title:
Parameterized Complexity Results for Exact Bayesian Network Structure
  Learning

abstract:
  Bayesian network structure learning is the notoriously difficult problem of
discovering a Bayesian network that optimally represents a given set of
training data. In this paper we study the computational worst-case complexity
of exact Bayesian network structure learning under graph theoretic restrictions
on the (directed) super-structure. The super-structure is an undirected graph
that contains as subgraphs the skeletons of solution networks. We introduce the
directed super-structure as a natural generalization of its undirected
counterpart. Our results apply to several variants of score-based Bayesian
network structure learning where the score of a network decomposes into local
scores of its nodes. Results: We show that exact Bayesian network structure
learning can be carried out in non-uniform polynomial time if the
super-structure has bounded treewidth, and in linear time if in addition the
super-structure has bounded maximum degree. Furthermore, we show that if the
directed super-structure is acyclic, then exact Bayesian network structure
learning can be carried out in quadratic time. We complement these positive
results with a number of hardness results. We show that both restrictions
(treewidth and degree) are essential and cannot be dropped without loosing
uniform polynomial time tractability (subject to a complexity-theoretic
assumption). Similarly, exact Bayesian network structure learning remains
NP-hard for "almost acyclic" directed super-structures. Furthermore, we show
that the restrictions remain essential if we do not search for a globally
optimal network but aim to improve a given network by means of at most k arc
additions, arc deletions, or arc reversals (k-neighborhood local search).


title: Short and Long Supports for Constraint Propagation

abstract:
  Special-purpose constraint propagation algorithms frequently make implicit
use of short supports -- by examining a subset of the variables, they can infer
support (a justification that a variable-value pair may still form part of an
assignment that satisfies the constraint) for all other variables and values
and save substantial work -- but short supports have not been studied in their
own right. The two main contributions of this paper are the identification of
short supports as important for constraint propagation, and the introduction of
HaggisGAC, an efficient and effective general purpose propagation algorithm for
exploiting short supports. Given the complexity of HaggisGAC, we present it as
an optimised version of a simpler algorithm ShortGAC. Although experiments
demonstrate the efficiency of ShortGAC compared with other general-purpose
propagation algorithms where a compact set of short supports is available, we
show theoretically and experimentally that HaggisGAC is even better. We also
find that HaggisGAC performs better than GAC-Schema on full-length supports. We
also introduce a variant algorithm HaggisGAC-Stable, which is adapted to avoid
work on backtracking and in some cases can be faster and have significant
reductions in memory use. All the proposed algorithms are excellent for
propagating disjunctions of constraints. In all experiments with disjunctions
we found our algorithms to be faster than Constructive Or and GAC-Schema by at
least an order of magnitude, and up to three orders of magnitude.


title: Safe Exploration of State and Action Spaces in Reinforcement Learning

abstract:
  In this paper, we consider the important problem of safe exploration in
reinforcement learning. While reinforcement learning is well-suited to domains
with complex transition dynamics and high-dimensional state-action spaces, an
additional challenge is posed by the need for safe and efficient exploration.
Traditional exploration techniques are not particularly useful for solving
dangerous tasks, where the trial and error process may lead to the selection of
actions whose execution in some states may result in damage to the learning
system (or any other system). Consequently, when an agent begins an interaction
with a dangerous and high-dimensional state-action space, an important question
arises; namely, that of how to avoid (or at least minimize) damage caused by
the exploration of the state-action space. We introduce the PI-SRL algorithm
which safely improves suboptimal albeit robust behaviors for continuous state
and action control tasks and which efficiently learns from the experience
gained from the environment. We evaluate the proposed method in four complex
tasks: automatic car parking, pole-balancing, helicopter hovering, and business
management.


title:
Irrelevant and independent natural extension for sets of desirable
  gambles

abstract:
  The results in this paper add useful tools to the theory of sets of desirable
gambles, a growing toolbox for reasoning with partial probability assessments.
We investigate how to combine a number of marginal coherent sets of desirable
gambles into a joint set using the properties of epistemic irrelevance and
independence. We provide formulas for the smallest such joint, called their
independent natural extension, and study its main properties. The independent
natural extension of maximal coherent sets of desirable gambles allows us to
define the strong product of sets of desirable gambles. Finally, we explore an
easy way to generalise these results to also apply for the conditional versions
of epistemic irrelevance and independence. Having such a set of tools that are
easily implemented in computer programs is clearly beneficial to fields, like
AI, with a clear interest in coherent reasoning under uncertainty using general
and robust uncertainty models that require no full specification.


title: Online Stochastic Optimization under Correlated Bandit Feedback

abstract:
  In this paper we consider the problem of online stochastic optimization of a
locally smooth function under bandit feedback. We introduce the high-confidence
tree (HCT) algorithm, a novel any-time $\mathcal{X}$-armed bandit algorithm,
and derive regret bounds matching the performance of existing state-of-the-art
in terms of dependency on number of steps and smoothness factor. The main
advantage of HCT is that it handles the challenging case of correlated rewards,
whereas existing methods require that the reward-generating process of each arm
is an identically and independent distributed (iid) random process. HCT also
improves on the state-of-the-art in terms of its memory requirement as well as
requiring a weaker smoothness assumption on the mean-reward function in compare
to the previous anytime algorithms. Finally, we discuss how HCT can be applied
to the problem of policy search in reinforcement learning and we report
preliminary empirical results.


title:
Evaluating Indirect Strategies for Chinese-Spanish Statistical Machine
  Translation

abstract:
  Although, Chinese and Spanish are two of the most spoken languages in the
world, not much research has been done in machine translation for this language
pair. This paper focuses on investigating the state-of-the-art of
Chinese-to-Spanish statistical machine translation (SMT), which nowadays is one
of the most popular approaches to machine translation. For this purpose, we
report details of the available parallel corpus which are Basic Traveller
Expressions Corpus (BTEC), Holy Bible and United Nations (UN). Additionally, we
conduct experimental work with the largest of these three corpora to explore
alternative SMT strategies by means of using a pivot language. Three
alternatives are considered for pivoting: cascading, pseudo-corpus and
triangulation. As pivot language, we use either English, Arabic or French.
Results show that, for a phrase-based SMT system, English is the best pivot
language between Chinese and Spanish. We propose a system output combination
using the pivot strategies which is capable of outperforming the direct
translation strategy. The main objective of this work is motivating and
involving the research community to work in this important pair of languages
given their demographic impact.


title:
A Hybrid LP-RPG Heuristic for Modelling Numeric Resource Flows in
  Planning

abstract:
  Although the use of metric fluents is fundamental to many practical planning
problems, the study of heuristics to support fully automated planners working
with these fluents remains relatively unexplored. The most widely used
heuristic is the relaxation of metric fluents into interval-valued variables
--- an idea first proposed a decade ago. Other heuristics depend on domain
encodings that supply additional information about fluents, such as capacity
constraints or other resource-related annotations. A particular challenge to
these approaches is in handling interactions between metric fluents that
represent exchange, such as the transformation of quantities of raw materials
into quantities of processed goods, or trading of money for materials. The
usual relaxation of metric fluents is often very poor in these situations,
since it does not recognise that resources, once spent, are no longer available
to be spent again. We present a heuristic for numeric planning problems
building on the propositional relaxed planning graph, but using a mathematical
program for numeric reasoning. We define a class of producer--consumer planning
problems and demonstrate how the numeric constraints in these can be modelled
in a mixed integer program (MIP). This MIP is then combined with a metric
Relaxed Planning Graph (RPG) heuristic to produce an integrated hybrid
heuristic. The MIP tracks resource use more accurately than the usual
relaxation, but relaxes the ordering of actions, while the RPG captures the
causal propositional aspects of the problem. We discuss how these two
components interact to produce a single unified heuristic and go on to explore
how further numeric features of planning problems can be integrated into the
MIP. We show that encoding a limited subset of the propositional problem to
augment the MIP can yield more accurate guidance, partly by exploiting
structure such as propositional landmarks and propositional resources. Our
results show that the use of this heuristic enhances scalability on problems
where numeric resource interaction is key in finding a solution.


title:
Lifted Variable Elimination: Decoupling the Operators from the
  Constraint Language

abstract:
  Lifted probabilistic inference algorithms exploit regularities in the
structure of graphical models to perform inference more efficiently. More
specifically, they identify groups of interchangeable variables and perform
inference once per group, as opposed to once per variable. The groups are
defined by means of constraints, so the flexibility of the grouping is
determined by the expressivity of the constraint language. Existing approaches
for exact lifted inference use specific languages for (in)equality constraints,
which often have limited expressivity. In this article, we decouple lifted
inference from the constraint language. We define operators for lifted
inference in terms of relational algebra operators, so that they operate on the
semantic level (the constraints extension) rather than on the syntactic level,
making them language-independent. As a result, lifted inference can be
performed using more powerful constraint languages, which provide more
opportunities for lifting. We empirically demonstrate that this can improve
inference efficiency by orders of magnitude, allowing exact inference where
until now only approximate inference was feasible.


title:
Incremental Clustering and Expansion for Faster Optimal Planning in
  Dec-POMDPs

abstract:
  This article presents the state-of-the-art in optimal solution methods for
decentralized partially observable Markov decision processes (Dec-POMDPs),
which are general models for collaborative multiagent planning under
uncertainty. Building off the generalized multiagent A* (GMAA*) algorithm,
which reduces the problem to a tree of one-shot collaborative Bayesian games
(CBGs), we describe several advances that greatly expand the range of
Dec-POMDPs that can be solved optimally. First, we introduce lossless
incremental clustering of the CBGs solved by GMAA*, which achieves exponential
speedups without sacrificing optimality. Second, we introduce incremental
expansion of nodes in the GMAA* search tree, which avoids the need to expand
all children, the number of which is in the worst case doubly exponential in
the nodes depth. This is particularly beneficial when little clustering is
possible. In addition, we introduce new hybrid heuristic representations that
are more compact and thereby enable the solution of larger Dec-POMDPs. We
provide theoretical guarantees that, when a suitable heuristic is used, both
incremental clustering and incremental expansion yield algorithms that are both
complete and search equivalent. Finally, we present extensive empirical results
demonstrating that GMAA*-ICE, an algorithm that synthesizes these advances, can
optimally solve Dec-POMDPs of unprecedented size.


