nils-herrmann/clean_scirepeval huggingface dataset:
Tunable 1.55 - 2.1 GHz 4-Pole Elliptic Bandpass Filter With Bandwidth Control and ${> 50}~{\hbox {dB}}$ Rejection for Wireless Systems

This paper presents a four-pole elliptic tunable combline bandpass filter with center frequency and bandwidth control. The filter is built on a Duroid substrate with εr=10.2 and h=25 mils, and the tuning is done using packaged Schottky diodes. A frequency range of 1.55-2.1 GHz with a 1-dB bandwidth tuning from 40-120 MHz (2.2-8% fractional bandwidth) is demonstrated. A pair of tunable transmission zeroes are synthesized at both passband edges and significantly improve the filter selectivity. The rejection level at both the lower and upper stopbands is >; 50 dB and no spurious response exists close to the passband. The measured third-order intermodulation intercept point (TOI) and 1-dB power compression point at midband (1.85 GHz) and a bandwidth of 110 MHz are >; 14& dBm and 6 dBm, respectively, and are limited by the Schottky diodes. It is believed that this is the first four-pole combline tunable bandpass filter with an elliptic function response and center frequency and bandwidth control. The application areas are in tunable filters for wireless systems and cognitive radios.
Machine-Learning-Based Identification of Defect Patterns in Semiconductor Wafer Maps: An Overview and Proposal

Wafers are formed from very thin layers of a semiconductor material, hence, they are highly susceptible to various kinds of defects. The defects are most likely to occur during the lengthy and complex fabrication process, which can include hundreds of steps. Wafer defects are generally caused by machine inaccuracy, chemical stains, physical damages, human mistakes, and atmospheric conditions. The defective chips tend to have several unique spatial patterns across the wafer, namely ring, spot, repetitive and cluster patterns. To locate such defect patterns, wafer maps are used to visualize and ultimately lead to better understanding of what happened during the process failure. To identify the unique patterns of defects and to find the point of manufacturing process that causes such defects accurately, nature-inspired model-free machine-learning techniques have been well accepted. This paper thus reviews the theoretical and experimental literature of such models with a focus on model learnability and efficiency-related issues involving data reduction and transformation techniques, which could be seen as the key model properties to deal with big data applications.
Generic Dynamic Models for Modeling Wind Power Plants and Other Renewable Technologies in Large-Scale Power System Studies

With the tremendous growth of wind power worldwide in the past decade, there has been an equally great demand for simplified, standard, and publicly available models for simulating wind power generators in commercially available power system simulation tools for stability analysis. Several efforts have been on the way to meet this need. The Western Electricity Coordinating Council's Renewable Energy Modeling Task Force has successfully achieved this goal and more recently has been working on expanding these models to include the ability to model complex plants, energy storage, and frequency response capabilities. This paper presents an account of these latest developments.
Characterization of Glass Fibre Reinforced Polymer Composite Prepared by Hand Layup Method

In this work, glass fiber reinforced epoxy composites were fabricated. Epoxy resin was used as polymer matrix material and glass fiber was used as reinforcing material. The main focus of this work was to fabricate this composite material by the cheapest and easiest way. For this, hand layup method was used to fabricate glass fiber reinforced epoxy resin composites and TiO2 material was used as filler material. Six types of compositions were made with and without filler material keeping the glass fiber constant and changing the epoxy resin with respect to filler material addition. Mechanical properties such as tensile, impact, hardness, compression and flexural properties were investigated. Additionally, microscopic analysis was done. The experimental investigations show that without filler material the composites exhibit overall lower value in mechanical properties than with addition of filler material in the composites. The results also show that addition of filler material increases the mechanical properties but highest values were obtained for different filler material addition. From the obtained results, it was observed that composites filled by 15wt% of TiO2 particulate exhibited maximum tensile strength, 20wt% of TiO2 particulate exhibited maximum impact strength, 25wt% of TiO2 particulate exhibited maximum hardness value, 25wt% of TiO2 particulate exhibited maximum compressive strength, 20wt% of TiO2 particulate exhibited maximum flexural strength.
LVDS driver design for high speed serial link in 0.13um CMOS technology

