nils-herrmann/clean_scirepeval huggingface dataset:
Samba: a smartphone-based robot system for energy-efficient aquatic environment monitoring

Monitoring aquatic environment is of great interest to the ecosystem, marine life, and human health. This paper presents the design and implementation of Samba -- an aquatic surveillance robot that integrates an off-the-shelf Android smartphone and a robotic fish to monitor harmful aquatic processes such as oil spill and harmful algal blooms. Using the built-in camera of on-board smartphone, Samba can detect spatially dispersed aquatic processes in dynamic and complex environments. To reduce the excessive false alarms caused by the non-water area (e.g., trees on the shore), Samba segments the captured images and performs target detection in the identified water area only. However, a major challenge in the design of Samba is the high energy consumption resulted from the continuous image segmentation. We propose a novel approach that leverages the power-efficient inertial sensors on smartphone to assist the image processing. In particular, based on the learned mapping models between inertial and visual features, Samba uses real-time inertial sensor readings to estimate the visual features that guide the image segmentation, significantly reducing energy consumption and computation overhead. Samba also features a set of lightweight and robust computer vision algorithms, which detect harmful aquatic processes based on their distinctive color features. Lastly, Samba employs a feedback-based rotation control algorithm to adapt to spatiotemporal evolution of the target aquatic process. We have implemented a Samba prototype and evaluated it through extensive field experiments, lab experiments, and trace-driven simulations. The results show that Samba can achieve 94% detection rate, 5% false alarm rate, and a lifetime up to nearly two months.
Non-invasive continuous core temperature measurement by zero heat flux.

Reliable continuous core temperature measurement is of major importance for monitoring patients. The zero heat flux method (ZHF) can potentially fulfil the requirements of non-invasiveness, reliability and short delay time that current measurement methods lack. The purpose of this study was to determine the performance of a new ZHF device on the forehead regarding these issues. Seven healthy subjects performed a protocol of 10 min rest, 30 min submaximal exercise (average temperature increase about 1.5 °C) and 10 min passive recovery in ambient conditions of 35 °C and 50% relative humidity. ZHF temperature (T(zhf)) was compared to oesophageal (T(es)) and rectal (T(re)) temperature. ΔT(zhf)-T(es) had an average bias ± standard deviation of 0.17 ± 0.19 °C in rest, -0.05 ± 0.18 °C during exercise and -0.01 ± 0.20 °C during recovery, the latter two being not significant. The 95% limits of agreement ranged from -0.40 to 0.40 °C and T(zhf) had hardly any delay compared to T(es). T(re) showed a substantial delay and deviation from T(es) when core temperature changed rapidly. Results indicate that the studied ZHF sensor tracks T(es) very well in hot and stable ambient conditions and may be a promising alternative for reliable non-invasive continuous core temperature measurement in hospital.
The mathematical foundations of quantum mechanics

Classical mechanics was first envisaged by Newton, formed into a powerful tool by Euler, and brought to perfection by Lagrange and Laplace. It has served as the paradigm of science ever since. Even the great revolutions of 19th century phys icsnamely, the FaradayMaxwell electro-magnetic theory and the kinetic t h e o r y w e r e viewed as further support for the complete adequacy of the mechanistic world view. The physicist at the end of the 19th century had a coherent conceptual scheme which, in principle at least, answered all his questions about the world. The only work left to be done was the computing of the next decimal. This consensus began to unravel at the beginning of the 20th century. The work of Planck, Einstein, and Bohr simply could not be made to fit. The series of ad hoc moves by Bohr, Eherenfest, et al., now called the old quantum theory, was viewed by all as, at best, a stopgap. In the period 1925-27 a new synthesis was formed by Heisenberg, Schr6dinger, Dirac and others. This new synthesis was so successful that even today, fifty years later, physicists still teach quantum mechanics as it was formulated by these men. Nevertheless, two foundational tasks remained: that of providing a rigorous mathematical formulation of the theory, and that of providing a systematic comparison with classical mechanics so that the full ramifications of the quantum revolution could be clearly revealed. These tasks are, of course, related, and a possible fringe benefit of the second task might be the pointing of the way 'beyond quantum theory'. These tasks were taken up by von Neumann as a consequence of a seminar on the foundations of quantum mechanics conducted by Hilbert in the fall of 1926. In papers published in 1927 and in his book, The Mathemat ical Foundations of Quantum Mechanics, von Neumann provided the first completely rigorous
Capturing user reading behaviors for personalized document summarization