title:
Efficient Computation of the Shapley Value for Game-Theoretic Network
  Centrality

abstract:
  The Shapley value---probably the most important normative payoff division
scheme in coalitional games---has recently been advocated as a useful measure
of centrality in networks. However, although this approach has a variety of
real-world applications (including social and organisational networks,
biological networks and communication networks), its computational properties
have not been widely studied. To date, the only practicable approach to compute
Shapley value-based centrality has been via Monte Carlo simulations which are
computationally expensive and not guaranteed to give an exact answer. Against
this background, this paper presents the first study of the computational
aspects of the Shapley value for network centralities. Specifically, we develop
exact analytical formulae for Shapley value-based centrality in both weighted
and unweighted networks and develop efficient (polynomial time) and exact
algorithms based on them. We empirically evaluate these algorithms on two
real-life examples (an infrastructure network representing the topology of the
Western States Power Grid and a collaboration network from the field of
astrophysics) and demonstrate that they deliver significant speedups over the
Monte Carlo approach. For instance, in the case of unweighted networks our
algorithms are able to return the exact solution about 1600 times faster than
the Monte Carlo approximation, even if we allow for a generous 10% error margin
for the latter method.


title:
Boolean Equi-propagation for Concise and Efficient SAT Encodings of
  Combinatorial Problems

abstract:
  We present an approach to propagation-based SAT encoding of combinatorial
problems, Boolean equi-propagation, where constraints are modeled as Boolean
functions which propagate information about equalities between Boolean
literals. This information is then applied to simplify the CNF encoding of the
constraints. A key factor is that considering only a small fragment of a
constraint model at one time enables us to apply stronger, and even complete,
reasoning to detect equivalent literals in that fragment. Once detected,
equivalences apply to simplify the entire constraint model and facilitate
further reasoning on other fragments. Equi-propagation in combination with
partial evaluation and constraint simplification provide the foundation for a
powerful approach to SAT-based finite domain constraint solving. We introduce a
tool called BEE (Ben-Gurion Equi-propagation Encoder) based on these ideas and
demonstrate for a variety of benchmarks that our approach leads to a
considerable reduction in the size of CNF encodings and subsequent speed-ups in
SAT solving times.


title: Description Logic Knowledge and Action Bases

abstract:
  Description logic Knowledge and Action Bases (KAB) are a mechanism for
providing both a semantically rich representation of the information on the
domain of interest in terms of a description logic knowledge base and actions
to change such information over time, possibly introducing new objects. We
resort to a variant of DL-Lite where the unique name assumption is not enforced
and where equality between objects may be asserted and inferred. Actions are
specified as sets of conditional effects, where conditions are based on
epistemic queries over the knowledge base (TBox and ABox), and effects are
expressed in terms of new ABoxes. In this setting, we address verification of
temporal properties expressed in a variant of first-order mu-calculus with
quantification across states. Notably, we show decidability of verification,
under a suitable restriction inspired by the notion of weak acyclicity in data
exchange.


title: A Feature Subset Selection Algorithm Automatic Recommendation Method

abstract:
  Many feature subset selection (FSS) algorithms have been proposed, but not
all of them are appropriate for a given feature selection problem. At the same
time, so far there is rarely a good way to choose appropriate FSS algorithms
for the problem at hand. Thus, FSS algorithm automatic recommendation is very
important and practically useful. In this paper, a meta learning based FSS
algorithm automatic recommendation method is presented. The proposed method
first identifies the data sets that are most similar to the one at hand by the
k-nearest neighbor classification algorithm, and the distances among these data
sets are calculated based on the commonly-used data set characteristics. Then,
it ranks all the candidate FSS algorithms according to their performance on
these similar data sets, and chooses the algorithms with best performance as
the appropriate ones. The performance of the candidate FSS algorithms is
evaluated by a multi-criteria metric that takes into account not only the
classification accuracy over the selected features, but also the runtime of
feature selection and the number of selected features. The proposed
recommendation method is extensively tested on 115 real world data sets with 22
well-known and frequently-used different FSS algorithms for five representative
classifiers. The results show the effectiveness of our proposed FSS algorithm
recommendation method.


title: Analysis of Watson's Strategies for Playing Jeopardy!

abstract:
  Major advances in Question Answering technology were needed for IBM Watson to
play Jeopardy! at championship level -- the show requires rapid-fire answers to
challenging natural language questions, broad general knowledge, high
precision, and accurate confidence estimates. In addition, Jeopardy! features
four types of decision making carrying great strategic importance: (1) Daily
Double wagering; (2) Final Jeopardy wagering; (3) selecting the next square
when in control of the board; (4) deciding whether to attempt to answer, i.e.,
"buzz in." Using sophisticated strategies for these decisions, that properly
account for the game state and future event probabilities, can significantly
boost a players overall chances to win, when compared with simple "rule of
thumb" strategies. This article presents our approach to developing Watsons
game-playing strategies, comprising development of a faithful simulation model,
and then using learning and Monte-Carlo methods within the simulator to
optimize Watsons strategic decision-making. After giving a detailed description
of each of our game-strategy algorithms, we then focus in particular on
validating the accuracy of the simulators predictions, and documenting
performance improvements using our methods. Quantitative performance benefits
are shown with respect to both simple heuristic strategies, and actual human
contestant performance in historical episodes. We further extend our analysis
of human play to derive a number of valuable and counterintuitive examples
illustrating how human contestants may improve their performance on the show.


title: Sharing Rewards in Cooperative Connectivity Games

abstract:
  We consider how selfish agents are likely to share revenues derived from
maintaining connectivity between important network servers. We model a network
where a failure of one node may disrupt communication between other nodes as a
cooperative game called the vertex Connectivity Game (CG). In this game, each
agent owns a vertex, and controls all the edges going to and from that vertex.
A coalition of agents wins if it fully connects a certain subset of vertices in
the graph, called the primary vertices. Power indices measure an agents ability
to affect the outcome of the game. We show that in our domain, such indices can
be used to both determine the fair share of the revenues an agent is entitled
to, and identify significant possible points of failure affecting the
reliability of communication in the network. We show that in general graphs,
calculating the Shapley and Banzhaf power indices is #P-complete, but suggest a
polynomial algorithm for calculating them in trees. We also investigate finding
stable payoff divisions of the revenues in CGs, captured by the game theoretic
solution of the core, and its relaxations, the epsilon-core and least core. We
show a polynomial algorithm for computing the core of a CG, but show that
testing whether an imputation is in the epsilon-core is coNP-complete. Finally,
we show that for trees, it is possible to test for epsilon-core imputations in
polynomial time.


title: Identifying the Class of Maxi-Consistent Operators in Argumentation

abstract:
  Dung's abstract argumentation theory can be seen as a general framework for
non-monotonic reasoning. An important question is then: what is the class of
logics that can be subsumed as instantiations of this theory? The goal of this
paper is to identify and study the large class of logic-based instantiations of
Dung's theory which correspond to the maxi-consistent operator, i.e. to the
function which returns maximal consistent subsets of an inconsistent knowledge
base. In other words, we study the class of instantiations where very extension
of the argumentation system corresponds to exactly one maximal consistent
subset of the knowledge base. We show that an attack relation belonging to this
class must be conflict-dependent, must not be valid, must not be
conflict-complete, must not be symmetric etc. Then, we show that some attack
relations serve as lower or upper bounds of the class (e.g. if an attack
relation contains canonical undercut then it is not a member of this class). By
using our results, we show for all existing attack relations whether or not
they belong to this class. We also define new attack relations which are
members of this class. Finally, we interpret our results and discuss more
general questions, like: what is the added value of argumentation in such a
setting? We believe that this work is a first step towards achieving our
long-term goal, which is to better understand the role of argumentation and,
particularly, the expressivity of logic-based instantiations of Dung-style
argumentation frameworks.


title: Learning to Predict from Textual Data

abstract:
  Given a current news event, we tackle the problem of generating plausible
predictions of future events it might cause. We present a new methodology for
modeling and predicting such future news events using machine learning and data
mining techniques. Our Pundit algorithm generalizes examples of causality pairs
to infer a causality predictor. To obtain precisely labeled causality examples,
we mine 150 years of news articles and apply semantic natural language modeling
techniques to headlines containing certain predefined causality patterns. For
generalization, the model uses a vast number of world knowledge ontologies.
Empirical evaluation on real news articles shows that our Pundit algorithm
performs as well as non-expert humans.


title: Reasoning about Explanations for Negative Query Answers in DL-Lite

abstract:
  In order to meet usability requirements, most logic-based applications
provide explanation facilities for reasoning services. This holds also for
Description Logics, where research has focused on the explanation of both TBox
reasoning and, more recently, query answering. Besides explaining the presence
of a tuple in a query answer, it is important to explain also why a given tuple
is missing. We address the latter problem for instance and conjunctive query
answering over DL-Lite ontologies by adopting abductive reasoning; that is, we
look for additions to the ABox that force a given tuple to be in the result. As
reasoning tasks we consider existence and recognition of an explanation, and
relevance and necessity of a given assertion for an explanation. We
characterize the computational complexity of these problems for arbitrary,
subset minimal, and cardinality minimal explanations.


title: Optimizing SPARQL Query Answering over OWL Ontologies

abstract:
  The SPARQL query language is currently being extended by the World Wide Web
Consortium (W3C) with so-called entailment regimes. An entailment regime
defines how queries are evaluated under more expressive semantics than SPARQLs
standard simple entailment, which is based on subgraph matching. The queries
are very expressive since variables can occur within complex concepts and can
also bind to concept or role names. In this paper, we describe a sound and
complete algorithm for the OWL Direct Semantics entailment regime. We further
propose several novel optimizations such as strategies for determining a good
query execution order, query rewriting techniques, and show how specialized OWL
reasoning tasks and the concept and role hierarchy can be used to reduce the
query execution time. For determining a good execution order, we propose a
cost-based model, where the costs are based on information about the instances
of concepts and roles that are extracted from a model abstraction built by an
OWL reasoner. We present two ordering strategies: a static and a dynamic one.
For the dynamic case, we improve the performance by exploiting an individual
clustering approach that allows for computing the cost functions based on one
individual sample from a cluster. We provide a prototypical implementation and
evaluate the efficiency of the proposed optimizations. Our experimental study
shows that the static ordering usually outperforms the dynamic one when
accurate statistics are available. This changes, however, when the statistics
are less accurate, e.g., due to nondeterministic reasoning decisions. For
queries that go beyond conjunctive instance queries we observe an improvement
of up to three orders of magnitude due to the proposed optimizations.


