Miracle-dz/UniFAR-FTUs huggingface dataset:
Day-by-Day Variability of Blood Pressure and Heart Rate at Home as a Novel Predictor of Prognosis: The Ohasama Study

Day-by-day blood pressure and heart rate variability defined as within-subject SDs of home measurements can be calculated from long-term self-measurement. We investigated the prognostic value of day-by-day variability in 2455 Ohasama, Japan, residents (baseline age: 35 to 96 years; 60.4% women). Home blood pressure and heart rate were measured once every morning for 26 days (median). A total of 462 deaths occurred over a median of 11.9 years, composing 168 cardiovascular deaths (stroke: n=83; cardiac: n=85) and 294 noncardiovascular deaths. Using Cox regression, we computed hazard ratios while adjusting for baseline characteristics, including blood pressure and heart rate level, sex, age, obesity, current smoking and drinking habits, history of cardiovascular disease, diabetes mellitus, hyperlipidemia, and treatment with antihypertensive drugs. An increase in systolic blood pressure variability of +1 between-subject SD was associated with increased hazard ratios for cardiovascular (1.27; P=0.002) and stroke mortality (1.41; P=0.0009) but not for cardiac mortality (1.13; P=0.26). Conversely, heart rate variability was associated with cardiovascular (1.24; P=0.002) and cardiac mortality (1.30; P=0.003) but not stroke mortality (1.17; P=0.12). Similar findings were observed for diastolic blood pressure variability. Additional adjustment of heart rate variability for systolic blood pressure variability and vice versa produced confirmatory results. Coefficient of variation, defined as within-subject SD divided by level of blood pressure or heart rate, displayed similar prognostic value. In conclusion, day-by-day blood pressure variability and heart rate variability by self-measurement at home make up a simple method of providing useful clinical information for assessing cardiovascular risk.
Utilising Semantically Rich Big Data to Enhance Book Recommendation Engines

This paper proposes a novel approach to book recommendation: we utilise big data created by thousands of book social cataloguing website users and treat it as a collectively written meta-annotation of a book. After learning semantic similarity between a large collection of books by applying algorithms of natural language processing (probabilistic topic models and semantic neural networks) to the symbolic text of meta-annotations, we show how to construct more precise and descriptive semantic recommendation engines and enrich content-based recommendation engines.
The ‘state of art’ of the research on Magnetic field teaching: a review of physics education literature between 1995 and 2015.

In this paper, we present a review of the Physics Education literature on the topic of Magnetic Field. We investigated eleven renowned national and international journals in the field over a period of twenty years, collecting papers that involve situations where magnetic field is present. Items collected were categorized within a qualitative literature perspective and three categories emerged naturally. It was noticed that students’ difficulties in understanding this concept is somewhat linked to their inability to visualize three-dimensional situations involving the magnetic field. The use of computer simulations was suggested as a possible means of addressing this. Despite criticism/ suggestions made by various authors regarding the use of Computer Simulations to teach this concept, there are few investigations theoretically and methodologically grounded on the educational applications of computer simulations to teach Magnetic field in the curricula of courses where this concept is deemed as important.
Thrombin generation in severely obese children

Since changes of hemostatic parameters in obese patients may contribute to the development of cardiovascular disease [1], we investigated whether there is a relationship between obesity in childhood and alterations in thrombin generation (TG). In a recently published pilot study we have shown that severely overweight children after a significantly prolonged lag phase and a significantly prolonged time to peak, generate significantly higher amounts of thrombin in comparison to age matched, normal weight, healthy controls. Furthermore, the ETP was significantly higher in obese children compared to controls [2].
Addressing challenges in automatic Language Identification of Romanized Text

Due to the diversity of documents on web, language identification is a vital task for web search engines during crawling and indexing of web documents. Among the current challenges in language-identification, the unsettled problem remains identifying Romanized text language. The challenge in Romanized text is the variations in word spellings and sounds in different dialects. We propose a Romanized text language identification system (RoLI) that addresses these challenges. RoLI uses an n-gram based approach and also exploits sound based similarity of words. RoLI does not rely on language intensive resources and is robust to Multilingual text. We focus on five Indian languages: Hindi, Telugu, Tamil, Kannada and Malayalam. Over the five languages, we achieve an average accuracy of 98.3%, despite the spelling variations as well as sound variations in Indian languages.
in Computer Science and Software Engineering

: Telecommunication companies routinely generate and store enormous amounts of high-quality data, have a very large customer base, and operate in a rapidly changing and highly competitive environment. These companies also face a number of data mining challenges due to the enormous size of their data sets, the sequential and temporal aspects of their data, and the need to predict very rare events—such as customer fraud and network failures—in real-time The main application areas of Business Intelligence and Data Mining in telecommunication industry include fraud detection, network fault isolation and improving market effectiveness.
Statistical models for battery recharging time in RF energy harvesting systems

