Miracle-dz/UniFAR-FTUs huggingface dataset:
Web-Scale Querying through Linked Data Fragments

To unlock the full potential of Linked Data sources, we need flexible ways to query them. Public sparql endpoints aim to fulfill that need, but their availability is notoriously problematic. We therefore introduce Linked Data Fragments, a publishing method that allows e cient o oading of query execution from servers to clients through a lightweight partitioning strategy. It enables servers to maintain availability rates as high as any regular http server, allowing querying to scale reliably to much larger numbers of clients. This paper explains the core concepts behind Linked Data Fragments and experimentally verifies their Web-level scalability, at the cost of increased query times. We show how trading server-side query execution for inexpensive data resources with relevant a ordances enables a new generation of intelligent clients.
Next to You: Monitoring Quality of Experience in Cellular Networks From the End-Devices

A quarter of the world population will be using smartphones to access the Internet in the near future. In this context, understanding the quality of experience (QoE) of popular apps in such devices becomes paramount to cellular network operators, who need to offer high-quality levels to reduce the risks of customers churning for quality dissatisfaction. In this paper, we address the problem of QoE provisioning in smartphones from a double perspective, combining the results obtained from subjective laboratory tests with end-device passive measurements and QoE crowd-sourced feedback obtained in operational cellular networks. The study addresses the impact of both access bandwidth and latency on the QoE of five different services and mobile apps: YouTube, Facebook, Web browsing through Chrome, Google Maps, and WhatsApp. We evaluate the influence of both constant and dynamically changing network access conditions, tackling in particular the case of fluctuating downlink bandwidth, which is typical in cellular networks. As a main contribution, we show that the results obtained in the laboratory are highly applicable in the live scenario, as mappings track the QoE provided by users in real networks. We additionally provide hints and bandwidth thresholds for good QoE levels on such apps, as well as discussion on end-device passive measurements and analysis. The results presented in this paper provide a sound basis to better understand the QoE requirements of popular mobile apps, as well as for monitoring the underlying provisioning network. To the best of our knowledge, this is the first paper providing such a comprehensive analysis of QoE in mobile devices, combining network measurements with users QoE feedback in laboratory tests, and operational networks.
Federation in Cloud Data Management: Challenges and Opportunities

Companies are increasingly moving their data processing to the cloud, for reasons of cost, scalability, and convenience, among others. However, hosting multiple applications and storage systems on the same cloud introduces resource sharing and heterogeneous data processing challenges due to the variety of resource usage patterns employed, the variety of data types stored, and the variety of query interfaces presented by those systems. Furthermore, real clouds are never perfectly symmetric - there often are differences between individual processors in their capabilities and connectivity. In this paper, we introduce a federation framework to manage such heterogeneous clouds. We then use this framework to discuss several challenges and their potential solutions.
Targeted Social Transparency as Global Corporate Strategy

Multinational enterprises (MNEs) are subject to a variety of U.S. laws that require public disclosure of their cross-border activities. Recent years have seen the emergence of mandatory disclosure regimes under U.S. federal securities law with the express purpose of advancing international human rights in the context of geographically-defined, issue-specific non-economic public policy objectives, which I collectively refer to as “targeted social transparency” (or “TST”) regimes. This Article addresses the appeal and shortcomings of mandatory disclosure as a means of regulating global corporate conduct, focusing on the unique challenges posed by TST. Two contemporary examples of TST are analyzed: (i) the “conflict minerals” provisions in the Dodd-Frank Act, which require the disclosure of minerals whose mining is associated with human rights violations in the Democratic Republic of Congo; and (ii) disclosure requirements under the Iran Threat Reduction and Syria Human Rights Act with respect to commercial activities associated with the Iranian government’s suppression of human rights. I present the concept of constructive discourse, which seeks to enhance the effectiveness of mandatory disclosure by addressing two related objectives: (a) how TST can catalyze internally-driven changes in corporate behavior to the mutual benefit of MNEs and stakeholders; and (b) how MNEs can use TST for strategic purposes. Using the concept of constructive discourse, this Article identifies and explores specific ways that TST regimes can shape socially-beneficial, strategically-rational corporate conduct.
Chronic Proteasome Inhibition Contributes to Coronary Atherosclerosis