We propose a new personalized document summarization method that observes a user's personal reading preferences. These preferences are inferred from the user's reading behaviors, including facial expressions, gaze positions, and reading durations that were captured during the user's past reading activities. We compare the performance of our algorithm with that of a few peer algorithms and software packages. The results of our comparative study show that our algorithm can produce more superior personalized document summaries than all the other methods in that the summaries generated by our algorithm can better satisfy a user's personal preferences.
Identifying parking spaces & detecting occupancy using vision-based IoT devices

The increasing number of vehicles in high density, urban areas is leading to significant parking space shortages. While systems have been developed to enable visibility into parking space vacancies for drivers, most rely on costly, dedicated sensor devices that require high installation costs. The proliferation of inexpensive Internet of Things (IoT) devices enables the use of compute platforms with integrated cameras that could be used to monitor parking space occupancy. However, even with camera-captured images, manual specification of parking space locations is required before such devices can be used by drivers after device installation. In this paper, we leverage machine learning techniques to develop a method to dynamically identify parking space topologies based on parked vehicle positions. More specifically, we designed and evaluated an occupation detection model to identify vacant parking spaces. We built a prototype implementation of the whole system using a Raspberry Pi and evaluated it on a real-world urban street near the University of Washington campus. The results show that our clustering-based learning technique coupled with our occupation detection pipeline is able to correctly identify parking spaces and determine occupancy without manual specication of parking space locations with an accuracy of 91%. By dynamically aggregating identied parking spaces from multiple IoT devices using Amazon Cloud Services, we demonstrated how a complete, city-wide parking management system can be quickly deployed at low cost.
NHTSA Driver Distraction Research: Past, Present And Future

Driver distraction has been identified as a highpriority topic by the National Highway Traffic Safety Administration, reflecting concerns about the compatibility of certain in-vehicle technologies with the driving task, whether drivers are making potentially dangerous decisions about when to interact with invehicle technologies while driving, and that these trends may accelerate as new technologies continue to become available. Since 1991, NHTSA has conducted research to understand the factors that contribute to driver distraction and to develop methods to assess the extent to which in-vehicle technologies may contribute to crashes. This paper summarizes significant findings from past NHTSA research in the area of driver distraction and workload, provides an overview of current ongoing research, and describes upcoming research that will be conducted, including research using the National Advanced Driving Simulator and work to be conducted at NHTSA’s Vehicle Research and Test Center. Preliminary results of the ongoing research are also presented.
A fast MST-inspired kNN-based outlier detection method

Today's real-world databases typically contain millions of items with many thousands of fields. As a result, traditional distribution-based outlier detection techniques have more and more restricted capabilities and novel k-nearest neighbors based approaches have become more and more popular. However, the problems with these k-nearest neighbors rankings for top n outliers, are very computationally expensive for large datasets, and doubts exist in general whether they would work well for high dimensional datasets. To partially circumvent these problems, we propose in this paper a new global outlier factor and a new local outlier factor and an efficient outlier detection algorithm developed upon them that is easy to implement and can provide competing performances with existing solutions. Experiments performed on both synthetic and real data sets demonstrate the efficacy of our method. & 2014 Elsevier Ltd. All rights reserved.
Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms

Achieving the upper limits of face identification accuracy in forensic applications can minimize errors that have profound social and personal consequences. Although forensic examiners identify faces in these applications, systematic tests of their accuracy are rare. How can we achieve the most accurate face identification: using people and/or machines working alone or in collaboration? In a comprehensive comparison of face identification by humans and computers, we found that forensic facial examiners, facial reviewers, and superrecognizers were more accurate than fingerprint examiners and students on a challenging face identification test. Individual performance on the test varied widely. On the same test, four deep convolutional neural networks (DCNNs), developed between 2015 and 2017, identified faces within the range of human accuracy. Accuracy of the algorithms increased steadily over time, with the most recent DCNN scoring above the median of the forensic facial examiners. Using crowd-sourcing methods, we fused the judgments of multiple forensic facial examiners by averaging their rating-based identity judgments. Accuracy was substantially better for fused judgments than for individuals working alone. Fusion also served to stabilize performance, boosting the scores of lower-performing individuals and decreasing variability. Single forensic facial examiners fused with the best algorithm were more accurate than the combination of two examiners. Therefore, collaboration among humans and between humans and machines offers tangible benefits to face identification accuracy in important applications. These results offer an evidence-based roadmap for achieving the most accurate face identification possible.
Single-Layered Circularly Polarized Substrate-Integrated Waveguide Horn Antenna Array

In this communication, a circularly polarized (CP) substrate-integrated waveguide horn antenna is proposed and studied. The CP horn antenna is implemented on a single-layer substrate with a thickness of  $0.12\lambda _{\mathrm {\mathbf {0}}}$  at the center frequency (1.524 mm) for 24 GHz system applications. It comprises of an integrated phase controlling and power dividing structure, two waveguide antennas, and an antipodal linearly tapered slot antenna. With such a phase controlling and power dividing structure fully integrated inside the horn antenna, two orthogonal electric fields of the equal amplitude with 90° phase difference are achieved at the aperture plane of the horn antenna, thus, yielding an even effective circular polarization in a compact single-layered geometry. The measured results of the prototyped horn antenna exhibit a 5% bandwidth (23.7–24.9 GHz) with an axial ratio below 3 dB and a VSWR below 2. The gain of the antenna is around 8.5 dBi.
Detecting novelties in time series through neural networks forecasting with robust confidence intervals

Novelty detection in time series is an important problem with application in a number of different domains such as machine failure detection and fraud detection in financial systems. One of the methods for detecting novelties in time series consists of building a forecasting model that is later used to predict future values. Novelties are assumed to take place if the difference between predicted and observed values is above a certain threshold. The problem with this method concerns the definition of a suitable value for the threshold. This paper proposes a method based on forecasting with robust confidence intervals for defining the thresholds for detecting novelties. Experiments with six real-world time series are reported and the results show that the method is able to correctly define the thresholds for novelty detection. r 2006 Elsevier B.V. All rights reserved.
Spectrum sharing methods for the coexistence of multiple RF systems: A survey

Due to the explosive growth of wireless devices and wireless traffic, the spectrum scarcity problem is becoming more urgent in numerous Radio Frequency (RF) systems. At the same time, many studies have shown that spectrum resources allocated to various existing RF systems are largely underutilized. As a potential solution to this spectrum scarcity problem, spectrum sharing among multiple, potentially dissimilar RF systems has been proposed. However, such spectrum sharing solutions are challenging to develop due to the lack of efficient coordination schemes and potentially different PHY/MAC properties. In this paper, we investigate existing spectrum sharing methods facilitating coexistence of various RF systems. The cognitive radio technique, which has been the subject of various surveys, constitutes a subset of our wider scope. We study more general coexistence scenarios and methods such as coexistence of communication systems with similar priorities, utilizing similar or different protocols or standards, as well as the coexistence of communication and non-communication systems using the same spectral resources. Finally, we explore open research issues on the spectrum sharing methods as well as potential approaches to resolving these issues.
The Exosomal/Total α-Synuclein Ratio in Plasma Is Associated With Glucocerebrosidase Activity and Correlates With Measures of Disease Severity in PD Patients

