Miracle-dz/UniFAR-FTUs huggingface dataset:
devant le jury composé de:

For several biomedical issues, including neurobiology, imaging with high spatial and temporal resolution in a physiologically relevant cellular context is necessary. To address these requirements, we have developed an original setup which associates total internal reflection fluorescence (TIRF) and fluorescence lifetime imaging microscopy (FLIM). Indeed, FLIM allows a dynamic analysis of molecular and metabolic processes within cells. Combined with TIRF microscopy, which provides a sub-wavelength axial resolution, FLIM enables to analyze the activity of cell membrane, with efficient fluorescent background rejection. The first aim of this project was to develop a fluorescence excitation source dedicated to lifetime measurement in time domain. We worked on a solution based on a solid state picosecond laser, and we used microstructured optical fibers to enlarge the spectrum by non linear effects. The second aspect of this work was the development and the characterization of the time-gated total internal reflection fluorescence microscope, which allows to measure fluorescence lifetime in wide field. Then, we used this setup to study using FRET probes the homodimerization of amyloid precursor protein (APP), which plays a crucial role in Alzheimer disease. te l-0 03 76 11 6, v er si on 1 16 A pr 2 00 9
Working Paper

In this article I analyse how the multinational oil company Shell has responded to the increasing institutional pressures (DiMaggio and Powell, 1983) related to corporate environmental governance. The corporate culture in Shell appears favourable (Hoffman, 2001) towards the adoption of corporate environmental governance practices. The Shell top management is to this end appearing sincere in the way they monitor (Meyer and Rowan, 1977) the progress in giving secondary stakeholders (Clarkson, 1995) access to environmental information and to environmental decisionmaking in Shell. Based on the Shell case I contribute in this article to descriptive stakeholder engagement theory by conceptualising a number of new internal influence strategies that engaged secondary stakeholders can use in their new face-to-face interactions with the corporations. These internal stakeholder influence strategies should be seen as adding to the list of external stakeholder influence strategies (e.g. Frooman, 1999) that secondary stakeholders can use in their traditional role of operating from the outside.
Visioning Workshop held May 30 – June 1 , 2018

There are a number of open challenges and research issues that need to be addressed both in the short and long term to ensure sustained storage systems efficacy and performance. The wide variety of applications of modern and emerging storage systems entail that the fundamental design of storage systems should be revisited to support application-specific and application-defined semantics. Such tailored design will address many of the shortcomings of using the extant one-size-for-all generic approach that is plagued with inefficiencies both in performance and storage capacity overhead. Another aspect is to reevaluate the use of standards and APIs such as POSIX, and design new sustainable data representations and APIs to support emerging applications such as IoT. Such redesign of the storage architecture is gradually being explored. For example, systems such as key-value and object stores are being used to design application-specific solutions. Management of metadata, indexing, and high-speed transactions for small data items are key to next generation storage systems as well. Nevertheless, more new methods and techniques are needed to support a wider range of emerging applications. This prompts a more general discussion on application data management and storage system co-design. Many important applications now arise in the space of Artificial Intelligence (AI), such as model training frameworks, and their needs are met poorly by current storage systems. New storage techniques and technologies tailored for AI workloads and AI usages of data are much needed. At the same time, AI-based methods hold great potential for building intelligent storage systems for meeting the challenging application demands and optimizing storage management as the systems become increasingly complex. Hardware advances are further driving the way storage systems are realized. Storage hybridization and heterogeneity is now a part of most large-scale storage deployments. However, the software subsystems for supporting and using such systems are lagging, and new management systems need to be designed and invented. Similarly, in-memory storage systems and persistent memory systems are giving rise to a new class of memory-only storage solutions. While such systems can be thought of as simply a tier of a traditional storage hierarchy, there is clearly a need for innovation to leverage the unique opportunities offered by new hardware advancements and designing the storage layer to maximize efficiency and performance. At the same time, the exponential growth of cold archival and cold primary data motivates intensive research of new hardware and software architectures that can store petabytes of data at the maximum possible cost-efficiency.
Contracting with Limited Commitment: Evidence from Employment-Based Health Insurance Contracts

Impediments to worker mobility serve to mitigate the attrition of healthy individuals from employer-sponsored insurance pools, thereby creating a de facto commitment mechanism that allows for more complete insurance of health risks than would be possible in the absence of such frictions. Using data on health insurance contracts obtained from the 1987 National Medical Expenditure Survey, we find that the quantity of insurance provided is positively related to the degree of worker commitment. These results illustrate the importance of commitment in the design of long-term contracts, and provide an additional rationale for the bundling of health insurance with employment.
Forthcoming: Emerging Markets Review Dove or Hawk? Characterizing Monetary Policy Regime Switches in India Introduction