The proteasome is responsible for the degradation of oxidized proteins, and proteasome inhibition has been shown to generate oxidative stress in vitro. Atherosclerosis is thought to be initiated as a consequence of increased endogenous oxidative stress. The current study was designed to assess whether chronic proteasome inhibition is associated with early coronary atherosclerosis. Female pigs, 3 months of age, were randomized to a normal (N) or high-cholesterol (HC) diet (2% cholesterol, 15% lard) without or with twice weekly subcutaneous injections of the proteasome inhibitor (PSI) MLN-273 (0.08 mg/kg, N+PSI and HC+PSI) for a period of 12 weeks (n=5 per group). Coronary vasorelaxation to bradykinin (10−10.5 to 10−6.5 mol/L) and sodium nitroprusside (10−9 to 10−5 mol/L) was assessed by in vitro organ chamber experiments, intima–media ratio by morphometric analysis of Elastica–van Gieson–stained slides, and intima superoxide production by dihydroethidium fluorescence. Vasorelaxation to 10−6.5 mol/L bradykinin was reduced in HC compared with N (69±7 versus 90±2%, P<0.05) and further reduced in N+PSI and HC+PSI (57±6 and 48±13%, P<0.05 versus N and HC for each). Compared with N (0.03±0.01), intima–media ratio was higher in N+PSI (0.09±0.04, P<0.01) and HC+PSI (0.15±0.06, P<0.05). Compared with N (0.6±0.9% of intima area), dihydroethidium fluorescence was higher in HC, N+PSI, and HC+PSI (8.9±1.6, 6.0±3.5, and 7.2±3.9% of intima area, P<0.05 for all). Thus, chronic proteasome inhibition is associated with increased coronary artery oxidative stress and early atherosclerosis. These findings support the significance of the proteasome and related protein quality control for vascular biology and pathology.
Star formation in the LMC: gravitational instability and dynamical triggering

Abstract Evidence for triggered star formation is difficult to establish because energy feedback from massive stars tend to erase the interstellar conditions that led to the star formation. Young stellar objects (YSOs) mark sites of current star formation whose ambient conditions have not been significantly altered. Spitzer observations of the Large Magellanic Cloud (LMC) effectively reveal massive YSOs. The inventory of massive YSOs, in conjunction with surveys of interstellar medium, allows us to examine the conditions for star formation: spontaneous or triggered. We examine the relationship between star formation and gravitational instability on a global scale, and we present evidence of triggered star formation on local scales in the LMC.
Carrot-Eaters and Creature-Believers: The Effects of Lexicalization on Children's Inferences About Social Categories

This article examines how language affects children's inferences about novel social categories. We hypothesized that lexicalization (using a noun label to refer to someone who possesses a certain property) would influence children's inferences about other people. Specifically, we hypothesized that when a property is lexicalized, it is thought to be more stable over time and over contexts. One hundred fifteen children (5- and 7-year-olds) learned about a characteristic of a hypothetical person (e.g., “Rose eats a lot of carrots”). Half the children were told a noun label for each character (e.g., “She is a carrot-eater”), whereas half heard a verbal predicate (e.g., “She eats carrots whenever she can”). The children judged characteristics as significantly more stable over time and over contexts when the characteristics were referred to by a noun than when they were referred to by a verbal predicate. Lexicalization (in the form of a noun) provides important information to children regarding the stability of personal characteristics.
A Co-Inductive Proof Method for Contextual Properties in Untyped ‚-Calculus with References and Deallocation✩

A fuel cell includes an electrolyte electrode assembly and a pair of first and second separators. First through fourth oxygen-containing gas holes, first through fourth coolant holes, and first through fourth fuel gas holes extend through the fuel cell. The first through fourth oxygen-containing gas holes are selectively used as an oxygen-containing gas supply port or an oxygen-containing gas discharge port to cause an oxygen-containing gas to flow circularly along an electrode surface in an oxygen-containing gas flow field.
Health Information and Social Security Entitlements

This study examines whether new health information, obtained through medical s creening, affects entitlements to Social Security benefits. Random assignment of information is derived from a unique feature of the Continuous National Health and Nutrition Examination Survey. To examine the effect of information on entitlements, the survey data are matched to administrative data from the Social Security Administration. The results suggest that new health information leads to delayed entitlements, particularly among workers near the early retirement age.
Customer classification and load profiling using data from Smart Meters

The paper presents a self-organization based integrated model for customer classification and load profiling in distribution systems. The consumer classification in consumption classes characterized by typical load profiles is made using information provided by Smart Meters. For determination of the consumption classes, every customer is characterized by the following primary information: daily (monthly) energy consumption, minimum and maximum loads. The proposed model was tested using household consumers from a rural area. The results demonstrate the ability of the methodology to efficiently used in distribution systems when information about the supplied customers is very poor (based only the data provided by classic meters).
Matrix Models of AdS Gravity

We explore the connection between anti-deSitter supergravity and gauge theory, in the context of bound states of many D1 and D5 branes. The near-horizon $AdS_3$ supergravity describes the identity sector of the conformal field theory produced by the brane dynamics. A variant of anomaly inflow (for the 2d conformal anomaly) is involved. Dynamical matter fields on $AdS_3$ couple to the chiral ring and its descendant fields on the branes. We propose a map between boundary conformal field theory and bulk supergravity/matter dynamics, which is strongly reminiscent of matrix models of 2d gravity.
A novel tree-based clustering algorithm for statistical language modeling

