nils-herrmann/clean_scirepeval huggingface dataset:
Comparing Alzheimer’s and Parkinson’s diseases networks using graph communities structure

Recent advances in large datasets analysis offer new insights to modern biology allowing system-level investigation of pathologies. Here we describe a novel computational method that exploits the ever-growing amount of “omics” data to shed light on Alzheimer’s and Parkinson’s diseases. Neurological disorders exhibit a huge number of molecular alterations due to a complex interplay between genetic and environmental factors. Classical reductionist approaches are focused on a few elements, providing a narrow overview of the etiopathogenic complexity of multifactorial diseases. On the other hand, high-throughput technologies allow the evaluation of many components of biological systems and their behaviors. Analysis of Parkinson’s Disease (PD) and Alzheimer’s Disease (AD) from a network perspective can highlight proteins or pathways common but differently represented that can be discriminating between the two pathological conditions, thus highlight similarities and differences. In this work we propose a strategy that exploits network community structure identified with a state-of-the-art network community discovery algorithm called InfoMap, which takes advantage of information theory principles. We used two similarity measurements to quantify functional and topological similarities between the two pathologies. We built a Similarity Matrix to highlight similar communities and we analyzed statistically significant GO terms found in clustered areas of the matrix and in network communities. Our strategy allowed us to identify common known and unknown processes including DNA repair, RNA metabolism and glucose metabolism not detected with simple GO enrichment analysis. In particular, we were able to capture the connection between mitochondrial dysfunction and metabolism (glucose and glutamate/glutamine). This approach allows the identification of communities present in both pathologies which highlight common biological processes. Conversely, the identification of communities without any counterpart can be used to investigate processes that are characteristic of only one of the two pathologies. In general, the same strategy can be applied to compare any pair of biological networks.
A Hybrid Intrusion Detection System: Integrating Hybrid Feature Selection Approach with Heterogeneous Ensemble of Intelligent Classifiers

This paper proposes Hybrid Feature Selection Approach – Heterogeneous Ensemble of Intelligent Classifiers (HyFSA-HEIC) for intelligent lightweight network intrusion detection system (NIDS). The purpose is to classify for anomaly from the incoming traffic. This system hierarchically integrates HyFSA and HEIC. The HyFSA will obtain the optimal number of features and then HEIC is built using these optimal features. HyFSA helps to decrease the computation time of the system and make it lightweight to work in real time. The aim of HEIC is to obtain accurate and robust classifier and enhance overall performance of the system. The results demonstrate that proposed system outperforms other ensemble and single classifier methods used in this paper. It has true positive rate (99.9%), accuracy (99.91%), precision (99.9%), receiver operating characteristics (99.9%), low false positive rate (0.1%) and lower root mean square error rate (3.06%) with a minimum number of selected 6 features. It also reduces time to build and time to test the model by 50.79% and 55.30% respectively on reduced features set. The results evince that detection rate, accuracy and precision of the system is increased by incorporating feature selection approach with heterogeneous ensemble of intelligent classifiers and significantly reduce the computation time.
Accurate and robust brain image alignment using boundary-based registration

The fine spatial scales of the structures in the human brain represent an enormous challenge to the successful integration of information from different images for both within- and between-subject analysis. While many algorithms to register image pairs from the same subject exist, visual inspection shows that their accuracy and robustness to be suspect, particularly when there are strong intensity gradients and/or only part of the brain is imaged. This paper introduces a new algorithm called Boundary-Based Registration, or BBR. The novelty of BBR is that it treats the two images very differently. The reference image must be of sufficient resolution and quality to extract surfaces that separate tissue types. The input image is then aligned to the reference by maximizing the intensity gradient across tissue boundaries. Several lower quality images can be aligned through their alignment with the reference. Visual inspection and fMRI results show that BBR is more accurate than correlation ratio or normalized mutual information and is considerably more robust to even strong intensity inhomogeneities. BBR also excels at aligning partial-brain images to whole-brain images, a domain in which existing registration algorithms frequently fail. Even in the limit of registering a single slice, we show the BBR results to be robust and accurate.
Towards a comprehensive theory of nurse/patient empowerment: applying Kanter's empowerment theory to patient care.

The purpose of this theoretical paper is to propose an integrated model of nurse/patient empowerment that could be used as a guide for creating high-quality nursing practice work environments that ensure positive outcomes for both nurses and their patients.