The past two decades have witnessed a worldwide move by emerging markets to adopt explicit or implicit inflation targeting regimes. A notable and often discussed exception to this trend, of course, is China which follows pegged exchange rate regime supported by capital controls. Another major exception is India. It is not clear how to characterize the monetary regime or identify the nominal monetary anchor in India. Is central bank policy in India following a predictable rule that is heavily influenced by a quasi inflation target? And how has the monetary regime been affected by the gradual process of financial liberalization in India? To address these points, we investigate monetary policy regime change in India using a Markov switching model to estimate a time-varying Taylor-type rule for the Reserve Bank of India. We find that the conduct of monetary policy over the last two decades can be characterized by two regimes, which we term 'Hawk' and 'Dove. ' In the first of these two regimes, the central bank reveals a greater relative (though not absolute) weight on controlling inflation vis-à-vis narrowing the output gap. The central bank however was found to be in the " Dove " regime about half of our sample period, focusing more on the output gap and exchange rate targets to stimulate exports, rather than on moderating inflation. India thus seems to be following its own direction in the conduct of monetary policy, seemingly not overly influenced by the emphasis on quasi-inflation targeting seen in many emerging markets.
Incorporating Social Relationship discovered from User’s Behavior into Collaborative Filtering

소셜 네트워크는 사용자들의 공통된 관심사, 경험 그리고 일상생활들을 함께 공유하기 위해 소셜 네트워크 상 사람들을 서로 연결시켜주는 거대한 커뮤니케이션 플랫폼이다. 소셜 네트워크상의 사용자들은 포스팅, 댓글, 인스턴스 메시지, 게임 소셜 이벤트 외에도 다양한 애플리케이션을 통해 다른 사용자들과 소통하고 개인 정보 관리하는데 많은 시간을 소비한다. 소셜 네트워크상의 풍부한 사용자 정보는 추천시스템이 추천 성능을 향상시키기 위해 필요한 큰 잠재력이 되었다. 대부분의 사용자들은 어떤 상품을 구매하기 전 가까운 관계이거나 같은 성향을 가진 사람들의 의견을 반영하여 의사 결정을 하게 된다. 그러므로 소셜 네트워크에서의 사용자 관계는 추천시스템을 위한 사용자 선호도 예측을 효율적으로 높이는데 중요한 요소라 할 수 있다. 일부 연구자들은 소셜 네트워크에서의 사용자와 다른 사용자들 사이의 상호작용 즉, 소셜 관계(social relationship)와 같은 소셜 데이터가 추천시스템에서 추천의 질에 어떠한 영향을 미치는가를 연구하고 있다. 추천시스템은 아마존, 이베이, Last. fm과 같은 큰 규모의 전자상거래 사이트 또한 채택하여 사용되는 시스템으로, 추천시스템을 위한 방법으로는 협업적 여과 방법과 내용 기반 여과 방법이 있다. 협업적 여과방법은 사용자들의 선호도 학습에 의해 사용자가 아직 평가하지 않은 아이템 중 선호할 수 있는 아이템을 정확하게 제안하기 위한 추천시스템 방법 중 하나이다. 협업적 여과는 사용자들의 데이터에 초점을 맞춘 방법으로 유사한 배경과 선호도를 가지는 사용자들로부터 정보를 수집하여 사용자들의 선호도 예측을 자동으로 발생시킨다. 특히 협업적 여과는 근접한 이웃 사용자들에 의해서 목적 사용자가 선호할 수 있는 아이템을 제시하는 것으로 유사한 이웃 사용자를 찾는 것이 중요하다. 좋은 이웃 사용자 발견은 사용자와 아이템을 고려하는 방법이 일반적이다. 각 사용자는 아이템 즉, 영화, 상품, 책 등에 자신의 선호도를 나타내기 위하여 평가 값을 입력하고, 시스템은 이를 바탕으로 사용자-평가 행렬을 구축한다. 이 사용자-평가 행렬은 목적 사용자와 유사하게 아이템을 평가한 사용자 그룹을 찾기 위한 것으로, 목적 사용자가 아직 평가하지 않은 아이템에 대하여 사용자-평가 매트릭스를 통해 그 평가 값을 예측한다. 현재 이 협업적 여과 방법은 전자상거래와 정보 검색에서 적용되어 개인화 시스템에 효율적으로 사용되고 있다. 하지만 초기 사용자 문제, 데이터 희박성 문제와 확장성 그리고 예측 정확도 향상 등 해결해야 할 과제가 여전히 남아 있다. 이러한 문제들을 해소하기 위해 많은 연구자들은 하이브리드, 신뢰기반, 소셜 네트워크 기반 협업적 여과와 같은 다양한 방법을 제안하였다. 본 논문에서는 전통적인 협업적 여과 방식의 예측 정확도와 추천 성능을 향상시키기 위해 소셜 네트워크에 존재하는 소셜 관계를 이용한 협업적 여과 시스템을 제안한다. 소셜 관계는 소셜 네트워크 서비스 중 하나인 페이스북 사용자들이 남긴 포스팅과 사용자의 소셜 네트워크 친구와 의견 교류 중 남긴 코멘트와 같은 사용자 행동을 기반으로 정의된다. 소셜 관계를 구축하기 위해 소셜 네트워크 사용자의 포스팅과 댓글 추출하고, 추출된 텍스트에 불용어 및 특수 기호 제거와 스테밍 등 전처리를 수행하였다. 특징 벡터는 TF-IDF를 이용하여 전처리된 텍스트에 나타난 각 단어에 대한 특징 점수를 계산함으로써 구축된다. 본 논문에서 이웃 사용자를 결정하기 위해 사용되는 사용자 간 유사도는 특징 벡터를 이용한 사용자 행동 유사도와 사용자의 영화 평가를 기반으로 한 전통적 방법의 유사도를 결합하여 계산된다. 제안하는 시스템은 목표 사용자와 제안한 방법을 통해 결정된 이웃 사용자 집단을 기반으로 목표 사용자가 평가하지 않은 아이템에 대한 선호도를 예측하고 Top-N 아이템을 선별하여 사용자에게 아이템을 추천하게 된다. 본 논문에서 제안하는 방법을 확인하고 평가하기 위하여 IMDB에서 제공하는 영화 정보 기반으로 영화 평가 시스템을 구축하였다. 예측 정확도를 평가하기 위해 MAE 값을 이용하여 제안하는 알고리즘이 얼마나 정확한 추천을 수행하는지에 대한 예측 정확도를 측정하였다. 그리고 정확도 재현율 및 F1값 등을 활용하여 시스템의 성능을 평가하였으며, 시스템의 추천 품질은 커버리지를 이용하여 평가되었다. 실험 결과로부터 본 논문에서 제안한 시스템이 보다 더 정확하고 좋은 성능으로 사용자에게 아이템을 추천하는 것을 볼 수 있었다. 특히 소셜 네트워크에서 사용자 행동을 기반으로 한 소셜 관계를 이용함으로써 추천 정확도를 6% 향상시킴을 보였다. 또한 벤치마크 알고리즘과의 성능비교 실험을 통해 7% 향상된 추천 성능의 결과를 보여준다. 그러므로 사용자의 행동으로부터 관찰된 소셜 관계를 CF방법과 결합한 제안한 방법이 정확한 추천시스템을 위해 유용하며, 추천시스템의 성능과 품질을 향상시킬 수 있음을 알 수 있다.
Authorship Identification of E-mail as a Multi-Class Task - Notebook for PAN at CLEF 2011.

