nils-herrmann/clean_scirepeval huggingface dataset:
POSSIBLE MECHANISM OF DEVELOPMENT OF SALT SENSITIVE ESSENTIAL HYPERTENSION.

It has been known that salt-sensitivity of blood pressure is defined genetically as well as can be developed secondary to either decreased renal function or by influence of other environmental factors. The aim of the study was to evaluate the possible mechanism for the development of salt-sensitive essential hypertension in the population of Georgia. The Case-Control study included 185 subjects, 94 cases with Essential Hypertension stage I (JNC7) without prior antihypertensive treatment, and 91 controls. Salt-sensitivity test was used to divide both case and control groups into salt-sensitive (n=112) and salt-resistant (n=73) subgroups. Endogenous cardiotonic steroids, sodium and PRA were measured in blood and urine samples at the different sodium conditions. Determinations of circulating levels of endogenous sodium pump inhibitors and PRA were carried out using the ELISA and RIA methods. Descriptive statistics were used to analyze the data. Differences in variables between sodium conditions were assessed using paired t-tests. Salt-sensitivity was found in 60.5% of total population investigated, with higher frequency in females. Salt-sensitivity positively correlated with age in females (r=0.262, p<0.01). Statistically significant positive correlation was found between 24 hour urine sodium concentration changes and salt-sensitivity r=0.334, p<0.01. Significant negative correlation was found between salt-sensitivity and PRA. Since no significant correlations were found between BMI and salt-sensitivity, we assume that BMI and salt-sensitivity should be discussed as different independent risk factors for the development of Essential Hypertension. Significant correlation was found between changes in GFR in salt-sensitive cases and controls p<0.01. This can be explained with comparable hyperfiltration of the kidneys at high sodium load and discussed as early sign of hypertensive nephropathy in salt-sensitive individuals. At the high sodium condition Endogenous MBG and OU were high in salt-sensitive subjects compared to salt-resistant. These compounds decreased after low salt diet in salt-sensitive cases as well as controls but remained within the same level in salt-resistant individuals. MBG and OU levels positively correlated with SBP in salt-sensitive individuals but salt-resistant subjects didn't show any changes. Our results support the idea that chronic high sodium loading (>200 mmol) which is typical in traditional Georgian as well as other diets switch those humoral and pathophysiological mechanisms that can lead to the development of certain type of hypertension in salt-sensitive individuals. Salt intake reduction can prevent development of hypertension in salt-sensitive subjects, although hypertension develops in the salt-resistant individuals but by other mechanism such as RAAS.
Advanced modelling and simulation methods for communication networks

This paper contains a brief overview of suitable simulation tools available for education and research on network technologies and protocols. Among the mentioned tools we present advanced methods for network simulations using the OPNET modeler simulation tool. This is one of the most widespread simulation tools for network simulations, appropriate for both for teaching and also for the researching procedures of new internet devices and protocols. The basic package is intended for simulating communication networks and for protocols and devices development. There are also additional specific modules, such as a module assigned to the simulation of wireless networks, an ACE module for application analyzing, 3DNV module for visualizing networks on virtual terrain and the “System in the loop” module for simulating networks with real communication equipment in the loop, in real-time.
A Model for Temporal Dependencies in Event Streams

We introduce the Piecewise-Constant Conditional Intensity Model, a model for learning temporal dependencies in event streams. We describe a closed-form Bayesian approach to learning these models, and describe an importance sampling algorithm for forecasting future events using these models, using a proposal distribution based on Poisson superposition. We then use synthetic data, supercomputer event logs, and web search query logs to illustrate that our learning algorithm can efficiently learn nonlinear temporal dependencies, and that our importance sampling algorithm can effectively forecast future events.
A Novel Image Classification Method with CNN-XGBoost Model

Image classification problem is one of most important research directions in image processing and has become the focus of research in many years due to its diversity and complexity of image information. In view of the existing image classification models’ failure to fully utilize the information of images, this paper proposes a novel image classification method of combining the Convolutional Neural Network (CNN) and eXtreme Gradient Boosting (XGBoost), which are two outstanding classifiers. The presented CNNXGBoost model provides more precise output by integrating CNN as a trainable feature extractor to automatically obtain features from input and XGBoost as a recognizer in the top level of the network to produce results. Experiments are implemented on the well-known MNIST and CIFAR-10 databases. The results prove that the new method performs better compared with other methods on the same databases, which verify the effectiveness of the proposed method in image classification problem.
Run, Skeleton, Run: Skeletal Model in a Physics-Based Simulation

