivity, and electrical
conductivity. Observations of neutron stars may be sensitive to these
properties, and can be be used to interpret observations of supernova
neutrinos, magnetic field decay, and crust cooling of accreting neutron stars.
We end by comparing nuclear pasta shapes with some similar shapes seen in
biological systems.

categories: astro-ph.HE cond-mat.mtrl-sci cond-mat.soft nucl-th


title: Training Recurrent Answering Units with Joint Loss Minimization for VQA

abstract:
  We propose a novel algorithm for visual question answering based on a
recurrent deep neural network, where every module in the network corresponds to
a complete answering unit with attention mechanism by itself. The network is
optimized by minimizing loss aggregated from all the units, which share model
parameters while receiving different information to compute attention
probability. For training, our model attends to a region within image feature
map, updates its memory based on the question and attended image feature, and
answers the question based on its memory state. This procedure is performed to
compute loss in each step. The motivation of this approach is our observation
that multi-step inferences are often required to answer questions while each
problem may have a unique desirable number of steps, which is difficult to
identify in practice. Hence, we always make the first unit in the network solve
problems, but allow it to learn the knowledge from the rest of units by
backpropagation unless it degrades the model. To implement this idea, we
early-stop training each unit as soon as it starts to overfit. Note that, since
more complex models tend to overfit on easier questions quickly, the last
answering unit in the unfolded recurrent neural network is typically killed
first while the first one remains last. We make a single-step prediction for a
new question using the shared model. This strategy works better than the other
options within our framework since the selected model is trained effectively
from all units without overfitting. The proposed algorithm outperforms other
multi-step attention based approaches using a single step prediction in VQA
dataset.


title:
Investigation of constraints on few-neutron forces in neutron matter by
  empirical information on the neutron skin of 48Ca and 208Pb

abstract:
  The neutron matter equation of state is calculated from two-neutron forces up
to fifth order of the chiral expansion and the order-by-order convergence of
the predictions is investigated. Based on these equations of state, the binding
energies and the neutron and proton density distributions in 208Pb and 48Ca are
derived, with particular attention to the neutron skins, the focal point of
this paper. Anticipating future experiments which will provide reliable
information on the weak charge density in nuclei, the theoretical uncertainties
and the possibility of constraining the size of few-neutron forces in neutron
matter are discussed.


title: Measure-theoretic sensitivity via finite partitions

abstract:
  For every positive integer $n\geq 2$, we introduce the concept of
measure-theoretic $n$-sensitivity for measure-theoretic dynamical systems via
finite measurable partitions, and show that an ergodic system is
measure-theoretically $n$-sensitive but not $(n+1)$-sensitive if and only if
its maximal pattern entropy is $\log n$.


title:
Generalized Variational Source Condition Associated with the Bregman
  Distance-I: Verification of the Variational Source Condition and Stability of
  the Total Error Estimation

abstract:
  A general deterministic analysis to state the necessary conditions with a
coefficient determination for the variational source condition to hold is
provided. Of particular interest in terms of the choice of the regularization
parameter, it is revealed that Morozov's discrepancy principle can be used both
for determining new stable lower and upper bounds for the regularization
parameter. With these bounds, it is also possible to establish quantitative
estimations for the index function as well as for the different definitions of
the Bregman distance. Inclusion of the variational source condition into the
stability analysis enables one to re-establish convergence and convergence rate
results in terms of the index function. The coefficient in the variational
source condition is explicitly defined as a multivariable function of constants
in Morozov's discrepancy principle. As expected, the results here are
applicable when any strictly convex, smooth/non-smooth objective functional is
considered.


title:
The product of dependent random variables with applications to a
  discrete-time risk model

abstract:
  Let $X$ be a real valued random variable with an unbounded distribution $F$
and let $Y$ be a nonnegative valued random variable with a unbounded
distribution $G$, which satisfy that
  \begin{eqnarray*} P(X>x|Y=y)\sim h(y)P(X>x) \end{eqnarray*} holds uniformly
for $y\geq0$ as $x\to \infty$. Under the condition that
$\overline{G}(bx)=o(\overline H(x))$ holds for all constant $b>0$, we proved
that $F\in\mathcal{L}(\gamma)$ for some $\gamma\geq 0$ implied $H\in
\mathcal{L}(\gamma/\beta_G)$ and that $F\in\mathcal{S}(\gamma)$ for some
$\gamma\geq 0$ implied $H\in \mathcal{S}(\gamma/\beta_G)$, where $H$ is the
distribution of the product $XY$, and $\beta_G$ is the right endpoint of $G$,
that is, $\beta_G=\sup\{y:~G(y)<1\}\in (0,\infty],$ and when $\beta_G=\infty$,
$\gamma/\beta_G$ is understood as 0.
  Furthermore, in a discrete-time risk model in which the net insurance loss
and the stochastic discount factor are equipped with a dependence structure, a
general asymptotic formula for the finite-time ruin probability is obtained
when the net insurance loss has a subexponential tail.


title: A Shorter Proof of Marten's Theorem

abstract:
  This note is intended to give a new proof on Marten's theorem stating that
$\dim(\wrd) \leq d-2r$ for any smooth curve with equality occurring exactly in
the case when C is hyper-elliptic. The proof follows the general lines of the
proof given in 'The Geometry of Algebraic Curves'. What is different is that it
uses Hopf's theorem to simplify the proof and strengthen the conclusions of the
theorem.


title: Stabilization of Kelvin-Voigt viscoelastic Fuid Fow model

abstract:
  In this article, stabilization result for the viscoelastic fluid flow problem
governed by Kelvin-Voigt model, that is, convergence of the unsteady solution
to a steady state solution is proved under the assumption that linearized
self-adjoint steady state eigenvalue problem has a minimal positive eigenvalue.
Both power and exponential convergence results are derived under various
conditions on the forcing function. It is shown that results are valid
uniformly in the time relaxation or some times called regularization parameter
$\kappa$ as $\kappa\to 0$, which in turn, establishes results for the
Navier-Stokes system.


title:
Imaging electrostatically confined Dirac fermions in graphene quantum
  dots

abstract:
  Electrostatic confinement of charge carriers in graphene is governed by Klein
tunneling, a relativistic quantum process in which particle-hole transmutation
leads to unusual anisotropic transmission at pn junction boundaries. Reflection
and transmission at these novel potential barriers should affect the quantum
interference of electronic wavefunctions near these boundaries. Here we report
the use of scanning tunneling microscopy (STM) to map the electronic structure
of Dirac fermions confined by circular graphene pn junctions. These effective
quantum dots were fabricated using a new technique involving local manipulation
of defect charge within the insulating substrate beneath a graphene monolayer.
Inside such graphene quantum dots we observe energy levels corresponding to
quasi-bound states and we spatially visualize the quantum interference patterns
of confined electrons. Dirac fermions outside these quantum dots exhibit
Friedel oscillation-like behavior. Bolstered with a theoretical model
describing relativistic particles in a harmonic oscillator potential, our
findings yield new insight into the spatial behavior of electrostatically
confined Dirac fermions.