In this paper, a new method to cluster words into classes is proposed in order to define a statistical language model. The purpose of this algorithm is to decrease the computational cost of the clustering task while not degrading speech recognition performance. The algorithm provides a bottom-up hierarchical clustering using the reciprocal neighbours method. This technique consists in merging several pairs of classes within a single iteration. Experiments on a spontaneous speech corpus are presented. Results are given both in terms of perplexity and word recognition error rate. We obtain a large reduction in the number of iterations necessary to build a classification tree and thus a CPU time reduction in building the model as well as a reduction in both perplexity and word error rate.
PROBING QUANTUM NONLINEARITIES THROUGH NEUTRINO OSCILLATIONS

We investigate potential quantum nonlinear corrections to Dirac's equation through its sub-leading effect on neutrino oscillation probabilities. Working in the plane-wave approximation and in the μ - τ sector, we explore various classes of nonlinearities, with or without an accompanying Lorentz violation. The parameters in our models are first delimited by current experimental data before they are used to estimate corrections to oscillation probabilities. We find that only a small subset of the considered nonlinearities has the potential to be relevant at higher energies and thus possibly detectable in future experiments. A falsifiable prediction of our models is an energy-dependent effective mass-squared, generically involving fractional powers of the energy.
Relationship between guaranteed rate server and latency rate server

To analyze scheduling algorithms in high-speed packet switching networks, two models have been proposed and widely used in the literature, namely the guaranteed rate (GR) server model and the latency rate (LR) server mode. While a lot scheduling algorithms have been proved to belong to GR or LR or both, it is not clear what is the relationship between them. The purpose of this paper is to investigate this relationship. We prove that if a scheduling algorithm that belongs to GR, it also belongs to LR and vice versa. In addition, we review several other models that are also used for analysis of scheduling algorithms and discuss their relationship with LR model and GR model.
Overview of the MPSoC design challenge

We review the design challenges faced by MPSoC designers at all levels. Starting at the application level, there is a need for programming models and communications APIs that allow applications to be easily re-configured for many different possible architectures without tedious rewriting, while at the same time ensuring efficient production code. Synchronisation and control of task scheduling may be provided by RTOS's or other scheduling methods, and the choice of programming and threading models, whether symmetric or asymmetric, has a heavy influence on how best to control task or thread execution. Debugging MP systems for the typical application developer becomes a much more complex job, when compared to traditional single-processor debug, or the debug of simple MP systems that are only very loosely coupled. The interaction between the system, applications and software views, and processor configuration and extension, adds a new dimension to the problem space. Zeroing in on the optimal solution for a particular MPSoC design demands a multi-disciplinary approach. After reviewing the design challenges, we end by focusing on the requirements for design tools that may ameliorate many of these issues, and illustrate some of the possible solutions, based on experiments
The Focused Organization of Advice Relations: A Study in Boundary Crossing

Organizations contain multiple social foci-settings for interaction providing members with occasions for structuring their social relations. In this paper we examine how identification with particular social foci within organizations influences the propensity of advice-seeking ties to crosscut the boundaries of organizational subunits. We propose and test a theory of relationship formation based on the strength of organizational members' identification with social foci. We expect that advice relations of organizational members identifying more strongly with local foci organizational subunits will be more likely to be contained within their boundaries. By contrast, we expect that advice relations of organizational members identifying more strongly with a global focus the organization as a whole will be more likely to crosscut the boundaries defined around local foci. We test these hypotheses on data we collected on advice-seeking relations among members of the top management team in an industrial multiunit group that comprises five distinct subsidiary companies. Results show that identification with social foci affects the formation of crosscutting network ties over and above the effect of the formal organizational boundaries that encircle the foci. More specifically, we find that organizational members who identify strongly with local foci subsidiaries, in our case tend to seek advice within such local foci, whereas organizational members who identify strongly with a global focus corporate, in our case tend to be sources of advice across the boundaries of the local foci in which they participate. Cross-boundary advice ties are less likely to occur among managers who identify strongly with their subsidiaries but weakly with the corporate group. As a consequence, identification with local foci constrains knowledge transfer relations within the boundaries of such foci. On the contrary, cross-boundary advice ties are more likely to occur among managers who identify strongly with the corporate group but weakly with their subsidiary. As a consequence, identification with a global focus activates knowledge transfer across the boundaries of local foci.
Vowel discrimination in early bilinguals : How plastic ?

