pinecone/core-2020-05-10-deduplication huggingface dataset:
Low-rank tensor methods for model order reduction

Parameter-dependent models arise in many contexts such as uncertainty
quantification, sensitivity analysis, inverse problems or optimization.
Parametric or uncertainty analyses usually require the evaluation of an output
of a model for many instances of the input parameters, which may be intractable
for complex numerical models. A possible remedy consists in replacing the model
by an approximate model with reduced complexity (a so called reduced order
model) allowing a fast evaluation of output variables of interest. This chapter
provides an overview of low-rank methods for the approximation of functions
that are identified either with order-two tensors (for vector-valued functions)
or higher-order tensors (for multivariate functions). Different approaches are
presented for the computation of low-rank approximations, either based on
samples of the function or on the equations that are satisfied by the function,
the latter approaches including projection-based model order reduction methods.
For multivariate functions, different notions of ranks and the corresponding
low-rank approximation formats are introduced.Comment: To appear as a book chapte
Rethinking place branding and place making through creative and cultural quarters

Cultural and creative quarters—from historic districts to new digital hubs—feature heavily in economic development and cluster studies, but their role in place making and branding is only recently being discovered. Essentially production based, they also tend to locate in industrial\ud
areas of cities which are also the subject of regeneration and transformation. As cities seek towiden their brand offer and diversify the range of destinations to a discerning visitor and residential market (local, domestic and international), these areas, which combine work and\ud
play, represent a distinct place brand. Through city case studies from several countries, this chapter critiques the emergence of the creative quarter through organic place making and their value as a place brand in areas of the city which have not traditionally been considered a\ud
destination. How far policy and planning can support and protect these post/new industrial quarters is also discussed, including the concomitant risks of gentrification and commodification that branding can infer
Introduction to the Analytic Hierarchy Process

The Analytic Hierarchy Process (AHP) has been one of the foremost mathematical methods for decision making with multiple criteria and has been widely studied in the operations research literature as well as applied to solve countless real-world problems. This book is meant to introduce and strengthen the readers’ knowledge of the AHP, no matter how familiar they may be with the topic.
This book provides a concise, yet self-contained, introduction to the AHP that uses a novel and more pedagogical approach. It begins with an introduction to the principles of the AHP, covering the critical points of the method, as well as some of its applications. Next, the book explores further aspects of the method, including the derivation of the priority vector, the estimation of inconsistency, and the use of AHP for group decisions. Each of these is introduced by relaxing initial assumptions. Furthermore, this booklet covers extensions of AHP, which are typically neglected in elementary expositions of the methods. Such extensions concern different numerical representations of preferences and the interval and fuzzy representations of preferences to account for uncertainty. During the whole exposition, an eye is kept on the most recent developments of the method.Peer reviewe
Practical Genericity: Writing Image Processing Algorithms Both Reusable and Efficient

International audienceAn important topic for the image processing and pattern recognition community is the construction of open source and efficient libraries. An increasing number of software frameworks are said to be generic: they allow users to write reusable algorithms compatible with many input image types. However, this design choice is often made at the expense of performance. We present an approach to preserve efficiency in a generic image processing framework, by leveraging data types features. Variants of generic algorithms taking advantage of image types properties can be defined, offering an adjustable trade-off between genericity and efficiency. Our experiments show that these generic optimizations can match dedicated code in terms of execution times, and even sometimes perform better than routines optimized by hand
Using time-sensitive rooted PageRank to detect hierarchical social relationships

We study the problem of detecting hierarchical ties in a social network by exploiting the interaction patterns between the actors (members) involved in the network. Motivated by earlier work using a rank-based approach, i.e., Rooted-PageRank, we introduce a novel time-sensitive method, called T-RPR, that captures and exploits the dynamics and evolution of the interaction patterns in the network in order to identify the underlying hierarchical ties. Experiments on two real datasets demonstrate the performance of T-RPR in terms of recall and show its superiority over a recent competitor method
The BioKET Biodiversity Data Warehouse: Data and Knowledge Integration and Extraction