title:
To $v_\infty$ and beyond! The He I absorption variability across the
  2014.6 periastron passage of $\eta$ Carinae

abstract:
  We have monitored the massive binary star $\eta$ Carinae with the CTIO/SMARTS
1.5~m telescope and CHIRON spectrograph from the previous apastron passage of
the system through the recent 2014.6 periastron passage. Our monitoring has
resulted in a large, homogeneous data set with an unprecedented time-sampling,
spectral resolving power, and signal-to-noise. This allowed us to investigate
temporal variability previously unexplored in the system and discover a
kinematic structure in the P Cygni absorption troughs of neutral helium wind
lines. The features observed occurred prior to the periastron passage and are
seen as we look through the trailing arm of the wind-wind collision shock cone.
We show that the bulk of the variability is repeatable across the last five
periastron passages, and that the absorption occurs in the inner 230 AU of the
system. In addition, we found an additional, high-velocity absorption component
super-imposed on the P Cygni absorption troughs that has been previously
un-observed in these lines, but which bears resemblance to the observations of
the He~I $\lambda$10830 \AA\ feature across previous cycles. Through a
comparison of the current smoothed particle hydrodynamical simulations, we show
that the observed variations are likely caused by instabilities in the
wind-wind collision region in our line-of-sight, coupled with stochastic
variability related to clumping in the winds.


title: Theory of dual fermion superconductivity in hole-doped cuprates

abstract:
  Since the discovery of the cuprate high-temperature superconductivity in
1986, a universal phase diagram has been constructed experimentally and
numerous theoretical models have been proposed. However, there remains no
consensus on the underlying physics thus far. Here, we theoretically
investigate the phase diagram of hole-doped cuprates based on an
itinerant-localized dual fermion model, with the charge carriers doped on the
oxygen sites and localized holes on the copper $d_{x^{2}-y^{2}}$ orbitals. We
analytically demonstrate that the puzzling anomalous normal state or the
strange metal could simply stem from a free Fermi gas of carriers bathing in
copper antiferromagnetic spin fluctuations. The short-range high-energy spin
excitations also act as the `magnetic glue' of carrier Cooper pairs and induce
$d$-wave superconductivity from the underdoped to overdoped regime, distinctly
diffrent from the conventional low-frequency magnetic fluctuation mechanism. We
further sketch out the characteristic dome-shaped critical temperature $T_c$
versus doping level. The emergence of the pseudogap is ascribed to the
localization of partial carriers coupled to the local copper moments or a
crossover from the strange metal to a nodal Kondo-like insulator. Our work
provides a consistent theoretical framework to understand the typical phase
diagram of hole-doped cuprates and paves a distinct way to the studies of both
non-Fermi liquid and unconventional superconductivity in strongly correlated
systems.


title:
InfoGAN: Interpretable Representation Learning by Information Maximizing
  Generative Adversarial Nets

abstract:
  This paper describes InfoGAN, an information-theoretic extension to the
Generative Adversarial Network that is able to learn disentangled
representations in a completely unsupervised manner. InfoGAN is a generative
adversarial network that also maximizes the mutual information between a small
subset of the latent variables and the observation. We derive a lower bound to
the mutual information objective that can be optimized efficiently, and show
that our training procedure can be interpreted as a variation of the Wake-Sleep
algorithm. Specifically, InfoGAN successfully disentangles writing styles from
digit shapes on the MNIST dataset, pose from lighting of 3D rendered images,
and background digits from the central digit on the SVHN dataset. It also
discovers visual concepts that include hair styles, presence/absence of
eyeglasses, and emotions on the CelebA face dataset. Experiments show that
InfoGAN learns interpretable representations that are competitive with
representations learned by existing fully supervised methods.


title: New Approaches for Calculating Moran's Index of Spatial Autocorrelation

abstract:
  Spatial autocorrelation plays an important role in geographical analysis,
however, there is still room for improvement of this method. The formula for
Moran's index is complicated, and several basic problems remain to be solved.
Therefore, I will reconstruct its mathematical framework using mathematical
derivation based on linear algebra and present four simple approaches to
calculating Moran's index. Moran's scatterplot will be ameliorated, and new
test methods will be proposed. The relationship between the global Moran's
index and Geary's coefficient will be discussed from two different vantage
points: spatial population and spatial sample. The sphere of applications for
both Moran's index and Geary's coefficient will be clarified and defined. One
of theoretical findings is that Moran's index is a characteristic parameter of
spatial weight matrices, so the selection of weight functions is very
significant for autocorrelation analysis of geographical systems. A case study
of 29 Chinese cities in 2000 will be employed to validate the innovatory models
and methods. This work is a methodological study, which will simplify the
process of autocorrelation analysis. The results of this study will lay the
foundation for the scaling analysis of spatial autocorrelation.


title:
Inverse Compton X-ray Emissions from TeV blazar Mrk421 during a
  Historical Low-Flux State Observed with NuSTAR

abstract:
  We report on the detection of excess hard X-ray emission from the TeV BL Lac
object Mrk421 during the historical low-flux state of the source in January
2013. NuSTAR observations were conducted four times between MJD56294 and
MJD56312 with a total exposure of 80.9 ksec. The source flux in the 3-40 keV
range was nearly constant except for MJD56307, when the average flux level
increased by a factor of three. Throughout the exposure, the X-ray spectra of
Mrk421 were well represented by a steep power-law model with a photon index of
3.1, although a significant excess was noted above 20 keV in the MJD56302 data
when the source was in its faintest state. Moreover, Mrk421 was detected at
more than the 4-sigma level in the 40-79 keV count maps for both MJD56307 and
MJD56302 but not during the remaining two observations. The detected excess
hard X-ray emissions connect smoothly with the extrapolation of the high-energy
gamma-ray continuum of the blazar constrained by Fermi-LAT during the source
quiescence. These findings indicate that, while the overall X-ray spectrum of
Mrk421 is dominated by the highest-energy tail of the synchrotron continuum,
the variable excess hard X-ray emission above 20 keV (on the timescale of a
week) is related to the inverse Compton emission component. We discuss the
resulting constraints on the variability and spectral properties of the
low-energy segment of the electron energy distribution in the source.


title: Validity of the Local Approximation in Iron- Pnictides and Chalcogenides

abstract:
  We introduce a cluster DMFT (Dynamical Mean Field Theory) approach to study
the normal state of the iron pnictides and chalcogenides. In the regime of
moderate mass renormalizations, the self-energy is very local, justifying the
success of single site DMFT for these materials and for other Hunds metals. We
solve the corresponding impurity model with CTQMC (Continuous Time Quantum
Monte-Carlo) and find that the minus sign problem is not severe in regimes of
moderate mass renormalization.


title: Existence of Self-Cheeger Sets on Riemannian Manifolds

abstract:
  Let $(\mathcal{M}, g)$ be a compact Riemannian manifold of dimension $N\geq
2$. We prove the existence of a family $(\Omega_\varepsilon)_{\varepsilon\in
(0,\varepsilon_0)}$ of self-Cheeger sets in $(\mathcal{M}, g)$ . The domains
$\Omega_\varepsilon\subset\mathcal{M}$ are perturbations of geodesic balls of
radius $\varepsilon$ centered at $p \in \mathcal{M}$, and in particular, if
$p_0$ is a non-degenerate critical point of the scalar curvature of $g$, then
the family $( \partial\Omega_\varepsilon)_{\varepsilon\in (0,\varepsilon_0)}$
constitutes a smooth foliation of a neighborhood of $p_0$.