title: A Survey on Latent Tree Models and Applications

abstract:
  In data analysis, latent variables play a central role because they help
provide powerful insights into a wide variety of phenomena, ranging from
biological to human sciences. The latent tree model, a particular type of
probabilistic graphical models, deserves attention. Its simple structure - a
tree - allows simple and efficient inference, while its latent variables
capture complex relationships. In the past decade, the latent tree model has
been subject to significant theoretical and methodological developments. In
this review, we propose a comprehensive study of this model. First we summarize
key ideas underlying the model. Second we explain how it can be efficiently
learned from data. Third we illustrate its use within three types of
applications: latent structure discovery, multidimensional clustering, and
probabilistic inference. Finally, we conclude and give promising directions for
future researches in this field.


title:
Natural Language Inference for Arabic Using Extended Tree Edit Distance
  with Subtrees

abstract:
  Many natural language processing (NLP) applications require the computation
of similarities between pairs of syntactic or semantic trees. Many researchers
have used tree edit distance for this task, but this technique suffers from the
drawback that it deals with single node operations only. We have extended the
standard tree edit distance algorithm to deal with subtree transformation
operations as well as single nodes. The extended algorithm with subtree
operations, TED+ST, is more effective and flexible than the standard algorithm,
especially for applications that pay attention to relations among nodes (e.g.
in linguistic trees, deleting a modifier subtree should be cheaper than the sum
of deleting its components individually). We describe the use of TED+ST for
checking entailment between two Arabic text snippets. The preliminary results
of using TED+ST were encouraging when compared with two string-based approaches
and with the standard algorithm.


title: Probabilistic Planning for Continuous Dynamic Systems under Bounded Risk

abstract:
  This paper presents a model-based planner called the Probabilistic Sulu
Planner or the p-Sulu Planner, which controls stochastic systems in a goal
directed manner within user-specified risk bounds. The objective of the p-Sulu
Planner is to allow users to command continuous, stochastic systems, such as
unmanned aerial and space vehicles, in a manner that is both intuitive and
safe. To this end, we first develop a new plan representation called a
chance-constrained qualitative state plan (CCQSP), through which users can
specify the desired evolution of the plant state as well as the acceptable
level of risk. An example of a CCQSP statement is go to A through B within 30
minutes, with less than 0.001% probability of failure." We then develop the
p-Sulu Planner, which can tractably solve a CCQSP planning problem. In order to
enable CCQSP planning, we develop the following two capabilities in this paper:
1) risk-sensitive planning with risk bounds, and 2) goal-directed planning in a
continuous domain with temporal constraints. The first capability is to ensures
that the probability of failure is bounded. The second capability is essential
for the planner to solve problems with a continuous state space such as vehicle
path planning. We demonstrate the capabilities of the p-Sulu Planner by
simulations on two real-world scenarios: the path planning and scheduling of a
personal aerial vehicle as well as the space rendezvous of an autonomous cargo
spacecraft.


title: On the Computation of Fully Proportional Representation

abstract:
  We investigate two systems of fully proportional representation suggested by
Chamberlin Courant and Monroe. Both systems assign a representative to each
voter so that the "sum of misrepresentations" is minimized. The winner
determination problem for both systems is known to be NP-hard, hence this work
aims at investigating whether there are variants of the proposed rules and/or
specific electorates for which these problems can be solved efficiently. As a
variation of these rules, instead of minimizing the sum of misrepresentations,
we considered minimizing the maximal misrepresentation introducing effectively
two new rules. In the general case these "minimax" versions of classical rules
appeared to be still NP-hard. We investigated the parameterized complexity of
winner determination of the two classical and two new rules with respect to
several parameters. Here we have a mixture of positive and negative results:
e.g., we proved fixed-parameter tractability for the parameter the number of
candidates but fixed-parameter intractability for the number of winners. For
single-peaked electorates our results are overwhelmingly positive: we provide
polynomial-time algorithms for most of the considered problems. The only rule
that remains NP-hard for single-peaked electorates is the classical Monroe
rule.


title:
Qualitative Order of Magnitude Energy-Flow-Based Failure Modes and
  Effects Analysis

abstract:
  This paper presents a structured power and energy-flow-based qualitative
modelling approach that is applicable to a variety of system types including
electrical and fluid flow. The modelling is split into two parts. Power flow is
a global phenomenon and is therefore naturally represented and analysed by a
network comprised of the relevant structural elements from the components of a
system. The power flow analysis is a platform for higher-level behaviour
prediction of energy related aspects using local component behaviour models to
capture a state-based representation with a global time. The primary
application is Failure Modes and Effects Analysis (FMEA) and a form of
exaggeration reasoning is used, combined with an order of magnitude
representation to derive the worst case failure modes. The novel aspects of the
work are an order of magnitude(OM) qualitative network analyser to represent
any power domain and topology, including multiple power sources, a feature that
was not required for earlier specialised electrical versions of the approach.
Secondly, the representation of generalised energy related behaviour as
state-based local models is presented as a modelling strategy that can be more
vivid and intuitive for a range of topologically complex applications than
qualitative equation-based representations.The two-level modelling strategy
allows the broad system behaviour coverage of qualitative simulation to be
exploited for the FMEA task, while limiting the difficulties of qualitative
ambiguity explanation that can arise from abstracted numerical models. We have
used the method to support an automated FMEA system with examples of an
aircraft fuel system and domestic a heating system discussed in this paper.


title: Scheduling a Dynamic Aircraft Repair Shop with Limited Repair Resources

abstract:
  We address a dynamic repair shop scheduling problem in the context of
military aircraft fleet management where the goal is to maintain a full
complement of aircraft over the long-term. A number of flights, each with a
requirement for a specific number and type of aircraft, are already scheduled
over a long horizon. We need to assign aircraft to flights and schedule repair
activities while considering the flights requirements, repair capacity, and
aircraft failures. The number of aircraft awaiting repair dynamically changes
over time due to failures and it is therefore necessary to rebuild the repair
schedule online. To solve the problem, we view the dynamic repair shop as
successive static repair scheduling sub-problems over shorter time periods. We
propose a complete approach based on the logic-based Benders decomposition to
solve the static sub-problems, and design different rescheduling policies to
schedule the dynamic repair shop. Computational experiments demonstrate that
the Benders model is able to find and prove optimal solutions on average four
times faster than a mixed integer programming model. The rescheduling approach
having both aspects of scheduling over a longer horizon and quickly adjusting
the schedule increases aircraft available in the long term by 10% compared to
the approaches having either one of the aspects alone.


title: Decentralized Anti-coordination Through Multi-agent Learning

abstract:
  To achieve an optimal outcome in many situations, agents need to choose
distinct actions from one another. This is the case notably in many resource
allocation problems, where a single resource can only be used by one agent at a
time. How shall a designer of a multi-agent system program its identical agents
to behave each in a different way? From a game theoretic perspective, such
situations lead to undesirable Nash equilibria. For example consider a resource
allocation game in that two players compete for an exclusive access to a single
resource. It has three Nash equilibria. The two pure-strategy NE are efficient,
but not fair. The one mixed-strategy NE is fair, but not efficient. Aumanns
notion of correlated equilibrium fixes this problem: It assumes a correlation
device that suggests each agent an action to take. However, such a "smart"
coordination device might not be available. We propose using a randomly chosen,
"stupid" integer coordination signal. "Smart" agents learn which action they
should use for each value of the coordination signal. We present a multi-agent
learning algorithm that converges in polynomial number of steps to a correlated
equilibrium of a channel allocation game, a variant of the resource allocation
game. We show that the agents learn to play for each coordination signal value
a randomly chosen pure-strategy Nash equilibrium of the game. Therefore, the
outcome is an efficient correlated equilibrium. This CE becomes more fair as
the number of the available coordination signal values increases.


title: NuMVC: An Efficient Local Search Algorithm for Minimum Vertex Cover

abstract:
  The Minimum Vertex Cover (MVC) problem is a prominent NP-hard combinatorial
optimization problem of great importance in both theory and application. Local
search has proved successful for this problem. However, there are two main
drawbacks in state-of-the-art MVC local search algorithms. First, they select a
pair of vertices to exchange simultaneously, which is time-consuming. Secondly,
although using edge weighting techniques to diversify the search, these
algorithms lack mechanisms for decreasing the weights. To address these issues,
we propose two new strategies: two-stage exchange and edge weighting with
forgetting. The two-stage exchange strategy selects two vertices to exchange
separately and performs the exchange in two stages. The strategy of edge
weighting with forgetting not only increases weights of uncovered edges, but
also decreases some weights for each edge periodically. These two strategies
are used in designing a new MVC local search algorithm, which is referred to as
NuMVC. We conduct extensive experimental studies on the standard benchmarks,
namely DIMACS and BHOSLIB. The experiment comparing NuMVC with state-of-the-art
heuristic algorithms show that NuMVC is at least competitive with the nearest
competitor namely PLS on the DIMACS benchmark, and clearly dominates all
competitors on the BHOSLIB benchmark. Also, experimental results indicate that
NuMVC finds an optimal solution much faster than the current best exact
algorithm for Maximum Clique on random instances as well as some structured
ones. Moreover, we study the effectiveness of the two strategies and the
run-time behaviour through experimental analysis.


title: AI Methods in Algorithmic Composition: A Comprehensive Survey

abstract:
  Algorithmic composition is the partial or total automation of the process of
music composition by using computers. Since the 1950s, different computational
techniques related to Artificial Intelligence have been used for algorithmic
composition, including grammatical representations, probabilistic methods,
neural networks, symbolic rule-based systems, constraint programming and
evolutionary algorithms. This survey aims to be a comprehensive account of
research on algorithmic composition, presenting a thorough view of the field
for researchers in Artificial Intelligence.


