Miracle-dz/UniFAR-FTUs huggingface dataset:
Smoking and incidence of glaucoma

Abstract Smoking is a serious global public health concern that has been related to many chronic diseases. However, the effect of smoking on eye disorders has been less studied. The aim of this cohort study was to assess the association between current tobacco smokers and the risk of developing glaucoma and furthermore to evaluate the relationship between passive or former smokers and glaucoma. In this prospective and dynamic cohort, 16,797 participants initially who were found not to have glaucoma were followed up for a median of 8.5 years. Validated data on lifestyle, including tobacco consumption, were assessed at baseline. Information about new diagnosis of glaucoma was collected by follow-up questionnaires every 2 years. The outcome was the incidence of self-reported glaucoma during the follow-up. A subsample was used to validate the glaucoma diagnosis. During the 8.5 years of follow-up, 184 new glaucoma cases were identified. Current smokers had a significantly higher risk of glaucoma compared to participants who had never smoked after controlling for potential confounders (Hazard ratio [HR] 1.88 [95% coefficient interval (CI): 1.26–2.81]; P = 0.002). A nonsignificant increased risk was found among former smokers (HR 1.27 [95% CI: 0.88–1.82]; P = 0.198). When we assessed the exposure as per the number of cigarette pack-years, a dose–response relationship between pack-years and the risk of glaucoma was found (HR for the 5th quintile versus the 1st quintile: 1.70 [95% IC: 1.10–2.64], P for trend, 0.009). However, no relationship was found between passive smokers and glaucoma. (HR 0.67 [95% CI: 0.37–1.21]; P = 0.189). Our results suggest a direct association between current smokers and the incidence of glaucoma. In particular, this association was related to the number of pack-years, which was not found in the case of former smokers nor in the case of passive smokers.
Communicating and Coordinating: Occasions for Information Technology in Loosely Coupled Organizations

This article uses the theory of loose coupling to explain failure in the adoption of a technology that was supposed to improve collaboration across one organization's internal boundaries. The research details an interpretive case study of a single organization, MacGregor Crane, in which relatively autonomous individuals are connected only loosely in terms of their daily interactions. The company implemented Lotus Notes® in an attempt to increase collaboration. However, this effort failed because employees in various units, particularly engineering, were reluctant to share information across unit boundaries. In light of these findings, it is suggested that the successful implementation of a collaborative IT within a loosely coupled organization should involve the reconsideration of the organizational members' roles and functions.
Radar sensor management for detection and tracking

Advancements in sensor technology provide new multi-sensor systems with increasing flexibility. The sensor management process aims to perform sensor actions that support the overall goal of the user of a multi-sensor system. Some sensors can support multiple functions. When the different sensor functions utilise shared resources then the sensor actions must be chosen as a compromise between competing actions. The problem of managing a multifunction electronically scanned array (ESA) radar is considered. The ESA radar is capable of performing surveillance (search) and tracking (revisit) functions. Two criteria are used to determine sensor actions with revisit actions based on the expected information gain and surveillance actions based on the probability of detection for previously undetected targets. The characteristics of the resulting sensor management policy are examined in an illustrative scenario.
Comparison of Network Intrusion Detection Systems in cloud computing environment

Computer Networks face a constant struggle against intruders and attackers. Attacks on distributed systems grow stronger and more prevalent each and every day. Intrusion detection methods are a key to control and potentially eradicate attacks on a system. An Intrusion detection system pertains to the methods used to identify an attack on a computer or computer network. In cloud computing environment the applications are user-centric and the customers should be confident about their applications stored in the cloud server. Network Intrusion Detection System (NIDS) plays an important role in providing the network security. They provide a defence layer which monitors the network traffic for pre-defined suspicious activity or pattern. In this paper Snort, Tcpdump and Network Flight Recorder which are the most famous NIDS in cloud system are examined and contrasted.
Scalar weighting optimal and adaptive information fusion kalman filter with feedback