title: Store Location Selection via Mining Search Query Logs of Baidu Maps

abstract:
  Choosing a good location when opening a new store is crucial for the future
success of a business. Traditional methods include offline manual survey, which
is very time consuming, and analytic models based on census data, which are un-
able to adapt to the dynamic market. The rapid increase of the availability of
big data from various types of mobile devices, such as online query data and
offline positioning data, provides us with the possibility to develop automatic
and accurate data-driven prediction models for business store placement. In
this paper, we propose a Demand Distribution Driven Store Placement (D3SP)
framework for business store placement by mining search query data from Baidu
Maps. D3SP first detects the spatial-temporal distributions of customer demands
on different business services via query data from Baidu Maps, the largest
online map search engine in China, and detects the gaps between demand and sup-
ply. Then we determine candidate locations via clustering such gaps. In the
final stage, we solve the location optimization problem by predicting and
ranking the number of customers. We not only deploy supervised regression
models to predict the number of customers, but also learn to rank models to
directly rank the locations. We evaluate our framework on various types of
businesses in real-world cases, and the experiments results demonstrate the
effectiveness of our methods. D3SP as the core function for store placement has
already been implemented as a core component of our business analytics platform
and could be potentially used by chain store merchants on Baidu Nuomi.


title: A Timed Process Algebra for Wireless Networks

abstract:
  This paper proposes a timed process algebra for wireless networks, an
extension of the Algebra for Wireless Networks. It combines treatments of local
broadcast, conditional unicast and data structures, which are essential
features for the modelling of network protocols. In this framework we model and
analyse the Ad hoc On-Demand Distance Vector routing protocol, and show that,
contrary to claims in the literature, it fails to be loop free. We also present
boundary conditions for a fix ensuring that the resulting protocol is indeed
loop free.


title: Weakly Supervised Scalable Audio Content Analysis

abstract:
  Audio Event Detection is an important task for content analysis of multimedia
data. Most of the current works on detection of audio events is driven through
supervised learning approaches. We propose a weakly supervised learning
framework which can make use of the tremendous amount of web multimedia data
with significantly reduced annotation effort and expense. Specifically, we use
several multiple instance learning algorithms to show that audio event
detection through weak labels is feasible. We also propose a novel scalable
multiple instance learning algorithm and show that its competitive with other
multiple instance learning algorithms for audio event detection tasks.


title:
Resource Allocation and Fairness in Wireless Powered Cooperative
  Cognitive Radio Networks

abstract:
  We integrate a wireless powered communication network with a cooperative
cognitive radio network, where multiple secondary users (SUs) powered
wirelessly by a hybrid access point (HAP) help a primary user relay the data.
As a reward for the cooperation, the secondary network gains the spectrum
access where SUs transmit to HAP using time division multiple access. To
maximize the sum-throughput of SUs, we present a secondary sum-throughput
optimal resource allocation (STORA) scheme. Under the constraint of meeting
target primary rate, the STORA scheme chooses the optimal set of relaying SUs
and jointly performs the time and energy allocation for SUs. Specifically, by
exploiting the structure of the optimal solution, we find the order in which
SUs are prioritized to relay primary data. Since the STORA scheme focuses on
the sum-throughput, it becomes inconsiderate towards individual SU throughput,
resulting in low fairness. To enhance fairness, we investigate three resource
allocation schemes, which are (i) equal time allocation, (ii) minimum
throughput maximization, and (iii) proportional time allocation. Simulation
results reveal the trade-off between sum-throughput and fairness. The minimum
throughput maximization scheme is the fairest one as each SU gets the same
throughput, but yields the least SU sum-throughput.


title:
Concentration at submanifolds for an elliptic Dirichlet problem near
  high critical exponents

abstract:
  Let $\Omega$ be a open bounded domain in $\mathbb{R}^n $ with smooth boundary
$\partial\Omega$. We consider the equation $ \Delta u +
u^{\frac{n-k+2}{n-k-2}-\varepsilon} =0\,\hbox{ in }\,\Omega $, under zero
Dirichlet boundary condition, where $\varepsilon$ is a small positive
parameter. We assume that there is a $k$-dimensional closed, embedded minimal
submanifold $K$ of $\partial\Omega$, which is non-degenerate, and along which a
certain weighted average of sectional curvatures of $\partial\Omega$ is
negative. Under these assumptions, we prove existence of a sequence
$\varepsilon=\varepsilon_j$ and a solution $u_{\varepsilon}$ which concentrate
along $K$, as $\varepsilon \to 0^+$, in the sense that $$
  |\nabla u_{\varepsilon} |^2\,\rightharpoonup \, S_{n-k}^{\frac{n-k}{2}}
\,\delta_K \quad \mbox{as} \ \ \varepsilon \to 0 $$ where $\delta_K $ stands
for the Dirac measure supported on $K$ and $S_{n-k}$ is an explicit positive
constant. This result generalizes the one obtained by del Pino-Musso-Pacard,
where the case $k=1$ is considered.


title:
Deep Reinforcement Learning with a Combinatorial Action Space for
  Predicting Popular Reddit Threads

abstract:
  We introduce an online popularity prediction and tracking task as a benchmark
task for reinforcement learning with a combinatorial, natural language action
space. A specified number of discussion threads predicted to be popular are
recommended, chosen from a fixed window of recent comments to track. Novel deep
reinforcement learning architectures are studied for effective modeling of the
value function associated with actions comprised of interdependent sub-actions.
The proposed model, which represents dependence between sub-actions through a
bi-directional LSTM, gives the best performance across different experimental
configurations and domains, and it also generalizes well with varying numbers
of recommendation requests.


title:
Spatial and Social Paradigms for Interference and Coverage Analysis in
  Underlay D2D Network

abstract:
  The homogeneous Poisson point process (PPP) is widely used to model spatial
distribution of base stations and mobile terminals. The same process can be
used to model underlay device-to-device (D2D) network, however, neglecting
homophilic relation for D2D pairing presents underestimated system insights. In
this paper, we model both spatial and social distributions of interfering D2D
nodes as proximity based independently marked homogeneous Poisson point
process. The proximity considers physical distance between D2D nodes whereas
social relationship is modeled as Zipf based marks. We apply these two
paradigms to analyze the effect of interference on coverage probability of
distance-proportional power-controlled cellular user. Effectively, we apply two
type of functional mappings (physical distance, social marks) to Laplace
functional of PPP. The resulting coverage probability has no closed-form
expression, however for a subset of social marks, the mark summation converges
to digamma and polygamma functions. This subset constitutes the upper and lower
bounds on coverage probability. We present numerical evaluation of these bounds
on coverage probability by varying number of different parameters. The results
show that by imparting simple power control on cellular user, ultra-dense
underlay D2D network can be realized without compromising the coverage
probability of cellular user.