title: Topic Segmentation and Labeling in Asynchronous Conversations

abstract:
  Topic segmentation and labeling is often considered a prerequisite for
higher-level conversation analysis and has been shown to be useful in many
Natural Language Processing (NLP) applications. We present two new corpora of
email and blog conversations annotated with topics, and evaluate annotator
reliability for the segmentation and labeling tasks in these asynchronous
conversations. We propose a complete computational framework for topic
segmentation and labeling in asynchronous conversations. Our approach extends
state-of-the-art methods by considering a fine-grained structure of an
asynchronous conversation, along with other conversational features by applying
recent graph-based methods for NLP. For topic segmentation, we propose two
novel unsupervised models that exploit the fine-grained conversational
structure, and a novel graph-theoretic supervised model that combines lexical,
conversational and topic features. For topic labeling, we propose two novel
(unsupervised) random walk models that respectively capture conversation
specific clues from two different sources: the leading sentences and the
fine-grained conversational structure. Empirical evaluation shows that the
segmentation and the labeling performed by our best models beat the
state-of-the-art, and are highly correlated with human annotations.


title: Asymmetric Distributed Constraint Optimization Problems

abstract:
  Distributed Constraint Optimization (DCOP) is a powerful framework for
representing and solving distributed combinatorial problems, where the
variables of the problem are owned by different agents. Many multi-agent
problems include constraints that produce different gains (or costs) for the
participating agents. Asymmetric gains of constrained agents cannot be
naturally represented by the standard DCOP model. The present paper proposes a
general framework for Asymmetric DCOPs (ADCOPs). In ADCOPs different agents may
have different valuations for constraints that they are involved in. The new
framework bridges the gap between multi-agent problems which tend to have
asymmetric structure and the standard symmetric DCOP model. The benefits of the
proposed model over previous attempts to generalize the DCOP model are
discussed and evaluated. Innovative algorithms that apply to the special
properties of the proposed ADCOP model are presented in detail. These include
complete algorithms that have a substantial advantage in terms of runtime and
network load over existing algorithms (for standard DCOPs) which use
alternative representations. Moreover, standard incomplete algorithms (i.e.,
local search algorithms) are inapplicable to the existing DCOP representations
of asymmetric constraints and when they are applied to the new ADCOP framework
they often fail to converge to a local optimum and yield poor results. The
local search algorithms proposed in the present paper converge to high quality
solutions. The experimental evidence that is presented reveals that the
proposed local search algorithms for ADCOPs achieve high quality solutions
while preserving a high level of privacy.


title:
A Refined View of Causal Graphs and Component Sizes: SP-Closed Graph
  Classes and Beyond

abstract:
  The causal graph of a planning instance is an important tool for planning
both in practice and in theory. The theoretical studies of causal graphs have
largely analysed the computational complexity of planning for instances where
the causal graph has a certain structure, often in combination with other
parameters like the domain size of the variables. Chen and Gimand#233;nez
ignored even the structure and considered only the size of the weakly connected
components. They proved that planning is tractable if the components are
bounded by a constant and otherwise intractable. Their intractability result
was, however, conditioned by an assumption from parameterised complexity theory
that has no known useful relationship with the standard complexity classes. We
approach the same problem from the perspective of standard complexity classes,
and prove that planning is NP-hard for classes with unbounded components under
an additional restriction we refer to as SP-closed. We then argue that most
NP-hardness theorems for causal graphs are difficult to apply and, thus, prove
a more general result; even if the component sizes grow slowly and the class is
not densely populated with graphs, planning still cannot be tractable unless
the polynomial hierachy collapses. Both these results still hold when
restricted to the class of acyclic causal graphs. We finally give a partial
characterization of the borderline between NP-hard and NP-intermediate classes,
giving further insight into the problem.


title:
Protecting Privacy through Distributed Computation in Multi-agent
  Decision Making

abstract:
  As large-scale theft of data from corporate servers is becoming increasingly
common, it becomes interesting to examine alternatives to the paradigm of
centralizing sensitive data into large databases. Instead, one could use
cryptography and distributed computation so that sensitive data can be supplied
and processed in encrypted form, and only the final result is made known. In
this paper, we examine how such a paradigm can be used to implement constraint
satisfaction, a technique that can solve a broad class of AI problems such as
resource allocation, planning, scheduling, and diagnosis. Most previous work on
privacy in constraint satisfaction only attempted to protect specific types of
information, in particular the feasibility of particular combinations of
decisions. We formalize and extend these restricted notions of privacy by
introducing four types of private information, including the feasibility of
decisions and the final decisions made, but also the identities of the
participants and the topology of the problem. We present distributed algorithms
that allow computing solutions to constraint satisfaction problems while
maintaining these four types of privacy. We formally prove the privacy
properties of these algorithms, and show experiments that compare their
respective performance on benchmark problems.


title: A Survey of Multi-Objective Sequential Decision-Making

abstract:
  Sequential decision-making problems with multiple objectives arise naturally
in practice and pose unique challenges for research in decision-theoretic
planning and learning, which has largely focused on single-objective settings.
This article surveys algorithms designed for sequential decision-making
problems with multiple objectives. Though there is a growing body of literature
on this subject, little of it makes explicit under what circumstances special
methods are needed to solve multi-objective problems. Therefore, we identify
three distinct scenarios in which converting such a problem to a
single-objective one is impossible, infeasible, or undesirable. Furthermore, we
propose a taxonomy that classifies multi-objective methods according to the
applicable scenario, the nature of the scalarization function (which projects
multi-objective values to scalar ones), and the type of policies considered. We
show how these factors determine the nature of an optimal solution, which can
be a single policy, a convex hull, or a Pareto front. Using this taxonomy, we
survey the literature on multi-objective methods for planning and learning.
Finally, we discuss key applications of such methods and outline opportunities
for future work.


title: Learning by Observation of Agent Software Images

abstract:
  Learning by observation can be of key importance whenever agents sharing
similar features want to learn from each other. This paper presents an agent
architecture that enables software agents to learn by direct observation of the
actions executed by expert agents while they are performing a task. This is
possible because the proposed architecture displays information that is
essential for observation, making it possible for software agents to observe
each other. The agent architecture supports a learning process that covers all
aspects of learning by observation, such as discovering and observing experts,
learning from the observed data, applying the acquired knowledge and evaluating
the agents progress. The evaluation provides control over the decision to
obtain new knowledge or apply the acquired knowledge to new problems. We
combine two methods for learning from the observed information. The first one,
the recall method, uses the sequence on which the actions were observed to
solve new problems. The second one, the classification method, categorizes the
information in the observed data and determines to which set of categories the
new problems belong. Results show that agents are able to learn in conditions
where common supervised learning algorithms fail, such as when agents do not
know the results of their actions a priori or when not all the effects of the
actions are visible. The results also show that our approach provides better
results than other learning methods since it requires shorter learning periods.


title:
The Earth as an extrasolar transiting planet - II: HARPS and UVES
  detection of water vapor, biogenic O$_2$, and O$_3$

abstract:
  The atmospheric composition of transiting exoplanets can be characterized
during transit by spectroscopy. For the transit of an Earth twin, models
predict that biogenic $O_2$ and $O_3$ should be detectable, as well as water
vapour, a molecule linked to habitability as we know it on Earth. The aim is to
measure the Earth radius versus wavelength $\lambda$ - or the atmosphere
thickness $h(\lambda)$ - at the highest spectral resolution available to fully
characterize the signature of Earth seen as a transiting exoplanet. We present
observations of the Moon eclipse of 21-12-2010. Seen from the Moon, the Earth
eclipses the Sun and opens access to the Earth atmosphere transmission
spectrum. We used HARPS and UVES spectrographs to take penumbra and umbra
high-resolution spectra from 3100 to 10400 Ang. A change of the quantity of
water vapour above the telescope compromised the quality of the UVES data. We
corrected for this effect in the data processing. We analyzed the data by 3
different methods. The 1st method is based on the analysis of pairs of penumbra
spectra. The 2nd makes use of a single penumbra spectrum, and the 3rd of all
penumbra and umbra spectra. Profiles $h(\lambda)$ are obtained with the three
methods for both instruments. The 1st method gives the best result, in
agreement with a model. The second method seems to be more sensitive to the
Doppler shift of solar spectral lines with respect to the telluric lines. The
3rd method makes use of umbra spectra which bias the result, but it can be
corrected for this a posteriori from results with the first method. The 3
methods clearly show the spectral signature of the Rayleigh scattering in the
Earth atmosphere and the bands of H$_2$O, O$_2$, and O$_3$. Sodium is detected.
Assuming no atmospheric perturbations, we show that the E-ELT is theoretically
able to detect the $O_2$ A-band in 8~h of integration for an Earth twin at
10pc.


title: On the mass of the exterior blow-up points

abstract:
  We consider the following problem on open set $\Omega$ of ${\mathbb R}^2$:
$$\left \{ \begin {split} -\Delta u_i & = V_i e^{u_i} \,\, &\text{in} \,\,
&\Omega \subset {\mathbb R}^2, \\ u_i & = 0 \,\, & \text{in} \,\, &\partial
\Omega.\end {split}\right.$$ We assume that $ \int_{\Omega} e^{u_i} dy \leq C$,
and $ 0 \leq V_i \leq b < + \infty$.
  On the other hand, if we assume that $V_i$ $s-$holderian with $1/2< s \leq
1$, then each exterior blow-up point is simple. As application, we have a
compactness result for the case when: $\int_{\Omega}V_i e^{u_i} dy \leq
40\pi-\epsilon , \,\, \epsilon >0$.


title: The non-Abelian geometric phase in the diamond nitrogen-vacancy center

abstract:
  This paper introduces a theoretical framework for understanding the
accumulation of non-Abelian geometric phases in rotating nitrogen-vacancy
centers in diamond. Specifically, we consider how degenerate states can be
achieved and demonstrate that the resulting geometric phase for multiple paths
is non-Abelian. We find that the non-Abelian nature of the phase is robust to
fluctuations in the path and magnetic field. In contrast to previous studies of
the accumulation of Abelian geometric phases for nitrogen-vacancy centers under
rotation we find that the limiting time-scale is $T_{1}$. As such a non-Abelian
geometric phase accumulation in nitrogen-vacancy centers has potential
advantages for applications as gyroscopes.