Based on multi-sensor optimal fusion criterion weighted by scalars in the linear minimum variance sense, a scalar weighting optimal fusion Kalman filter with feedback is given for discrete linear stochastic system with multiple sensors, and it has a two-layer fusion structure. Further, an adaptive information fusion filter with feedback is also given when the process noise covariance is unknown. The fused filter and process noise covariance in the fusion center is spread to all local subsystems by feedback at each time step. And the fusion filters have better precision than any local filter does. Applying them to a radar tracking system shows their effectiveness.
Isotopic Abundance Analysis of Biofield Treated Benzene, Toluene and p-Xylene Using Gas Chromatography-Mass Spectrometry (GC-MS)

Benzene, toluene and p-xylene are derivatives of benzene, generally produced from crude petroleum and have numerous applications in industry. The aim of the present study was to evaluate the impact of biofield treatment on isotopic abundance of these benzene derivatives by gas chromatography-mass spectrometry (GC-MS). Benzene, toluene and p-xylene samples were divided into two parts: control and treatment. Control part was remained as untreated and treatment part was subjected to Mr. Trivedi’s biofield treatment. Control and treated samples were characterized using GC-MS. GC-MS data revealed that isotopic abundance ratio of 13C/12C or 2H/1H (PM+1/PM) of treated samples were significantly increased from un-substituted to substituted benzene rings (where, PM- primary molecule, PM+1- isotopic molecule either for 13C/12C and/or 2H/1H). The isotopic abundance ratio of 13C/12C or 2H/1H (PM+1/PM) in benzene was decreased significantly by 42.14% as compared to control. However, the isotopic abundance ratio of (PM+1/PM) in treated toluene and p-xylene was significantly increased up to 531.61% and 134.34% respectively as compared to their respective control. Thus, overall data suggest that biofield treatment has significantly altered the isotopic abundance ratio of (PM+1/PM) in a different way for un-substituted and substituted benzenes.
CONDITIONAL AND UNCONDITIONAL RISK MANAGEMENT ESTIMATES FOR EUROPEAN STOCK INDEX FUTURES

Accurate forecasting of risk is the key to successful risk management techniques. Correct modelling of a variable’s extreme values located at the distributional tails accounting for the fat-tail phenomena is paramount, and this paper presents an overview of a theoretically and statistically robust approach to this problem. Underpinned by Extreme Value Theory that explicitly allows for fat-tailed densities, this paper presents four precise measures of downside risk. Two Value at Risk and two Excess Loss Probability estimators evolve from the conditional and unconditional distributions. Fitting an AR(1)-GARCH(1, 1) filter provides the description of the current volatility climate. Results are presented for a panorama of twelve stock index futures traded on European bourses.
Modern to Historic Image Matching : ORB / SURF an Effective Matching Technique

Matching modern to historical images requires a special effort in the sense of dealing with historical photos. While there are many robust matching techniques which are vectoror binary-based that perform effectively on modern images, they are not accurate on historic photos. Photos of different landmarks were gathered for a long timeline taken in different conditions of illuminations, position, and weather. Images were matched using standard vector-based matching techniques SIFT and SURF and a binary-based technique: ORB. Match results of these sets of images were recorded and analyzed. Generally, these results shows successful matching of the modern digital images, while matching historic photos to modern ones shows poor matching results. A novel application of a hybrid ORB/SURF matching technique was applied in matching modern to historic images showed more accurate results and performs more effectively.
Robust recruitment near the edge of chaos and an application to mine sweeping

The field of robotics is in rapid development. As robots become cheaper to build, new applications involving many robots systems can be envisioned. One reason for using many robots is to achieve robustness. Having many robots, however, does not ensure robustness. A control strategy and robot behaviors must be engineered to incorporate robustness into the system. Swarm intelligence based approaches are popular for developing optimal and robust control strategies for systems of robots. Here we analyze the behavior of a swarm of robots modeled after a swarm of ants, where tasks are spatially distributed in the environment and robots/ants are recruited through short-range recruitment. For ants that move probabilistically in response to the short range signal and who adjust their probabilities such that they are near a phase change boundary, or edge-of-chaos, in the mean field analysis of their motions, we find a significant improvement in the robustness of the system.
Computing the Minimum EDF Feasible Deadline in Periodic Systems

