nils-herrmann/clean_scirepeval huggingface dataset:
VIRAM1: A MediaOriented Vector Processor with Embedded DRAM

Processors for mobile multimedia devices must be low power while having excellent performance on media applications. Our processor, VIRAM1, accomplishes this by combining vector processing with embedded DRAM. VIRAM1 includes a scalar core, 13 megabytes (104 megabits) of DRAM, and four vector datapaths. It consumes 2 watts at 200 MHz and executes up to 9.6 giga-ops (16 bit) per second.
Addressing the cold start problem in tag-based recommender systems

Folksonomies have become a powerful tool to describe, discover, search, and navigate online resources (e.g., pictures, videos, blogs) on the Social Web. Unlike taxonomies and ontologies, which impose a hierarchical categorisation on content, folksonomies directly allow end users to freely create and choose the categories (in this case, tags) that best describe a piece of information. However, the freedom afforded to users comes at a cost: as tags are defined informally, the retrieval of information becomes more challenging. Different solutions have been proposed to help users discover content in this highly dynamic setting. However, they have proved to be effective only for users who have already heavily used the system (active users) and who are interested in popular items (i.e., items tagged by many other users). In this thesis we explore principles to help both active users and more importantly new or inactive users (cold starters) to find content they are interested in even when this content falls into the long tail of medium-to-low popularity items (cold start items). We investigate the tagging behaviour of users on content and show how the similarities between users and tags can be used to produce better recommendations. We then analyse how users create new content on social tagging websites and show how preferences of only a small portion of active users (leaders), responsible for the vast majority of the tagged content, can be used to improve the recommender system’s scalability. We also investigate the growth of the number of users, items and tags in the system over time. We then show how this information can be used to decide whether the benefits of an update of the data structures modelling the system outweigh the corresponding cost. In this work we formalize the ideas introduced above and we describe their implementation. To demonstrate the improvements of our proposal in recommendation efficacy and efficiency, we report the results of an extensive evaluation conducted on three different social tagging websites: CiteULike, Bibsonomy and MovieLens. Our results demonstrate that our approach achieves higher accuracy than state-of-the-art systems for cold start users and for users searching for cold start items. Moreover, while accuracy of our technique is comparable to other techniques for active users, the computational cost that it requires is much smaller. In other words our approach is more scalable and thus more suitable for large and quickly growing settings.
A Novel Reference Signal Generation Method for Power-Quality Improvement of Unified Power-Quality Conditioner

This paper proposes a novel reference signal generation method for the unified power quality conditioner (UPQC) adopted to compensate current and voltage-quality problems of sensitive loads. The UPQC consists of a shunt and series converter having a common dc link. The shunt converter eliminates current harmonics originating from the nonlinear load side and the series converter mitigates voltage sag/swell originating from the supply side. The developed controllers for shunt and series converters are based on an enhanced phase-locked loop and nonlinear adaptive filter. The dc link control strategy is based on the fuzzy-logic controller. A fast sag/swell detection method is also presented. The efficacy of the proposed system is tested through simulation studies using the Power System Computer Aided Design/Electromagnetic Transients dc analysis program. The proposed UPQC achieves superior capability of mitigating the effects of voltage sag/swell and suppressing the load current harmonics under distorted supply conditions.
Eye movement analysis and cognitive processing: detecting indicators of conversion to Alzheimer’s disease

A great amount of research has been developed around the early cognitive impairments that best predict the onset of Alzheimer's disease (AD). Given that mild cognitive impairment (MCI) is no longer considered to be an intermediate state between normal aging and AD, new paths have been traced to acquire further knowledge about this condition and its subtypes, and to determine which of them have a higher risk of conversion to AD. It is now known that other deficits besides episodic and semantic memory impairments may be present in the early stages of AD, such as visuospatial and executive function deficits. Furthermore, recent investigations have proven that the hippocampus and the medial temporal lobe structures are not only involved in memory functioning, but also in visual processes. These early changes in memory, visual, and executive processes may also be detected with the study of eye movement patterns in pathological conditions like MCI and AD. In the present review, we attempt to explore the existing literature concerning these patterns of oculomotor changes and how these changes are related to the early signs of AD. In particular, we argue that deficits in visual short-term memory, specifically in iconic memory, attention processes, and inhibitory control, may be found through the analysis of eye movement patterns, and we discuss how they might help to predict the progression from MCI to AD. We add that the study of eye movement patterns in these conditions, in combination with neuroimaging techniques and appropriate neuropsychological tasks based on rigorous concepts derived from cognitive psychology, may highlight the early presence of cognitive impairments in the course of the disease.
Factors influencing teachers ’ adoption and integration of information and communication technology into teaching : A review of the literature