Intensive research efforts in the field of Parkinson's disease (PD) are focusing on identifying reliable biomarkers which possibly help physicians in predicting disease onset, diagnosis, and progression as well as evaluating the response to disease-modifying treatments. Given that abnormal alpha-synuclein (α-syn) accumulation is a primary component of PD pathology, this protein has attracted considerable interest as a potential biomarker for PD. Alpha-synuclein can be detected in several body fluids, including plasma, where it can be found as free form or in association with exosomes, small membranous vesicles secreted by virtually all cell types. Together with α-syn accumulation, lysosomal dysfunctions seem to play a central role in the pathogenesis of PD, given the crucial role of lysosomes in the α-syn degradation. In particular, heterozygous mutations in the GBA1 gene encoding lysosomal enzyme glucocerebrosidase (GCase) are currently considered as the most important risk factor for PD. Different studies have found that GCase deficiency leads to accumulation of α-syn; whereas at the same time, increased α-syn may inhibit GCase function, thus inducing a bidirectional pathogenic loop. In this study, we investigated whether changes in plasma total and exosome-associated α-syn could correlate with disease status and clinical parameters in PD and their relationship with GCase activity. We studied 39 PD patients (mean age: 65.2 ± 8.9; men: 25), without GBA1 mutations, and 33 age-matched controls (mean age: 61.9 ± 6.2; men: 15). Our results showed that exosomes from PD patients contain a greater amount of α-syn compared to healthy subjects (25.2 vs. 12.3 pg/mL, p < 0.001) whereas no differences were found in plasma total α-syn levels (15.7 vs. 14.8 ng/mL, p = 0.53). Moreover, we highlighted a significant increase of plasma exosomal α-syn/total α-syn ratio in PD patients (1.69 vs. 0.89, p < 0.001), which negatively correlates with disease severity (p = 0.014). Intriguingly, a significant inverse correlation between GCase activity and this ratio in PD subjects was found (p = 0.006). Additional and large-scale studies comparing GCase activity and pathological protein levels will be clearly needed to corroborate these data and determine whether the association between key players in the lysosomal system and α-syn can be used as diagnostic or prognostic biomarkers for PD.
Free and Open Source Software for Industrial Process Control Systems

Scilab and Scicos are open-source and free software packages for design, simulation and realization of industrial process control systems. They can be used as the center of an integrated platform for the complete development process, including running controller with real plant (ScicosHIL: Hardware In the Loop) and automatic code generation for real time embedded platforms (Linux, RTAI/RTAI-Lab, ). These tools are mature, working alternatives to closed source, proprietary solutions for educational, academic, research and industrial applications. We present, using a working example, a complete development chain, from the design tools to the automatic code generation of stand alone embedded control and user interface program.
In Search for the Core of the Business Ecosystem Concept: A Conceptual Comparison of Business Ecosystem, Industry, Cluster, and Inter Organizational Network

The concept of ecosystem emanates from ecology and subsequently has been broadly used in business studies to describe and investigate complex interrelationships between companies and other organizations. Concepts that are transferred from other disciplines (and used both in research and in practice) can, however, be ambiguous and problematic. For example, the use of the ecosystem concept has been questioned in the literature. To better understand the potential ambiguities between the business ecosystem concept and other related concepts, this study presents a conceptual analysis of business ecosystem. We continue by analytically comparing business ecosystem with other concepts used to describe business relationships, namely industry, population, cluster, and inter-organizational network. The results indicate a need for conceptual clarity when describing business networks. We conclude with a synthesis and discuss under what circumstances using the business ecosystem concept may add value for research and practice. The paper contributes to the business ecosystem literature by positioning the business ecosystem concept in relation to other closely related concepts
Chip-on-Flex Packaging of Optoelectronic Devices in Polymer-Based Planar Optical Interconnects