In this paper, we present an approach to solve a physicsbased reinforcement learning challenge “Learning to Run” with objective to train physiologically-based human model to navigate a complex obstacle course as quickly as possible. The environment is computationally expensive, has a high-dimensional continuous action space and is stochastic. We benchmark state of the art policy-gradient methods and test several improvements, such as layer normalization, parameter noise, action and state reflecting, to stabilize training and improve its sample-efficiency. We found that the Deep Deterministic Policy Gradient method is the most efficient method for this environment and the improvements we have introduced help to stabilize training. Learned models are able to generalize to new physical scenarios, e.g. different obstacle courses.
Full Metal Jacket : A Pure Visual Dataflow Language Built on Top of Lisp

Full Metal Jacket is a general-purpose visual dataflow language currently being developed on top of Emblem, a Lisp dialect strongly influenced by Common Lisp but smaller and more type-aware, and with support for CLOS-style object orientation, graphics, event handling and multi-threading. Methods in Full Metal Jacket Jacket are directed acyclic graphs. Data arriving at ingates from the calling method flows along edges through vertices, at which it gets transformed by applying Emblem functions or methods, or methods defined in Full Metal Jacket, before it finally arrives at outgates where it is propagated back upwards to the calling method. The principal difference between Full Metal Jacket and existing visual dataflow languages such as Prograph is that Full Metal Jacket is a pure dataflow language, with no special syntax being required for control constructs such as loops or conditionals, which resemble ordinary methods except in the number of times they generate outputs. This uniform syntax means that, like Lisp and Prolog, methods in Full Metal Jacket are themselves data structures and can be manipulated as such.
Autonomous vision-guided approach for the analysis and grading of vertical suspension tests during Hammersmith Infant Neurological Examination (HINE)

Computer vision assisted diagnostic systems are gaining popularity in different healthcare applications. This paper presents a video analysis and pattern recognition framework for the automatic grading of vertical suspension tests on infants during the Hammersmith Infant Neurological Examination (HINE). The proposed vision-guided pipeline applies a color-based skin region segmentation procedure followed by the localization of body parts before feature extraction and classification. After constrained localization of lower body parts, a stick-diagram representation is used for extracting novel features that correspond to the motion dynamic characteristics of the infant's leg movements during HINE. This set of pose features generated from such a representation includes knee angles and distances between knees and hills. Finally, a time-series representation of the feature vector is used to train a Hidden Markov Model (HMM) for classifying the grades of the HINE tests into three predefined categories. Experiments are carried out by testing the proposed framework on a large number of vertical suspension test videos recorded at a Neuro-development clinic. The automatic grading results obtained from the proposed method matches the scores of experts at an accuracy of 74%.
Providing the basis for human-robot-interaction: a multi-modal attention system for a mobile robot

In order to enable the widespread use of robots in home and office environments, systems with natural interaction capabilities have to be developed. A prerequisite for natural interaction is the robot's ability to automatically recognize when and how long a person's attention is directed towards it for communication. As in open environments several persons can be present simultaneously, the detection of the communication partner is of particular importance. In this paper we present an attention system for a mobile robot which enables the robot to shift its attention to the person of interest and to maintain attention during interaction. Our approach is based on a method for multi-modal person tracking which uses a pan-tilt camera for face recognition, two microphones for sound source localization, and a laser range finder for leg detection. Shifting of attention is realized by turning the camera into the direction of the person which is currently speaking. From the orientation of the head it is decided whether the speaker addresses the robot. The performance of the proposed approach is demonstrated with an evaluation. In addition, qualitative results from the performance of the robot at the exhibition part of the ICVS'03 are provided.
From Cybersecurity to Collaborative Resiliency

This paper presents the holistic approach to cyber resilience as a means of preparing for the "unknown unknowns". Principles of augmented cyber risks management and resilience management model at national level are presented, with elaboration on multi-stakeholder engagement and partnership for the implementation of national cyber resilience collaborative framework. The complementarity of governance, law, and business/industry initiatives is outlined, with examples of the collaborative resilience model for the Bulgarian national strategy and its multi-national engagements.
Determining the Epipolar Geometry and its Uncertainty: A Review

Two images of a single scene/object are related by the epipolar geometry, which can be described by a 3×3 singular matrix called the essential matrix if images' internal parameters are known, or the fundamental matrix otherwise. It captures all geometric information contained in two images, and its determination is very important in many applications such as scene modeling and vehicle navigation. This paper gives an introduction to the epipolar geometry, and provides a complete review of the current techniques for estimating the fundamental matrix and its uncertainty. A well-founded measure is proposed to compare these techniques. Projective reconstruction is also reviewed. The software which we have developed for this review is available on the Internet.
Parabix: Boosting the efficiency of text processing on commodity processors