title: Scene Labeling with Contextual Hierarchical Models

abstract:
  Scene labeling is the problem of assigning an object label to each pixel. It
unifies the image segmentation and object recognition problems. The importance
of using contextual information in scene labeling frameworks has been widely
realized in the field. We propose a contextual framework, called contextual
hierarchical model (CHM), which learns contextual information in a hierarchical
framework for scene labeling. At each level of the hierarchy, a classifier is
trained based on downsampled input images and outputs of previous levels. Our
model then incorporates the resulting multi-resolution contextual information
into a classifier to segment the input image at original resolution. This
training strategy allows for optimization of a joint posterior probability at
multiple resolutions through the hierarchy. Contextual hierarchical model is
purely based on the input image patches and does not make use of any fragments
or shape examples. Hence, it is applicable to a variety of problems such as
object segmentation and edge detection. We demonstrate that CHM outperforms
state-of-the-art on Stanford background and Weizmann horse datasets. It also
outperforms state-of-the-art edge detection methods on NYU depth dataset and
achieves state-of-the-art on Berkeley segmentation dataset (BSDS 500).


title: Critical constraint on inflationary magnetogenesis

abstract:
  Recently, there are several reports that the cosmic magnetic fields on Mpc
scale in void region is larger than $\sim 10^{-15}$G with an uncertainty of a
few orders from the current blazar observations. On the other hand, in
inflationary magnetogenesis models, additional primordial curvature
perturbations are inevitably produced from iso-curvature perturbations due to
generated electromagnetic fields. We explore such induced curvature
perturbations in a model independent way and obtained a severe upper bound for
the energy scale of inflation from the observed cosmic magnetic fields and the
observed amplitude of the curvature perturbation, as $\rho_{\rm inf}^{1/4} <
30{\rm GeV} \times (B_{\rm obs}/10^{-15}{\rm G})^{-1}$ where $B_{\rm obs}$ is
the strength of the magnetic field at present. Therefore, without a dedicated
low energy inflation model or an additional amplification of magnetic fields
after inflation, inflationary magnetogenesis on Mpc scale is generally
incompatible with CMB observations.


title: Surface Phase Transition in Anomalous Fluid in Nanoconfinement

abstract:
  We explore by molecular dynamic simulations the thermodynamical behavior of
an anomalous fluid confined inside rigid and flexible nanopores. The fluid is
modeled by a two length scale potential. In the bulk this system exhibits the
density and diffusion anomalous behavior observed in liquid water. We show that
the anomalous fluid confined inside rigid and flexible nanopores forms layers.
As the volume of the nanopore is decreased the rigid surface exhibits three
consecutive first order phase transitions associated with the change in the
number of layers. These phase transitions are not present for flexible
confinement. Our results indicate that the nature of confinement is relevant
for the properties of the confined liquid what suggests that confinement in
carbon nanotubes should be quite different from confinement in biological
channels.


title: The Lucas Numbers and other Gibonacci Sequences mod $m$

abstract:
  We study Gibonacci sequences mod $m$, giving special attention to the Lucas
numbers. It is known which $m$ have the property that the Fibonacci sequence
contains all residues mod $m$. When $m$ has this property, we say that the
Fibonacci sequence is complete mod $m$. We extend this work to all Gibonacci
sequences, concluding by determining the set of $m$ in which a generic
Gibonacci sequence containing the relatively prime consecutive terms $a, b$ is
complete mod $m$.


title: Stochastic Event-triggered Sensor Schedule for Remote State Estimation

abstract:
  We propose an open-loop and a closed-loop stochastic event-triggered sensor
schedule for remote state estimation. Both schedules overcome the essential
difficulties of existing schedules in recent literature works where, through
introducing a deterministic event-triggering mechanism, the Gaussian property
of the innovation process is destroyed which produces a challenging nonlinear
filtering problem that cannot be solved unless approximation techniques are
adopted. The proposed stochastic event-triggered sensor schedules eliminate
such approximations. Under these two schedules, the MMSE estimator and its
estimation error covariance matrix at the remote estimator are given in a
closed-form. Simulation studies demonstrate that the proposed schedules have
better performance than periodic ones with the same sensor-to-estimator
communication rate.


title:
Self-gravitating Bose-Einstein condensates and the Thomas-Fermi
  approximation

abstract:
  Self-gravitating Bose-Einstein condensates (BEC) have been proposed in
various astrophysical contexts, including Bose-stars and BEC dark matter halos.
These systems are described by a combination of the Gross-Pitaevskii and
Poisson equations (the GPP system). In the analysis of these hypothetical
objects, the Thomas-Fermi (TF) approximation is widely used. This approximation
is based on the assumption that in the presence of a large number of particles,
the kinetic term in the Gross-Pitaevskii energy functional can be neglected,
yet it is well known that this assumption is violated near the condensate
surface. We also show that the total energy of the self-gravitating condensate
in the TF-approximation is positive. The stability of a self-gravitating system
is dependent on the total energy being negative. Therefore, the
TF-approximation is ill suited to formulate initial conditions in numerical
simulations. As an alternative, we offer an approximate solution of the full
GPP system.


title: The Complexity of Synchronous Notions of Information Flow Security

abstract:
  The paper considers the complexity of verifying that a finite state system
satisfies a number of definitions of information flow security. The systems
model considered is one in which agents operate synchronously with awareness of
the global clock. This enables timing based attacks to be captured, whereas
previous work on this topic has dealt primarily with asynchronous systems.
Versions of the notions of nondeducibility on inputs, nondeducibility on
strategies, and an unwinding based notion are formulated for this model. All
three notions are shown to be decidable, and their computational complexity is
characterised.


title: Tight Bounds on the Accessible Information and the Informational Power

abstract:
  The accessible information quantifies the amount of classical information
that can be extracted from an ensemble of quantum states. Analogously, the
informational power quantifies the amount of classical information that can be
extracted by a quantum measurement. For both quantities, we provide upper and
lower bounds that depend only on the dimension of the system, and we prove
their tightness. In the case of symmetric informationally complete (SIC)
ensembles and measurements, stronger bounds are provided and their tightness
proved for qubits and qutrits. From our upper bounds, we notice, perhaps
surprisingly, that the statistics generated by SIC ensembles or measurements in
arbitrary dimension, though optimal for tomographic purposes, in fact never
contains more than just one bit of information, the rest being constituted by
completely random bits. On the other hand, from our lower bounds, we obtain an
explicit strategy beating the so-called "pretty-good" one for the extraction of
mutual information in the case of SIC ensembles and measurements.


title:
X-ray Spectroscopy of the Mixed Morphology Supernova Remnant W28 with
  XMM-Newton

abstract:
  We report on spatially resolved X-ray spectroscopy of the north-eastern part
of the mixed morphology supernova remnant (SNR) W28 with {\it XMM-Newton}. The
observed field of view includes a prominent and twisted shell emission forming
the edge of this SNR as well as part of the center-filled X-ray emission
brightening toward the south-west edge of the field of view. The shell region
spectra are in general represented by an optically thin thermal plasma emission
in collisional ionization equilibrium with a temperature of $\sim$0.3~keV and a
density of $\sim$10~cm$^{-3}$, which is much higher than the density obtained
for inner parts. In contrast, we detected no significant X-ray flux from one of
the TeV $\gamma$-ray peaks with an upper-limit flux of 2.1$\times$10$^{-14}$
erg cm$^{-2}$ s$^{-1}$ in the 2--10~keV band. The large flux ratio of TeV to
X-ray, larger than 16, and the spatial coincidence of the molecular cloud and
the TeV $\gamma$-ray emission site indicate that the TeV $\gamma$-ray of W28 is
$\pi^{0}$-decay emission originating from collisions between accelerated
protons and molecular cloud protons. Comparing the spectrum in the TeV band and
the X-ray upper limit, we obtained a weak upper limit on the magnetic field
strength $B\lesssim$ 1500~$\mu$G.


title: Resonances and lower resolvent bounds

abstract:
  We show how the presence of resonances close to the real axis implies
exponential lower bounds on the norm of the cut-off resolvent on the real axis.


title: Phonon spectrum of SrFe2As2 determined using multizone phonon refinement

abstract:
  The ferropnictide superconductors exhibit a sensitive interplay between the
lattice and magnetic degrees of freedom, including a number of phonon modes
that are much softer than predicted by nonmagnetic calculations using density
functional theory (DFT). However, it is not known what effect, if any, the
long-range magnetic order has on phonon frequencies above 23 meV, where several
phonon branches are very closely spaced in energy and it is challenging to
isolate them from each other. We measured these phonons using inelastic
time-of-flight neutron scattering in ~40 Brillouin zones, and developed a
technique to determine their frequencies. We find this method capable of
determining phonon energies to ~0.1 meV accuracy, and that the DFT calculations
using the experimental structure yield qualitatively correct energies and
eigenvectors. We do not find any effect of the magnetic transition on these
phonons.


title:
ANOVA (analysis of variance) in the quantum linguistic formulation of
  statistics

abstract:
  Recently, we proposed quantum language (or, measurement theory), which is
characterized as the linguistic turn of the Copenhagen interpretation of
quantum mechanics. We believe that this language has a great powet of
description, and therefore, even statistics can be described by quantum
language. In this paper, we show that ANOVA (analysis of variance (one-way and
two-way)) can be formulated in quantum language. Since quantum language is
suited for theoretical arguments, we believe that our results are visible and
understandable. For example, we can answer the question "What kind of role does
Kolmogorov's probability theory play in ANOVA?" That is, the readers find that
Kolmogorov's probability theory is merely used in order to calculate
multi-dimenstional Gauss integrals, and thus, they can avoid to confuse the
relation between Kolmogorov's probability theory and statistics.


title: Effects of kination and scalar-tensor cosmologies on sterile neutrinos

abstract:
  We study the effects of kination and scalar-tensor pre-Big Bang
Nucleosynthesis cosmologies on the non-resonant production of sterile
neutrinos. We show that if the peak of the production rate of sterile neutrinos
occurs during the non-standard cosmological phase, the relic number density of
sterile neutrinos could be reduced with respect to the number expected in the
standard cosmology. Consequently, current bounds on active-sterile neutrino
mixing derived from the relic energy density of sterile neutrinos could be
greatly relaxed. In particular, we show that the sterile neutrinos which could
explain the anomalies found in short-baseline neutrino experiments are
compatible with recent joint Planck upper limits on their contribution to the
energy density of the Universe in a scalar-tensor or a low-reheating
temperature pre-Big Bang Nucleosynthesis cosmology.


