Miracle-dz/UniFAR-FTUs huggingface dataset:
YA-MAC: Handling unified unicast and broadcast traffic in Multi-hop Wireless Sensor Networks

This paper introduces YA-MAC, an agile Medium Access Control (MAC) protocol to provide high throughput for both unicast and broadcast traffic in Duty-Cycled Multi-hop Wireless Sensor Networks (DCM-WSN). YA-MAC is implemented under the UPMA framework in TinyOS and is evaluated on TelosB and MicaZ testbeds. We observe that YA-MAC significantly outperforms the state-of-the-art SCP-MAC protocol in terms of throughput by 60%, while tolerating a more dynamic network, at a small cost to duty-cycle performance. Further, we show that YA-MAC's idle listening radio power consumption is 35% less than RI-MAC's, while achieving similar throughput and latency.
Experimental and numerical investigation of saltwater intrusion dynamics in flux‐controlled groundwater systems

Sea level rise and reduction of groundwater fluxes due to changes in rainfall patterns are the two major climate change–induced hydrological variables that can severely affect saltwater intrusion in coastal aquifers. In this study we use a combination of laboratory experiments and numerical simulations to study the impacts of changes in one of these climate change–induced hydrological variables, groundwater flux, on saltwater intrusion process. We have completed experiments in a laboratory‐scale model to study the changes in two types of groundwater fluxes—areal recharge flux and regional flux. The experimental results were modeled using the numerical code SEAWAT. The transient data sets reported in this study are useful benchmarks for testing numerical models that employ flux‐type boundary conditions. Also, based on the experimental observations, we hypothesize that when the fluxes are perturbed, it would require relatively less time for a salt wedge to recede from an aquifer when compared to the time required to advance into the aquifer. This rather counterintuitive hypothesis implies that saltwater intrusion and receding processes are asymmetric and the timescales associated with these processes will be different. We use a combination of laboratory and numerical experiments to test this hypothesis and use the resulting data set to explain the reason for the difference in intrusion rates.
What is 'Information' Beyond a Definition?

The need to further conceptual engagement with information has been raised repeatedly over the last forty years. Approaches to understanding information in Information Systems have so far focused on defining what information is. In this paper we propose a fundamentally different approach. Using Wittgenstein’s philosophy of language we develop a descriptive theory of information. Reviewing how information is used and what it is used for in the literature we state two claims about information: (1) information is used to describe intended information, potential information and information-in-use; (2) different facets of information can be described in relation to pragmatic, semantic, semiotic and empiric aspects of signs. This descriptive theory of information offers an alternative understanding of information for researchers, practitioners, and educators. Our research provides a first step for an alternative approach to information that is grounded in a non-representational understanding of language where words do not have definite meanings.
The Role of Inflammatory and Anti-Inflammatory Cytokines in the Pathogenesis of Osteoarthritis

Osteoarthritis (OA) is the most common chronic disease of human joints. The basis of pathologic changes involves all the tissues forming the joint; already, at an early stage, it has the nature of inflammation with varying degrees of severity. An analysis of the complex relationships indicates that the processes taking place inside the joint are not merely a set that (seemingly) only includes catabolic effects. Apart from them, anti-inflammatory anabolic processes also occur continually. These phenomena are driven by various mediators, of which the key role is attributed to the interactions within the cytokine network. The most important group controlling the disease seems to be inflammatory cytokines, including IL-1β, TNFα, IL-6, IL-15, IL-17, and IL-18. The second group with antagonistic effect is formed by cytokines known as anti-inflammatory cytokines such as IL-4, IL-10, and IL-13. The role of inflammatory and anti-inflammatory cytokines in the pathogenesis of OA with respect to inter- and intracellular signaling pathways is still under investigation. This paper summarizes the current state of knowledge. The cytokine network in OA is put in the context of cells involved in this degenerative joint disease. The possibilities for further implementation of new therapeutic strategies in OA are also pointed.
An automated method to build a corpus of rhetorically-classified sentences in biomedical texts

The rhetorical classification of sentences in biomedical texts is an important task in the recognition of the components of a scientific argument. Generating supervised machine learned models to do this recognition requires corpora annotated for the rhetorical categories Introduction (or Background), Method, Result, Discussion (or Conclusion). Currently, a few, small annotated corpora exist. We use a straightforward feature of co-referring text using the word “this” to build a selfannotating corpus extracted from a large biomedical research paper dataset. The corpus is annotated for all of the rhetorical categories except Introduction without involving domain experts. In a 10-fold cross-validation, we report an overall F
The Halo Occupation Distribution: Toward an Empirical Determination of the Relation between Galaxies and Mass