Global investment in ICT to improve teaching and learning in schools have been initiated by many governments. Despite all these investments on ICT infrastructure, equipments and professional development to improve education in many countries, ICT adoption and integration in teaching and learning have been limited. This article reviews personal, institutional and technological factors that encourage teachers’ use of computer technology in teaching and learning processes. Also teacher-level, school-level and system-level factors that prevent teachers from ICT use are reviewed. These barriers include lack of teacher ICT skills; lack of teacher confidence; lack of pedagogical teacher training; l lack of suitable educational software; limited access to ICT; rigid structure of traditional education systems; restrictive curricula, etc. The article concluded that knowing the extent to which these barriers affect individuals and institutions may help in taking a decision on how to tackle them.
Acculturation and Latino adolescent mental health: integration of individual, environmental, and family influences.

In order to address the mental health disparities that exist for Latino adolescents in the United States, psychologists must understand specific factors that contribute to the high risk of mental health problems in Latino youth. Given the significant percentage of Latino youth who are immigrants or the children of immigrants, acculturation is a key factor in understanding mental health among this population. However, limitations in the conceptualization and measurement of acculturation have led to conflicting findings in the literature. Thus, the goal of the current review is to examine and critique research linking acculturation and mental health outcomes for Latino youth, as well as to integrate individual, environmental, and family influences of this relationship. An integrated theoretical model is presented and implications for clinical practice and future directions are discussed.
A Novel High-Gain Tetrahedron Origami

In this letter, a novel high-gain tetrahedron origami antenna is introduced. The antenna comprises a triangular-shaped monopole, a reflector, and two parasitic strip directors on a paper substrate. The directors and the reflector are employed to increase the antenna gain. The step-by-step origami folding procedure is presented in detail. The proposed design of antenna is verified by both simulations and measurements with a fabricated prototype. The antenna exhibits a 10-dB impedance bandwidth of 66% (2–4 GHz) and a peak gain of 9.5 dBi at 2.6 GHz.
Choosing a first programming language

Today, among other challenges, teaching students how to write computer programs for the first time can be an important criterion for whether students in computing will remain in their program of study, i.e. Computer Science or Information Technology. Not learning to program a computer as a computer scientist or information technologist can be compared to a mathematician not learning algebra. For a mathematician this would be an extremely limiting situation. For a computer scientist, not learning to program imposes a similar severe limitation on the budding computer scientist. Therefore it is not a question as to whether programming should be taught rather it is a question of how to maximize aspects of teaching programming so that students are less likely to be discouraged when learning to program. Different criteria have been used to select first programming languages. Computer scientists have attempted to establish criteria for selecting the first programming language to teach a student. This paper examines the criteria used to select first programming languages and the issues that novices face when learning to program in an effort to create a more comprehensive model for selecting first programming languages.
An operational definition of learning

An operational definition offered in this paper posits learning as a multi-dimensional and multi-phase phenomenon occurring when individuals attempt to solve what they view as a problem. To model someone’s learning accordingly to the definition, it suffices to characterize a particular sequence of that person’s disequilibrium–equilibrium phases in terms of products of a particular mental act, the characteristics of the mental act inferred from the products, and intellectual and psychological needs that instigate or result from these phases. The definition is illustrated by analysis of change occurring in three thinking-aloud interviews with one middle-school teacher. The interviews were about the same task: “Make up a word problem whose solution may be found by computing 4/5 divided by 2/3.”  An operational definition is a showing of something—such as a variable, term, or object—in terms of the specific process or set of validation tests used to determine its presence and quantity. Properties described in this manner must be publicly accessible so that persons other than the definer can independently measure or test for them at will. An operational definition is generally designed to model a conceptual definition (Wikipedia)
Towards webcam-based tracking for interventional navigation

Optical tracking is a commonly used tool in computer assisted surgery and surgical training; however, many current generation commercially available tracking systems are prohibitively large and expensive for certain applications. We developed an open source optical tracking system using the Intel RealSense SR300 webcam with integrated depth sensor. In this paper, we assess the accuracy of this tracking system.  The PLUS toolkit was extended to incorporate the ArUco marker detection and tracking library. The depth data obtained from the infrared sensor of the Intel RealSense SR300 was used to improve accuracy. We assessed the accuracy of the system by comparing this tracker to a high accuracy commercial optical tracker.  The ArUco based optical tracking algorithm had median errors of 20.0mm and 4.1 degrees in a 200x200x200mm tracking volume. Our algorithm processing the depth data had a positional error of 17.3mm, and an orientation error of 7.1 degrees in the same tracking volume. In the direction perpendicular to the sensor, the optical only tracking had positional errors between 11% and 15%, compared to errors in depth of 1% or less. In tracking one marker relative to another, a fused transform from optical and depth data produced the best result of 1.39% error.  The webcam based system does not yet have satisfactory accuracy for use in computer assisted surgery or surgical training.
Support Vector Machines in Bioinformatics: a Survey