title: Variable-length compression allowing errors

abstract:
  This paper studies the fundamental limits of the minimum average length of
lossless and lossy variable-length compression, allowing a nonzero error
probability $\epsilon$, for lossless compression. We give non-asymptotic bounds
on the minimum average length in terms of Erokhin's rate-distortion function
and we use those bounds to obtain a Gaussian approximation on the speed of
approach to the limit which is quite accurate for all but small blocklengths:
$$(1 - \epsilon) k H(\mathsf S) - \sqrt{\frac{k V(\mathsf S)}{2 \pi} } e^{-
\frac {(Q^{-1}(\epsilon))^2} 2 }$$ where $Q^{-1}(\cdot)$ is the functional
inverse of the standard Gaussian complementary cdf, and $V(\mathsf S)$ is the
source dispersion. A nonzero error probability thus not only reduces the
asymptotically achievable rate by a factor of $1 - \epsilon$, but this
asymptotic limit is approached from below, i.e. larger source dispersions and
shorter blocklengths are beneficial. Variable-length lossy compression under an
excess distortion constraint is shown to exhibit similar properties.


title:
Experimental observation of Dirac-like surface states and topological
  phase transition in Pb$_{1-x}$Sn$_x$Te(111) films

abstract:
  The surface of a topological crystalline insulator (TCI) carries an even
number of Dirac cones protected by crystalline symmetry. We epitaxially grew
high quality Pb$_{1-x}$Sn$_x$Te(111) films and investigated the TCI phase by
in-situ angle-resolved photoemission spectroscopy. Pb$_{1-x}$Sn$_x$Te(111)
films undergo a topological phase transition from trivial insulator to TCI via
increasing the Sn/Pb ratio, accompanied by a crossover from n-type to p-type
doping. In addition, a hybridization gap is opened in the surface states when
the thickness of film is reduced to the two-dimensional limit. The work
demonstrates an approach to manipulating the topological properties of TCI,
which is of importance for future fundamental research and applications based
on TCI.


title: Remarks on the collapsing of torus fibered Calabi-Yau manifolds

abstract:
  One of the main results of the paper arXiv:1108.0967 by Gross-Tosatti-Zhang
establishes estimates on the collapsing of Ricci-flat Kahler metrics on
holomorphic torus fibrations. We remove a projectivity assumption from these
estimates and simplify some of the underlying analysis.


title: Metric measure limits of spheres and complex projective spaces

abstract:
  We study the limits of sequences of spheres and complex projective spaces
with unbounded dimensions. A sequence of spheres (resp. complex projective
spaces) either is a Levy family, infinitely dissipates, or converges to (resp.
the Hopf quotient of) a virtual infinite-dimensional Gaussian space, depending
on the size of the spaces. These are the first discovered examples with the
property that the limits are drastically different from the spaces in the
sequence. For the proof, we introduce a metric on Gromov's compactification of
the space of metric measure spaces.


title:
Properties of Intrinsic Polarization Angle Ambiguities in Faraday
  Tomography

abstract:
  Faraday tomography is a powerful method to diagnose polarizations and Faraday
rotations along the line of sight. The quality of Faraday tomography is,
however, limited by several conditions. Recently, it is reported that Faraday
tomography indicates false signals in some specific situations. In this paper,
we systematically investigate the condition of the appearance of false signals
in Faraday tomography. We study this by pseudo-observing two sources within a
beam, and change in the intrinsic polarization angles, rotation measures,
intensities, and frequency coverage. We find that false signals arise when
rotation measure between the sources is less than 1.5 times the full width at
half maximum of the rotation measure spread function. False signals also depend
on the intensity ratio between the sources and are reduced for large ratio. On
the other hand, the appearance of false signals does not depend on frequency
coverage, meaning that the uncertainty should be correctly understood and taken
into consideration even with future wide-band observations such as Square
Kilometer Array (SKA).


title: Bounds of the logarithmic mean

abstract:
  We give tight bounds for logarithmic mean. We also give new Frobenius norm
inequalities for two positive semidefinite matrices. In addition, we give some
matrix inequalities on matrix power mean.


title: Vector Bin-and-Cancel for MIMO Distributed Full-Duplex

abstract:
  In a multi-input multi-output (MIMO) full-duplex network, where an in-band
full-duplex infrastruc- ture node communicates with two half-duplex mobiles
supporting simultaneous up- and downlink flows, the inter-mobile interference
between the up- and downlink mobiles limits the system performance. We study
the impact of leveraging an out-of-band side-channel between mobiles in such
network under different channel models. For time-invariant channels, we aim to
characterize the generalized degrees- of-freedom (GDoF) of the side-channel
assisted MIMO full-duplex network. For slow-fading channels, we focus on the
diversity-multiplexing tradeoff (DMT) of the system with various assumptions as
to the availability of channel state information at the transmitter (CSIT). The
key to the optimal performance is a vector bin-and-cancel strategy leveraging
Han-Kobayashi message splitting, which is shown to achieve the system capacity
region to within a constant bit. We quantify how the side-channel improve the
GDoF and DMT compared to a system without the extra orthogonal spectrum. The
insights gained from our analysis reveal: i) the tradeoff between spatial
resources from multiple antennas at different nodes and spectral resources of
the side-channel, and ii) the interplay between the channel uncertainty at the
transmitter and use of the side-channel.


title:
A proper total coloring distinguishing adjacent vertices by sums of some
  product graphs

abstract:
  In this article, we consider a proper total coloring distinguishes adjacent
vertices by sums, if every two adjacent vertices have different total sum of
colors of the edges incident to the vertex and the color of the vertex.
Pilsniak and Wozniak \cite{PW} first introduced this coloring and made a
conjecture that the minimal number of colors need to have a proper total
coloring distinguishes adjacent vertices by sums is less than or equal to the
maximum degree plus $3$. We study proper total colorings distinguishing
adjacent vertices by sums of some graphs and their products. We find that these
graphs satisfy the conjecture.


title:
Dimensional Crossover of the Fulde-Ferrell-Larkin-Ovchinnikov State in
  Strongly Pauli-Limited Quasi-One-Dimensional Superconductors

abstract:
  The Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state is examined in
quasi-one-dimensional s-wave and d-wave superconductors with particular
attention paid to the effect of the Fermi-surface anisotropy. The upper
critical field $H_{c2}(T)$ is found to exhibit a qualitatively different
behavior depending on the ratio of the hopping energies $t_b/t_a$ and the
direction of the FFLO modulation vector q, where $t_a$ and $t_b$ are the intra-
and interchain hopping energies, respectively. In particular, when $t_b/t_a <
0.1$ and $\mathbf{q} \parallel \mathbf{a}$, we find a novel dimensional
crossover of $H_{c2}(T)$ from one dimension to two dimensions, where a is the
lattice vector of the most conductive chain. Just below the tricritical
temperature $T^*$, the upper critical field $H_{c2}(T)$ increases steeply as in
one-dimensional systems, but when the temperature decreases, the rate of
increase in $H_{c2}(T)$ diminishes and a shoulder appears. Near $T = 0$,
$H_{c2}(T)$ shows a behavior typical of the FFLO state in two-dimensional
systems, i.e., an upturn with a finite field at $T = 0$. When the angle between
$\mathbf{q}$ and $\mathbf{a}$ is large, the upper critical field curve is
convex upward at low temperatures, as in three-dimensional systems, but the
magnitude is much larger than that of a three-dimensional isotropic system. For
$t_b/t_a > 0.15$, the upper critical fields exhibit a two-dimensional behavior,
except for a slight shoulder in the range of $0.2 > t_b/t_a > 0.15$. The upper
critical field is maximum for $\mathbf{q} \parallel \mathbf{a}$ both for s-wave
and d-wave pairings, while it is only slightly larger than the Pauli
paramagnetic limit for $\mathbf{q} \perp \mathbf{a}$. The relevance of the
present results to the organic superconductor ${\rm (TMTSF)_2ClO_4}$ is
discussed.


title:
Possible Evolution of Antiferromagnetism in Zn-Doped Heavy-Fermion
  Superconductor CeCoIn5

abstract:
  We have succeeded in growing single crystals of the heavy-fermion
superconductor CeCo(In1-xZnx)5 with x<=0.07. Measurements of specific heat,
electrical resistivity, dc magnetization and ac susceptibility revealed that
the superconducting (SC) transition temperature Tc decreases from 2.25 K (x=0)
to 1.8 K (x=0.05) by doping Zn into CeCoIn5. Furthermore, these measurements
indicate a development of a new ordered phase below T_o ~ 2.2 K for x=>0.05,
characterized by the reduced magnetization and electrical resistivity in the
ordered phase, and the enhancement of specific heat at T_o. This phase
transition can be also recognized by the shoulder-like anomaly seen at H_o ~ 55
kOe in the field variations of the magnetization at low temperatures, which is
clearly distinguished from the superconducting critical fields Hc2=49 kOe for
x=0.05 and 42 kOe for x=0.07. We suggest from these results that the
antiferromagnetic (AFM) order is generated by doping Zn, and the interplay
between the SC and AFM orders is realized in CeCo(In1-xZnx)5.


title:
Measurement of e+e- to hadrons cross sections at BABAR, and implication
  for the muon g-2

abstract:
  The BaBar Collaboration has an intensive program of studying hadronic cross
sections at low-energy $e^+e^-$ collisions, accessible via initial-state
radiation. Our measurements allow significant improvements in the precision of
the predicted value of the muon anomalous magnetic moment, that shed light on
the current $\approx$ 3.5 sigma difference between the predicted and the
experimental values. We have published results on a number of processes with
two to six hadrons in the final state. We report here the results of recent
studies with the final states that constitute the main contribution to the
hadronic cross section below 3 GeV, as $e^+e^- \rightarrow \pi^+\pi^-$,
$K^+K^-$, $K_S K_L$ and $e^+e^- \rightarrow 4$ hadrons.