We investigate galaxy bias in the framework of the "halo occupation distribution" (HOD), which defines the bias of a population of galaxies by the conditional probability P(N|M) that a dark matter halo of virial mass M contains N galaxies, together with prescriptions that specify the relative spatial and velocity distributions of galaxies and dark matter within halos. By populating the halos of a cosmological N-body simulation using a variety of HOD models, we examine the sensitivity of different galaxy clustering statistics to properties of the HOD. The galaxy correlation function responds to different aspects of P(N|M) on different scales. Obtaining the observed power-law form of ξg(r) requires rather specific combinations of HOD parameters, implying a strong constraint on the physics of galaxy formation; the success of numerical and semianalytic models in reproducing this form is entirely nontrivial. Other clustering statistics such as the galaxy-mass correlation function, the bispectrum, the void probability function, the pairwise velocity dispersion, and the group multiplicity function are sensitive to different combinations of HOD parameters and thus provide complementary information about galaxy bias. We outline a strategy for determining the HOD empirically from redshift survey data. This method starts from an assumed cosmological model, but we argue that cosmological and HOD parameters will have nondegenerate effects on galaxy clustering, so that a substantially incorrect cosmological model will not reproduce the observations for any choice of HOD. Empirical determinations of the HOD as a function of galaxy type from the Two-Degree Field (2dF) and Sloan Digital Sky Survey (SDSS) redshift surveys will provide a detailed target for theories of galaxy formation, insight into the origin of galaxy properties, and sharper tests of cosmological models.
Intelligent image preloading by predicting user navigation through medical image stacks using a neural network

The data volumes produced by instruments used in the field of medical imaging has grown faster than the hardware used to store, communicate and display the data. This problem calls for more intelligent data management. A PACS (Picture Archiving and Communication System) organizes and presents information associated with medical imaging. In this thesis a neural network was developed and used to investigate the feasibility of using the network to make automatic predictions of user interactions in order to increase the perceived PACS performance, and reduce network utilization. It explores what is required from a solution to the problem and presents several types of predictions. These are tested and evaluated using real user interaction data collected at the Örebro University hospital. The suggested approaches provide insight into the problem and pave way for future work on the subject.
Optimizing queries on files

We present a framework which allows the user to access and manipulate data uniformly, regardless of whether it resides in a database or in the file system (or in both). A key issue is the performance of the system. We show that text indexing, combined with newly developed optimization techniques, can be used to provide an efficient high level interface to information stored in files. Furthermore, using these techniques, some queries can be evaluated significantly faster than in standard database implementations. We also study the tradeoff between efficiency and the amount of indexing.
Path based p-cycles for resilient MPLS network design

Resilient networks are those capable of continuously offering telecommunication services in the presence of network failures. New paradigms for reliable network design have been emerging and constantly improving network survivability. Failure-Independent Path-Protecting (FIPP) p-cycles are a path based extension of the well-known p-cycle concept and inherit attractive properties of ring-like recovery speed and mesh-like capacity efficiency. They are suitable for application to the Multi-Protocol Label Switching (MPLS) protocol that is widely used in Next Generation Networks, in that it provides shared, failure independent, end-to-end protection to whole working paths. We consider two basic models: known as FIPP-SCP and FIPP-DRS respectively and this is followed by an introduction to a Joint Capacity Allocation design that is based on the FIPP-SCP model, which we assert is more appropriate for use in MPLS networks. The network design is referred to as the MFIPP-JCA model and involves three specific cases.
SPEX: scalable spatial publish/subscribe for distributed virtual worlds without borders

Existing architectures designed to host large-scale virtual environments (VEs) use a variety of approaches, but they often limit the interaction range with other users or with the VE. How densely users can populate a given region is also limited by the hosting machine's CPU or bandwidth resources. We are motivated to remove such restrictions and present SPEX, an infrastructure that supports scalable spatial publish/subscribe for VE applications. SPEX is scalable and fault-tolerant, with adaptive load balancing and low latency as its key features. It is designed for the state and overlay management in VEs with many concurrent users. We evaluate a practical SPEX implementation within Amazon's EC2 Cloud and present a feasible approach to supporting 750 users across a continent with low latency, opening the possibility for hosting fast-paced games (e.g., first-person shooters) or applications on a large-scale.
Maximising Acute Kidney Injury Alerts – A Cross-Sectional Comparison with the Clinical Diagnosis