title: Color-based Segmentation of Sky/Cloud Images From Ground-based Cameras

abstract:
  Sky/cloud images captured by ground-based cameras (a.k.a. whole sky imagers)
are increasingly used nowadays because of their applications in a number of
fields, including climate modeling, weather prediction, renewable energy
generation, and satellite communications. Due to the wide variety of cloud
types and lighting conditions in such images, accurate and robust segmentation
of clouds is challenging. In this paper, we present a supervised segmentation
framework for ground-based sky/cloud images based on a systematic analysis of
different color spaces and components, using partial least squares (PLS)
regression. Unlike other state-of-the-art methods, our proposed approach is
entirely learning-based and does not require any manually-defined parameters.
In addition, we release the Singapore Whole Sky IMaging SEGmentation Database
(SWIMSEG), a large database of annotated sky/cloud images, to the research
community.


title:
The Probability That All Eigenvalues are Real for Products of Truncated
  Real Orthogonal Random Matrices

abstract:
  The probability that all eigenvalues of a product of $m$ independent $N
\times N$ sub-blocks of a Haar distributed random real orthogonal matrix of
size $(L_i+N) \times (L_i+N)$, $(i=1,\dots,m)$ are real is calculated as a
multi-dimensional integral, and as a determinant. Both involve Meijer
G-functions. Evaluation formulae of the latter, based on a recursive scheme,
allow it to be proved that for any $m$ and with each $L_i$ even the probability
is a rational number. The formulae furthermore provide for explicit computation
in small order cases.


title:
Segmentation of scanning electron microscopy images from natural rubber
  samples with gold nanoparticles using starlet wavelets

abstract:
  Electronic microscopy has been used for morphology evaluation of different
materials structures. However, microscopy results may be affected by several
factors. Image processing methods can be used to correct and improve the
quality of these results. In this paper we propose an algorithm based on
starlets to perform the segmentation of scanning electron microscopy images. An
application is presented in order to locate gold nanoparticles in natural
rubber membranes. In this application, our method showed accuracy greater than
85% for all test images. Results given by this method will be used in future
studies, to computationally estimate the density distribution of gold
nanoparticles in natural rubber samples and to predict reduction kinetics of
gold nanoparticles at different time periods.


title:
Comparison of Several Sparse Recovery Methods for Low Rank Matrices with
  Random Samples

abstract:
  In this paper, we will investigate the efficacy of IMAT (Iterative Method of
Adaptive Thresholding) in recovering the sparse signal (parameters) for linear
models with missing data. Sparse recovery rises in compressed sensing and
machine learning problems and has various applications necessitating viable
reconstruction methods specifically when we work with big data. This paper will
focus on comparing the power of IMAT in reconstruction of the desired sparse
signal with LASSO. Additionally, we will assume the model has random missing
information. Missing data has been recently of interest in big data and machine
learning problems since they appear in many cases including but not limited to
medical imaging datasets, hospital datasets, and massive MIMO. The dominance of
IMAT over the well-known LASSO will be taken into account in different
scenarios. Simulations and numerical results are also provided to verify the
arguments.


title:
Approximate Controllability of a Class of Partial Integro-Differential
  Equations of Parabolic Type

abstract:
  In this paper, we discuss the distributed control problem governed by the
following parabolic integro-differential equation (PIDE) in the abstract form
\begin{eqnarray*} \frac{\partial y}{\partial t} + A y &=& \int_0^t B(t, s) y(s)
ds + Gu, \;\, t \in [0, T], \;\;\;\;\;\;\;\;\;\;\;\;\;\;\, \hfill{(\ast)}\\
y(0) &=& y_0 \, \in X, \nonumber \end{eqnarray*} where, $y$ denotes the state
space variable, $u$ is the control variable, $A$ is a self adjoint, positive
definite linear (not necessarily bounded) operator in a Hilbert space $X$ with
dense domain $D(A) \subset X,$ $B(t,s)$ is an unbounded operator, smooth with
respect to $t$ and $s$ with $D(A) \subset D(B(t,s)) \subset X$ for $0 \leq s
\leq t \leq T$ and $G$ is a bounded linear operator from the control space to
$X.$ Assuming that the corresponding evolution equation ($B \equiv 0$ in
($\ast$)) is approximately controllable, it is shown that the set of
approximate controls of the distributed control problem ($\ast$) is nonempty.
The problem is first viewed as constrained optimal control problem and then it
is approximated by unconstrained problem with a suitable penalty function. The
optimal pair of the constrained problem is obtained as the limit of optimal
pair sequence of the unconstrained problem. The approximation theorems, which
guarantee the convergence of the numerical scheme to the optimal pair sequence,
are also proved.


title:
Critical Echo State Networks that Anticipate Input using Morphable
  Transfer Functions

abstract:
  The paper investigates a new type of truly critical echo state networks where
individual transfer functions for every neuron can be modified to anticipate
the expected next input. Deviations from expected input are only forgotten
slowly in power law fashion. The paper outlines the theory, numerically
analyzes a one neuron model network and finally discusses technical and also
biological implications of this type of approach.


title: Reconnection brightenings in the quiet solar photosphere

abstract:
  We describe a new quiet-Sun phenomenon which we call "Quiet-Sun Ellerman-like
Brightenings" (QSEB). QSEBs are similar to Ellerman bombs (EB) in some respects
but differ significantly in others. EBs are transient brightenings of the wings
of the Balmer H-alpha line that mark strong-field photospheric reconnection in
complex active regions. QSEBs are similar but smaller and less intense
Balmer-wing brightenings that occur in quiet areas away from active regions. In
the H-alpha wing we measure typical lengths of less than 0.5 arcsec, widths of
0.21 arcsec, and lifetimes of less than a minute. We discovered them using
high-quality H-alpha imaging spectrometry from the Swedish 1-m Solar Telescope
(SST) and show that in lesser-quality data they cannot be distinguished from
more ubiquitous facular brightenings, nor in the ultraviolet diagnostics
currently available from space platforms. We add evidence from concurrent SST
spectropolarimetry that QSEBs also mark photospheric reconnection events, but
in quiet regions on the solar surface.


title: External Lexical Information for Multilingual Part-of-Speech Tagging

abstract:
  Morphosyntactic lexicons and word vector representations have both proven
useful for improving the accuracy of statistical part-of-speech taggers. Here
we compare the performances of four systems on datasets covering 16 languages,
two of these systems being feature-based (MEMMs and CRFs) and two of them being
neural-based (bi-LSTMs). We show that, on average, all four approaches perform
similarly and reach state-of-the-art results. Yet better performances are
obtained with our feature-based models on lexically richer datasets (e.g. for
morphologically rich languages), whereas neural-based results are higher on
datasets with less lexical variability (e.g. for English). These conclusions
hold in particular for the MEMM models relying on our system MElt, which
benefited from newly designed features. This shows that, under certain
conditions, feature-based approaches enriched with morphosyntactic lexicons are
competitive with respect to neural methods.