In this paper, we describe a multi-class text categorization approach to authorship attribution and test it on sets of e-mail collections. The PAN 2011 competition data consists of e-mails of variable length, written by various candidate authors, with some represented by significantly longer or more e-mails than others. Rather than construct a classifier for each separate author to discriminate it from the others (i.e. binary classification), we adopt a multi-class scheme where all authorship classes are learned simultaneously. We explore the effect of the selection of feature types and of the C parameter in the SVM learning algorithm. Variable-length lexical features showed promising results, nevertheless our authorship attribution approach only scored a mid position amongst the other competitors, for the SMALL as well as the LARGE test sets.
Title: A model selection approach for expression quantitative trait loci (eQTL) mapping

Identifying the genetic basis of complex traits remains an important and challenging problem with the potential to impact a broad range of biological endeavors. A number of statistical methods are available for mapping quantitative trait loci (QTL), but their application to high throughput phenotypes has been limited as most require user input and interaction. Recently, methods have been developed speciﬁcally for expression QTL (eQTL) mapping, but they too are limited in that they do not allow for interactions and QTL of moderate effect. We here propose an automated model-selection based approach that identiﬁes multiple expression quantitative trait loci in experimental populations, allowing for eQTL of moderate effect and interactions. Output can be used to identify groups of transcripts that are likely co-regulated, as demonstrated in a study of diabetes in mouse.
AIS Electronic