This paper presents a LVDS (low voltage differential signal) driver, which works at 2Gbps, with a pre-emphasis circuit compensating the attenuation of limited bandwidth of channel. To make the output common-mode (CM) voltage stable over process, temperature, and supply voltage variations, a closed-loop negative feedback circuit is added in this work. The LVDS driver is designed in 0.13um CMOS technology using both thick (3.3V) and thin (1.2V) gate oxide device, simulated with transmission line model and package parasitic model. The simulated results show that this driver can operate up to 2Gbps with random data patterns.
Intuitive experimentation in the physical world

Many aspects of our physical environment are hidden. For example, it is hard to estimate how heavy an object is from visual observation alone. In this paper we examine how people actively "experiment" within the physical world to discover such latent properties. In the first part of the paper, we develop a novel framework for the quantitative analysis of the information produced by physical interactions. We then describe two experiments that present participants with moving objects in "microworlds" that operate according to continuous spatiotemporal dynamics similar to everyday physics (i.e., forces of gravity, friction, etc.). Participants were asked to interact with objects in the microworlds in order to identify their masses, or the forces of attraction/repulsion that governed their movement. Using our modeling framework, we find that learners who freely interacted with the physical system selectively produced evidence that revealed the physical property consistent with their inquiry goal. As a result, their inferences were more accurate than for passive observers and, in some contexts, for yoked participants who watched video replays of an active learner's interactions. We characterize active learners' actions into a range of micro-experiment strategies and discuss how these might be learned or generalized from past experience. The technical contribution of this work is the development of a novel analytic framework and methodology for the study of interactively learning about the physical world. Its empirical contribution is the demonstration of sophisticated goal directed human active learning in a naturalistic context.
Wireless Body Area Network for Heart Attack Detection [Education Corner]

This article describes a body area network (BAN) for measuring an electrocardiogram (ECG) signal and transmitting it to a smartphone via Bluetooth for data analysis. The BAN uses a specially designed planar inverted F-antenna (PIFA) with a small form factor, realizable with low-fabricationcost techniques. Furthermore, due to the human body's electrical properties, the antenna was designed to enable surface-wave propagation around the body. The system utilizes the user's own smartphone for data processing, and the built-in communications can be used to raise an alarm if a heart attack is detected. This is managed by an application for Android smartphones that has been developed for this system. The good functionality of the system was confirmed in three real-life user case scenarios.
Singularity Analysis of 3-DOF Planar Parallel Mechanisms via Screw Theory

This paper presents the results of a detailed study of the singular configurations of 3planar parallel mechanisms with three identical legs. Only prismatic and revolute jo are considered. From the point of view of singularity analysis, there are ten diffe architectures. All of them are examined in a compact and systematic manner using p screw theory. The nature of each possible singular configuration is discussed an singularity loci for a constant orientation of the mobile platform are obtained. For so architectures, simplified designs with easy to determine singularities are identified.
Bridging the gap. The separate worlds of evidence-based medicine and patient-centered medicine.

Modern medical care is influenced by two paradigms: 'evidence-based medicine' and 'patient-centered medicine'. In the last decade, both paradigms rapidly gained in popularity and are now both supposed to affect the process of clinical decision making during the daily practice of physicians. However, careful analysis shows that they focus on different aspects of medical care and have, in fact, little in common. Evidence-based medicine is a rather young concept that entered the scientific literature in the early 1990s. It has basically a positivistic, biomedical perspective. Its focus is on offering clinicians the best available evidence about the most adequate treatment for their patients, considering medicine merely as a cognitive-rational enterprise. In this approach the uniqueness of patients, their individual needs and preferences, and their emotional status are easily neglected as relevant factors in decision-making. Patient-centered medicine, although not a new phenomenon, has recently attracted renewed attention. It has basically a humanistic, biopsychosocial perspective, combining ethical values on 'the ideal physician', with psychotherapeutic theories on facilitating patients' disclosure of real worries, and negotiation theories on decision making. It puts a strong focus on patient participation in clinical decision making by taking into account the patients' perspective, and tuning medical care to the patients' needs and preferences. However, in this approach the ideological base is better developed than its evidence base. In modern medicine both paradigms are highly relevant, but yet seem to belong to different worlds. The challenge for the near future is to bring these separate worlds together. The aim of this paper is to give an impulse to this integration. Developments within both paradigms can benefit from interchanging ideas and principles from which eventually medical care will benefit. In this process a key role is foreseen for communication and communication research.
Facebook © Adoption as Computer-Mediated Communication for University Students