Many scientists and researchers have been considering Support Vector Machines (SVMs) as one of the most powerful and robust algorithm in machine learning. For this reason, they have been used in many fields, such as pattern recognition, image processing, robotics, and many others. Since their appearance in 1995, from an idea of Vladimir Vapnik, bioinformatics community started to use this new technique to solve the most common classification and clustering problems in the biomolecular domain. In this document, we first give a general description of Support Vector Machine technique, a technique based on the statistical learning theory (Section 1). Then we provide a survey of the many applications of the algorithm in the bioinformatics domain (Section 2). Finally, we report a short list of SVM implementation codes available on the internet (Section 3). About this survey This document is freely available and can be download from http://www.DavideChicco.it author’s website. Alessandro Lazaric (INRIA, Lille, France, EU) kindly supervised and corrected this document before publication.
Support Theory : A Nonextensional Representation of Subjective Probability

This article presents a new theory of subjective probability according to which different descriptions of the same event can give rise to different judgments. The experimental evidence confirms the major predictions of the theory. First, judged probability increases by unpacking the focal hypothesis and decreases by unpacking the alternative hypothesis. Second, judged probabilities are complementary in the binary case and subadditive in the general case, contrary to both classical and revisionist models of belief. Third, subadditivity is more pronounced for probability judgments than for frequency judgments and is enhanced by compatible evidence. The theory provides a unified treatment of a wide range of empirical findings. It is extended to ordinal judgments and to the assessment of upper and lower probabilities.
Fluorescent pigments in corals are photoprotective

All reef-forming corals depend on the photosynthesis performed by their algal symbiont, and such corals are therefore restricted to the photic zone. The intensity of light in this zone declines over several orders of magnitude—from high and damaging levels at the surface to extreme shade conditions at the lower limit. The ability of corals to tolerate this range implies effective mechanisms for light acclimation and adaptation. Here we show that the fluorescent pigments (FPs) of corals provide a photobiological system for regulating the light environment of coral host tissue. Previous studies have suggested that under low light, FPs may enhance light availability. We now report that in excessive sunlight FPs are photoprotective; they achieve this by dissipating excess energy at wavelengths of low photosynthetic activity, as well as by reflecting of visible and infrared light by FP-containing chromatophores. We also show that FPs enhance the resistance to mass bleaching of corals during periods of heat stress, which has implications for the effect of environmental stress on the diversity of reef-building corals, such as enhanced survival of a broad range of corals allowing maintenance of habitat diversity.
A Cluster-based TDMA System for Inter-Vehicle Communications

This paper presents a cluster-based TDMA (CBT) system for inter-vehicle communications. In intra-cluster communications, the proposed CBT uses a simple transmitand-listen scheme to fast elect a VC (VANET Coordinator) and it allows a VN (VANET node) to randomly choose a time slot for bandwidth requests (BR) without limiting the number of VNs. In inter-cluster communications, when two clusters are approaching, the CBT can quickly resolve the collisions by re-allocating time slots in one of the clusters. To analyze the performance of the proposed CBT, we derive mathematical equations using probability. The performance metrics of our interests include the average number of time slots for electing a VC, the average number of time slots required for BR, and the total number of time slots required before data can be successfully transmitted. The analytical results are finally validated by a simulation. Both the analytical and simulation results show that the proposed CBT spends less time to form a small-sized cluster than IEEE 802.11p. Additionally, when the number of joining VNs is increased, CBT takes less waiting time before a VN can effectively transmit data.
Efficient Stochastic Inference of Bitwise Deep Neural Networks

Recently published methods enable training of bitwise neural networks which allow reduced representation of down to a single bit per weight. We present a method that exploits ensemble decisions based on multiple stochastically sampled network models to increase performance figures of bitwise neural networks in terms of classification accuracy at inference. Our experiments with the CIFAR-10 and GTSRB datasets show that the performance of such network ensembles surpasses the performance of the high-precision base model. With this technique we achieve 5.81% best classification error on CIFAR-10 test set using bitwise networks. Concerning inference on embedded systems we evaluate these bitwise networks using a hardware efficient stochastic rounding procedure. Our work contributes to efficient embedded bitwise neural networks.
Visual Speech Enhancement using Noise-Invariant Training

