Miracle-dz/UniFAR-FTUs huggingface dataset:
STATES AS MOLDERS OF INFORMAL RELATIONS?

ABSTRACT This article studies the impact of a range of state institutions on citizens’ contacts with family and close friends in 20 countries. Recent studies have shown large country level differences in social participation. We aim to explain these differences from an actor centered institutionalist perspective. We present two lines of reasoning. According to the first, a high level of social security crowds out social participation, as intimate networks are no longer needed as an economic safety net. The second line of reasoning proposes that corruption or a lack of civil rights drive citizens to seek refuge in their secure intimate contacts. In a comparative, multi-level design we focus on participation in the nuclear family, in the extended family, and with the best friend. We test the two lines of reasoning simultaneously on ISSP 2001 data. We find that states matter. State institutions are an important determinant of social participation. Our findings mainly confirm the second line of reasoning, whereas the crowding out thesis is only supported for contact with the extended family. Moreover, we find that the contextual effects are not similar across social groups: the poor are more strongly affected by the institutional design than the rich.
Centralized versus distributed schedulers for multiple bag-of-task applications

Multiple applications that execute concurrently on heterogeneous platforms compete for CPU and network resources. In this paper, we consider the problem of scheduling applications to ensure fair and efficient execution on a distributed network of processors. We limit our study to the case where communication is restricted to a tree embedded in the network, and the applications consist of a large number of independent tasks that originate at the tree's root. The tasks of a given application all have the same computation and communication requirements, but these requirements can vary for different applications. Each application is given a weight that quantifies its relative value. The goal of scheduling is to maximize throughput while executing tasks from each application in the same ratio as their weights. We can find the optimal asymptotic rates by solving a linear program that expresses all necessary problem constraints, and we show how to construct a periodic schedule. For single-level trees, the solution is characterized by processing tasks with larger communication-to-computation ratios at children with larger bandwidths. For multi-level trees, this approach requires global knowledge of all application and platform parameters. For large-scale platforms, such global coordination by a centralized scheduler may be unrealistic. Thus, we also investigate decentralized schedulers that use only local information at each participating resource. We assess their performance via simulation, and compare to a centralized solution obtained via linear programming. The best of our decentralized heuristics achieves the same performance on about two-thirds of our test cases, but is far worse in a few cases. While our results are based on simplistic assumptions and do not explore all parameters (such as buffer size), they provide insight into the important question of fairly and optimally co-scheduling heterogeneous applications on heterogeneous grids
A Layered-Rings Overlay Network for On-demand Media Streaming Service and Its Performance Analysis

Peer-to-peer media streaming service has gained-tremendous momentum in recent years. However, a number of challenges intrinsic in peer-to-peer media streaming have not been addressed. In this paper, we propose a peer-to-peer substrate for supplying on-demand media streaming service, called LRONet, which mainly addresses the key technical issue-constructing a locality-aware P2P overlay network with high scalability and manageability. Based on modeling the underlying physical network as a random graph with power-law distributions, we theoretically analyze the performance of LRONet. Numerical results show that LRONet can fully utilize the locality of underlying physical network so that the bandwidth consumption can be effectively saved
High density SNP association study of a major autism linkage region on chromosome 17.

A region on chromosome 17 has recently been highlighted as linked to autism (MIM[209850]) in multiple studies and evidence has accumulated suggesting that male-only families (those families that have produced only affected males) provide the major contribution to linkage at this locus. In an attempt to comprehensively test for association of common variants to autism within the region on chromosome 17 defined in Stone et al. (Stone, J.L., Merriman, B., Cantor, R.M., Yonan, A.L., Gilliam, T.C., Geschwind, D.H. and Nelson, S.F. (2004) Evidence for sex-specific risk alleles in autism spectrum disorder. Am. J. Hum. Genet. , 75, 1117-1123), a dense panel of single nucleotide polymorphisms (SNPs) was selected across the linkage peak and analyzed in a trio-based study design. SNPs were genotyped in 219 independent trios at an average intermarker distance of 6.1 kb across the 13.7 Mb interval. This provided ~80% coverage of common HapMap variation present in Caucasians, testing exonic, intronic, promoter and intergenic regions, as knowledge of important functional regions within the genome is currently limited. In this comprehensive association study of a linkage region in autism, no single SNP or haplotype association was sufficient to account for the initial linkage signal. Nominally significant single SNP and/or haplotype-based association results were detected in 15 genes, of which, MYO1D, ACCN1 and LASP1 stand out as genes with autism risk alleles requiring further study, with potential GRRs in the range of 1.34-2.29.
Fuzzy Unordered Rules Induction Algorithm Used as Missing Value Imputation Methods for K-Mean Clustering on Real Cardiovascular Data

Missing value imputation is one of the biggest tasks of data pre-processing when performing data mining. Most medical datasets are usually incomplete. Simply removing the cases from the original datasets can bring more problems than solutions. A suitable method for missing value imputation can help to produce good quality datasets for better analysing clinical trials. In this paper we explore the use of a machine learning technique as a missing value imputation method for incomplete cardiovascular data. Mean/mode imputation, fuzzy unordered rule induction algorithm imputation, decision tree imputation and other machine learning algorithms are used as missing value imputation and the final datasets are classified using K-Mean clustering. The experiment shows that final classifier performance is improved when the fuzzy
Lattice-Matched to InP .