This paper investigates the usage of radio frequency (RF) signal as a source in energy harvesting systems. The open issue in the related literature is the characterization of battery recharging time of an energy harvesting receiver node. RF energy harvesting has challenges due to the wireless propagation environment and conversion efficiency. On the propagation side, the different channel models between source and harvesting node should be taken into account in order to obtain realistic results. The main goal of this paper is to propose statistical models for battery recharging time for the Nakagami-m and the generalized-K fading channels. We also include the effects of lognormal shadowing. We derive the associated closed form probability density function, cumulative distribution function, moment generation function, mean and variance expressions for battery recharging time. The simulations are used to verify the theoretical results.
Cytoplasmic Bodies Are Highly Dynamic Structures

*Department of Cell and Molecular Biology, Northwestern University, Chicago, IL 60611-3008; §Department of Microbiology and Immunology, University of Illinois at Chicago, Chicago, IL 60612; ‡Division of Virology, Medical Research Council National Institute for Medical Research, London, United Kingdom NW7 1AA; and Department of Infectious Diseases, Guy’s Hospital, King’s College London School of Medicine, London, United Kingdom SE1 9RT
On Why Coarse Class Classification is Bottleneck in Noun Compound Interpretation

Sequences of long nouns, i.e., noun compounds, occur frequently and are productive. Their interpretation is important for a variety of tasks located at various layers of NLP. Major reasons behind the poor performance of automatic noun compound interpretation are: (a) lack of a well deﬁned inventory of semantic relations and (b) non-availability of sufﬁcient, annotated, high-quality dataset. Tratz and Hovy (2010) presented an inventory of semantic relations. They compared existing inventories with their two-level hierarchy, and created a large annotated dataset. We performed both theoretical as well as data-driven analysis of this inventory. Theoretical analysis reveal ambiguities in the coarse relations. Data-driven analysis report similar performance for coarse as well as ﬁne relations prediction. Our experiments show that improving the coarse classiﬁcation accuracy can improve the performance of ﬁne class predictor by 13 to 30 points in F-score.
The Transmembrane Protein Semaphorin 6A Repels Embryonic Sympathetic Axons

Semaphorin 6A (Sema6A) (previously named Semaphorin VIa) is the originally described member of the vertebrate semaphorin class 6, a group of transmembrane semaphorins homologous to the insect semaphorin class 1. Although Sema-1a (previously named semaphorin I) has been implicated in axon guidance in insects, the function of Sema6A is currently unknown. We have expressed the extracellular domain of Sema6A in mammalian cells as either a monomeric or a dimeric fusion protein and tested for potential axon guidance effects on two populations of embryonic neurons in growth cone collapse and collagen matrix chemorepulsion assays. Sema6A was observed to induce growth cone collapse of sympathetic neurons with an EC50 of ∼200 pm, although a 10-fold higher (EC50 of ∼2 nm) concentration was necessary to induce growth cone collapse of dorsal root ganglion neurons. The activity of Sema6A is likely to depend on protein dimerization or oligomerization. Although Sema6A mRNA is expressed in complex patterns during embryonic development, it is strikingly absent from sympathetic ganglia. Sema6A is, however, expressed in areas avoided by sympathetic axons and in areas innervated by sympathetics, but before their arrival. Our results demonstrate that transmembrane semaphorins, like the secreted ones, can act as repulsive axon guidance cues. Our findings are consistent with a role for Sema6A in channeling sympathetic axons into the sympathetic chains and controlling the temporal sequence of sympathetic target innervation.
Multi-classifier decision fusion for enhancing melanoma recognition accuracy

This paper proposes an automated non-invasive multi-classifier system for skin cancer (melanoma) detection. The proposed system fuses the results obtained from three classification systems to enhance the melanoma detection rate. All of the classification systems use Support Vector Machine classifier. However, the image feature sets used in each classification system are different. The features sets used are Wavelets and Color features, Curvelets features and Grey Level Co-occurrence Matrices features. The output class labels or class probabilities of the three classification systems are combined using Majority Voting or Averaging Fusion to obtain enhanced classification rates. The dataset used include digital images for benign and malignant skin lesions. Experimental results show that the proposed multi-classifier fusion method outperforms standalone Skin Lesion classification systems in terms of recognition accuracy. Consequently, this can increase the chances of non-invasive melanoma detection from digital images.
Predicting Personality from Social Media Text

