Miracle-dz/UniFAR-FTUs huggingface dataset:
Ascaris lumbricoides Infection and Its Relation to Environmental Factors in the Mbeya Region of Tanzania, a Cross-Sectional, Population-Based Study

Background With one quarter of the world population infected, the intestinal nematode Ascaris lumbricoides is one of the most common infectious agents, especially in the tropics and sub-tropics. Infection is caused by oral intake of eggs and can cause respiratory and gastrointestinal problems. To identify high risk areas for intervention, it is necessary to understand the effects of climatic, environmental and socio-demographic conditions on A. lumbricoides infection. Methodology Cross-sectional survey data of 6,366 study participants in the Mbeya region of South-Western Tanzania were used to analyze associations between remotely sensed environmental data and A. lumbricoides infection. Non-linear associations were accounted for by using fractional polynomial regression, and socio-demographic and sanitary data were included as potential confounders. Principal Findings The overall prevalence of A. lumbricoides infection was 6.8%. Our final multivariable model revealed a significant non-linear association between rainfall and A. lumbricoides infection with peak prevalences at 1740 mm of mean annual rainfall. Mean annual land surface temperature during the day was linearly modeled and negatively associated with A. lumbricoides infection (odds ratio (OR) = 0.87, 95% confidence interval (CI) = 0.78–0.97). Furthermore, age, which also showed a significant non-linear association (infection maximum at 7.7 years), socio-economic status (OR = 0.82, CI = 0.68–0.97), and latrine coverage around the house (OR = 0.80, CI = 0.67–0.96) remained in the final model. Conclusions A. lumbricoides infection was associated with environmental, socio-demographic and sanitary factors both in uni- and multivariable analysis. Non-linear analysis with fractional polynomials can improve model fit, resulting in a better understanding of the relationship between environmental conditions and helminth infection, and more precise predictions of high prevalence areas. However, socio-demographic determinants and sanitary conditions should also be considered, especially when planning public health interventions on a smaller scale, such as the community level.
A Theory for Rapid Exact Signal Scanning with Deep Multi-Scale Convolutional Neural Networks

We introduce and analyze a rigorous formulation of the dynamics of a signal processing scheme that aims at dense scanning of large input signals. Recently proposed methodologies lack a satisfactory discussion of whether they actually produce the correct results according to their definition, especially in the context of Convolutional Neural Networks. We improve on this through an exact characterization of the requirements for a sound sliding window approach. The tools developed in this paper are especially beneficial if Convolutional Neural Networks are employed, but can also be used as a more general framework to validate related approaches to signal scanning. The contributed theory helps to eliminate redundant computations and renders special case treatment unnecessary, resulting in a dramatic boost in efficiency particularly on massively parallel processors.
Towards agile security risk management in RE and beyond

Little attention has been given so far to the process of security risk management at the early stages of system development. Security has been addressed by isolated security assurance practices, some of which consider risks and mitigations but they do not provide an overview of the overall security state of the system being developed. This paper takes the position that (1) these isolated security assurance practices should be fully integrated and should be embedded in short iterations of risk assessment, treatment and acceptance, providing input for updating security requirements and for security risk management, and that (2) available empirical data from public catalogs and databases should be used as a source of expertise, to leverage past experiences, and therefore reduce, although not eliminate, subjectivity of human judgment. Borrowing from the agile software development and project management philosophy, we introduce the idea of a light weight, agile approach to security risk management integrated to the development life cycle.
Semi-Supervised Top-k Query in Wireless Sensor Networks

This paper focuses on top-k query in wireless sensor networks and proposes a semi-supervised top-k query approach called CAV. Based on a spanning tree model, CAV adopts histogram technique and an aggregate-verify mechanism to guide query and filter the useless sensing data so as to reduce energy consumption. Besides, this paper further proposes two evaluation schemes of histogram. Compared with existing approaches, CAV is semi-supervised and neednpsilat set any parameters in advance. Performance analysis and simulation experiment results show the performance of CAV is much superior to that of TAG and it is superior to smooth data distribution than it is to random data distribution.
ALIGNMENT OF BILINGUAL ANTHROPONYMS AND TOPONYMS IN PARALLEL CORPORA FRENCH – ARABIC

Researches in the field of Named Entity recognition and alignment are of strong interest for various applications of natural language processing, such as Cross Lingual Information Retrieval, document management, question-answering systems, data mining etc. But in the processing of Arabic language, the task is particularly difficult and few resources are available to cope with these difficulties. In this paper, we present a simple method of character transcoding a kind of transliteration that we call character reduction which could improve an aligning system for Named Entities such as anthroponyms and toponyms. This system has been applied and evaluated on a French-Arabic parallel corpus that has been used during the Arcade 2 evaluation campaign. The purpose of this method is to bring the graphic forms of both languages close together as much as possible, in order to increase aligning precision. An outcome of such aligning is the ability to project on the target language (Arabic) annotations that has been done on the source language, for which more tools and resources are available (French, English, etc.).
Long-term treatment effects of the FR-2 appliance: a prospective evalution 7 years post-treatment.