International audienceBiodiversity datasets are generally stored in different for-mats. This makes it difficult for biologists to combine and integrate them to retrieve useful information for the purpose of, for example, efficiently classify specimens. In this paper, we present BioKET, a data warehouse which is a consolidation of heterogeneous data sources stored in different formats. For the time being, the scopus of BioKET is botanical. We had, among others things, to list all the existing botanical ontologies and re-late terms in BioKET with terms in these ontologies. We demonstrate the usefulness of such a resource by applying FIST, a combined biclus-tering and conceptual association rule extraction method on a dataset extracted from BioKET to analyze the risk status of plants endemic to Laos. Besides, BioKET may be interfaced with other resources, like GeoCAT, to provide a powerful analysis tool for biodiversity data
Exploiting novel properties of space-filling curves for data analysis

Using space-filling curves to order multidimensional data  has been found to be useful in a variety of application domains.\ud
This paper examines the  space-filling curve induced ordering of multidimensional data that has been transformed using shape  preserving transformations. It is demonstrated that, although the orderings are not invariant under these transformations, the probability of an ordering is dependent on the geometrical configuration of the multidimensional data. This novel property extends the potential applicability of space-filling curves and is demonstrated by constructing novel features for shape matching
Exploiting the network for securing personal devices

Personal devices (such as smartphones and laptops) often experience incoherent levels of security due to the different protection applications available on the various devices. This paper presents a novel approach that consists in offloading security applications from personal devices and relocating them inside the network; this will be achieved by enriching network devices with the appropriate computational capabilities to execute generic security applications. This approach is fostered by the Secured project, which will define the architecture, data and protocols needed to turn this vision into realit
Secure message delivery games for device-to-device communications

Device-to-Device (D2D) communication is expected to be a key feature supported by next generation cellular networks. D2D can extend the cellular coverage allowing users to communicate when telecommunications infrastructure are highly congested or absent. In D2D networks, any message delivery from a source to a destination relies exclusively on intermediate devices. Each device can run different kinds of mobile security software, which offer protection against viruses and other harmful programs by using real-time scanning in every file entering the device. In this paper, we investigate the best D2D network path to deliver a potentially malicious message from a source to a destination. Although our primary objective is to increase security, we also investigate the contribution of energy costs and quality-of-service to the path selection. To this end, we propose the Secure Message Delivery (SMD) protocol, whose main functionality is determined by the solution of the Secure Message Delivery Game (SMDG). This game is played between the defender (i.e., the D2D network) which abstracts all legitimate network devices and theattackerwhich abstracts any adversary that can inject different malicious messages into the D2D network in order, for instance, to infect a device with malware. Simulation results demonstrate the degree of improvement that SMD introduces as opposed to a shortest path routing protocol. This improvement has been measured in terms of the defender’s expected cost as defined in SMDGs. This cost includes security expected damages, energy consumption incurred due to messages inspection, and the quality-of-service of the D2D message communications
Cybersecurity Games and Investments: A Decision Support Approach

In this paper we investigate how to optimally invest in cybersecurity controls. We are particularly interested in examining cases where the organization suffers from an underinvestment problem or inefficient spending on cybersecurity. To this end, we first model the cybersecurity environment of an organization. We then model non-cooperative cybersecurity control-games between the defender which abstracts all defense mechanisms of the organization and the attacker which can exploit different vulnerabilities at different network locations. To implement our methodology we use the SANS Top 20 Critical Security Controls and the 2011 CWE/SANS top 25 most dangerous software errors. Based on the profile of an organization, which forms its preferences in terms ofindirect costs, its concerns about different kinds of threats and the importance of the assets given their associated risks we derive the Nash Equilibria of a series of control-games. These game solutions are then handled by optimization techniques, in particular multi-objective, multiple choice Knapsack to determine the optimal cybersecurity investment. Our methodology provides security effective and cost efficient solutions especially against commodity attacks. We believe our work can be used to advise security managers on how they should spend an available cybersecurity budget given their organization profile
Synthetic Test Data Generation for Hierarchical Graph Clustering Methods

Recent achievements in graph-based clustering algorithms\ud
revealed the need for large-scale test data sets. This paper introduces a\ud
procedure that can provide synthetic but realistic test data to the hi-\ud
erarchical Markov clustering algorithm. Being created according to the\ud
structure and properties of the SCOP95 protein sequence data set, the\ud
synthetic data act as a collection of proteins organized in a four-level\ud
hierarchy and a similarity matrix containing pairwise similarity values\ud
of the proteins. An ultimate high-speed TRIBE-MCL algorithm was em-\ud
ployed to validate the synthetic data. Generated data sets have a healthy\ud
amount of variability due to the randomness in the processing, and are\ud
suitable for testing graph-based clustering algorithms on large-scale data
Sensor Drift Compensation Using Fuzzy Interference System and Sparse-Grid Quadrature Filter in Blood Glucose Control