There are few integrated theoretical approaches to nurse and patient empowerment in the literature, although nurse empowerment is assumed to positively affect patient outcomes.



The constructs described in Kanter's (1993) work empowerment theory are conceptually consistent with the nursing care process and can be logically extended to nurses' interactions with their patients and the outcomes of nursing care.



We propose a model of nurse/patient empowerment derived from Kanter's theory that suggests that empowering working conditions increase feelings of psychological empowerment in nurses, resulting in greater use of patient empowerment strategies by nurses, and, ultimately, greater patient empowerment and better health outcomes.



Empirical testing of the model is recommended prior to use of the model in clinical practice.



We argue that empowered nurses are more likely to empower their patients, which results in better patient and system outcomes. Strategies for managers to empower nurses and for nurses to empower patients are suggested.
A Survey on Feature Selection Algorithms

One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and feature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations.
Probabilistic Topic Modelling with Semantic Graph

In this paper we propose a novel framework, topic model with semantic graph (TMSG), which couples topic model with the rich knowledge from DBpedia. To begin with, we extract the disambiguated entities from the document collection using a document entity linking system, i.e., DBpedia Spotlight, from which two types of entity graphs are created from DBpedia to capture local and global contextual knowledge, respectively. Given the semantic graph representation of the documents, we propagate the inherent topic-document distribution with the disambiguated entities of the semantic graphs. Experiments conducted on two real-world datasets show that TMSG can significantly outperform the state-of-the-art techniques, namely, author-topic Model (ATM) and topic model with biased propagation (TMBP).
Prevalence of autism in a US metropolitan area.

Concern has been raised about possible increases in the prevalence of autism. However, few population-based studies have been conducted in the United States.



To determine the prevalence of autism among children in a major US metropolitan area and to describe characteristics of the study population.



Study of the prevalence of autism among children aged 3 to 10 years in the 5 counties of metropolitan Atlanta, Ga, in 1996. Cases were identified through screening and abstracting records at multiple medical and educational sources, with case status determined by expert review.



Autism prevalence by demographic factors, levels of cognitive functioning, previous autism diagnoses, special education eligibility categories, and sources of identification.



A total of 987 children displayed behaviors consistent with Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria for autistic disorder, pervasive developmental disorder-not otherwise specified, or Asperger disorder. The prevalence for autism was 3.4 per 1000 (95% confidence interval [CI], 3.2-3.6) (male-female ratio, 4:1). Overall, the prevalence was comparable for black and white children (black, 3.4 per 1000 [95% CI, 3.0-3.7] and white, 3.4 per 1000 [95% CI, 3.2-3.7]). Sixty-eight percent of children with IQ or developmental test results (N = 880) had cognitive impairment. As severity of cognitive impairment increased from mild to profound, the male-female ratio decreased from 4.4 to 1.3. Forty percent of children with autism were identified only at educational sources. Schools were the most important source for information on black children, children of younger mothers, and children of mothers with less than 12 years of education.



The rate of autism found in this study was higher than the rates from studies conducted in the United States during the 1980s and early 1990s, but it was consistent with those of more recent studies.
Improved marketing decision making in a customer churn prediction context using generalized additive models

Nowadays, companies are investing in a well-considered CRM strategy. One of the cornerstones in CRM is customer churn prediction, where one tries to predict whether or not a customer will leave the company. This study focuses on how to better support marketing decision makers in identifying risky customers by using Generalized Additive Models (GAM). Compared to Logistic Regression, GAM relaxes the linearity constraint which allows for complex non-linear fits to the data. The contributions to the literature are three-fold: (i) it is shown that GAM is able to improve marketing decision making by better identifying risky customers; (ii) it is shown that GAM increases the interpretability of the churn model by visualizing the non-linear relationships with customer churn identifying a quasi-exponential, a U, an inverted U or a complex trend and (iii) marketing managers are able to significantly increase business value by applying GAM in this churn prediction context.
Implementation of continuous VLC modulation schemes on commercial LED spotlights

When modulated with a continuous signal, the intensity of light emitted by a Light Emitting Diode needs to be adjusted such that linearity is maintained between the modulating voltage and the intensity of the emitted light. This paper presents a low-cost solution that is able to maintain linearity over virtually the entire intensity range. Extending the range of linearity is accomplished by incorporating a feedback loop that compensates for the non-linear parameters of the circuit's active components, including the LED. The technique is applied to convert a commercially available LED spotlight into a device that allows for the transmission of continuous Visible Light Communication schemes.
Demand Side Management in Smart Grid Using Heuristic Optimization

Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.
Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records

The ability to identify the risk factors related to an adverse condition, e.g., heart failures (HF) diagnosis, is very important for improving care quality and reducing cost. Existing approaches for risk factor identification are either knowledge driven (from guidelines or literatures) or data driven (from observational data). No existing method provides a model to effectively combine expert knowledge with data driven insight for risk factor identification.