In most real-time applications, deadlines are artifices that need to be enforced to meet different performance requirements. For example, in periodic task sets, jitter requirements can be met by assigning suitable relative deadlines and guaranteeing the feasibility of the schedule. This paper presents a method (called minD) for calculating the minimum EDF-feasible deadline of a real-time task. More precisely, given a set of periodic tasks with hard real-time requirements, which is feasible under EDF, the proposed algorithm allows computing the shortest deadline that can be assigned to an arbitrary task in the set, or to a new incoming task (periodic or aperiodic), still preserving the EDF feasibility of the new task set. The algorithm has a pseudo polynomial complexity and handles arbitrary relative deadlines, which can be less than, equal to, or greater than periods
Improvement of a structured language model: arbori-context tree

In this paper we present an extention of a context tree for a structured language model (SLM), which we call an arbori-context tree. The state-of-the-art SLM predicts the next word from a (cid:12)xed partial tree of the history tree, such as two exposed heads, etc. An arbori-context tree allows us to select an opti-mum partial tree of a history tree for the next word prediction depending on the e(cid:11)ectiveness in the similar way that a context tree selects the length of the history (n of n-gram). The experiment we conducted showed that the test set perplexity of the SLM based on an arbori-context tree (79.98) was lower than that of the SLM with a (cid:12)xed history (101.56).
Computationally efficient statistical face model in the feature space

In this paper, we present a computationally efficient statistical face modeling approach. The efficiency of our proposed approach is the result of mathematical simplifications in the core formula of a previous face modeling method and the use of the singular value decomposition. In order to reduce the errors in our resulting models, we preprocess the facial images to normalize for pose and illumination and remove little occlusions. Then, the statistical face models for the enrolled subjects are obtained from the normalized face images. The effects of the variations in pose, facial expression, and illumination on the accuracy of the system are studied. Experimental results demonstrate the reduction in the computational complexity of the new approach and its efficacy in modeling the face images.
Manganese exposure and the neuropsychological effect on children and adolescents: a review.

OBJECTIVES Manganese (Mn) is an essential element, but overexposure can have neurotoxic effects. METHODS In this article, we review and summarize studies on exposure to Mn and nervous system impairments in children. RESULTS We identified 12 original articles published between 1977 and 2007. Overexposure to Mn was suspected to occur through diverse sources: infant milk formula, drinking water, industrial pollution, and mining wastes. The most common bioindicator of exposure to Mn was hair Mn content, but some studies measured Mn in blood, urine, or dentin; one study on prenatal exposure measured Mn content in cord blood. Most studies indicate that higher postnatal exposure to Mn is associated with poorer cognitive functions and hyperactive behavior. CONCLUSIONS The limitations of the existing studies are numerous: most were cross-sectional, had a modest sample size, and lacked adjustment for important confounders. Future investigations should be performed on a larger sample size and include a more detailed exposure assessment, addressing multiple sources of exposure such as food, water, and airborne particulates.
A visual calendar for scheduling group meetings

Scheduling group meetings requires access to participants' calendars, typically located in scattered pockets or desks. Placing participants' calendars on-line and using a rule-based scheduler to find a time slot would alleviate the problem to some extent, but it often is difficult to trust the results, because correct scheduling rules are elusive, varying with the participants and the agenda of a particular meeting. What's needed is a comprehensive scheduling system that summarizes the available information for quick, flexible, and reliable scheduling. We have developed a prototype of a priority-based, graphical scheduling system called Visual Scheduler (VS). A controlled experiment comparing automatic scheduling with VS to manual scheduling demonstrated the former to be faster and less error prone. A field study conducted over six weeks at the UNC-CH Computer Science Department showed VS to be a generally useful system and provided valuable feedback on ways to enhance the functionality of the system to increase its value as a groupwork tool. In particular, users found priority-based time-slots and access to scheduling decision reasoning advantageous. VS has been in use by more than 75 faculty, staff, and graduate students since Fall 1987.
A QoS architecture integrating mobile Ad-Hoc and infrastructure networks