A variety of applications in the use of optical and optoelectronic devices as well as their integrated circuits are increasingly penetrating into our daily routine. One of the most demanding fields is the sensing engineering. Meanwhile, the demand for more mechanical flexibility of systems and lower manufacturing budgets is continuously growing. Since the optical transparency is desired for coupling and transmission of optical signals, cost-effective transparent polymeric films are the promising candidates as carrier substrates. In this work, we aim to establish short-distance planar optical interconnects consisting of light sources/detectors and waveguides on the flexible transparent polymeric films for optical sensing functions. To achieve a miniaturized structure and ensure the flexibility of interconnects, bare chips of optoelectronic light sources/detectors are employed. Here, packaging of these chips carries the burden of all responsibilities in terms of ensuring the mechanical strength, electrical connection, thermal stability as well as the optical performance. It leads to the concept of chip-on-flex (CoF) packaging of optoelectronic devices. We present CoF packaging of a bare edge-emitting laser diode using the previously developed novel optodic bonding. While operating CoF packages, the optical performance of an active diode is strongly impaired by inefficient heat dissipation due to the extremely low thermal conductivity of employed polymeric films. Addressing this challenge, different concepts of thermomanagement are implemented. We elaborate the characterization results that evaluate the performance of CoF packages in terms of mechanical, electrical, thermo-optical, and opto-electronic properties. Two prototypes of planar optical interconnects are presented, with multi- and single-mode polymeric waveguide.
RNY (YRNA)-derived small RNAs regulate cell death and inflammation in monocytes/macrophages

The recent discovery of new classes of small RNAs has opened unknown territories to explore new regulations of physiopathological events. We have recently demonstrated that RNY (or Y RNA)-derived small RNAs (referred to as s-RNYs) are an independent class of clinical biomarkers to detect coronary artery lesions and are associated with atherosclerosis burden. Here, we have studied the role of s-RNYs in human and mouse monocytes/macrophages and have shown that in lipid-laden monocytes/macrophages s-RNY expression is timely correlated to the activation of both NF-κB and caspase 3-dependent cell death pathways. Loss- or gain-of-function experiments demonstrated that s-RNYs activate caspase 3 and NF-κB signaling pathways ultimately promoting cell death and inflammatory responses. As, in atherosclerosis, Ro60-associated s-RNYs generated by apoptotic macrophages are released in the blood of patients, we have investigated the extracellular function of the s-RNY/Ro60 complex. Our data demonstrated that s-RNY/Ro60 complex induces caspase 3-dependent cell death and NF-κB-dependent inflammation, when added to the medium of cultured monocytes/macrophages. Finally, we have shown that s-RNY function is mediated by Toll-like receptor 7 (TLR7). Indeed using chloroquine, which disrupts signaling of endosome-localized TLRs 3, 7, 8 and 9 or the more specific TLR7/9 antagonist, the phosphorothioated oligonucleotide IRS954, we blocked the effect of either intracellular or extracellular s-RNYs. These results position s-RNYs as relevant novel functional molecules that impacts on macrophage physiopathology, indicating their potential role as mediators of inflammatory diseases, such as atherosclerosis.
Probabilistic rectangular-shape estimation for extended object tracking

This paper presents new methods for the representation of a vehicle's contour by an oriented rectangle, also known as the bounding box. The parameters of this bounding box are originally modeled probabilistically by a single multivariate Gaussian distribution. This approach incorporates the sensor uncertainties, where the problem of estimating the parameters of this distribution from range measurements is addressed. Additionally, a transformation of the parameters into the measurement space is introduced. This representation is used to perform probabilistic updates by new measurements. The proposed method can handle strong parameter changes which might be affected by object occlusion. Experiments on real-world data demonstrate the robustness and accuracy of the probabilistic approach integrated in a tracking framework incorporating the Doppler measurements of automotive radars and laser measurements.