AIM To examine the long-term effects induced by treatment with the function regulator (FR-2) appliance 7 years post-treatment compared with untreated class II subjects. SUBJECTS AND METHODS The FR-2 sample was collected prospectively and comprised 17 subjects (10 boys and 7 girls, mean age 10.8 years) who were treated with the FR-2 appliance for 1.7 years and re-evaluated 7.1 years after treatment. The step-by-step mandibular advancement was performed gradually (increments up to 3-4 mm), until a 'super class I' molar relationship was obtained. The control group consisted of 17 class II subjects (9 boys and 8 girls, mean age 11.3 years) with class II malocclusion, excessive overjet, and class II molar relationship, matched to the treated group as to ages at all times, gender distribution, and stages of skeletal maturity (evaluated by the cervical vertebral maturation method). The lateral cephalograms were analysed at T1 (initial), T2 (final), and T3 (7.1 years post-treatment). The compatibility between the groups and the comparisons of their changes at T1-T2, T2-T3, and T1-T3 intervals were examined by independent sample t-tests (P < 0.05). RESULTS FR-2 treatment provided a significant improvement in the maxillomandibular relationship due to an increase in mandibular length compared with controls, which remained stable over time. Also overjet, overbite, and molar relationship corrections demonstrated stability. Among dentoalveolar changes, only the increased mesial movement of the mandibular molars in the FR-2 group demonstrated stability. CONCLUSIONS Correction of class II malocclusion remained stable 7 years after FR-2 treatment mainly due to the stability of the skeletal changes.
The effects of stereoscopic 3D on an incidence response training game

A critical incident is defined as one of the most serious situations that can occur at any institution. Among these critical incidents are fire hazards, which resulted in 224 civilian deaths, and approximately 1.5$ billion dollars in direct property damage in Canada in 2011 alone. As a result of these costs, much effort is currently being placed in both fire safety prevention and fire safety evacuation. However, many current training techniques come at a high cost, and remain ineffective for real life fire situations, especially those which take place in large institutions. An example of this can be seen in university chemical labs, which hold many hazardous and flammable materials. In this thesis, the development of an incidence response training game for chemical lab fires is described. Serious games leverage the power of computer games to captivate and engage players/learners for a specific purpose such as to develop new knowledge or skills. Not only do serious games allow for higher engagement rates, and easier distribution than current training techniques, but by utilizing stereoscopic 3D (S3D) technologies it is hypothesized by many researchers that even higher engagement rates as well as knowledge retention levels can be reached. However, there is a lack of research examining the effects of S3D within an incidence response serious game. In this thesis, three experiments were conducted to examine potential benefits of employing S3D within a serious game for incidence response: i) engagement effects with S3D, ii) calibration of S3D settings, and iii) knowledge retention levels with S3D. The results of these experiments revealed that users were neither more engaged, nor did they increase their knowledge retention with the incorporation of S3D, contrary to prior research. Furthermore, allowing a user to define their own S3D settings is critical, and the inability to do so may create visual discomfort or an unnoticeable S3D effect.
Influence of ischemic and infarcted tissue on the surface potential

The influence of ischemic and infarcted tissue on the body-surface potential was investigated. A cellular automaton was employed for modeling the heart's specialized conduction system, junction points, action potentials, and fiber orientation. The cellular automaton generated a transmembrane potential distribution providing the source distribution for the electrocardiographic forward problem. The body-surface potential was calculated with a bidomain-based forward model of a patient's volume conductor. Several scenarios were simulated including anterior and posterior myocardial infarction. The simulations demonstrated the influence on the body-surface potential displaying ST elevations and depressions as well as changes in polarity depending on the location of the ischemic region and electrode position.
Modeling the effects of manufacturing variation on high-speed microprocessor interconnect performance

This paper contributes the first study of manufacturing variation on interconnect timing performance in a high speed microprocessor. Also new in this paper is a methodology using timing analysis in conjunction with post-extraction net adjustment to account for interconnect structure variation (e.g., that arising due to pattern dependencies); this methodology is efficient enough to enable thousands of nets to be analyzed for variation and is compatible with current CAD tools.
Gradient-Limited Affine Projection Algorithm for Double-Talk-Robust and Fast-Converging Acoustic Echo Cancellation