The organizational principles of open source software (OSS) development have challenged traditional theories in economics, organization research and information systems. In a seminal paper, Benkler (2002) provided a comprehensive framework to structure and explain these OSS principles. Coined Commons-Based Peer Production (CBPP), his framework has inspired a large stream of research on OSS. The objective of our paper is to determine whether CBPP also provides a viable framework to investigate projects of open innovation in non-software related domains. Using a case study approach, we focus on four projects that attempt to operate in line with the OSS phenomenon, but deal with tangible outputs (biotechnology, automobiles, entertainment hardware, and public patent review). We show that in general the CBPP framework is well-suited to explain "open" value creation in these domains. However, we also find several factors which limit its adoption to non-software related arenas.
Original Papers

The aim of the study was to follow the implicit patterns in children’s responses to metaphor describing human by means of a name of animal. The main problem in present study was: which traits of topic (human) would be spontaneously used by children from three age groups? The study followed a quasi-experimental design. The subjects were 77 children from three age groups: 5;6-6;0, 8;0-8;6, 9;6-10;0. The dependent variable: the level of comprehension of 18 metaphors with vehicles from the animal domain and one topic – human. The variable was measured through individual Piagetian interviews. The study confirmed the hypothesis that the ability to activate metaphorical thinking in order to describe human attributes increases with age, with a turning point around 8 year of life. The traits mentioned by subjects could be classified into five categories: unambiguous evaluations, physical features, behavior, behavioral traits, dispositions (intellectual, emotional, communion) and agency. Older children assigned more human dispositional traits, thoughts and preferences to the objects of metaphors. Younger ones often focused on the physical features of animals. With age, the tendency to give positive evaluations to the objects of metaphors increases.
On distribution of and hybridisation between the newts Triturus vulgaris and T. montandoni in western Ukraine

A case of natural hybridisation between the newts Triturus vulgaris and T. montandoni was recorded in the Maloye Opolie area, Lvov province, western Ukraine, where the latter species is represented by a population geographically isolated from the main range in the Carpathian mountains. A female hybrid, probably of the first generation, was identified by means of DNA flow cytometry. No hybrids were found in the Ukrainian Carpathians. Syntopic occurrence of T. montandoni and T. vulgaris in western Ukraine is discussed.
Ontologies for Modeling and Simulation: Initial Framework

[JOHN - redo this last. ] Many fields have or are developing ontologies for their subdomains. The Gene Ontology (GO) is now considered to be a great success in biology, a field that has already developed several extensive ontologies. Similar advantages could accrue to the simulation and modeling community. Ontologies provide a way to establish common vocabularies and capture domain knowledge for organizing the domain with a community wide agreement or with the context of agreement between leading domain experts. They can the be used to provide significantly improved (semantic) search and browsing, integration of heterogeneous information sources, and improved analytics and knowledge discovery capabilities. Such knowledge can be used to establish common vocabularies, nomenclatures and taxonomies with links to detailed information sources. In this paper, the design and development of a draft ontology for modeling and simulation called the Discreteevent Modeling Ontology (DeMO) are discussed, which can form a basis for achieving a broader community agreement and adoption.
Data-Driven Market Segmentation – A Structure-Based Conceptual Framework for Managerial Decision Support

Market segmentation increasingly uses homogeneous groups of consumers determined on the basis of empirical market data as target segments ( a posteriori -, data-driven, post hoc segmentation) rather than splitting individuals according to single, typically socio-demographic or geographic, criteria ( a priori -, commen sense segmentation). A vast amount of contributions has been made to improve methodology of identifying or constructing data-based market segments. However, real world data sets often do not contain clearly separated density clusters. Therefore all techniques used in data-based market segmentation can render multiple solutions of similar quality. So far no attempt has been made to construct a framework enabling managers to systematically choose between different segmentation solutions with regard to their practical usefulness. We propose a framework of such kind.
Filling Your Shelves: Synthesizing Diverse Style-Preserving Artifact Arrangements.

Our homes and workspaces are filled with collections of dozens of artifacts laid out on surfaces such as shelves, counters, and mantles. The content and layout of these arrangements reflect both context, e.g. kitchen or living room, and style, e.g. neat or messy. Manually assembling such arrangements in virtual scenes is highly time consuming, especially when one needs to generate multiple diverse arrangements for numerous support surfaces and living spaces. We present a datadriven method especially designed for artifact arrangement which automatically populates empty surfaces with diverse believable arrangements of artifacts in a given style. The input to our method is an annotated photograph or a 3D model of an exemplar arrangement, that reflects the desired context and style. Our method leverages this exemplar to generate diverse arrangements reflecting the exemplar style for arbitrary furniture setups and layout dimensions. To simultaneously achieve scalability, diversity and style preservation, we define a valid solution space of arrangements that reflect the input style. We obtain solutions within this space using barrier functions and stochastic optimization.
A Complete Dynamic Malware Analysis