Diabetes mellitus is a serious chronic condition of the human\ud
metabolism. The development of an automated treatment has reached\ud
clinical phase in the last few years. The goal is to keep the blood glucose\ud
concentration within a certain region with minimal interaction required\ud
by the patient or medical personnel. However, there are still several prac-\ud
tical problems to solve. One of these would be that the available sensors\ud
have significant noise and drift. The latter is rather difficult to manage,\ud
because the deviating signal can cause the controller to drive the glu-\ud
cose concentration out of the safe region even in the case of frequent\ud
calibration. In this study a linear-quadratic-Gaussian (LQG) controller\ud
is employed on a widely used diabetes model and enhanced with an ad-\ud
vanced Sparse-grid quadratic filter and a fuzzy interference system-based\ud
calibration supervisor
GREENLION Project: Advanced  Manufacturing Processes for Low Cost Greener Li-Ion Batteries

GREENLION is a Large Scale Collaborative Project within the FP7\ud
 (GC.NMP.2011-1) leading to the manufacturing of greener and cheaper Li-Ion batteries for electric vehicle applications via the use of water soluble, fluorine-free, high thermally stable binders, which would eliminate the use of VOCs and reduce the cell assembly cost. The project has 6 key objectives: (i) development of new active and inactive battery materials viable for water processes (green chemistry); (ii) development of innovative processes (coating from aqueous slurries) capable of reducing electrode production cost and avoid environmental pollution; (iii) development of new assembly procedures (including laser cutting and high temperature pre-treatment) capable of substantially reduce the time and the cost of cell fabrication; (iv) lighter battery modules with easier disassembly through eco-designed bonding techniques; (v) waste reduction, which, by making use of the watersolubility of the binder, allows the extensive recovery of the active and inactive battery materials; and (vi) development of automated process and construction of fully integrated battery module for electric vehicle applications with optimized electrodes, cells, and other ancillaries. Achievements during the first 18 months of the project, especially on materials development and water-based electrode fabri cation are reported herein
A Precise and Abstract Memory Model for C Using Symbolic Values

International audienceReal life C programs are often written using C dialects which, for the ISO C standard, have undefined behaviours. In particular, according to the ISO C standard, reading an uninitialised variable has an undefined behaviour and low-level pointer operations are implementation defined. We propose a formal semantics which gives a well-defined meaning to those behaviours for the C dialect of the CompCert compiler. Our semantics builds upon a novel memory model leveraging a notion of symbolic values. Symbolic values are used by the semantics to delay the evaluation of operations and are normalised lazily to genuine values when needed. We show that the most precise normalisation is computable and that a slightly relaxed normalisation can be efficiently implemented using an SMT solver. The semantics is executable and our experiments show that the enhancements of our semantics are mandatory to give a meaning to low-levels idioms such as those found in the allocation functions of a C standard library
Logic Programming and Logarithmic Space

International audienceWe present an algebraic view on logic programming, related to proof theory and more specifically linear logic and geometry of interaction. Within this construction, a characterization of logspace (deterministic and non-deterministic) computation is given via a syntactic restriction, using an encoding of words that derives from proof theory. We show that the acceptance of a word by an observation (the counterpart of a program in the encoding) can be decided within logarithmic space, by reducing this problem to the acyclicity of a graph. We show moreover that observations are as expressive as two-ways multi-head finite automata, a kind of pointer machine that is a standard model of logarithmic space computation
On RAF Sets and Autocatalytic Cycles in Random Reaction Networks

The emergence of autocatalytic sets of molecules seems to have played an
important role in the origin of life context. Although the possibility to
reproduce this emergence in laboratory has received considerable attention,
this is still far from being achieved. In order to unravel some key properties
enabling the emergence of structures potentially able to sustain their own
existence and growth, in this work we investigate the probability to observe
them in ensembles of random catalytic reaction networks characterized by
different structural properties. From the point of view of network topology, an
autocatalytic set have been defined either in term of strongly connected
components (SCCs) or as reflexively autocatalytic and food-generated sets
(RAFs). We observe that the average level of catalysis differently affects the
probability to observe a SCC or a RAF, highlighting the existence of a region
where the former can be observed, whereas the latter cannot. This parameter
also affects the composition of the RAF, which can be further characterized
into linear structures, autocatalysis or SCCs. Interestingly, we show that the
different network topology (uniform as opposed to power-law catalysis systems)
does not have a significantly divergent impact on SCCs and RAFs appearance,
whereas the proportion between cleavages and condensations seems instead to
play a role. A major factor that limits the probability of RAF appearance and
that may explain some of the difficulties encountered in laboratory seems to be
the presence of molecules which can accumulate without being substrate or
catalyst of any reaction.Comment: pp 113-12
A log analysis study of 10 years of ebook consumption in academic library collections