We propose a gradient-limited affine projection algorithm (GL-APA), which can achieve fast and double-talk-robust convergence in acoustic echo cancellation. GL-APA is derived from the M-estimation-based nonlinear cost function extended for evaluating multiple error signals dealt with in the affine projection algorithm (APA). By considering the nonlinearity of the gradient, we carefully formulate an update equation consistent with multiple input-output relationships, which the conventional APA inherently satisfies to achieve fast convergence. We also newly introduce a scaling rule for the nonlinearity, so we can easily implement GL-APA by using a predetermined primary function as a basis of scaling with any projection order. This guarantees a linkage between GL-APA and the gradient-limited normalized least-mean-squares algorithm (GL-NLMS), which is a conventional algorithm that corresponds to the GL-APA of the first order. The performance of GL-APA is demonstrated with simulation results.
Homophyly Networks - A Structural Theory of Networks

A grand challenge in network science is apparently the missing of a structural theory of networks. The authors have showed that the existence of community structures is a universal phenomenon in real networks, and that neither randomness nor preferential attachment is a mechanism of community structures of network \footnote{A. Li, J. Li, and Y. Pan, Community structures are definable in networks, and universal in the real world, To appear. }. This poses a fundamental question: What are the mechanisms of community structures of real networks? Here we found that homophyly is the mechanism of community structures and a structural theory of networks. We proposed a homophyly model. It was shown that networks of our model satisfy a series of new topological, probabilistic and combinatorial principles, including a fundamental principle, a community structure principle, a degree priority principle, a widths principle, an inclusion and infection principle, a king node principle, and a predicting principle etc, leading to a structural theory of networks. Our model demonstrates that homophyly is the underlying mechanism of community structures of networks, that nodes of the same community share common features, that power law and small world property are never obstacles of the existence of community structures in networks, and that community structures are definable in networks.
Self organization in content delivery networks

The Content Delivery Network paradigm with a centralized approach shows its limits in large and dynamic systems. Decentralized algorithms and protocols, such as peer-to-peer (P2P) and multi agent systems, can be usefully employed. In this work, an algorithm exploiting bio-inspired agents to organize the contents in Content Delivery Networks, is proposed. Agents move and reorganize the metadata that describe the contents to improve information retrieval. Preliminary results confirm the efficacy of the approach.
SCoPe: A Decision System for Large Scale Container Provisioning Management

Operating system (OS) containers provide a process level virtualization in a multi-tenant Cloud environment. Such containers are becoming increasingly popular in developer community as they facilitate fast development and delivery of enterprise class Cloud services. Furthermore, these containers share a common OS and hence, they have a low resource foot-print leading to reduced provisioning time. In this paper, we investigate such promise of containers while provisioning large scale 3-tier applications. First, through benchmarking, we observe that at very large scale, several application scaling factors (e.g., number of containers of an application provisioned in parallel, application load) and system state parameters (e.g., number of applications and containers running on a system) introduce variability in application provisioning time because of resource bottleneck in general, and specifically due to the OS process overhead. To address such variability, we propose a provisioning decision management system SCoPe that provides an application partitioning and provisioning strategy where we determine the maximum number of containers of every application that can be provisioned in parallel across physical machines, while meeting the service level agreement (SLA) on provisioning time and Cloud provider specific objectives (e.g., maximize consolidation of applications, minimize operating cost). This joint partitioning and provisioning problem is NP-hard and we propose a greedy heuristic solution. Using real data set and through extensive experiments, we demonstrate the performance of SCoPe for large scale container based application provisioning. Compared to other well-known heuristics, SCoPe can reduce the partitioning by 5x or more, while meeting SLAs.
Learning Relevant Features for Manipulation Skills using Meta-Level Priors

Robots can generalize manipulation skills between different scenarios by adapting to the features of the objects being manipulated. Selecting the set of relevant features for generalizing skills has usually been performed manually by a human. Alternatively, a robot can learn to select relevant features autonomously. However, feature selection usually requires a large amount of training data, which would require many demonstrations. In order to learn the relevant features more efficiently, we propose using a meta-level prior to transfer the relevance of features from previously learned skills. The experiments show that the meta-level prior more than doubles the average precision and recall of the feature selection when compared to a standard uniform prior. The proposed approach was used to learn a variety of manipulation skills, including pushing, cutting, and pouring.
Twig Query Processing Under Concurrent Updates

An appropriate database language characteristics leading to the success of declarative query processing - and, in turn, to the rise of relational DBMSs in general - always provides more than one way of evaluating a query. This counts for structurally different but logically equivalent query evaluation plans (QEPs) as well as for different implementations of the same logical operator. This principle surely holds for the novel XML database management systems (XDBMSs): Recently proposed operators for XML query processing can be grouped into the logical operators Structural Join [1, 22] and Holistic Twig Join [3, 6, 16]. Depending on available internal system mechanisms, a lot of opportunities exist how to implement these operators (two of which are presented in this paper.
Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013