title: Large deviation principles for 3D stochastic primitive equations

abstract:
  In this paper, we establish the large deviation principle for 3D stochastic
primitive equations with small perturbation multiplicative noise. The proof is
mainly based on the weak convergence approach.


title: The green light generation by self-frequency-doubled Yb:YCOB crystal

abstract:
  Taking advantages of the broad emission bands of a Yb3+ doped calcium yttrium
oxoborate (Yb:YCOB) crystal cut along the optimized direction out of principle
planes with the maximum effective nonlinear coefficient, the
self-frequency-doubled green light based on the self-frequency-doubling
behavior of Yb:YCOB was achieved with a maximum output power of 710 mW at 523
nm.


title:
Diffusive to quasi-ballistic random laser: incoherent and coherent
  models

abstract:
  We study the crossover between the diffusive and quasi-ballistic regimes of
random lasers. In particular, we compare incoherent models based on the
diffusion equation and the radiative transfer equation (RTE), which neglect all
wave effects, with a coherent wave model for the random laser threshold. We
show that both the incoherent and the coherent models predict qualitatively
similar thresholds, with a smooth transition from a diffuse to a
quasi-ballistic regime. The shape of the intensity distribution in the sample
as predicted by the RTE model at threshold is also in good agreement with the
coherent model. The approximate incoherent models thus provide useful
analytical predictions for the threshold of random lasers as well as the shape
of the random laser modes at threshold.


title:
Variable Neighborhood Search Based Algorithm for University Course
  Timetabling Problem

abstract:
  In this paper a variable neighborhood search approach as a method for solving
combinatoric optimization problems is presented. A variable neighborhood search
based algorithm for solving the problem concerning the university course
timetable design has been developed. This algorithm is used to solve the real
problem regarding the university course timetable design. It is compared with
other algorithms that are tested on the same sets of input data. The object and
the methodology of study are presented. The main objectives of the experiment
are formulated. The conditions for conducting the experiment are specified. The
results are analyzed and appropriate conclusions are made. The future trends of
work in this field are presented.


title:
The Spectral Temperature of Optically Thick Outflows with Application to
  Light Echo Spectra from $\eta$~Carinae's Giant Eruption

abstract:
  The detection by Rest et al. (2012) of light echoes from $\eta$ Carinae has
provided important new observational constraints on the nature of its 1840's
era giant eruption. Spectra of the echoes suggest a relatively cool spectral
temperature of about 5500K, lower than the lower limit of about 7000K suggested
in the optically thick wind outflow analysis of Davidson (1987). This has lead
to a debate about the viability of this steady wind model relative to
alternative, explosive scenarios. Here we present an updated analysis of the
wind outflow model using newer low-temperature opacity tabulations and
accounting for the stronger mass loss implied by the $>$10 Msun mass now
inferred for the Homunculus. A major conclusion is that, because of the sharp
drop in opacity due to free electron recombination for $T<$6500K, a low
temperature of about 5000K is compatible with, and indeed expected from, a wind
with the extreme mass loss inferred for the eruption. Within a spherical gray
model in radiative equilibrium, we derive spectral energy distributions for
various assumptions for the opacity variation of the wind, providing a basis
for comparisons with observed light echo spectra. The scaling results here are
also potentially relevant for other highly optically thick outflows, including
those from classical novae, giant eruptions of LBVs and SN Type IIn precursors.
A broader issue therefore remains whether the complex, variable features
observed from such eruptions are better understood in terms of a steady or
explosive paradigm, or perhaps a balance of these idealizations.


title: Interlayer Resistance of Misoriented MoS2

abstract:
  Interlayer misorientation in transition metal dichalcogenides alters the
interlayer distance, the electronic band structure, and the vibrational modes,
but, its effect on the interlayer resistance is not known. This work analyzes
the coherent interlayer resistance of misoriented 2H-MoS2 for low energy
electrons and holes as a function of the misorientation angle. The electronic
interlayer resistance monotonically increases with the supercell lattice
constant by several orders of magnitude similar to that of misoriented bilayer
graphene. The large hole coupling gives low interlayer hole resistance that
weakly depends on the misorientation angle. Interlayer rotation between an
n-type region and a p-type region will suppress the electron current with
little effect on the hole current. We estimate numerical bounds and explain the
results in terms of the orbital composition of the bands at high symmetry
points. Density functional theory calculations provide the interlayer coupling
used in both a tunneling Hamiltonian and a non-equilibrium Green function
calculation of the resistivity.


title: On the Muskat flow

abstract:
  Of concern is the motion of two fluids separated by a free interface in a
porous medium, where the velocities are given by Darcy's law. We consider the
case with and without phase transition. It is shown that the resulting models
can be understood as purely geometric evolution laws, where the motion of the
separating interface depends in a non-local way on the mean curvature. It turns
out that the models are volume preserving and surface area reducing, the latter
property giving rise to a Lyapunov function. We show well-posedness of the
models, characterize all equilibria, and study the dynamic stability of the
equilibria. Lastly, we show that solutions which do not develop singularities
exist globally and converge exponentially fast to an equilibrium.


title: The Verigin problem with and without phase transition

abstract:
  Isothermal compressible two-phase flows with and without phase transition are
modeled, employing Darcy's and/or Forchheimer's law for the velocity field. It
is shown that the resulting systems are thermodynamically consistent in the
sense that the available energy is a strict Lyapunov functional. In both cases,
the equilibria are identified and their thermodynamical stability is
investigated by means of a variational approach. It is shown that the problems
are well-posed in an $L_p$-setting and generate local semiflows in the proper
state manifolds. It is further shown that a non-degenerate equilibrium is
dynamically stable in the natural state manifold if and only if it is
thermodynamically stable. Finally, it is shown that a solution which does not
develop singularities exists globally and converges to an equilibrium in the
state manifold.


title: Efficient KLMS and KRLS Algorithms: A Random Fourier Feature Perspective

abstract:
  We present a new framework for online Least Squares algorithms for nonlinear
modeling in RKH spaces (RKHS). Instead of implicitly mapping the data to a RKHS
(e.g., kernel trick), we map the data to a finite dimensional Euclidean space,
using random features of the kernel's Fourier transform. The advantage is that,
the inner product of the mapped data approximates the kernel function. The
resulting "linear" algorithm does not require any form of sparsification,
since, in contrast to all existing algorithms, the solution's size remains
fixed and does not increase with the iteration steps. As a result, the obtained
algorithms are computationally significantly more efficient compared to
previously derived variants, while, at the same time, they converge at similar
speeds and to similar error floors.


title: Does Having More Options Mean Harder to Reach Consensus?

abstract:
  We generalize a binary majority-vote model on adaptive networks to a
plurality-vote counterpart. When opinions are uniformly distributed in the
population of voters in the initial state, it is found that having more
available opinions in the initial state actually accelerate the time to
consensus. In particular, we investigate the three-state plurality-vote model.
While time to consensus in two state model scales exponentially with population
size $N$, for finite-size system, there is a non-zero probability that either
the population reaches the consensus state in a time that is very short and
independent of $N$ (in the heterophily regime), or in a time that scales
exponentially with $N$ but is still much faster than two-state model.