Even though libraries have been offering eBooks for more than a decade, very little is known about eBook access and consumption in academic library collections. This paper addresses this gap with a log analysis study of eBook access at the library of the University of Waikato. This in-depth analysis covers a period spanning 10 years of eBook use at this university. We draw conclusions about the use of eBooks at this institution and compare the results with other published studies of eBook usage at tertiary institutes
Family visits to libraries and bookshops: observations and implications for digital libraries

This paper explores how families select books for leisure reading. We recruited 17 families (adults and children) for this study, and spent time with each in both bookshops and public libraries. Our research aims to add to understanding of how families interact with books and bookshelves in these places, and how digital libraries might best support the shared needs of these inter-generational users. Much of our understanding of how an eBook should look and feel comes from generalizations about books and assumptions about the needs of those individuals who read them. We explore how children and adults search and browse for books together, with specific focus on the type of information seeking tasks that families undertake and on the families’ shared search and browsing strategies. We further explore the implications of this study for the development of digital libraries for children and families
Shared reading of children's interactive picture books

We report on a study of children and parents shared reading of interactive printed books. We investigated the differences between books with interactive features and books with expressive typography in order to evaluate which features within a book encouraged interaction between the reading participants and the book. 11 parent and child groups took part in the study that involved three observed reading sessions. From our observations we offer suggestions for the development of books and eBooks to encourage shared reading practices
A New Combination Method Based on Adaptive Genetic Algorithm for Medical Image Retrieval

Medical image retrieval could be based on the text describing the image as the caption or the title. The use of text terms to retrieve images have several disadvantages such as term-disambiguation. Recent studies prove that representing text into semantic units (concepts) can improve the semantic representation of textual information. However, the use of conceptual representation has other problems as the miss or erroneous semantic relation between two concepts. Other studies show that combining textual and conceptual text representations leads to better accuracy. Popularly, a score for textual representation and a score for conceptual representation are computed and then a combination function is used to have one score. Although the existing of many combination methods of two scores, we propose in this paper a new combination method based on adaptive version of the genetic algorithm. Experiments are carried out on Medical Information Retrieval Task of the ImageCLEF 2009 and 2010. The results confirm that the combination of both textual and conceptual scores allows best accuracy. In addition, our approach outperforms the other combination methods
Design of low-cost sensors for industrial pocesses energy consumption measurement. Application to the gas flow consumed by a boiler

International audienceThe demand for energy is becoming increasingly important, and who says strong demands for energy says rising CO 2 emissions. Everyone agrees that a great part of the energy consumed by industry and households can be saved. The energy savings can take many forms. In addition to the necessity to build equipments more and more energy efficient, it is also necessary to get a clear view of how the energy is used. This obviously involves the implementation of an energy flow measuring system for long lasting optimization solutions. It is precisely in this context that the project CHIC (Low cost industry utilities monitoring systems for energy savings), funded by the French National Research Agency (ANR), emerged. The objective of this project is to develop and test low-cost non-intrusive sensors to monitor and analyze the energy consumption of major flows used in the manufacturing sector (electricity, gas, compressed air). With such sensors, it should be possible to tool up a factory, equipment by equipment, which is not feasible with intrusive sensors. The ultimate goal is the long term consumption monitoring and the detection of the consumption deviations rather than a precise measurement. The measurement accuracy is fixed to 5%. These developments are based on the recent approaches in system identification and parametric estimation. This project, concretely, involves the design of new low-cost sensors in the following areas: current sensors, voltage, power, and gas flow, relying on the international ISO 50001 standard for Energy Management Systems. The work presented in this chapter focuses on the modeling of the gas flow supplied to a boiler in order to implement a soft sensor. This implementation requires the estimation of a mathematical model that expresses the flow rate from the control signal of the solenoid valve and the gas pressure and temperature measurements. Two types of models are studied: LPV (Linear Parameter Varying) model with pressure and temperature as scheduling variables and a non-parametric model based on Gaussian processes