The 13th St Gallen International Breast Cancer Conference (2013) Expert Panel reviewed and endorsed substantial new evidence on aspects of the local and regional therapies for early breast cancer, supporting less extensive surgery to the axilla and shorter durations of radiation therapy. It refined its earlier approach to the classification and management of luminal disease in the absence of amplification or overexpression of the Human Epidermal growth factor Receptor 2 (HER2) oncogene, while retaining essentially unchanged recommendations for the systemic adjuvant therapy of HER2-positive and ‘triple-negative’ disease. The Panel again accepted that conventional clinico-pathological factors provided a surrogate subtype classification, while noting that in those areas of the world where multi-gene molecular assays are readily available many clinicians prefer to base chemotherapy decisions for patients with luminal disease on these genomic results rather than the surrogate subtype definitions. Several multi-gene molecular assays were recognized as providing accurate and reproducible prognostic information, and in some cases prediction of response to chemotherapy. Cost and availability preclude their application in many environments at the present time. Broad treatment recommendations are presented. Such recommendations do not imply that each Panel member agrees: indeed, among more than 100 questions, only one (trastuzumab duration) commanded 100% agreement. The various recommendations in fact carried differing degrees of support, as reflected in the nuanced wording of the text below and in the votes recorded in supplementary Appendix S1, available at Annals of Oncology online. Detailed decisions on treatment will as always involve clinical consideration of disease extent, host factors, patient preferences and social and economic constraints.
An Improved Electromagnetism-like Algorithm for Recurrent Neural Fuzzy Controller Design

This paper introduces an improved electromagnetism-like algorithm (IEM) for recurrent fuzzy neural controller design. The hybrid IEM algorithm combines the advantages of the electromagnetism-like (EM) algorithm and the genetic algorithm (GA). The proposed IEM is composed of initialization, local search, total force calculation, movement, and evaluation. For recurrent fuzzy neural controller design, IEM simulates the “attraction” and “repulsion” of charged particles based on the electromagnetism theory by considering each fuzzy neural system as an electrical charge. IEM algorithm involves replacing the neighborhood randomly local search with a competitive concept and GA. IEM can treat the optimization of fuzzy neural systems for gradient information free systems. In addition, IEM has the capability of rapidly convergence and reduces the computation complexity of EM. Finally, two illustrative examples are presented to demonstrate the performance and effectiveness of IEM.
Time variation of the fine structure constant in the early universe and the Bekenstein model

Aims. We calculate the bounds on the variation in the fine structure constant at the time of primordial nucleosynthesis and at the time of neutral hydrogen formation. We used these bounds and other bounds from the late universe to test the Bekenstein model. Methods. We modified the Kawano code, CAMB, and CosmoMC to include the possible variation in the fine structure constant. We used observational primordial abundances of D, 4 He, and 7 Li, recent data from the cosmic microwave background, and the 2dFGRS power spectrum, to obtain bounds on the variation in α. We calculated a piecewise solution to the scalar field equation of the Bekenstein model in two different regimes: i) matter and radiation, ii) matter and cosmological constant. We match both solutions with the appropriate boundary conditions. We performed a statistical analysis, using the bounds obtained from the early universe and other bounds from the late universe to constrain the free parameters of the model. Results. Results are consistent with no variation in α for the early universe. Limits on α are inconsistent with the scale length of the theory l being larger than the Planck scale. Conclusions. In order to fit all observational and experimental data, the assumption l > Lp implied in Bekenstein’s model has to be relaxed.
Dynamic range Analysis of HH Model for Excitable Neurons

Brain exercises control over other organs of the body. This complex control process, is completed in steps which involves commands issued to the muscles to execute movements, receipt of feedback from sensors reporting the actual state of the musculature & skeletal elements, and inputs from the senses to monitor progress towards the goal. This is made possible by neurons through communicating with each other. Neurons are cells specialised for transmitting and receiving information. Exchange of information between neurons is either in the form of chemical or electrical signals. Electrically information is conveyed in the form of neural electrical signal know as action potential. Hodgkin and Huxley conducted Voltage Clamped experiments to study mechanism for generation and propagation of action potential in giant Axon of Squid and proposed a simple mathematical model known as HH model. Since information is conveyed through time and frequency of action potential, dynamic range becomes an important criterion for analysis. This paper analyse the dynamic range of the HH model through simulation, for its ability to convey information within and between neurons for effective control by brain over other organs.