Accurate and numerically efficient models for the complex dielectric function versus wavelength and material characteristics are essential for the use of nondestructive optical techniques such as spectroscopic ellipsometry or reflectometry. These optical techniques are commonly used for real-time and run-to-run monitoring and control of growth and etch processes to determine a material's composition and thickness. In this work, we discuss an improved composition-dependent model for the complex dielec-tric function for lattice-matched In 1,x Ga x As y P 1,y /InP systems valid over the entire composition range 0 y 1. We describe our model, which is an extension of the critical point parabolic band method and is based on the model proposed by Charles Kim et. al. for the Al x Ga 1,x As/GaAs system. We demonstrate the quality of the model in fitting optical data for individual compositions , and compare our results to other established models including the harmonic oscillator approximation and the model of Adachi. Using results obtained from the individual fits, we generate a composition-dependent model that is valid for the entire range of lattice-matched compositions. Also, we show how this model can be used to accurately determine the composition (0.01) of an unknown material whose dielectric response has been obtained using spectroscopic ellipsometry or a similar technique.
Information privée sur les marchés financiers : une étude de la prime de risque dans un cadre général

La problematique de cette these est d’etudier la prime de risque d’information sur les marches financiers, d’un point de vue theorique, empirique et pratique. Theoriquement, cette these introduit un nouveau modele d’equilibre a anticipations rationnelles pour evaluer les actifs financiers dans un contexte d’asymetrie informationnelle. Ce modele permet d’etudier la prime de risque d’information dans un cadre tres general, prenant en compte plusieurs titres et plusieurs sources de correlation entre ceux-ci. La specification du modele permet de formuler de nombreuses conclusions theoriques, nouvelles dans la litterature, ainsi que des hypotheses testables empiriquement. Les applications du modele sont multiples. Le modele permet par exemple d’expliquer de nombreuses « anomalies » sur les marches ainsi que des phenomenes en finance internationale. L’objectif de l’etude empirique est de tester la presence d’un facteur informationnel sur les marches financiers ainsi que d’analyser le poids de ce facteur. Le facteur informationnel est approche par une nouvelle mesure d’asymetrie informationnelle directement basee sur les modeles a anticipations rationnelles en asymetrie informationnelle. Nous analysons l’impact du facteur informationnel sur la rentabilite des titres afin de mettre en evidence l’existence d’une prime de risque d’information. Une deuxieme etude etudie la performance des fonds mutuels investis en actions ainsi que leur degre de specialisation, en fonction du degre d’asymetrie informationnelle des titres qui les composent. Les resultats empiriques corroborent les implications du modele theorique.
Design-Space Exploration of Reconfigurable Hardware for Analysis

Stream analysis and stream processing has such wide spread applicability as multimedia processing to network security to program profiling and system analysis. Many proposed run-time systems rely on profile information to make informed design and optimization decisions. Procedure and data placement, trace scheduling, value specialization, network load balancing, dynamic compilation, and a whole host of power management techniques can all be guided by an accurate picture of what a program is doing and how it is interacting with the system. While program and system profiling themselves pose challenges, processing streams in network systems adds yet another dimension to this problem, especially when applications require line speed processing. For example, IP-lookup module needs to access huge routing tables and make decisions for every packet seen on the card. Worm detection, anomaly detection, pattern matching for intrusion detection, etc., all provide a novel set of challenges to hardware and software designers striving to build systems which can satisfy these applications.
Efficient metric learning for the analysis of motion data

We investigate metric learning in the context of dynamic time warping (DTW), the by far most popular dissimilarity measure used for the comparison and analysis of motion capture data. While metric learning enables a problem-adapted representation of data, the majority of methods has been proposed for vectorial data only. In this contribution, we extend the popular principle offered by the large margin nearest neighbours learner (LMNN) to DTW by treating the resulting component-wise dissimilarity values as features. We demonstrate, that this principle greatly enhances the classification accuracy in several benchmarks. Further, we show that recent auxiliary concepts such as metric regularisation can be transferred from the vectorial case to component-wise DTW in a similar way. We illustrate, that metric regularisation constitutes a crucial prerequisite for the interpretation of the resulting relevance profiles.
TCP SYN-based DDoS attack on EKG signals monitored via a wireless sensor network