: This paper replicates text-based Big Five personality score predictions generated by the Receptiviti API—a tool built on and tied to the popular psycholinguistic analysis tool Linguistic Inquiry and Word Count (LIWC). We use four social media datasets with posts and personality scores for nearly 9,000 users to determine the accuracy of the Receptiviti predictions. We found Mean Absolute Error rates in the 15–30% range, which is a higher error rate than other personality prediction algorithms in the literature. Preliminary analysis suggests relative scores between groups of subjects may be maintained, which may be sufficient for many applications.
On Real Quadratic Number Fields Suitable for Cryptography

We present empirical results that suggest that there are real quadratic fields with properties similar to imaginary quadratic fields in terms of size and structure of the class group. Therefore, these class groups can also be used for encryption schemes such as the ElGamal scheme, where up to now, only class groups of imaginary quadratic fields have been considered. Some security aspects are also addressed.
An exploration of Thai public perceptions of defenses in cases of women who kill their domestically violent spouses

The present study investigated the issue of domestic violence in Thailand and in particular the reactions of the Thai populace (male versus female) to a battered woman who killed her abusive spouse, as well as how such reactions could be influenced by the presentation of expert testimony related to the battered woman syndrome. Cluster sampling conducted within the Bangkok metropolitan area in Thailand yielded a sample of 1190 participants who voluntarily filled in the study’s questionnaire. Multi-group path analysis showed no significant gender difference in the direct influence of battered woman syndrome information on the verdict/judgment rendered and suggests that the provision of such information in spousal homicide trials within the Thai context may not be efficacious in influencing the participants’ verdict judgments. Results however did indicate an indirect effect of the battered woman syndrome information on the verdict/judgment rendered being mediated by the defense strategy of self-defense. This finding indicated that the battered woman syndrome information presented aided the participants in contextualizing the defendant’s killing in a frame of self-defense, rather than inappropriately applying a form of “reasonableness” in their verdict/judgment. These findings are discussed in relation to their implications for the presentation of battered woman syndrome information in trials of Thai battered women who killed their abusive spouses.
Geometry and Graphics for Developing a Multimodal , Multidimensional Desktop Virtual Reality Framework

This paper describes how geometry and graphics are employed to design and implement an intermodal multidimensional framework. Geometry and graphics can be judiciously juxtaposed within desktop virtual environments to facilitate information visualization, extraction, and analysis. This paper demonstrates a desktop VR system that exploits the potential of geometric shapes and graphics functionalities to depict a multidimensional framework for visualizing a campus map. This framework facilitates visualizing at different levels of detail (LOD) and switching among different modes of visualization. 3-D virtual models of buildings have been integrated into the framework to facilitate immersion and navigation. The intermodal denotes that various forms (modes) such as simple graphics, photo-realistic, and non-photorealistic renderings have been incorporated into the application. The entire application has been built using Web-friendly technologies such as VRML/X3-D, Java, and JavaScript to facilitate hosting the application online. Overall, the framework demonstrated in this study significantly aids information visualization and analysis. The authors believe that this notion can be extended for a wide range of applications across multiple disciplines.
Visibility Analysis on theWeb as an Indicator for Public Relations and Marketing Evaluation

One component of an effective marketing strategy is the evaluation of public relations and publicity campaign efforts. We provide instruments for Web-based marketing evaluation by introducing the concept of topic visibility based on search engine hit counts. This allows to monitor changes in public awareness of certain topics like products or companies in time. Using covisibilities and domain restrictions provides fine-granular evaluation regarding selected markets. A number of case studies illustrate our approach
Robust recursive network clock synchronization

A simple and statistically robust method for passive clock synchronization in sensor networks is presented. The method is not limited to passive (one-way communication) synchronization, but this scenario justifies the method. The recursive nature of the method and the targeted passive setup mean that it adds a minimum of requirements on the system in which it is used. Statistical characteristics of the method are quantified and real measurements are used to illustrate the robustness and performance gain relative to a naive Kalman filter based clock synchronization. Finally, C++ code that implements the suggested clock synchronization method, is provided in this article.
Widespread Disulfide Bonding in Proteins from Thermophilic Archaea

Disulfide bonds are generally not used to stabilize proteins in the cytosolic compartments of bacteria or eukaryotic cells, owing to the chemically reducing nature of those environments. In contrast, certain thermophilic archaea use disulfide bonding as a major mechanism for protein stabilization. Here, we provide a current survey of completely sequenced genomes, applying computational methods to estimate the use of disulfide bonding across the Archaea. Microbes belonging to the Crenarchaeal branch, which are essentially all hyperthermophilic, are universally rich in disulfide bonding while lesser degrees of disulfide bonding are found among the thermophilic Euryarchaea, excluding those that are methanogenic. The results help clarify which parts of the archaeal lineage are likely to yield more examples and additional specific data on protein disulfide bonding, as increasing genomic sequencing efforts are brought to bear.
Energy in ICT - Trends and research directions