Background Acute kidney injury (AKI) is serious and widespread across healthcare (1 in 7 hospital admissions) but recognition is often delayed causing avoidable harm. Nationwide automated biochemistry alerts for AKI stages 1-3 have been introduced in England to improve recognition. We explored how these alerts compared with clinical diagnosis in different hospital settings. Methods We used a large population cohort of 4464 patients with renal impairment. Each patient had case-note review by a nephrologist, using RIFLE criteria to diagnose AKI and chronic kidney disease (CKD). We identified and staged AKI alerts using the new national NHS England AKI algorithm and compared this with nephrologist diagnosis across hospital settings. Results Of 4464 patients, 525 had RIFLE AKI, 449 had mild AKI, 2185 had CKD (without AKI) and 1305 were of uncertain chronicity. NHS AKI algorithm criteria alerted for 90.5% of RIFLE AKI, 72.4% of mild AKI, 34.1% of uncertain cases and 14.0% of patients who actually had CKD.The algorithm identified AKI particularly well in intensive care (95.5%) and nephrology (94.6%), but less well on surgical wards (86.4%). Restricting the algorithm to stage 2 and 3 alerts reduced the over-diagnosis of AKI in CKD patients from 14.0% to 2.1%, but missed or delayed alerts in two-thirds of RIFLE AKI patients. Conclusion Automated AKI detection performed well across hospital settings, but was less sensitive on surgical wards. Clinicians should be mindful that restricting alerts to stages 2-3 may identify fewer CKD patients, but including stage 1 provides more sensitive and timely alerting.
An Introduction to Front-End Processing and Acoustic Features for Automatic Speech Recognition

This is a term paper in the course Speech Technology within the Swedish national graduate school of language technology, GSLT. It gives an introduction to front-end processing and acoustic features for automatic speech recognition. That is, it describes the process of transforming the physical sound signal of speech into a feature vector that is suitable for further processing. The process is divided into a few steps that are described separately. For some of the steps different approaches are described.
Towards a fibre-based constitutive law for the myocardium

According to Streeter, myocardial bres are organized in a nested set of layers and they are geodesics of these toroidal surfaces. In a previous work, using experimental data, we checked several consequences of this conjecture for the particular case of the left ventricle. We used a bre reconstruction algorithm that we have now generalized in a surface reconstruction algorithm which can be used for both ventricles and which leads to a better visualization of the layers. In the same time, we are interested in the derivation of a constitutive law for the myocardium which should take into account both the bre structure and the electric activation. In a rst step in this direction, we use a discrete homogenization technique. R esum e. Les bres myocardiques sont, selon Streeter, des g eod esiques de surfaces torodales embo^t ees. Partant de donn ees exp erimentales, nous avons, dans une premi ere etape, v eri e sur le ventricule gauche plusieurs cons equences de cette conjecture. Nous avons maintenant d evelopp e un algorithme de recons- truction de surfaces qui permet une visualisation des couches de bres. Par ailleurs, nous proposons d'utiliser une technique d'homog en eisation discr ete pour etablir une loi de comportement du muscle cardiaque prenant en compte la structure breuse.
Result checking in global computing systems

Global Computing is a particular modality of Grid Computing targeting massive parallelism, Internet computing and cycle-stealing. This new computing infrastructure has been shown to be exposed to a new type of attacks, where authentication is not relevant, network security techniques are not sufficient, and result-checking algorithms may be unavailable. The behavior of a Global Computing System, which is likely to be bimodal, nevertheless offers an opportunity for a probabilistic verification process that is efficient in the most frequent cases, and degrades gracefully as the problem becomes more difficult. For the two cases of a system based on anonymous volunteers, and a better controlled system, we propose probabilistic tests which self-adapt to the behavior of the computing entities.
Liquidity, Economic Activity, and Mortality

Abstract We document a within-month mortality cycle where deaths decline before the first day of the month and spike after the first. This cycle is present across a wide variety of causes and demographic groups. A similar cycle exists for a range of economic activities, suggesting the mortality cycle may be due to short-term variation in levels of economic activity. We provide evidence that the within-month activity cycle is generated by liquidity. Our results suggest a causal pathway whereby liquidity problems reduce activity, which in turn reduces mortality. These relationships may help explain the procyclical nature of mortality.
Induction of L1 mRNA in PC12 cells by NGF is modulated by cell-cell contact and does not require the high-affinity NGF receptor

We examined the effects of nerve growth factor (NGF) and cell-cell contact on expression of the neural cell adhesion molecule L1 in PC12 cells. After 7 d exposure to NGF, but not after exposure to EGF, FGF, TGF beta, or dibutyryl cAMP (dbcAMP), L1 mRNA levels increased fourfold. This increase was not blocked by K252a, an inhibitor of the high-affinity NGF receptor, although neurite extension was completely inhibited. L1 mRNA levels also increased in NGF-treated mutant PC12 cells (PC12nnr5) that lack the high-affinity NGF receptor. The effect of NGF on L1 mRNA was greatest in cells cultured at high density, but its effect on cells cultured at low density was augmented by antibody to L1 (to mimic L1 homophilic binding). Various extracellular matrix components had no differential effects on L1 mRNA levels in either the presence or absence of NGF. Together, these findings suggest that NGF regulates L1 expression by a mechanism that is independent of the high- affinity NGF receptor and that this regulation is modulated by cell- cell contact but not by cell-extracellular matrix interactions.
Rapid eye movement sleep does not seem to unbind memories from their emotional context GA