—This paper proposes an Ad-hoc QoS architecture, using as basis some concepts from SWAN, extending it to fulfill our requirements. It also proposes the modules required in the network elements and its interaction to provide optimized QoS in the ad-hoc network and integration between both networks. We also present the overall integration QoS signaling protocol and the differentiation mechanisms to address end-to-end QoS for real-time multimedia applications. Furthermore, the proposed solution addresses the use of multipath routing in order to provide load balancing and increased network reliability.
Semi-supervised temporal-spatial filter based on MRP for brain-computer interfaces

In brain-computer interface (BCI) studies, if the number of training trails is small, the discriminative patterns of movement related potentials (MRPs) can not be appropriately extracted by temporal-spatial filter (TSF) algorithm. Thus in this paper, we proposed a semi-supervised TSF (ssTSF) algorithm which employed self-training scheme to induce the unlabelled trails with high confidences and learn the discriminative patterns of MRPs iteratively. We compared TSF and ssTSF algorithm on the data from BCI competition I. The results demonstrated the effectiveness of the ssTSF, especially for small training sets.
Enriching spaces in practice-based education to support collaboration while mobile: the case of teacher education

Practice-based education is gaining a growing popularity in fields as diverse as, for example, software engineering, pedagogy and medical studies. In practice-based education learning takes place across different learning arenas and requires cooperation among all the actors involved in the learning process. However, mobility of students across these arenas impact deeply on cooperation patterns, and therefore on the learning process. In this paper we investigate the usage of shared display systems to promote cooperation among students in practice-based education. Our focus is on teacher education and the paper is based on our experiences with the teacher education programme at our university. Based on our observations of students out in practice, we discuss the importance of common spaces and the role of bulletin boards of different types. We then define high-level requirements for a shared display system to support practice-based education and we illustrate the main concepts with a demonstrator. Strengths and weaknesses of our approach are pointed out through an evaluation of the demonstrator.
Interest of Tumor-Specific CD4 T Helper 1 Cells for Therapeutic Anticancer Vaccine

Nowadays, immunotherapy represents one promising approach for cancer treatment. Recently, spectacular results of cancer immunotherapy clinical trials have confirmed the crucial role of immune system in cancer regression. Therapeutic cancer vaccine represents one widely used immunotherapy strategy to stimulate tumor specific T cell responses but clinical impact remains disappointing in targeting CD8 T cells. Although CD8 T cells have been initially considered to be the main protagonists, it is now clear that CD4 T cells also play a critical role in antitumor response. In this article, we discuss the role of tumor antigen-specific CD4 T cell responses and how we can target these cells to improve cancer vaccines.
A technology mapper for depth-constrained FPGA logic cells

In the last decade, progress in logic synthesis has brought about new advantageous circuit representations. These representations, such as And-Inverter Graphs in the ubiquitous open-source synthesizer ABC, have inspired new designs of Field Programmable Gate Arrays (FPGAs), which, instead of using Look-Up Tables (LUTs), mimic the topology of the circuit representation in the basic logic cells. More recent examples are Majority-Inverter Graphs, another uniform representation which has triggered considerable interest in synthesis and which naturally suggests new logic cells. Yet, in this paper we observe how naïvely adapting technology mapping solutions for classic LUT-based FPGAs to these new architectures incurs severe shortcomings. The key issue is that LUTs are inherently input-constrained (the logic function they implement is irrelevant) and have generally a single output; on the other hand, logic cells made of uniform networks of some fundamental logic function (e.g., And-Invert) are constrained in terms of logic depth and multiple outputs are an integral feature. We introduce novel and effective solutions to address these differences; the result is a highly versatile mapper - thus enabling further research in these new architectures - with a significantly better performance than what is described in literature for one such architecture. Specifically, when we compare with the state of the art on one sample architecture, we obtain a significant decrease in area (on average 18% over several benchmarks) while also improving slightly the critical path (a reduction of 3%).
A Distinct Spectrum of Copy Number Aberrations in Pediatric High-Grade Gliomas