Internet distributed denial of service (DDoS) attacks are known to exhaust computing resources of a computer. In many cases, even if the attack is not sufficiently strong to completely crash a computing system, its adverse effects can be devastating for the information being processed on the system. When such information is too critical as an electrocardiogram (EKG) signal, the signal itself becomes vulnerable to such attacks also. In this experimental paper, we evaluate the adverse effects of TCP SYN-based DDoS attacks on EKG signals remotely collected by using wireless sensor networks (WSNs). In lab, we deployed two applications (Serial Forwarder and Oscilloscope GUI) to display the EKG signal on remote monitoring station. Such EKG signal was delivered by a WSN (motes running a modified version of OscilloscopeRF with an analog channel enabled and TOSBase) to collect and send the EKG data. It was found that even though the computing resource of the remote computer running the applications to display the EKG signals was almost exhausted under a DDoS attack, the system was still responding (alive) and displaying the collected EKG signal. However, the normal EKG signal pattern was distorted due to the resource exhaustion caused by the DDoS attack, which in turn made the EKG signal unreliable for medical diagnosis and remote monitoring. In order to understand effect of TCP SYN attack on the EKG signal pattern, the attack was launched at different loads in a controlled lab environment. Finally, an Intrusion Prevention System was deployed in the network to mitigate the adverse effects of attack in the network in an attempt to recover a good EKG signal. Copyright © 2011 John Wiley & Sons, Ltd.
When the Walk is Not Random: Commodity Prices and Exchange Rates

We show that there is a distinct commodity-related driver of exchange rate movements, even at fairly high frequencies. Commodity prices predict exchange rate movements of 11 commodity-exporting countries in an in-sample panel setting for horizons up to two months. We also find evidence of systematic (pseudo) out-of-sample predictability, overturning the results of Meese and Rogoff (1983): information embedded in our country-specific commodity price indices clearly helps improving upon the predictive accuracy of the random walk in the majority of countries. We further show that the link between commodity prices and exchange rates is not driven by changes in global risk appetite or carry.
A Mixed Interaction Platform for Supporting Large-Scale Crisis Storytelling

Crowdsourcing is a distributed approach in which people participate and share information over the Web (e.g. Facebook or Twitter). This distributed model has proved to be helpful in accelerating the perception of what occurred, where it happened, and who needed help in recent disasters such as the Boston Marathon bombs, the Oklahoma tornado or the Central Europe floods. Nevertheless, this kind of sources might provide unreliable, partial, ambiguous and inconsistent information that do not necessarily help to understand the situation. An alternative may be the combination of crowdsourcing activities and more official flows of information to make up a coherent big picture that support the needs of decision makers. This paper introduces a software approach conceived to convey stories created from different sources –official and unofficial- as a way of depicting situations in a collaborative manner.
Convergence rates for posterior distributions and adaptive estimation

The goal of this paper is to provide theorems on convergence rates of posterior distributions that can be applied to obtain good convergence rates in the context of density estimation as well as regression. We show how to choose priors so that the posterior distributions converge at the optimal rate without prior knowledge of the degree of smoothness of the density function or the regression function to be estimated.
GAS CHROMATOGRAPHIC–MASS SPECTROMETRIC ANALYSIS OF BIOGENIC AMINES IN IDENTIFIED NEURONS AND TISSUES OF HIRUDO MEDICINALIS

—Previous workers have reported that the colossal cells of Retzius in the segmental ganglia of the medicinal leech contain about 2.3 pmol of 5‐hydroxytryptamme (5‐HT)/cell body. We verify the identify of 5‐HT in the Retzius cells by gas chromatography‐mass spectrometry and derive concentrations of 1.3–4.1 pmol/neuron by analyses of eight individually dissected Retzius cell bodies. The Retzius cell bodies contain about 30% of the 5‐HT in each ganglion. An average of 25 pmol 5‐HT/mg tissue, a concentration about 500 times lower than that in the Retzius cell, was found in the fibrous, pigmented tissue surrounding the leech nervous system. We could not detect γ‐aminobutyric acid, octopamine, dopamine or norepinephrine in the Retzius cells, in the pigmented tissue, or, with the possible exception of dopamine (±0.4 pmol/ganglion), in whole ganglia. Furthermore, we could not detect 5‐HT in pooled samples of 100 non‐chromaffin control neurons.
Context-based proofs of termination for typed delimited-control operators