Power consumption of IC T equipment has a growing impact, both on economic and environmental level. To reduce its energy footprint, drastic actions will be required on four fronts: energy-efficient components, power management techniques, new network paradigms and policy supporting actions. This paper outlines the cur rent trends and research for each category.
A Compact Quad-Band Microstrip Slot Antenna for WLAN / WIMAX Applications

AbstractA compact quad-band microstrip slot antenna applied to WLAN/WiMAX applications is proposed in this letter. This antenna has a simpler structure than other antennas designed for realizing quad -band characteristics. It is just composed of a microstrip feed line, a substrate, and a ground plane on which some simple slots are etched. Then, to prove the validation of the design, a prototype is simulated. The experimental data show that the antenna can provide four impedance bandwidths of 100 MHz centered at 2.5 GHz, 230 MHz centered at 3.5 GHz, 150 MHz centered at 5 GHz and 450 MHz centered at 5.8 GHz. The proposed antenna has good radiation characteristics, radiation efficiency, improved return loss and low dimensions in the four operating bands. The optimum results of proposed antenna verify and tested in HFSS simulator.
Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data

Genome‐wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source of data for Mendelian randomization investigations. We demonstrate how such coefficients from multiple variants can be combined in a Mendelian randomization analysis to estimate the causal effect of a risk factor on an outcome. The bias and efficiency of estimates based on summarized data are compared to those based on individual‐level data in simulation studies. We investigate the impact of gene–gene interactions, linkage disequilibrium, and ‘weak instruments’ on these estimates. Both an inverse‐variance weighted average of variant‐specific associations and a likelihood‐based approach for summarized data give similar estimates and precision to the two‐stage least squares method for individual‐level data, even when there are gene–gene interactions. However, these summarized data methods overstate precision when variants are in linkage disequilibrium. If the P‐value in a linear regression of the risk factor for each variant is less than 1×10−5 , then weak instrument bias will be small. We use these methods to estimate the causal association of low‐density lipoprotein cholesterol (LDL‐C) on coronary artery disease using published data on five genetic variants. A 30% reduction in LDL‐C is estimated to reduce coronary artery disease risk by 67% (95% CI: 54% to 76%). We conclude that Mendelian randomization investigations using summarized data from uncorrelated variants are similarly efficient to those using individual‐level data, although the necessary assumptions cannot be so fully assessed.
ASAR: Ant-based spectrum aware routing for cognitive radio networks

The new dynamic spectrum environment in cognitive radio networks necessities novel routing protocols which should be spectrum-aware, self-configured, high-adaptive and robust to match the dynamic nature of Cognitive Radio Networks (CRN) and exploit the available spectrum efficiently. In this paper, we present ASAR, which is a biologically inspired routing solution for CRN. In our routing algorithm, paths are discovered, observed and learned by guided ants communicating in an indirect way. F-ants are used to exploit spectrum feasible paths to the destinations and B-ants are used to collect information about the network and update routing table in the nodes. Furthermore, a reinforcement learning function is integrated into our algorithm for accelerating convergence. Simulation results show the validity of our algorithm in solving routing problems under high spectrum dynamic conditions.
Extracting Traffic Parameters at Intersections Through Computer Vision

A vision-based system for extracting important traffic parameters such as vehicle count, density, type and location at a three-way junction is presented. A single digital camera is used to acquire the video clip at the junction. In order to cover a wide area of the junction and to minimize the occlusion of vehicles, the camera is placed on top of a building near the junction. The accuracy of the system in vehicle counting is 91% and that of vehicle classification is 89% for small, medium and large vehicles. The errors in vehicle counting and classification occur mainly due to the occlusion of the moving vehicles. Errors also increase when the distance from the camera to the moving vehicle is high. Many of the problems in real-time implementation can be avoided to a great extent by using a multi camera system that can cover a large field of view.
Security Scheme for Sensitive Data in Management-Type SaaS

In this paper, we proposed the potential security problem of sensitive data in management-type SaaS which have no valid evidence when service provider embezzling or responsibility confirmation in service level agreement dispute, and proposed a scheme to ensure sensitive data safety by taking advantage of encryption and signature technique. Non-credible PKG of identity-based signature mechanism is used as signature mechanism for sensitive data in the scheme. Based on non-credible PKG of identity-based signature, a double-layer PKG framework is built by using the sub-PKG of service provider and tenants according to characteristic of the security responsibility of sensitive data is supervised by both service provider and tenants which have ownership and use right of the software respectively. The symmetric cryptography which ensures confidentiality of sensitive data is employed based on balance efficiency and safety. The related questions are also discussed. Security and the feasibility analysis show that this scheme ensures the confidentiality, integrity and non-repudiation of sensitive data. It can also provide valid evidence for service level agreement dispute and impel the application and the development of management-type SaaS. The proposed scheme can be widely applied to interactive distributed system of sensitive data.