Visual speech enhancement is used on videos shot in noisy environments to enhance the voice of a visible speaker and to reduce background noise. While most existing methods use audio-only inputs, we propose an audio-visual neural network model for this purpose. The visible mouth movements are used to separate the speaker’s voice from the background sounds. Instead of training our speech enhancement model on a wide range of possible noise types, we train the model on videos where other speech samples of the target speaker are used as background noise. A model trained using this paradigm generalizes well to various noise types, while also substantially reducing training time. The proposed model outperforms prior audio visual methods on two public lipreading datasets. It is also the first to be demonstrated on a general dataset not designed for lipreading. Our dataset was composed of weekly addresses of Barack Obama.
Spatially Variant Apodization for Squinted Synthetic Aperture Radar Images

Spatially variant apodization (SVA) is a nonlinear sidelobe reduction technique that improves sidelobe level and preserves resolution at the same time. This method implements a bidimensional finite impulse response filter with adaptive taps depending on image information. Some papers that have been previously published analyze SVA at the Nyquist rate or at higher rates focused on strip synthetic aperture radar (SAR). This paper shows that traditional SVA techniques are useless when the sensor operates with a squint angle. The reasons for this behaviour are analyzed, and a new implementation that largely improves the results is presented. The algorithm is applied to simulated SAR images in order to demonstrate the good quality achieved along with efficient computation.
Agaricus subrufescens: A review.

Medicinal mushrooms have currently become a hot issue due to their various therapeutic properties. Of these, Agaricus subrufescens, also known as the "almond mushroom", has long been valued by many societies (i.e., Brazil, China, France, and USA). Since its discovery in 1893, this mushroom has been cultivated throughout the world, especially in Brazil where several strains of A. subrufescens have been developed and used as health food and alternative medicine. This article presents up-to-date information on this mushroom including its taxonomy and health promoting benefits. Medicinal properties of A. subrufescens are emphasized in several studies which are reviewed here. In addition, safety issues concerning the use of this fungus will be discussed.
Effects of blood pressure lowering on outcome incidence in hypertension: 2. Effects at different baseline and achieved blood pressure levels--overview and meta-analyses of randomized trials.

Relevant clinical questions not approached by randomized controlled trials (RCTs) of blood pressure (BP)-lowering treatment can be explored by meta-analyses stratified by clinical criteria.



Investigating whether all grades of hypertension benefit from BP-lowering treatment and which are the target BP levels to maximize outcome reduction.



Of the 68 RCTs of intentional and nonintentional BP-lowering, those without baseline antihypertensive drugs were stratified by the average baseline SBP and DBP (hypertension grades 1, 2, and 3). RCTs with or without baseline treatment were considered for investigating the effects of mean achieved SBP/DBP across three SBP cutoffs and two DBP cutoffs. Risk ratios (RR) and 95% confidence interval (CI) (random-effects model), standardized to 10/5 mmHg SBP/DBP reduction, and absolute risk reductions of seven fatal and nonfatal outcomes were calculated. Differences between relative and absolute risk reductions in the different strata of baseline or achieved SBP/DBP were evaluated by trend or heterogeneity analyses.



In 32 RCTs (104,359 individuals), significant outcome reductions were found independently of the hypertension grade, with no trend toward risk ratio changes with increasing baseline BP. A secondary analysis limited to RCTs on grade 1 hypertension at low-to-moderate risk showed significant outcome reductions [risk ratio: stroke 0.33 (0.11-0.98), coronary events 0.68 (0.48-0.95), and death 0.53 (0.35-0.80)]. In 32 RCTs (128,232 individuals), relative and absolute outcome reductions were significant for the SBP differences across 150 and 140 mmHg cutoffs. Below 130 mmHg, only stroke and all-cause death were significantly reduced. Absolute outcome reduction showed a significant trend to decrease, the lower the SBP cutoff considered. In 29 RCTs (107,665 individuals), outcomes were significantly reduced across DBP cutoffs of 90 and 80 mmHg. After excluding RCTs with baseline DBP less than 90 mmHg, only stroke reduction was significant at achieved DBP less than 80 mmHg.



Meta-analyses favor BP-lowering treatment even in grade 1 hypertension at low-to-moderate risk, and lowering SBP/DBP to less than 140/90 mmHg. Achieving less than 130/80 mmHg appears safe, but only adds further reduction in stroke.