We present direct proofs of termination of evaluation for typed delimited-control operators shift and reset using a variant of Tait's method with context-based reducibility predicates. We address both call by value and call by name, and for each reduction strategy we consider a type-and-effect system a la Danvy and Filinski as well as a system with a fixed answer type. The call-by-value type-and-effect system we present is a refinement of Danvy and Filinski's original type system, whereas the call-by-name type-and-effect system is new. From the normalization proofs, we extract call-by-value and call-by-name evaluators in continuation-passing style with two layers of continuations; by construction, these evaluators are instances of normalization by evaluation.
The Scoring Scheme of the Informant Questionnaire on Cognitive Decline in the Elderly Needs Revision: Results of Rasch Analysis

The purpose of this study was to examine the optimal scoring scheme (category use), unidimensionality, item fit, and redundancy of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) in a cohort of Hong Kong Chinese stroke survivors. At 3 months after the index stroke, a research assistant administered the IQCODE to relatives of 284 Chinese patients with acute stroke who were consecutively admitted to a general hospital. A psychiatrist, who was blinded to the IQCODE scores, interviewed all 284 patients and made DSM-IV diagnosis of dementia, which served as the benchmark for judging the performance of IQCODE in screening dementia. The results suggest that the optimal IQCODE scoring scheme has 2 rather than the original 5 categories. Although the IQCODE was unidimensional overall, there was evidence of item redundancy, thus indicating that a shortened version is desirable.
Communication Characteristics in the NAS Parallel Benchmarks

In this paper, we investigate the communication characteristics of the Message Passing Interface (MPI) implementation of the NAS parallel benchmarks and study the effectiveness of compiled communication for MPI programs. Compiled communication is a technique that utilizes the compiler knowledge of both the application communication requirement and the underlying network architecture to significantly optimize the performance of communications whose information can be determined at compile time (static communications). The results indicate that compiled communication can be applied to a large portion of the communications in the benchmarks. In particular, the majority of collective communications are static.
AQUATIC BIOMASS RESOURCES AND CARBON DIOXIDE TRAPPING

Intensively managed microalgal production facilities are capable of fixing several-fold more carbon dioxide per unit area than trees or crops. Although CO, is still released when fuels derived from algal biomass are burned, integration of microalgal farms for flue gas capture approximately doubles the amount of energy produced per unit of CO, released. Materials derived from microalgal biomass also can be used for other long-term uses, serving to sequester CO,. Flue gas has the potential to provide sufficient quantities of CO, for such large-scale microalgae farms. Viewing microalgae farms as a means to reduce the effects of a greenhouse gas (carbon dioxide, CO,) changes the view of the economics of the process. Instead of requiring that microalgae-derived fuel be cost competitive with fossil fuels, the process economics must be compared with those of other technologies proposed to deal with the problem of CO, pollution. However, development of alternative, environmentally safer energy production technologies will benefit society whether or not global climate change actually occurs. Microalgal biomass production has great potential to contribute to world energy supplies, and to control CO, emissions as the demand for energy increases. This technology makes productive use of arid and semi-arid lands and highly saline water, resources that are not suitable for agriculture and other biomass technologies. Keywords-Global climate change, greenhouse gas. microalgae, biomass
An Interview with Giorgio Agamben

Agamben: The State of Exception belongs to a series of genealogical essays that follow on from Homo Sacer and which should form a tetralogy. Regarding the content, it deals with two points. The first is a historical matter: the state of exception or state of emergency has become a paradigm of government today. Originally understood as something extraordinary, an exception, which should have validity only for a limited period of time, but a historical transformation has made it the normal form of governance. I wanted to show the consequence of this change for the state of the democracies in which we live. The second is of a philosophical nature and deals with the strange relationship of law and lawlessness, law and anomy. The state of exception establishes a hidden but fundamental relationship between law and the absence of law. It is a void, a blank and this empty space is constitutive of the legal system.
Lab-in-a-Phone: Smartphone-Based Portable Fluorometer for pH Measurements of Environmental Water

A novel portable fluorometer combining the attributes of a smartphone with an easy-fit, simple and compact sample chamber fabricated using 3-D printing has been developed for pH measurements of environmental water in the field. A color filter attached over the camera white light light-emitting diode selects an excitation band centered around λ ~ 450 nm with a 3-dB bandwidth, Δλ ~ 21 nm. An application-specific, temperature-stable probe based on the 4-aminonaphthalimide fluorophore was synthesized to absorb at this wavelength while emitting in the green region of the visible spectrum. The green emission is readily detected using the smartphone camera and a simple red-green-blue Android application. Suppression of the green emission increases with increasing pH enabling a straightforward pH sensor. The system was calibrated against a commercial spectrofluorometer and pH measurements were taken at various locations around Sydney. The results were then compared directly with those obtained using conventional electrode-based measurements. The data can be stored in the phone's available memory or transmitted by phone back to base for further real-time analysis.