title:
Precise estimation of critical exponents from real-space renormalization
  group analysis

abstract:
  We develop a novel real-space renormalization group (RG) scheme which
accurately estimates correlation length exponent $\nu$ near criticality of
higher-dimensional quantum Ising and Potts models in a transverse field. Our
method is remarkably simple (often analytical), grouping only a few spins into
a block spin so that renormalized Hamiltonian has a closed form. A previous
difficulty of spatial anisotropy and unwanted terms is avoided by incorporating
rotational invariance and internal $\mathbb{Z}_q$ symmetries of the
Hamiltonian. By applying this scheme to the (2+1)-dim Ising model on a
triangular lattice and solving an analytical RG equation, we obtain $\nu\approx
0.6300$. This value is within statistical errors of the current best
Monte-Carlo result, 25th-order high-temperature series expansions,
$\phi^4$-theory estimation which considers up to seven-loop corrections and
experiments performed in low-Earth orbits. We also apply the scheme to
higher-dimensional Potts models for which ordinary Monte-Carlo methods are not
effective due to strong hysteresis and suppression of quantum fluctuation in a
weak first-order phase transition.


title: Almost-Ramanujan Graphs and Prime Gaps

abstract:
  The method of Murty and Cioab\u{a} shows how one can use results about gaps
between primes to construct families of almost-Ramanujan graphs. In this paper
we give a simpler construction which avoids the search for perfect matchings
and thus eliminates the need for computation. A couple of recent explicit
bounds on the gap between consecutive primes are then used to give the
construction of $k$-regular families with explicit lower bounds on the spectral
gaps. We then show that a result of Ben-Aroya and Ta-Shma can be improved using
our simpler construction on the assumption of the Riemann Hypothesis, which
sheds some more light on a question raised by Reingold, Vadhan and Widgerson.


title: Negative Optical Torque

abstract:
  Maxwell noted that light carries angular momentum, and as such it can exert
torques on material objects. This was subsequently proved by Beth in 1936.
Applications of these opto-mechanical effects were limited initially due to
their smallness in magnitude, but later enabled by the invention of laser.
Novel and practical approaches for harvesting light for particle rotation have
been demonstrated, where the structure is subjected to a positive optical
torque along a certain axis21 if the incident angular momentum has a positive
projection on the same axis. We report here a counter-intuitive phenomenon of
negative optical torque, meaning that incoming photons carrying angular
momentum rotate an object in the opposite sense. Surprisingly this can be
realized quite straightforwardly in simple planar structures. Field retardation
is a necessary condition. The optimal conditions are explored and explained.


title: Divide-And-Conquer Computation of Cylindrical Algebraic Decomposition

abstract:
  We present a divide-and-conquer version of the Cylindrical Algebraic
Decomposition (CAD) algorithm. The algorithm represents the input as a Boolean
combination of subformulas, computes cylindrical algebraic decompositions of
solution sets of the subformulas, and combines the results. We propose a
graph-based heuristic to find a suitable partitioning of the input and present
empirical comparison with direct CAD computation.


title: Exotic hadron production in hard exclusive reactions

abstract:
  We consider hard exclusive production of exotic hadrons to study their
internal structure. Revisiting the constituent-counting rule for the
large-angle exclusive scattering, we discuss general features expected for the
production cross section of exotic hadrons whose leading Fock states are given
by multi-quark states other than the ordinary baryon ($qqq$) or meson
($q\bar{q}$) states.We take the production of $\Lambda(1405)$ as an example and
propose to study its partonic configuration from the asymptotic scaling of the
cross section, which is measurable at J-PARC. We also discuss the production of
a pair of the light-hadrons such as $f_0(980)$s and $a_0(980)$s in
$\gamma^*\gamma$ collisions in the framework of QCD factorization, in which the
cross section is expressed as a convolution of the perturbative coefficients
and the generalized distribution amplitudes (GDAs). We demonstrate how the
internal structure of $f_0(980)$ or $a_0(980)$ can be explored by measuring the
GDAs at $e^+e^-$ experiments such as the B-factories.


title:
Symmetries of Multipartite Entanglement Evolution in Many-Sided Local
  Channels

abstract:
  Symmetries of the initial state of a quantum system and the quantum channels,
which simultaneously affect parts of the system, can significantly simplify the
description of the entanglement evolution. Using concurrence as the
entanglement measure, we study the entanglement evolution of few qubit systems,
when each of the qubits is affected by a local channel independently of the
others. We show that, for low-rank density matrices of the final quantum state,
such complex entanglement dynamics can be completely described by a combination
of independent factors representing the evolution of entanglement of the
initial state, when just one of the qubits is affected by a local channel.


title: Complete Tidal Evolution of Pluto-Charon

abstract:
  Both Pluto and its satellite Charon have rotation rates synchronous with
their orbital mean motion. This is the theoretical end point of tidal evolution
where transfer of angular momentum has ceased. Here we follow Pluto's tidal
evolution from an initial state having the current total angular momentum of
the system but with Charon in an eccentric orbit with semimajor axis $a \approx
4R_P$ (where $R_P$ is the radius of Pluto), consistent with its impact origin.
Two tidal models are used, where the tidal dissipation function $Q \propto$
1/frequency and $Q=$ constant, where details of the evolution are strongly
model dependent. The inclusion of the gravitational harmonic coefficient
$C_{22}$ of both bodies in the analysis allows smooth, self consistent
evolution to the dual synchronous state, whereas its omission frustrates
successful evolution in some cases. The zonal harmonic $J_2$ can also be
included, but does not cause a significant effect on the overall evolution. The
ratio of dissipation in Charon to that in Pluto controls the behavior of the
orbital eccentricity, where a judicious choice leads to a nearly constant
eccentricity until the final approach to dual synchronous rotation. The tidal
models are complete in the sense that every nuance of tidal evolution is
realized while conserving total angular momentum - including temporary capture
into spin-orbit resonances as Charon's spin decreases and damped librations
about the same.


title: Starobinsky Model in N=2 Supergravity

abstract:
  The Starobinsky inflationary model of $(R+R^2)$ gravity is extended to the
new-minimal (40+40) off-shell 4D, N=2 supergravity in N=2 chiral (curved)
superspace. In its dual formulation, as a matter-coupled N=2 Einstein
supergravity, inflaton belongs to a massive N=2 vector multiplet.


title: Natural Inflation and Universal Hypermultiplet

abstract:
  A novel framework is proposed for embedding the natural inflation into the
type IIA superstrings compactified on a Calabi-Yau (CY) three-fold. Inflaton is
identified with axion of the Universal Hypermultiplet (UH). The other UH
scalars (including dilaton) are stabilized by the CY fluxes whose impact can be
described by gauging of the abelian isometry associated with the axion, when
the NS5-brane instanton contributions are suppressed. Then the stabilizing
scalar potential is controlled by the integrable three-dimensional Toda
equation, and leads to spontaneous N=2 SUSY breaking. The inflationary scalar
potential of the UH axion is dynamically generated at a lower scale in the
natural inflation via the non-perturbative quantum field effects such as
gaugino condensation. The natural inflation has two scales that allow any
values of the CMB observables $(n_s,r)$.


title: Implications of non minimal lepton mass textures for Dirac neutrinos

abstract:
  In the light of the recent measurement of the leptonic mixing angle
$\theta_{13}$, implications of the latest mixing data have been investigated
for non-minimal textures of lepton mass matrices pertaining to Dirac neutrinos.
All these texture specific lepton mass matrices have been examined for their
compatibility with the latest data in the cases of normal hierarchy, inverted
hierarchy and degenerate scenario of neutrino masses. The implications of all
the three lepton mixing angles have been investigated on the lightest neutrino
mass as well as the Jarlskog's CP violating parameter in the leptonic sector.


title: Ramsey fringes in a thermal beam of Yb atoms

abstract:
  We use the Ramsey separated oscillatory fields (SOF) technique in a
$400^\circ$C thermal beam of Yb atoms to measure the Larmor precession
frequency with high precision. For the experiment, we use the strongly-allowed
${^1S_0} \rightarrow {^1P_1}$ transition at $399$ nm, and choose the odd
isotope $^{171}$Yb with nuclear spin $I=1/2$, so that the ground state has only
two magnetic sublevels $m_F = \pm 1/2$. With a magnetic field of $22.2$ G and a
separation of about $400$ mm between the oscillatory fields, the central Ramsey
fringe is at $16.64$ kHz and has a width of $350$ Hz. The technique can be
readily adapted to a cold atomic beam, and should be useful in experiments
searching for a permanent electric dipole moment (EDM) in atoms.


title:
Controlling Citizens Cyber Viewing Using Enhanced Internet Content
  Filters

abstract:
  Information passing through internet is generally unrestricted and
uncontrollable and a good web content filter acts very much like a sieve. This
paper looks at how citizens internet viewing can be controlled using content
filters to prevent access to illegal sites and malicious contents in Nigeria.
Primary data were obtained by administering 100 questionnaires. The data was
analyzed by a software package called Statistical Package for Social Sciences
(SPSS). The result of the study shows that 66.4% of the respondents agreed that
the internet is been abused and the abuse can be controlled by the use of
content filters. The PHP, MySQL and Apache were used to design a content filter
program. It was recommended that a lot still need to be done by public
organizations, academic institutions, government and its agencies especially
the Economic and Financial Crime Commission (EFCC) in Nigeria to control the
internet abuse by the under aged and criminals.


title:
LWRP: Low Power Consumption Weighting Replacement Policy using Buffer
  Memory

abstract:
  As the performance gap between memory and processors has increased, then it
leads to the poor performance. Efficient virtual memory can overcome this
problem. And the efficiency of virtual memory depends on the replacement policy
used for cache. In this paper, our algorithm not only based on the time to last
access and frequency index but, we also consider the power consumption. We show
that Low Power Consumption Weighting Replacement Policy (LWRP) has better
performance and low power consumption.