title: A Simple Extension of Dirac's Theorem on Hamiltonicity

abstract:
  The classical Dirac theorem asserts that every graph $G$ on $n$ vertices with
minimum degree $\delta(G) \ge \lceil n/2 \rceil$ is Hamiltonian. The lower
bound of $\lceil n/2 \rceil$ on the minimum degree of a graph is tight. In this
paper, we extend the classical Dirac theorem to the case where $\delta(G) \ge
\lfloor n/2 \rfloor $ by identifying the only non-Hamiltonian graph families in
this case. We first present a short and simple proof. We then provide an
alternative proof that is constructive and self-contained. Consequently, we
provide a polynomial-time algorithm that constructs a Hamiltonian cycle, if
exists, of a graph $G$ with $\delta(G) \ge \lfloor n/2 \rfloor$, or determines
that the graph is non-Hamiltonian. Finally, we present a self-contained proof
for our algorithm which provides insight into the structure of Hamiltonian
cycles when $\delta(G) \ge \lfloor n/2 \rfloor$ and is promising for extending
the results of this paper to the cases with smaller degree bounds.


title:
Combining maximal regularity and energy estimates for time
  discretizations of quasilinear parabolic equations

abstract:
  We analyze fully implicit and linearly implicit backward difference formula
(BDF) methods for quasilinear parabolic equations, without making any
assumptions on the growth or decay of the coefficient functions. We combine
maximal parabolic regularity and energy estimates to derive optimal-order error
bounds for the time-discrete approximation to the solution and its gradient in
the maximum norm and energy norm.


title: Deformed Matter Bounce with Dark Energy Epoch

abstract:
  We extend the Loop Quantum Cosmology matter bounce scenario in order to
include a dark energy era, which ends abruptly at a Rip singularity where the
scale factor and the Hubble rate diverge. In the "deformed matter bounce
scenario", the Universe is contracting from an initial non-causal matter
dominated era until it reaches a minimal radius. After that it expands in a
decelerating way, until at late times, where it expands in an accelerating way,
thus the model is described by a dark energy era that follows the matter
dominated era. Depending on the choice of the free parameters of the model, the
dark energy era is quintessential like which follows the matter domination era,
and eventually it crosses the phantom divide line and becomes phantom. At the
end of the dark energy era, a Rip singularity exists, where the scale factor
and Hubble rate diverge, however the physical system cannot reach the
singularity, since the effective energy density and pressure become complex.
This indicates two things, firstly that the ordinary Loop Quantum Cosmology
matter bounce evolution stops, thus ending the infinite repetition of the
ordinary matter bounce scenario. Secondly, the fact that both the pressure and
the density become complex indicates probably that the description of the
cosmic evolution within the theoretical context of Loop Quantum Cosmology,
ceases to describe the physics of the system and possibly a more fundamental
theory of quantum gravity is needed near the would be Rip singularity. We
describe the qualitative features of the model and we also investigate how this
cosmology could be realized by a viscous fluid and scalar field in the context
of LQC.


title:
Engineering single-phonon number states of a mechanical oscillator via
  photon subtraction

abstract:
  We introduce an optomechanical scheme for the probabilistic preparation of
single-phonon Fock states of mechanical modes based on photo-subtraction. The
quality of the produced mechanical state is confirmed by a number of
indicators, including phonon statistics and conditional fidelity. We assess the
detrimental effect of parameters such as the temperature of the mechanical
system and address the feasibility of the scheme with state-of-the-art
technology.


title: On the Kodaira-Spencer map of abelian schemes

abstract:
  Let $A$ be an abelian scheme over a smooth affine complex variety $S$,
$\varOmega_A$ the $\sO_S$-module of $1$-forms of the first kind on $A$,
$\sD_S\varOmega_A$ the $\sD_S$-module spanned by $\varOmega_A$ in the first
algebraic De Rham cohomology module, and $\theta_\partial: \varOmega_A \to
\sD_S\varOmega_A/\varOmega_A$ the Kodaira-Spencer map attached to a tangent
vector field $\partial$ on $S$. We compare the rank of
$\sD_S\varOmega_A/\varOmega_A$ to the maximal rank of $\theta_\partial$ when
$\partial$ varies: we show that both ranks do not change when one passes to the
"modular case", \ie when one replaces $S$ by the smallest weakly special
subvariety of $\sA_g$ containing the image of $S$ (assuming, as one may up to
isogeny, that $A/S$ is principally polarized), we then analyse the "modular
case" and deduce, for instance, that {\it for any abelian pencil of relative
dimension $g$ with Zariski-dense monodromy in $Sp_{2g}$}, {\it the derivative
with respect to a parameter of a non zero abelian integral of the first kind is
never of the first kind}.


title:
Runge-Kutta time discretization of nonlinear parabolic equations studied
  via discrete maximal parabolic regularity

abstract:
  For a large class of fully nonlinear parabolic equations, which include
gradient flows for energy functionals that depend on the solution gradient, the
semidiscretization in time by implicit Runge-Kutta methods such as the Radau
IIA methods of arbitrary order is studied. Error bounds are obtained in the
$W^{1,\infty}$ norm uniformly on bounded time intervals and, with an improved
approximation order, in the parabolic energy norm. The proofs rely on discrete
maximal parabolic regularity. This is used to obtain $W^{1,\infty}$ estimates,
which are the key to the numerical analysis of these problems.


title:
Phase separation of metastable CoCrFeNi high entropy alloy at
  intermediate temperatures

abstract:
  The CoCrFeNi alloy is widely accepted as an exemplary stable base for high
entropy alloys (HEAs). Although various investigations prove it to be stable
solid solution, its phase stability is still suspicious. Here, we identified
that the CoCrFeNi HEA was thermally metastable at intermediate temperatures,
and composition decomposition occurred after annealed at 750oC for 800 hrs. The
increased lattice distortion induced by minor addition of Al into the CoCrFeNi
base accelerated the composition decomposition and a second fcc phase with a
different lattice constant occurred in the long time annealed CoCrFeNiAl0.1
HEA. A Cr-rich {\sigma} phase also precipitated from the CoCrFeNiAl0.1 HEA. The
Al element can induce the instability of CoCrFeNi HEA. The revealed metastable
CoCrFeNi at intermediate temperatures will greatly change the way of HEAs
development.


title: Critical exact solutions for self-gravitating Dirac fields

abstract:
  We consider the Einstein-Dirac field equations describing a self-gravitating
massive neutrino, looking for axially-symmetric exact solutions; in the search
of general solutions, we find some that are specific and which have critical
features, such as the fact that the space-time curvature turns out to be flat
and the spinor field gives rise to a vanishing bi-linear scalar
$\overline{\psi}\psi=0$ with non-vanishing bi-linear pseudo-scalar
$i\overline{\psi}\gamma^5\psi\not=0$: because in quantum field theory general
computational methods are built on plane-wave solutions, for which bi-linear
pseudo-scalar vanishes while the bi-linear scalar does not vanish, then the
solutions we found cannot be treated with the usual machinery of quantum field
theory. This means that for the Einstein-Dirac system there exist admissible
solutions which nevertheless cannot be quantized with the common prescriptions;
we regard this situation as yet another issue of tension between Einstein
gravity and quantum principles. Possible ways to quench this tension can be
seen either in enlarging the validity of quantum field theory or by restricting
the space of the solutions of the Einstein-Dirac system of field equations.