Previous research has suggested a lack of plasticity for second-language (L2) perceptual learning even in individuals who learned their L2 in childhood. This study examined the discrimination of English vowels by native (L1) Spanish speakers who learned English in childhood. Results showed that early bilinguals’ performance was much more similar to that of English monolinguals (though differences were found) than to that of Spanish monolinguals, suggesting extensive plasticity for L2 perceptual learning in early childhood.
A neural network approach towards reinforcing smart home security

In today's context, it is common to leave the house unattended as people are busy catching up with their tight daily schedule. Therefore, most people have chosen the home security system as the most reliable way to protect their home. However, the existing security mechanism provided by smart home is lack of intelligence for higher level decision making and action taking. Furthermore, the current security management of smart home is usually predefined by user. Thus, for consequences that are out of the defined scopes, chances to trigger false alarm are very high. Besides, the environment of each smart home is difference and the culture is also different from country to country, so the predefined security instructions may not work well or they may not suitable for all scenarios. Therefore, an intelligent algorithm with self learning ability must be adopted for smart home system in order to take adequate actions against the evaluated environment and decide for the necessary actions. Neural networks have been proposed in this paper where they are adaptive statistical models based on an analogy with the structure of the brain. The intelligence of the smart home should be able to analyze the information gathered by the sensors and detectors and to respond based on the analysis and the experiences gained via the neural networks. Neural network is chosen because they have the learning ability to estimate the parameters of some populations using a small number of examples at a time. When come to neural network learning process, there are several learning algorithms that could be applied, which can learn through pattern recognition, movement recognition, action recognition and so forth.
Evaluating FPGA Virtex-II Board using Dynamic Partial Reconfiguration

The Field Programmable Gate Array (FPGA) offer effective suppleness and performance because of reconfigurable hardware but consume more power in contrast to the Application Specific Integrated Circuit (ASIC). At run time reconfiguration of hardware in FPGAs can not only be very economical but can be real alternative for ASICs. The designers are reluctant to use Dynamic Partial Reconfiguration (DPR) in FPGA due to lack of adequate tools provided by the vendors. DPR has been in academic use for more over a decade. DPR offers reduction in power consumption, area, cost as well as increase in flexibility, efficiency and fault tolerance but has an application dependent overhead. In this work prior performance of DPR is evaluated using Xilinx Virtex II Pro in order to realize whether it is suitable for an application rather than at later complex design stages of a system design having the DPR employed. The evaluation is based on the reconfiguration speed and the resource utilization. The DPR shows an improvement of resource utilization by 22.5 % (in terms of slices) as well as speedup in comparison to Non-DPR design. Keyword
Spectra of graph neighborhoods and scattering

Let (Gε)ε>0 be a family of ‘ε‐thin’ Riemannian manifolds modeled on a finite metric graph G, for example, the ε‐neighborhood of an embedding of G in some Euclidean space with straight edges. We study the asymptotic behavior of the spectrum of the Laplace–Beltrami operator on Gε, as ε→0, for various boundary conditions. We obtain complete asymptotic expansions for the kth eigenvalue and the eigenfunctions, uniformly for k⩽Cε−1, in terms of scattering data on a non‐compact limit space. We then use this to determine the quantum graph which is to be regarded as the limit object, in a spectral sense, of the family (Gε). Our method is a direct construction of approximate eigenfunctions from the scattering and graph data, and the use of a priori estimates to show that all eigenfunctions are obtained in this way.
Fuzzy-genetic decision optimization for positioning of military combat units

Fuzzy-genetic decision optimization solved a problem of positioning military combat units for optimum performance. It used a simulation model to evaluate solutions, a fuzzy logic module to map simulation outputs to a single fitness value, and a genetic algorithm to search the terrain for a near-optimal combination of unit positions. In this study, the fuzzy-genetic system outperformed a human expert during a simulated battle.
Introduction to "College Choices: The Economics of Where to Go, When to Go, and How to Pay For It"

Aspiring college students and their families have many options. A student can attend an in-state or an out-of-state school, a public or private college, a two-year community college program or a four-year university program. Students can attend full-time and have a Bachelor of Arts degree by the age of twenty-two or mix college and work, progressing toward a degree more slowly. To make matters more complicated, the array of financial aid available is more complex than ever. Students and their families must weigh federal grants, state merit scholarships, college tax credits, and college savings accounts, just to name a few. In College Choices, Caroline Hoxby and a distinguished group of economists show how students and their families really make college decisions - how they respond to financial aid options, how peer relationships figure in the decision-making process, and even whether they need mentoring to get through the admissions process. Students of all sorts are considered - from poor students, who may struggle with applications and whether to continue on to college, to high-aptitude students who are offered "free rides" at elite schools. College Choices utilizes the best methods and latest data to analyze the college decision-making process, while explaining how changes in aid and admissions practices inform those decisions as well.