We present a systematic approach to enhance known knowledge-based risk factors with additional potential risk factors derived from data. The core of our approach is a sparse regression model with regularization terms that correspond to both knowledge and data driven risk factors.



The approach is validated using a large dataset containing 4,644 heart failure cases and 45,981 controls. The outpatient electronic health records (EHRs) for these patients include diagnosis, medication, lab results from 2003-2010. We demonstrate that the proposed method can identify complementary risk factors that are not in the existing known factors and can better predict the onset of HF. We quantitatively compare different sets of risk factors in the context of predicting onset of HF using the performance metric, the Area Under the ROC Curve (AUC). The combined risk factors between knowledge and data significantly outperform knowledge-based risk factors alone. Furthermore, those additional risk factors are confirmed to be clinically meaningful by a cardiologist.



We present a systematic framework for combining knowledge and data driven insights for risk factor identification. We demonstrate the power of this framework in the context of predicting onset of HF, where our approach can successfully identify intuitive and predictive risk factors beyond a set of known HF risk factors.
Generic and Scalable Framework for Automated Time-series Anomaly Detection

This paper introduces a generic and scalable framework for automated anomaly detection on large scale time-series data. Early detection of anomalies plays a key role in maintaining consistency of person's data and protects corporations against malicious attackers. Current state of the art anomaly detection approaches suffer from scalability, use-case restrictions, difficulty of use and a large number of false positives. Our system at Yahoo, EGADS, uses a collection of anomaly detection and forecasting models with an anomaly filtering layer for accurate and scalable anomaly detection on time-series. We compare our approach against other anomaly detection systems on real and synthetic data with varying time-series characteristics. We found that our framework allows for 50-60% improvement in precision and recall for a variety of use-cases. Both the data and the framework are being open-sourced. The open-sourcing of the data, in particular, represents the first of its kind effort to establish the standard benchmark for anomaly detection.
A ternary content-addressable memory (TCAM) based on 4T static storage and including a current-race sensing scheme

A 256 144-bit TCAM is designed in 0.18m CMOS. The proposed TCAM cell uses 4T static storage for increased density. The proposed match-line (ML) sense scheme reduces power consumption by minimizing switching activity of search-lines and limiting voltage swing of MLs. The scheme achieves a match-time of 3 ns and operates at a minimum supply voltage of 1.2 V.
Validated assessment scales for the lower face.

Aging in the lower face leads to lines, wrinkles, depression of the corners of the mouth, and changes in lip volume and lip shape, with increased sagging of the skin of the jawline. Refined, easy-to-use, validated, objective standards assessing the severity of these changes are required in clinical research and practice.



To establish the reliability of eight lower face scales assessing nasolabial folds, marionette lines, upper and lower lip fullness, lip wrinkles (at rest and dynamic), the oral commissure and jawline, aesthetic areas, and the lower face unit.



Four 5-point rating scales were developed to objectively assess upper and lower lip wrinkles, oral commissures, and the jawline. Twelve experts rated identical lower face photographs of 50 subjects in two separate rating cycles using eight 5-point scales. Inter- and intrarater reliability of responses was assessed.



Interrater reliability was substantial or almost perfect for all lower face scales, aesthetic areas, and the lower face unit. Intrarater reliability was high for all scales, areas and the lower face unit.