title: Nearest Neighbour Distance Distribution in Hard-Core Point Processes

abstract:
  In this paper we present an analytic framework for formulating the
statistical distribution of the nearest neighbour distance in hard-core point
processes. We apply this framework to Mat\'{e}rn hard-core point process (MHC)
to derive the cumulative distribution function of the contact distance in three
cases. The first case is between a point in an MHC process and its nearest
neighbour from the same process. The second case is between a point in an
independent Poisson point process and the nearest neighbour from an MHC
process. The third case is between a point in the complement of an MHC process
and its sibling MHC process. We test the analytic results against Monte-Carlo
simulations to verify their consistency.


title: A Novel Approach to Probability

abstract:
  When P indistinguishable balls are randomly distributed among L
distinguishable boxes, and considering the dense system in which P much greater
than L, our natural intuition tells us that the box with the average number of
balls has the highest probability and that none of boxes are empty; however in
reality, the probability of the empty box is always the highest. This fact is
with contradistinction to sparse system in which the number of balls is smaller
than the number of boxes (i.e. energy distribution in gas) in which the average
value has the highest probability. Here we show that when we postulate the
requirement that all possible configurations of balls in the boxes have equal
probabilities, a realistic "long tail" distribution is obtained. This formalism
when applied for sparse systems converges to distributions in which the average
is preferred. We calculate some of the distributions resulted from this
postulate and obtain most of the known distributions in nature, namely, Zipf
law, Benford law, particles energy distributions, and more. Further
generalization of this novel approach yields not only much better predictions
for election polls, market share distribution among competing companies and so
forth, but also a compelling probabilistic explanation for Planck's famous
empirical finding that the energy of a photon is hv.


title:
Electromagnetic Wave Propagation in a Quasi-1D Rhombic Linear Optical
  Waveguide Array

abstract:
  The quasi-one-dimensional rhombic array of the waveguides is considered.
System of equations describing coupled waves in the waveguide in the linear
limit is solved exactly. The electric field distribution was found both for the
diffractionless (or dispersionless) flat band modes and for the dispersive
modes.


title: Nonsymmorphic symmetry-required band crossings in topological semimetals

abstract:
  We show that for two-band systems nonsymmorphic symmetries may enforce the
existence of band crossings in the bulk, which realize Fermi surfaces of
reduced dimensionality. We find that these unavoidable crossings originate from
the momentum dependence of the nonsymmorphic symmetry, which puts strong
restrictions on the global structure of the band configurations. Three
different types of nonsymmorphic symmetries are considered: (i) a unitary
nonsymmorphic symmetry, (ii) a nonsymmorphic magnetic symmetry, and (iii) a
nonsymmorphic symmetry combined with inversion. For nonsymmorphic symmetries of
the latter two types, the band crossings are located at high-symmetry points of
the Brillouin zone, with their exact positions being determined by the algebra
of the symmetry operators. To characterize these band degeneracies we introduce
a \emph{global} topological charge and show that it is of $\mathbb{Z}_2$ type,
which is in contrast to the \emph{local} topological charge of Fermi points in,
say, Weyl semimetals. To illustrate these concepts, we discuss the $\pi$-flux
state as well as the SSH model at its critical point and show that these two
models fit nicely into our general framework of nonsymmorphic two-band systems.


title: Zero-point energy of ultracold atoms

abstract:
  We analyze the divergent zero-point energy of a dilute and ultracold gas of
atoms in D spatial dimensions. For bosonic atoms we explicitly show how to
regularize this divergent contribution, which appears in the Gaussian
fluctuations of the functional integration, by using three different
regularization approaches: dimensional regularization, momentum-cutoff
regularization and convergence-factor regularization. In the case of the ideal
Bose gas the divergent zero-point fluctuations are completely removed, while in
the case of the interacting Bose gas these zero-point fluctuations give rise to
a finite correction to the equation of state. The final convergent equation of
state is independent of the regularization procedure but depends on the
dimensionality of the system and the two-dimensional case is highly nontrivial.
We also discuss very recent theoretical results on the divergent zero-point
energy of the D-dimensional superfluid Fermi gas in the BCS-BEC crossover. In
this case the zero-point energy is due to both fermionic single-particle
excitations and bosonic collective excitations, and its regularization gives
remarkable analytical results in the BEC regime of composite bosons. We compare
the beyond-mean-field equations of state of both bosons and fermions with
relevant experimental data on dilute and ultracold atoms quantitatively
confirming the contribution of zero-point-energy quantum fluctuations to the
thermodynamics of ultracold atoms at very low temperatures.


title:
Effect of vacancies on the mechanical properties of zirconium: An ab
  initio investigation

abstract:
  It is well known that the irradiation-induced defects strongly influence the
mechanical properties of zirconium (Zr) or its alloys in nuclear reactors.
However, how the point defect changes the mechanical properties has been rarely
studied. Here, we systematically investigated the effect of vacancies on the
mechanical properties of alpha-Zr based on density functional theory (DFT).
Both uniformly distributed vacancies and vacancy clusters were considered. Our
results reveal that the existence of vacancy will reduce the bulk modulus,
while enhance the shear and Young's moduli. Based on these moduli, the
ductility and hardness were further calculated. With the introduction of
vacancy, the ductility decreases, but the hardness increases. However, when the
vacancy concentration is larger than a critical value, a rise in the ductility
and a reduction in the hardness occur, which indicates the degeneration of the
material. Moreover, it was found that the vacancies lead to a more isotropic
distribution of Young's modulus in 3D space. To further investigate how the
clustering of vacancies influences the mechanical properties, the most stable
configurations of di- and trivacancy clusters have been predicted, which
correspond to the most compact distribution of vacancies. Compared with the
uniform distribution of vacancies, clustering of vacancies will strengthen the
above changes of elastic moduli, ductility and hardness. Our results indicate
that the increase of the vacancy concentration may be the basic cause for the
changes of the mechanical properties under irradiation, while the formation of
vacancy clusters intensifies these changes.


title:
IT Governance in Actor-Network Mode of Collaboration : Cost Management
  Process Based on Game Theory

abstract:
  IT governance, like global governance of projects, requires cooperation
between several actors. In general, such cooperation builds a collaboration
network between entities. Many works in the literature interested in
collaboration network, but no one of them were focused directly on how to build
a network in an IT governance perspective. In this paper, we investigate how
game theory can be exploited to provide a formal implementation of Cost
Management Process, while highlighting Actor-Network as a framework of
collaboration and its incentive stage as a key step for network construction.
Our objective is to propose an approach of network establishment, by inciting
actors through cost savings. For that, we use Shapley Value to answer the
question: For the sake of IT governance, which coalitions are likely to form in
order to ensure best cost-saving objectives in ANT mode of collaboration? A
graphical tool is developed also to visualize and simulate networks evolution.