title:
MUSIC: A Hybrid Computing Environment for Burrows-Wheeler Alignment for
  Massive Amount of Short Read Sequence Data

abstract:
  High-throughput DNA sequencers are becoming indispensable in our
understanding of diseases at molecular level, in marker-assisted selection in
agriculture and in microbial genetics research. These sequencing instruments
produce enormous amount of data (often terabytes of raw data in a month) that
requires efficient analysis, management and interpretation. The commonly used
sequencing instrument today produces billions of short reads (upto 150 bases)
from each run. The first step in the data analysis step is alignment of these
short reads to the reference genome of choice. There are different open source
algorithms available for sequence alignment to the reference genome. These
tools normally have a high computational overhead, both in terms of number of
processors and memory. Here, we propose a hybrid-computing environment called
MUSIC (Mapping USIng hybrid Computing) for one of the most popular open source
sequence alignment algorithm, BWA, using accelerators that show significant
improvement in speed over the serial code.


title: On a finite 2,3-generated group of period 12

abstract:
  Using calculations in computer algebra systems along with some theoretic
results, we construct the largest finite group of period 12 generated by an
element of order 2 and an element of order 3. In particular, we prove that this
group has order $2^{66}.3^7$.


title: Multiple choice of gauge generators and consistency of interactions

abstract:
  It is usually assumed that any consistent interaction either deforms or
retains the gauge symmetries of the corresponding free theory. We propose a
simple model where an obvious irreducible gauge symmetry does not survive an
interaction, while the interaction is consistent as it preserves the number of
physical degrees of freedom. The model turns out admitting a less obvious
reducible set of gauge generators which is compatible with the interaction and
smooth in coupling constant. Possible application to gravity models is
discussed.


title: Generalization and Exploration via Randomized Value Functions

abstract:
  We propose randomized least-squares value iteration (RLSVI) -- a new
reinforcement learning algorithm designed to explore and generalize efficiently
via linearly parameterized value functions. We explain why versions of
least-squares value iteration that use Boltzmann or epsilon-greedy exploration
can be highly inefficient, and we present computational results that
demonstrate dramatic efficiency gains enjoyed by RLSVI. Further, we establish
an upper bound on the expected regret of RLSVI that demonstrates
near-optimality in a tabula rasa learning context. More broadly, our results
suggest that randomized value functions offer a promising approach to tackling
a critical challenge in reinforcement learning: synthesizing efficient
exploration and effective generalization.


title:
Asymptotic expansion of the expected volume of the Wiener sausage in
  even dimensions

abstract:
  We consider the Wiener sausage for a Brownian motion up to time $t$
associated with a closed ball in even dimensional cases. We obtain the
asymptotic expansion of the expected volume of the Wiener sausage for large
$t$. The result says that the expansion has many $\log$ terms, which do not
appear in odd dimensional cases.


title:
Modeling of a tunable-barrier non-adiabatic electron pump beyond the
  decay cascade model

abstract:
  We generalize the decay cascade model of charge capture statistics for a
tunable-barrier non-adiabatic electron pump dominated by the backtunneling
error at the quantum dot decoupling stage. The energy scales controlling the
competition between the thermal and the dynamical mechanisms for accurate
trapped charge quantization are discussed. Empirical fitting formula
incorporating quantum dot re-population errors due to particle-hole
fluctuations in the source lead is suggested and tested against an exactly
solvable rate equation model.


title:
Status and motivation of Raman LIDARs development for the CTA
  Observatory

abstract:
  The Cherenkov Telescope Array (CTA) is the next generation of Imaging
Atmospheric Cherenkov Telescopes. It would reach unprecedented sensitivity and
energy resolution in very-high-energy gamma-ray astronomy. In order to reach
these goals, the systematic uncertainties derived from the varying atmospheric
conditions shall be reduced to the minimum. Different instruments may help to
account for these uncertainties. Several groups in the CTA consortium are
currently building Raman LIDARs to be installed at the CTA sites. Raman LIDARs
are devices composed of a powerful laser that shoots into the atmosphere, a
collector that gathers the backscattered light from molecules and aerosols, a
photosensor, an optical module that spectrally select wavelengths of interest,
and a read-out system. Raman LIDARs can reduce the systematic uncertainties in
the reconstruction of the gamma-ray energies down to 5 % level. All Raman
LIDARs subject of this work, have design features that make them different than
typical Raman LIDARs used in atmospheric science, and are characterized by
large collecting mirrors ($\sim2~$m$^2$). They have multiple elastic and Raman
read-out channels (at least 4) and custom-made optics design. In this paper,
the motivation for Raman LIDARs, the design and the status of advance of these
technologies are described.


title: Monotonicity properties of some Dini functions

abstract:
  In this note our aim is to deduce some new monotonicity properties for a
special combination of Bessel functions of the first kind by using a recently
developed Mittag-Leffler expansion for the derivative of a normalized Bessel
function of the first kind. These monotonicity properties are used to obtain
some new inequalities for Bessel functions of the first kind.


title:
Alignment-free comparison of next-generation sequencing data using
  compression-based distance measures

abstract:
  Enormous volumes of short reads data from next-generation sequencing (NGS)
technologies have posed new challenges to the area of genomic sequence
comparison.
  The multiple sequence alignment approach is hardly applicable to NGS data due
to the challenging problem of short read assembly.
  Thus alignment-free methods need to be developed for the comparison of NGS
samples of short reads.
  Recently, new $k$-mer based distance measures such as {\it CVTree},
$d_{2}^{S}$, {\it co-phylog} have been proposed to address this problem.
  However, those distances depend considerably on the parameter $k$, and how to
choose the optimal $k$ is not trivial since it may depend on different aspects
of the sequence data.
  Hence, in this paper we consider an alternative parameter-free approach:
compression-based distance measures.
  These measures have shown impressive performance on long genome sequences in
previous studies, but they have not been tested on NGS short reads.
  In this study we perform extensive validation and show that the
compression-based distances are highly consistent with those distances obtained
from the $k$-mer based methods, from the alignment-based approach, and from
existing benchmarks in the literature.
  Moreover, as these measures are parameter-free, no optimization is required
and they still perform consistently well on multiple types of sequence data,
for different kinds of species and taxonomy levels.
  The compression-based distance measures are assembly-free, alignment-free,
parameter-free, and thus represent useful tools for the comparison of long
genome sequences and NGS samples of short reads.


title:
Bounds on pressure profile and internal compactness of static perfect
  fluid spheres with a positive cosmological constant

abstract:
  Some theorems for a static prefect fluid sphere, i.e. a star, in the presence
of a positive cosmological constant are proved. These theorems put bounds on
the pressure profile and internal compactness of the star.


title:
kappa_SQ: A Matlab package for randomized sampling of matrices with
  orthonormal columns

abstract:
  The kappa_SQ software package is designed to assist researchers working on
randomized row sampling. The package contains a collection of Matlab functions
along with a GUI that ties them all together and provides a platform for the
user to perform experiments. In particular, kappa_SQ is designed to do
experiments related to the two-norm condition number of a sampled matrix,
$\kappa(SQ)$, where $S$ is a row sampling matrix and $Q$ is a tall and skinny
matrix with orthonormal columns. Via a simple GUI, kappa_SQ can generate test
matrices, perform various types of row sampling, measure $\kappa(SQ)$,
calculate bounds and produce high quality plots of the results. All of the
important codes are written in separate Matlab function files in a standard
format which makes it easy for a user to either use the codes by themselves or
incorporate their own codes into the kappa_SQ package.


title:
Faster Algorithms for Multivariate Interpolation with Multiplicities and
  Simultaneous Polynomial Approximations

abstract:
  The interpolation step in the Guruswami-Sudan algorithm is a bivariate
interpolation problem with multiplicities commonly solved in the literature
using either structured linear algebra or basis reduction of polynomial
lattices. This problem has been extended to three or more variables; for this
generalization, all fast algorithms proposed so far rely on the lattice
approach. In this paper, we reduce this multivariate interpolation problem to a
problem of simultaneous polynomial approximations, which we solve using fast
structured linear algebra. This improves the best known complexity bounds for
the interpolation step of the list-decoding of Reed-Solomon codes,
Parvaresh-Vardy codes, and folded Reed-Solomon codes. In particular, for
Reed-Solomon list-decoding with re-encoding, our approach has complexity
$\mathcal{O}\tilde{~}(\ell^{\omega-1}m^2(n-k))$, where $\ell,m,n,k$ are the
list size, the multiplicity, the number of sample points and the dimension of
the code, and $\omega$ is the exponent of linear algebra; this accelerates the
previously fastest known algorithm by a factor of $\ell / m$.


title: Ricci curvature on polyhedral surfaces via optimal transportation

abstract:
  The problem of defining correctly geometric objects such as the curvature is
a hard one in discrete geometry. In 2009, Ollivier defined a notion of
curvature applicable to a wide category of measured metric spaces, in
particular to graphs. He named it coarse Ricci curvature because it coincides,
up to some given factor, with the classical Ricci curvature, when the space is
a smooth manifold. Lin, Lu & Yau, Jost & Liu have used and extended this notion
for graphs giving estimates for the curvature and hence the diameter, in terms
of the combinatorics. In this paper, we describe a method for computing the
coarse Ricci curvature and give sharper results, in the specific but crucial
case of polyhedral surfaces.


title: Local Gaussian Regression

abstract:
  Locally weighted regression was created as a nonparametric learning method
that is computationally efficient, can learn from very large amounts of data
and add data incrementally. An interesting feature of locally weighted
regression is that it can work with spatially varying length scales, a
beneficial property, for instance, in control problems. However, it does not
provide a generative model for function values and requires training and test
data to be generated identically, independently. Gaussian (process) regression,
on the other hand, provides a fully generative model without significant formal
requirements on the distribution of training data, but has much higher
computational cost and usually works with one global scale per input dimension.
Using a localising function basis and approximate inference techniques, we take
Gaussian (process) regression to increasingly localised properties and toward
the same computational complexity class as locally weighted regression.


title:
Rashba coupling induced spin susceptibility and magnetic phase
  transition of conduction electrons in monolayer graphene

abstract:
  Using the Kubo formalism, the magnetic properties of the system in the linear
regime have been investigated. Mainly the effect of non-magnetic substrate on
the spin susceptibility is calculated. Results show that the Rashba coupling
interaction has a central role in the magnetic response function of the system
and it is really remarkable since this type of spin orbit coupling can be
effectively controlled by an external gate voltage. Most importantly it was
shown that, in the presence of the Rashba interaction a magnetic phase
transition could be observed. This magnetic phase corresponds to a magnetic
order of conduction electrons that takes place at some especial frequency of
external magnetic field.