Our rating scales are reliable tools for valid and reproducible assessment of the aging process in lower face areas.
SPINS : Security Protocols for Sensor Networks

Adrian Perrig, Robert Szewczyk, Victor Wen, David Culler, J. D. Tygar.  Security Protocols for Sensor Networks, Mobicom 2001. • Chris Karlof and David Wagner, Secure Routing in Sensor Networks: Attacks and Countermeasures • Sasha Slijepcevic and Miodrag Potkonjak and Vlasios Tsiatsis and Scott Zimbeck and Mani B. Srivastava. On Communication Security in Wireless Ad-Hoc Sensor Networks. 11th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises. • Jiejun Kong, Petros Zerfos, Haiyun Luo, Songwu Lu, Lixia Zhang. Providing Robust and Ubiquitous Security Support for Mobile Ad-Hoc Networks. 9th International Conference on Network Protocols, Nov 2001. • Y.W. Law, S. Dulman, S. Etalle and P. Havinga. Assessing Security-Critical Energy-Efficient Sensor, Department of Computer Science, University of Twente, Technical Report TRCTIT-02-18, Jul 2002. • Anthony D. Wood, John A. Stankovic, Denial of Service in Sensor Networks References
A switching ringing suppression scheme of SiC MOSFET by Active Gate Drive

This paper proposes an Active Gate Drive (AGD) to reduce unwanted switching ringing of Silicon Carbide (SiC) MOSFET module with a rating of 120 A and 1200 V. While SiC MOSFET can be operated under high switching frequency and high temperature with very low power losses, one of the key challenges for SiC MOSFET is the electromagnetic interference (EMI) caused by steep switching transients and continuous switching ringing. Compared to Si MOSFET, the higher rate of SiC MOSFET drain current variation introduces worse EMI problems. To reduce EMI generated from the switching ringing, this paper investigates the causes of switching ringing by considering the combined impact of parasitic inductances, capacitances, and low circuit loop resistance. In addition, accurate mathematical expressions are established to explain the ringing behavior and quantitative analysis is carried out to investigate the relationship between the switching transient and gate drive voltage. Thereafter, an AGD method for mitigating SiC MOSFET switching ringing is presented. Substantially reduced switching ringing can be observed from circuit simulations. As a result, the EMI generation is mitigated.
Impact of Depression and Antidepressant Treatment on Heart Rate Variability: A Review and Meta-Analysis

Depression is associated with an increase in the likelihood of cardiac events; however, studies investigating the relationship between depression and heart rate variability (HRV) have generally focused on patients with cardiovascular disease (CVD). The objective of the current report is to examine with meta-analysis the impact of depression and antidepressant treatment on HRV in depressed patients without CVD.



Studies comparing 1) HRV in patients with major depressive disorder and healthy control subjects and 2) the HRV of patients with major depressive disorder before and after treatment were considered for meta-analysis.



Meta-analyses were based on 18 articles that met inclusion criteria, comprising a total of 673 depressed participants and 407 healthy comparison participants. Participants with depression had lower HRV (time frequency: Hedges' g = -.301, p < .001; high frequency: Hedges' g = -.293, p < .001; nonlinear: Hedges' g = -1.955, p = .05; Valsalva ratio: Hedges' g = -.712, p < .001) than healthy control subjects, and depression severity was negatively correlated with HRV (r = -.354, p < .001). Tricyclic medication decreased HRV, although serotonin reuptake inhibitors, mirtazapine, and nefazodone had no significant impact on HRV despite patient response to treatment.



Depression without CVD is associated with reduced HRV, which decreases with increasing depression severity, most apparent with nonlinear measures of HRV. Critically, a variety of antidepressant treatments do not resolve these decreases despite resolution of symptoms, highlighting that antidepressant medications might not have HRV-mediated cardioprotective effects and the need to identify individuals at risk among patients in remission.
Spatial delineation of soil erosion vulnerability in the Lake Tana Basin , Ethiopia

The main objective of this study was to identify the most vulnerable areas to soil erosion in the Lake Tana Basin, Blue Nile, Ethiopia using the Soil and Water Assessment Tool (SWAT), a physically based distributed hydrological model, and a Geographic Information System based decision support system that uses multi-criteria evaluation (MCE). The SWAT model was used to estimate the sediment yield within each sub-basin and identify the most sediment contributing areas in the basin. Using the MCE analysis, an attempt was made to combine a set of factors (land use, soil, slope and river layers) to make a decision according to the stated objective. On the basis of simulated SWAT, sediment yields greater than 30 tons/ha for each of the sub-basin area, 18Ð4% of the watershed was determined to be high erosion potential area. The MCE results indicated that 12–30Ð5% of the watershed is high erosion potential area. Both approaches show comparable watershed area with high soil erosion susceptibility. The output of this research can aid policy and decision makers in determining the soil erosion ‘hot spots’ and the relevant soil and water conservation measures.