Social communications and collaborations have been ranked as one of key information technology trends. Social networks are social collaboration tools that have shown clear benefits for education in terms of providing informal learning, joining students to communities, exchanging support in studying, and promoting relationships in classes. Therefore, major objectives of this paper are to investigate factors affecting Facebook acceptance as the computer-mediated communication for courses, to explore different levels of the factors’ influence, and to guide instructors for effectively applying social networks in education. The survey approach using questionnaires along with multiple regressions were applied to reveal results. The results show that two perception factor: perceived usefulness, perceived ease of use, and one instructor factor: instructor characteristics are determinants of the course Facebook pages adoption, but student factors which are student characteristics and past behavior are not determinants. In terms of theoretical contribution, this research has extended TAM and TPB models with some aspects of teachers and learners.
Quantification of acetaminophen (paracetamol) in human plasma and urine by stable isotope-dilution GC-MS and GC-MS/MS as pentafluorobenzyl ether derivative.

We report on the quantitative determination of acetaminophen (paracetamol; NAPAP-d(0)) in human plasma and urine by GC-MS and GC-MS/MS in the electron-capture negative-ion chemical ionization (ECNICI) mode after derivatization with pentafluorobenzyl (PFB) bromide (PFB-Br). Commercially available tetradeuterated acetaminophen (NAPAP-d(4)) was used as the internal standard. NAPAP-d(0) and NAPAP-d(4) were extracted from 100-μL aliquots of plasma and urine with 300 μL ethyl acetate (EA) by vortexing (60s). After centrifugation the EA phase was collected, the solvent was removed under a stream of nitrogen gas, and the residue was reconstituted in acetonitrile (MeCN, 100 μL). PFB-Br (10 μL, 30 vol% in MeCN) and N,N-diisopropylethylamine (10 μL) were added and the mixture was incubated for 60 min at 30 °C. Then, solvents and reagents were removed under nitrogen and the residue was taken up with 1000 μL of toluene, from which 1-μL aliquots were injected in the splitless mode. GC-MS quantification was performed by selected-ion monitoring ions due to [M-PFB](-) and [M-PFB-H](-), m/z 150 and m/z 149 for NAPAP-d(0) and m/z 154 and m/z 153 for NAPAP-d(4), respectively. GC-MS/MS quantification was performed by selected-reaction monitoring the transition m/z 150 → m/z 107 and m/z 149 → m/z 134 for NAPAP-d(0) and m/z 154 → m/z 111 and m/z 153 → m/z 138 for NAPAP-d(4). The method was validated for human plasma (range, 0-130 μM NAPAP-d(0)) and urine (range, 0-1300 μM NAPAP-d(0)). Accuracy (recovery, %) ranged between 89 and 119%, and imprecision (RSD, %) was below 19% in these matrices and ranges. A close correlation (r>0.999) was found between the concentrations measured by GC-MS and GC-MS/MS. By this method, acetaminophen can be reliably quantified in small plasma and urine sample volumes (e.g., 10 μL). The analytical performance of the method makes it especially useful in pediatrics.
4 Staining of Amyloid Beta ( Abeta ) Using ( Immuno ) Histochemical Techniques and Abeta 42 Specific Peptides