Now a days thousands of malware samples are received by anti-malware companies on daily basis. And these large numbers are send for analysis by a number of automated analysis tools. These tool automatically execute a program in a controlled environment and generate a report describing the program’s behaviour. This research paper is a contribution towards the Dynamic Malware analysis. The aim is to provide the general malware features found in recent malware by performing dynamic malware analysis using cuckoo sandbox executed on Windows XP (SP3). This paper also discusses the detailed information about techniques & tools used in dynamic malware analysis.
Mathematics in Action

In this work, we present an automatic classiﬁer of wafer defects for the semiconductor industry. Hopefully defects are rare, but this puts the classifying problem in a small data context. We propose a fast and fully reproducible approach based on decision trees. The main interest of using decision trees lies in obtaining a highly explicable classiﬁer, which makes the origin of the defect easy to identify.
Artificial radionuclides in the troposphere of Seville (Spain) due to the Fukushima accident, associated fallout and impact on the trophic chain

Radioactive emissions into the atmosphere from the damaged reactors of the Fukushima Daiichi nuclear power complex were transported in part across the Pacific Ocean to North America and then to Europe, where arrived quite diluted due to the dispersion experimented by the masses of contaminated air and their washing by rainfall during their displacement. This paper shows the magnitude and temporal evolution of the radionuclide concentrations detected in the cited contaminated air masses at their arrival to Seville (Spain), where there is a station of the nation's environmental monitoring network equipped with high sensitivity devices for uninterrupted radiation detection. In the collected aerosol filters and during about two weeks were detected the presence of the following radionuclides: 134 Cs, 136 Cs, 137 Cs, 131 I and 132 Te (together with its short-lived daughter 132 I) at minute levels and with characteristics 134 Cs/ 137 Cs and 136 Cs/ 137 Cs isotope ratios. The associated 131 I fallout due to this episode was also roughly estimated from additional wet and dry deposition measurements, while the presence of 131 I in gaseous form was evaluated through its collection with a pumping system equipped with a charcoal filter It should also be noted that in Seville and surroundings, the presence of 131 I with origin in the Fukushima episode was detected in several key links in the human food chain: samples of milk (goat and cow) and derivative dairy products, as well as in various broadleaf plants. The maximum levels measured for this radionuclide were 1.11 Bq/l in samples of milk and 1.42 Bq/kg wet wt in broadleaf plants, with obviously negligible radiological implications. However, no presence of 132 Te or 134 Cs was detected in the samples of products for human consumption tested, while the possible changes in 137 Cs activities were in most
Isotope Constraint for Genetic Types of Geothermal Water in the Center Part of Guanzhong Basin, NW China

The study area is the centre part of Guanzhong basin, which is located in the middle of Shanxi Province NW China and mainly consists of Xianli terrace, Xi 'an depression and Gushi depression. The main geothermal aquifer in the basin is Lantian-Bahe Group (N2l+b). The method of environmental isotope and hydrogeochemistry are used to study the genetic types of deep geothermal water, in order to deepen and enrich the understanding of the genetic types of geothermal water in sedimentary basin in China. The results show that δD and δ 18 O of Huayin 051 in the Gushi depression have significant drift; d value is as low as -41‰, which significantly deviates from the local meteoric water line and is the result of an extreme isotope hydrogeochemistry evolution process in closed environment. In addition, rNa/rCl 293, rSO 2- 4 /rCl=1.09 and rCa/rMg=12.26, which meets the characteristics of sedimentary water. Therefore, the genetic type of geothermal water of Huayin 051 is residual continental sedimentary water. The δD and δ 18 O of geothermal water of Weinan City in Gushi depression and the Eastern Xianli terrace fall within the range of δ values of sedimentary water. And the storage environment is relatively closed, so the genetic types are ancient infiltrating water mixed with a small amount of continental sedimentary water. The genetic types of geothermal water in the Xi'an depression and Western Xianli terrace are ancient infiltrating water mixed with modern meteoric water. In addition, the sedimentary evolution history of basin in the study area also supports the above conclusion.
Mobile Broadband Multimedia Networks: Techniques, Models and Tools for 4G

Preface List of contributors and editors List of acronyms 1 Introduction 2 Transmission techniques 3 Signal processing 4 Propagation modelling and channel characterisation 5 Antennas and diversity: from narrow band to ultra wide band 6 MIMO channel modelling 7 MIMO systems 8 Radio network aspects 9 UMTS radio networks Index