Purpose: As genome-scale technologies begin to unravel the complexity of the equivalent tumors in adults, we can attempt detailed characterization of high-grade gliomas in children, that have until recently been lacking. Toward this end, we sought to validate and extend investigations of the differences between pediatric and adult tumors. Experimental Design: We carried out copy number profiling by array comparative genomic hybridization using a 32K bacterial artificial chromosome platform on 63 formalin-fixed paraffin-embedded cases of high-grade glioma arising in children and young people (<23 years). Results: The genomic profiles of these tumors could be subclassified into four categories: those with stable genomes, which were associated with a better prognosis; those with aneuploid and those with highly rearranged genomes; and those with an amplifier genotype, which had a significantly worse clinical outcome. Independent of this was a clear segregation of cases with 1q gain (more common in children) from those with concurrent 7 gain/10q loss (a defining feature of adults). Detailed mapping of all the amplification and deletion events revealed numerous low-frequency amplifications, including IGF1R, PDGFRB, PIK3CA, CDK6, CCND1, and CCNE1, and novel homozygous deletions encompassing unknown genes, including those at 5q35, 10q25, and 22q13. Despite this, aberrations targeting the “core signaling pathways” in adult glioblastomas are significantly underrepresented in the pediatric setting. Conclusions: These data highlight that although there are overlaps in the genomic events driving gliomagenesis of all ages, the pediatric disease harbors a distinct spectrum of copy number aberrations compared with adults. Clin Cancer Res; 16(13); 3368–77. ©2010 AACR.
JEAF: A Java Evolutionary Algorithm Framework

There are not many tools in the evolutionary computing field that allow researchers to implement, modify or compare different algorithms. Additionally, those tools usually lack flexibility, maintenance or some other characteristic, so researchers program their own solutions most of the time, reimplementing algorithms that have already been implemented hundreds of times. This paper introduces a new framework for evolutionary computation called JEAF (Java Evolutionary Algorithm Framework) that tries to offer a platform to facilitate the tasks of comparing, analyzing, modifying and implementing evolutionary algorithms, reusing components and programming as few as possible. JEAF also aims to be a tool for evolutionary algorithm users that employ these algorithms to solve other problems not related with evolutionary computation. In this sense, JEAF provides methods to distribute an evolutionary process and to plug external tools to perform the evaluation of candidate solutions.
A Study on Online Social Networks Theme Semantic Computing Model

Online information sources including Weibo and Wechat bring huge impact to the society. Only a few words of network information can expand rapidly and catalyze the generation of a huge amount of information. Semantic computing on online social networks and research on topics about emergencies have great significance. In this article, a numerical model of text semantic analysis based on artificial neural network is proposed, and a semantic computational algorithm for social network texts as well as a discovery algorithm for emergencies is provided with reference to the information provided by the social nodes itself and the semantic of the text. Semantic vector of micro-information for nodes and closure extension of semantic extensions are defined in order to build up an equivalence of short sentences, and in turn realize the discovery of emergencies.
Westward Spread of Echinococcus multilocularis in Foxes, France, 2005–2010

During 2005–2010, we investigated Echinococcus multilocularis infection within fox populations in a large area in France. The parasite is much more widely distributed than hitherto thought, spreading west, with a much higher prevalence than previously reported. The parasite also is present in the large conurbation of Paris.