In the elderly, Alzheimer’s disease (AD) is the most common form of dementia (Hebert et al., 2003). The two pathologies that characterize the disease are the presence of large numbers of intracellular neurofibrillary tangles (NFTs) and extracellular neuritic plaques in the brain (e.g., Braak and Braak, 1991; 1998; Selkoe, 2001). Neurofibrillary tangles consist of hyperphosphorylated, twisted filaments of the cytoskeletal protein tau (e.g., Duff, 2006), whereas plaques are primarily made up of amyloid ┚(A┚ [Selkoe, 2001; Dickson and Vickers, 2002]), a 39-43 amino acid long peptide derived from the proteolytic processing of the amyloid precursor protein (APP [Selkoe, 2001; Vetrivel and Thinakaran, 2006]). When APP is sequentially cleaved by the ┚-secretase and ┛-secretase, one of the resulting breakdown product is A┚, in contrast, initial cleavage by ┙-secretase (in the middle of the A┚ sequence) leads to production of APPs┙ and the C83 peptide (Selkoe, 2001). Most cases of AD are sporadic, however approximately 5 % of AD cases are familial (Price and Sisodia, 1995; Selkoe, 2001), these cases are related to mutations in the genes for APP, and presenilin 1 and 2 (PS1 and PS2 [Price and Sisodia, 1995; Hardy, 1997; Selkoe, 2001]). Transgenic mice expressing mutated human AD genes offer a powerful model to study the role of A┚ in the development of pathology (e.g., Duff and Suleman, 2004; McGowan et al, 2006). The present study employs three lines of transgenic mice expressing both human APPswe and/or PS1 mutations. These lines of mice develop elevated levels of A┚42 at different ages, and at different locations (Van Groen et al., 2005; Wang et al., 2003).
Dynamic and Static Prototype Vectors for Semantic Composition

Compositional Distributional Semantic methods model the distributional behavior of a compound word by exploiting the distributional behavior of its constituent words. In this setting, a constituent word is typically represented by a feature vector conflating all the senses of that word. However, not all the senses of a constituent word are relevant when composing the semantics of the compound. In this paper, we present two different methods for selecting the relevant senses of constituent words. The first one is based on Word Sense Induction and creates a static multi prototype vectors representing the senses of a constituent word. The second creates a single dynamic prototype vector for each constituent word based on the distributional properties of the other constituents in the compound. We use these prototype vectors for composing the semantics of noun-noun compounds and evaluate on a compositionality-based similarity task. Our results show that: (1) selecting relevant senses of the constituent words leads to a better semantic composition of the compound, and (2) dynamic prototypes perform better than static prototypes.
Endogenous Stratification in Randomized Experiments

Policymakers are often interested in estimating how policy interventions affect the outcomes of those most in need of help. This concern has motivated the practice of disaggregating experimental results by groups constructed on the basis of an index of baseline characteristics that predicts the values of individual outcomes without the treatment. This paper shows that substantial biases may arise in practice if the index is estimated by regressing the outcome variable on baseline characteristics for the full sample of experimental controls. We propose alternative methods that correct this bias and show that they behave well in realistic scenarios.
NeuroGym: A Scientific Executive Review Introduction

Neuroplasticity has replaced the formerly held position that the brain is a physiologically static organ. By actively being mindful and partaking in cognitive exercises, the brain is able to improve its functions by growing new neurons and forming new connections between existing neurons [11]. Founded in 2011 and guided by its advisory board of neuroscientists, psychologists, medical doctors and researchers, NeuroGym effectively utilizes the natural neuroplasticity of the brain to create commercially available products based on available science.
Complementary Set Variational Autoencoder for Supervised Anomaly Detection

Anomalies have broad patterns corresponding to their causes. In industry, anomalies are typically observed as equipment failures. Anomaly detection aims to detect such failures as anomalies. Although this is usually a binary classification task, the potential existence of unseen (unknown) failures makes this task difficult. Conventional supervised approaches are suitable for detecting seen anomalies but not for unseen anomalies. Although, unsupervised neural networks for anomaly detection now detect unseen anomalies well, they cannot utilize anomalous data for detecting seen anomalies even if some data have been made available. Thus, providing an anomaly detector that finds both seen and unseen anomalies well is still a tough problem. In this paper, we introduce a novel probabilistic representation of anomalies to solve this problem. The proposed model defines the normal and anomaly distributions using the analogy between a set and the complementary set. We applied these distributions to an unsupervised variational autoencoder (VAE)-based method and turned it into a supervised VAE-based method. We tested the proposed method with well-known data and real industrial data to show that the proposed method detects seen anomalies better than the conventional unsupervised method without degrading the detection performance for unseen anomalies.
Autophagy and genomic integrity