GA ETANE DEL I ENS 1 , 2 , D AN I E L NEU 2 , 3 and PH I L I P PE PE IGNEUX 1 Neuropsychology and Functional Neuroimaging Research Group at Center for Research in Cognition and ULB Neurosciences Institute, Universit e Libre de Bruxelles (ULB), Brussels, Belgium, Sleep Laboratory and Unit for Chronobiology U78, Brugmann University Hospital, Universit e Libre de Bruxelles (ULB) and Vrije Universiteit Brussel (VUB), Brussels, Belgium and Laboratory of Medical Psychology ULB312, Faculty of Medicine, Neuroscience Institute U.L. B. , Brussels, Belgium
High-impact Refactoring Based on Architecture Violations

Software refactoring has been identified as a key technique for the maintenance and evolution of object-oriented systems. Most interesting are high-impact refactorings, that is, refactorings that have a strong impact on the quality of the system's architecture. "Bad smells " and code metrics have been suggested as means for identifying refactoring needs. According to our experience these techniques are useful yet, in order to spot opportunities for high-impact refactorings, they should be complemented with the analysis of architectural violations. The subject of this report is a mid-sized Java enterprise application from the telecommunications domain whose functionality had to be radically extended We show how we combined several tools and techniques to identify' opportunities for high-impact refactorings, and discuss the resulting architecture, the refactoring process, tool support as well as related experiences
INCORPORATING SEMANTIC KNOWLEDGE TO THE LANGUAGE MODEL IN A SPEECH UNDERSTANDING SYSTEM

The majority of the understanding systems follows an architecture based on two modules, a speech recognition module and an understanding module. Usually, only syntactic restrictions are incorporated to the speech recognition module through the language model and the semantic restrictions are incorporated in the understanding module. In this work, we present an approach to language understanding where the semantic knowledge involved in the understanding process is incorporated through an adequate definition of the language mo del of the automatic speech recognition module. Then, both the recognition and understanding processes incorporate semantic knowledge. An evaluation of the behavior of the proposed understanding system in the framework of a dialog system is also presented. The results show that the use of semantic information in the language model of the speech recognizer provides for the best performance.
Winner-Take-All and Proportional-Prize Contests: Theory and Experimental Results

This study provides a unified framework to compare three canonical forms of competition: winner-take-all contests won by the best performer, winner-take-all lotteries where probability of success is proportional to performance, and proportional-prize contests in which rewards are shared in proportion to performance. Performance is affected by random noise, reflecting imperfect information. We derive equilibria and observe outcomes from each contest in a laboratory experiment. Equilibrium and observed efforts are highest in winner-take-all contests. Lotteries and proportional-prize contests have the same Nash equilibrium, but empirically, lotteries induce contestants to choose higher efforts and receive lower, more unequal payoffs. This result may explain why contest designers who seek only to elicit effort offer lump-sum prizes, even though contestants would be better off with proportional rewards. Finally, we collect measures of contestants’ risk aversion, other-regarding preferences, and utility of winning a contest, and use them to partially explain deviations from standard and homogeneous equilibrium models of effort choice in the three contests.
Detection of user relationships in networks using self-organization approach

For the purpose of monitoring and knowing the extent of use of objects in the network, generally each network operation is stored for subsequent analysis to determine which objects and how often are they accessed and modified. The self-organization of such multi-state operations is used in this work to help clustering of relationships amongst users or between users and network objects; and enhance the performance of network management. The attributes of involved users used in clustering helps in generating alerts well in advance due to misuse of objects. The experimental setup is built to demonstrate the effectiveness of this approach.
A Sentence Classification System for Multi Biomedical Literature Summarization

A PubMed search often returns a long list of queryrelated papers that a researcher cannot cope with in a short time. As a first step to address this issue by summarizing retrieved papers, we developed a system to classify sentences of abstracts obtained from the MEDLINE database into five rhetorical statuses: background, purpose, method, result, or conclusion. We used Support Vector Machine (SVM) classifiers and trained each of them for a different rhetorical status on structured abstracts. A structured abstract is one that has labels indicating rhetorical statuses of the sentences, while an unstructured abstract does not. The classifiers were tested on both structured and unstructured abstracts. The former were randomly obtained from the MEDLINE database and the latter were manually labeled by humans. We compared our method with a previously reported one. In addition, we combined them and evaluated the combined method. Our method outperformed the previously reported one, and the combined method showed even better results. Classified abstracts can be used for multi-document summarization that provides researchers with a way of learning a research topic efficiently and effectively.