Modern applications employ text files widely for providing data storage in a readable format for applications ranging from database systems to mobile phones. Traditional text processing tools are built around a byte-at-a-time sequential processing model that introduces significant branch and cache miss penalties. Recent work has explored an alternative, transposed representation of text, Parabix (Parallel Bit Streams), to accelerate scanning and parsing using SIMD facilities. This paper advocates and develops Parabix as a general framework and toolkit, describing the software toolchain and run-time support that allows applications to exploit modern SIMD instructions for high performance text processing. The goal is to generalize the techniques to ensure that they apply across a wide variety of applications and architectures. The toolchain enables the application developer to write constructs assuming unbounded character streams and Parabix's code translator generates code based on machine specifics (e.g., SIMD register widths). The general argument in support of Parabix technology is made by a detailed performance and energy study of XML parsing across a range of processor architectures. Parabix exploits intra-core SIMD hardware and demonstrates 2×-7× speedup and 4× improvement in energy efficiency when compared with two widely used conventional software parsers, Expat and Apache-Xerces. SIMD implementations across three generations of x86 processors are studied including the new SandyBridge. The 256-bit AVX technology in Intel SandyBridge is compared with the well established 128-bit SSE technology to analyze the benefits and challenges of 3-operand instruction formats and wider SIMD hardware. Finally, the XML program is partitioned into pipeline stages to demonstrate that thread-level parallelism enables the application to exploit SIMD units scattered across the different cores, achieving improved performance (2× on 4 cores) while maintaining single-threaded energy levels.
Survival Analysis Approach For Early Prediction Of Student Dropout

Computer Science Degree: Master of Science Retention of students at colleges and universities has long been a concern for educators for many decades. The consequences of student attrition are significant for both students, academic staffs and the overall institution. Thus, increasing student retention is a long term goal of any academic institution. The most vulnerable students at all institutions of higher education are the freshman students, who are at the highest risk of dropping out at the beginning of their study. Consequently, the early identification of “at-risk” students is a crucial task that needs to be addressed precisely. In this thesis, we develop a framework for early prediction of student success using survival analysis approach. We propose time-dependent Cox (TD-Cox), which is based on the Cox proportional hazard regression model and also captures time-varying factors to address the challenge of predicting dropout students as well as the semester that the dropout will occur, to enable proactive interventions. This is critical in student retention problem because not only correctly classifying whether student is going to dropout is important but also when this is going to happen is crucial to investigate. We evaluate our method on real student data collected at Wayne State University. The results show that the proposed Cox-based framework can predict the student dropout and the semester of dropout with high accuracy and precision compared to the other alternative state-of-the-art methods.
Multilevel image thresholding by fireworks algorithm

This paper presents implementation of the recent fireworks algorithm adjusted for solving multilevel image thresholding problem. This is an important problem since it is often used in image processing for the purpose of image segmentation. Since the number of possible threshold combinations grows exponentially with the number of desirable thresholds, standard deterministic methods could not generate satisfying results when tackling this problem. To test the performance of our proposed approach, we employed Kapur's maximum entropy thresholding function on standard benchmark images where the optimal solutions are known (up to five thresholding points) from the exhaustive search. Results show that our approach has great potential in this field.
Caffeine intake and semen quality in a population of 2,554 young Danish men.

The authors examined the association between semen quality and caffeine intake among 2,554 young Danish men recruited when they were examined to determine their fitness for military service in 2001-2005. The men delivered a semen sample and answered a questionnaire including information about caffeine intake from various sources, from which total caffeine intake was calculated. Moderate caffeine and cola intakes (101-800 mg/day and < or =14 0.5-L bottles of cola/week) compared with low intake (< or =100 mg/day, no cola intake) were not associated with semen quality. High cola (>14 0.5-L bottles/week) and/or caffeine (>800 mg/day) intake was associated with reduced sperm concentration and total sperm count, although only significant for cola. High-intake cola drinkers had an adjusted sperm concentration and total sperm count of 40 mill/mL (95% confidence interval (CI): 32, 51) and 121 mill (95% CI: 92, 160), respectively, compared with 56 mill/mL (95% CI: 50, 64) and 181 mill (95% CI: 156, 210) in non-cola-drinkers, which could not be attributed to the caffeine they consumed because it was <140 mg/day. Therefore, the authors cannot exclude the possibility of a threshold above which cola, and possibly caffeine, negatively affects semen quality. Alternatively, the less healthy lifestyle of these men may explain these findings.
High-Resolution Underwater Robotic Vision-Based Mapping and Three-Dimensional Reconstruction for Archaeology