DNA lesions, constantly produced by endogenous and exogenous sources, activate the DNA damage response (DDR), which involves detection, signaling and repair of the damage. Autophagy, a lysosome-dependent degradation pathway that is activated by stressful situations such as starvation and oxidative stress, regulates cell fate after DNA damage and also has a pivotal role in the maintenance of nuclear and mitochondrial genomic integrity. Here, we review important evidence regarding the role played by autophagy in preventing genomic instability and tumorigenesis, as well as in micronuclei degradation. Several pathways governing autophagy activation after DNA injury and the influence of autophagy upon the processing of genomic lesions are also discussed herein. In this line, the mechanisms by which several proteins participate in both DDR and autophagy, and the importance of this crosstalk in cancer and neurodegeneration will be presented in an integrated fashion. At last, we present a hypothetical model of the role played by autophagy in dictating cell fate after genotoxic stress.
Voxelized Shadow Volumes

Efficient shadowing algorithms have been sought for decades, but most shadow research focuses on quickly identifying shadows on surfaces. This paper introduces a novel algorithm to efficiently sample light visibility at points inside a volume. These voxelized shadow volumes (VSVs) extend shadow maps to allow efficient, simultaneous queries of visibility along view rays, or can alternately be seen as a discretized shadow volume. We voxelize the scene into a binary, epipolar-space grid where we apply a fast parallel scan to identify shadowed voxels. Using a view-dependent grid, our GPU implementation looks up 128 visibility samples along any eye ray with a single texture fetch. We demonstrate our algorithm in the context of interactive shadows in homogeneous, single-scattering participating media.
Superposition benchmark: innovative SSRTGI lighting in real time

Superposition Benchmark is a next-generation GPU benchmark that continues the line of UNIGINE benchmarks, famous for outstanding and innovative 3D graphics. The UNIGINE team developed a brand-new lighting effect that makes an interactive real-time-rendered environment look photorealistic: screen-space ray tracing global illumination (SSRTGI), a technology that brings rendered ray tracing closer to real physics. Ordinary screen-space effects, such as SSAO and SSGI, do not treat objects as obstacles for light rays, so they are not able to provide realistic lighting simulation. They produce only a rough imitation, while SSRTGI provides real 180-degree ray tracing for each pixel in the scene with a fixed number of steps per ray to define occlusions. This ray-tracing technique provides ambient occlusion with more realistic shadows between the objects. The use of bent normals helps to reduce the noise and smooth borders of shadows, while global illumination recreates light reflections from surfaces. Working together, these effects provide incredibly realistic lighting and shadow-play simulation for real-time interactive rendering.
IoTPredict: Collaborative QoS Prediction in IoT

Internet of Things (IoT) applications can be built from a number of heterogeneous services provided by a range of devices, which are potentially resource constrained and/or mobile. As these services and applications continue to be more widespread, a key research question is how to predict user-side quality of service (QoS), to ensure the optimal selection, composition and adaptation of IoT services. The exponential growth in the number of these services means that it is not practical to invoke all candidate services to test their QoS, especially during runtime service adaptation. QoS can vary by time and location, which makes it difficult for service providers to give accurate estimates of how the service will perform for users located in changing network topologies. We propose IoTPredict, a novel neighbourhood-based prediction approach for the IoT, which uses an alternative similarity computation mechanism. Our collaborative approach requires no additional invocation of services, which is a key requirement for resource constrained devices in the IoT. We evaluate our algorithm on a QoS dataset and show that it achieves higher QoS prediction accuracy than other state of the art approaches.