title: L-Infinity optimization in tropical geometry and phylogenetics

abstract:
  We investigate uniqueness issues that arise in $l^\infty$-optimization to
linear spaces and Bergman fans of matroids. For linear spaces, we give a
polyhedral decomposition of $\mathbb{R}^n$ based on the dimension of the set of
$l^\infty$-nearest neighbors. This implies that the $l^\infty$-nearest neighbor
in a linear space is unique if and only if the underlying matroid is uniform.
For Bergman fans of matroids, we show that the set of $l^\infty$-nearest points
is a tropical polytope and give an algorithm to compute its tropical vertices.
A key ingredient here is a notion of topology that generalizes tree topology.
These results have practical implications for distance-based phylogenetic
reconstruction using the $l^\infty$-metric. We analyze the possible dimensions
of the set of $l^\infty$-nearest equidistant tree metrics to an arbitrary
dissimilarity map and the number of tree topologies represented in this set.
For both 3 and 4-leaf trees, we decompose the space of dissimilarity maps
relative to the tree topologies represented.


title: Fractional lattice charge transport

abstract:
  We consider the dynamics of noninteracting electrons on a square lattice in
the presence of a magnetic flux {\alpha} and a dc electric field E oriented
along the lattice diagonal. In general, the adiabatic dynamics of an electron
will be characterized by Bloch oscillations in the electrical field direction
and dispersive ballistic transport in the perpendicular direction. For rational
values of {\alpha} and a corresponding discrete set of values of E({\alpha})
vanishing gaps in the spectrum induce a fractionalization of the charge in the
perpendicular direction - while left movers are still performing dispersive
ballistic transport, the complementary fraction of right movers is propagating
in a dispersionless relativistic manner in the opposite direction.
Generalizations to other field directions, lattice symmetries and the possible
probing of the effect with atomic Bose-Einstein condensates and photonic
networks are discussed.


title: Knots and links of complex tangents

abstract:
  It is shown that every knot or link is the set of complex tangents of a
3-sphere smoothly embedded in the three-dimensional complex space. We show in
fact that a one-dimensional submanifold of a closed orientable 3-manifold can
be realised as the set of complex tangents of a smooth embedding of the
3-manifold into the three-dimensional complex space if and only if it
represents the trivial integral homology class in the 3-manifold. The proof
involves a new application of singularity theory of differentiable maps.


title: Counting saddle connections in flat surfaces with poles of higher order

abstract:
  Flat surfaces that correspond to $k$-differentials on compact Riemann
surfaces are of finite area provided there is no pole of order $k$ or higher.
We denote by \textit{flat surfaces with poles of higher order} those surfaces
with flat structures defined by a $k$-differential with at least one pole of
order at least $k$. Flat surfaces with poles of higher order have different
geometrical and dynamical properties than usual flat surfaces of finite area.
In particular, they can have a finite number of saddle connections. We give
lower and upper bounds for the number of saddle connections and related
quantities. In the case $k=1\ or\ 2$, we provide a combinatorial
characterization of the strata for which there can be an infinite number of
saddle connections.


title: Veech groups of flat surfaces with poles

abstract:
  Flat surfaces that correspond to meromorphic $1$-forms or to meromorphic
quadratic differentials containing poles of order two and higher are surfaces
of infinite area. We classify groups that appear as Veech groups of translation
surfaces with poles. We characterize those surfaces such that their
$GL^{+}(2,\mathbb{R})$-orbit or their $SL(2,\mathbb{R})$-orbit is closed.
Finally, we provide a way to determine the Veech group for a typical infinite
surface in a given chamber of a stratum.


title: Tropical Count of Curves on Abelian Varieties

abstract:
  We investigate the problem of counting tropical genus g curves in
g-dimensional tropical abelian varieties. For g = 2, 3, we prove that the
tropical count matches the count provided by G\"ottsche, Bryan-Leung, and
Lange-Sernesi in the complex setting.


title:
Non-zero density-velocity consistency relations for large scale
  structures

abstract:
  We present exact kinematic consistency relations for cosmological structures
that do not vanish at equal times and can thus be measured in surveys. These
rely on cross-correlations between the density and velocity, or momentum,
fields. Indeed, the uniform transport of small-scale structures by long
wavelength modes, which cannot be detected at equal times by looking at density
correlations only, gives rise to a shift in the amplitude of the velocity field
that could be measured. These consistency relations only rely on the weak
equivalence principle and Gaussian initial conditions. They remain valid in the
non-linear regime and for biased galaxy fields. They can be used to constrain
non-standard cosmological scenarios or the large-scale galaxy bias.


title: Mean field games of timing and models for bank runs

abstract:
  The goal of the paper is to introduce a set of problems which we call mean
field games of timing. We motivate the formulation by a dynamic model of bank
run in a continuous-time setting. We briefly review the economic and game
theoretic contributions at the root of our effort, and we develop a
mathematical theory for continuous-time stochastic games where the strategic
decisions of the players are merely choices of times at which they leave the
game, and the interaction between the strategic players is of a mean field
nature.


title:
Using SDO/HMI magnetograms as a source of the solar mean magnetic field
  data

abstract:
  The solar mean magnetic field (SMMF) provided by the Wilcox Solar Observatory
(WSO) is compared with the SMMF acquired by the \textit{Helioseismic and
Magnetic Imager} (HMI) onboard the \textit{Solar Dynamic Observatory} (SDO). We
found that despite the different spectral lines and measurement techniques used
in both instruments the Pearson correlation coefficient between these two
datasets equals 0.86 while the conversion factor is very close to unity: B(HMI)
= 0.99(2)B(WSO). We also discuss artifacts of the SDO/HMI magnetic field
measurements, namely the 12 and 24-hour oscillations in SMMF and in sunspots
magnetic fields that might be caused by orbital motions of the spacecraft. The
artificial harmonics of SMMF reveal significant changes in amplitude and the
nearly stable phase. The connection between the 24-hour harmonic amplitude of
SMMF and the presence of sunspots is examined. We also found that opposite
phase artificial 12 and/or 24-hour oscillations exist in sunspots of opposite
polarities.


title: Etienne B\'ezout on Elimination Theory

abstract:
  B\'ezout's name is attached to his famous theorem. B\'ezout's Theorem states
that the degree of the eliminand of a system a $n$ algebraic equations in $n$
unknowns, when each of the equations is generic of its degree, is the product
of the degrees of the equations. The eliminand is, in the terms of XIXth
century algebra, an equation of smallest degree resulting from the elimination
of $(n-1)$ unknowns. B\'ezout demonstrates his theorem in 1779 in a treatise
entitled "Th\'eorie g\'en\'erale des \'equations alg\'ebriques". In this text,
he does not only demonstrate the theorem for $n>2$ for generic equations, but
he also builds a classification of equations that allows a better bound on the
degree of the eliminand when the equations are not generic. This part of his
work is difficult: it appears incomplete and has been seldom studied. In this
article, we shall give a brief history of his theorem, and give a complete
justification of the difficult part of B\'ezout's treatise.