Documenting underwater archaeological sites is an extremely challenging problem. Sites covering large areas are particularly daunting for traditional techniques. In this paper, we present a novel approach to this problem using both an autonomous underwater vehicle (AUV) and a diver-controlled stereo imaging platform to document the submerged Bronze Age city at Pavlopetri, Greece. The result is a three-dimensional (3D) reconstruction covering 26,600 m2 at a resolution of 2 mm/pixel, the largest-scale underwater optical 3D map, at such a resolution, in the world to date. We discuss the advances necessary to achieve this result, including i) an approach to color correct large numbers of images at varying altitudes and over varying bottom types; ii) a large-scale bundle adjustment framework that is capable of handling upward of 400,000 stereo images; and iii) a novel approach to the registration and rapid documentation of an underwater excavations area that can quickly produce maps of site change. We present visual and quantitative comparisons to the authors’ previous underwater mapping approaches.
Effects of a rumen protected B vitamin blend upon milk production and component yield in lactating dairy cows

Results from 12 switchback field trials involving 1216 cows were combined to assess the effects of a protected B vitamin blend (BVB) upon milk yield (kg), fat percentage (%), protein %, fat yield (kg) and protein yield (kg) in primiparous and multiparous cows. Trials consisted of 3 test periods executed in the order control-test-control. No diet changes other than the inclusion of 3 grams/cow/ day of the BVB during the test period occurred. Means from the two control periods were compared to results obtained during the test period using a paired T test. Cows include in the analysis were between 45 and 300 days in milk (DIM) at the start of the experiment and were continuously available for all periods. The provision of the BVB resulted in increased (P < 0.05) milk, fat %, protein %, fat yield and protein yield. Regression models showed that the amount of milk produced had no effect upon the magnitude of the increase in milk components. The increase in milk was greatest in early lactation and declined with DIM. Protein and fat % increased with DIM in mature cows, but not in first lactation cows. Differences in fat yields between test and control feeding periods did not change with DIM, but the improvement in protein yield in mature cows declined with DIM. These results indicate that the BVB provided economically important advantages throughout lactation, but expected results would vary with cow age and stage of lactation.
Combined SPR and SERS microscopy in the Kretschmann configuration.

A novel hybrid spectroscopic technique is proposed, combining surface plasmon resonance (SPR) with surface-enhanced Raman scattering (SERS) microscopy. A standard Raman microscope is modified to accommodate the excitation of surface plasmon-polaritons (SPPs) on flat metallic surfaces in the Kretschmann configuration, while retaining the capabilities of Raman microscopy. The excitation of SPPs is performed as in standard SPR-microscopy; namely, a beam with TM-polarization traverses off-axis a high numerical aperture oil immersion objective, illuminating at an angle the metallic film from the (glass) substrate side. The same objective is used to collect the full Kretschmann cone containing the SERS emission on the substrate side. The angular dispersion of the plasmon resonance is measured in reflectivity for different coupling conditions and, simultaneously, SERS spectra are recorded from Nile Blue (NB) molecules adsorbed onto the surface. A trade-off is identified between the conditions of optimum coupling to SPPs and the spot size (which is related to the spatial resolution). This technique opens new horizons for SERS microscopy with uniform enhancement on flat surfaces.
RFMS: Real-time Flood Monitoring System with wireless sensor networks

In this paper, we present RFMS, the real-time flood monitoring system with wireless sensor networks, which is deployed in two volcanic islands Ulleung-do and Dok-do located in the East Sea near to the Korean peninsula and developed for flood monitoring. RFMS measures river and weather conditions through wireless sensor nodes equipped with different sensors. Measured information is employed for early-warning via diverse types of services such as SMS (short message service) and a Web service.
A nested PID steering control for lane keeping in vision based autonomous vehicles

In this paper a nested PID steering control for lane keeping in vision based autonomous vehicles is designed to perform path following in the case of roads with an uncertain curvature. The control input is the steering wheel angle: it is designed on the basis of the yaw rate, measured by a gyroscope, and the lateral offset, measured by the vision system as the distance between the road centerline and a virtual point at a fixed distance from the vehicle. No lateral acceleration and no lateral speed measurements are required. A PI active front steering control on the yaw rate tracking error is used to reject constant disturbances and the overall effect of parameter variations while improving vehicle steering dynamics. The yaw rate reference is viewed as the control input in an external control loop: it is designed using a PID control on the lateral offset to reject the disturbances on the curvature which increase linearly with respect to time. The robustness is investigated with respect to speed variations and uncertain vehicle physical parameters: it is shown that the controlled system is asymptotically stable for all perturbations in the range of interest. Several simulations are carried out on a standard big sedan CarSim vehicle model to explore the robustness with respect to unmodelled effects such as combined lateral and longitudinal tire forces, pitch and roll. The simulations show reduced lateral offset and new stable μ-split braking manoeuvres in comparison with the CarSim model predictive steering controller implemented by CarSim.