nils-herrmann/clean_scirepeval huggingface dataset:
REAR SEAT OCCUPANT PROTECTION IN FRONTAL CRASHES AND ITS FEASIBILITY

As part of NHTSAs Rear Seat Occupant Protection Research Program, the Fatality Analysis Reporting System (FARS) and State Data System (SDS) for Florida, Pennsylvania and Maryland were utilized to estimate relative fatality rates and injury risk ratios between the front and rear seat passengers. In addition, a parametric study of rear-seat restraint parameters was performed to assess chest deflection and head excursion trends for different belt load limits, pretensioner location(s) and stroke, and impact speeds with the Hybrid III (HIII) 50 percentile male and 5 percentile female dummies. Simulation data were validated using 48 km/h frontal impact sled tests with a standard belt system in outboard rear seats of a mid-size passenger car buck. The real world data suggests that the fatality and serious injury risk in frontal crashes is higher for older occupants in rear seats than for those in front seats. In addition, the relative effectiveness (to mitigate serious injury and death) of rear seats with respect to front seats for restrained adult occupants in newer vehicle models is less than it is in older models, presumably due to the advances in restraint technology that have been incorporated into the front seat position. The simulations demonstrated that adult dummy injury measures in the rear seat can be reduced by incorporating restraint technology (load limiting and pretensioning) used in the front seat, even in the absence of an air bag and knee bolster for load sharing. A force-limiting belt with a pretensioner in the rear seat can maintain or reduce head excursion relative to a standard belt, while significantly reducing chest deformation and thoracic injury risk. In fact, 42 sets of restraint parameters were identified that reduced both head excursion and chest deflection of the 50 percentile male relative to the baseline belt.
EM Algorithms for PCA and SPCA

I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large collections of high dimensional data. It is computationally very efficient in space and time. It also naturally accommodates missing information. I also introduce a new variant of PCA calledsensibleprincipal component analysis (SPCA) which defines a proper density model in the data space. Learning for SPCA is also done with an EM algorithm. I report results on synthetic and real data showing that these EM algorithms correctly and efficiently find the leading eigenvectors of the covariance of datasets in a few iterations using up to hundreds of thousands of datapoints in thousands of dimensions.
Estimation, planning, and mapping for autonomous flight using an RGB-D camera in GPS-denied environments

RGB-D cameras provide both color images and per-pixel depth stimates. The richness of this data and the recent development of low-c ost sensors have combined to present an attractive opportunity for mobile robot ics research. In this paper, we describe a system for visual odometry and mapping using an RGB-D camera, and its application to autonomous flight. By leveraging resu lts from recent stateof-the-art algorithms and hardware, our system enables 3D fl ight in cluttered environments using only onboard sensor data. All computation an d se sing required for local position control are performed onboard the vehicle, r educing the dependence on unreliable wireless links. However, even with accurate 3 D sensing and position estimation, some parts of the environment have more percept ual structure than others, leading to state estimates that vary in accuracy across the environment. If the vehicle plans a path without regard to how well it can localiz e itself along that path, it runs the risk of becoming lost or worse. We show how the Belief Roadmap (BRM) algorithm (Prentice and Roy, 2009), a belief space extensio n of the Probabilistic Roadmap algorithm, can be used to plan vehicle trajectories that incorporate the sensing model of the RGB-D camera. We evaluate the effective ness of our system for controlling a quadrotor micro air vehicle, demonstrate its use for constructing detailed 3D maps of an indoor environment, and discuss its li mitations. Abraham Bachrach and Samuel Prentice contributed equally to this work. Abraham Bachrach, Samuel Prentice, Ruijie He, Albert Huang a nd Nicholas Roy Computer Science and Artificial Intelligence Laboratory, Ma ss chusetts Institute of Technology, Cambridge, MA 02139.  abachrac, ruijie, albert, prentice, nickroy@mit.ed u Peter Henry, Michael Krainin and Dieter Fox University of Washington, Department of Computer Science & Engi neering, Seattle, WA.  peter, mkrainin, fox@cs.washington.edu. Daniel Maturana The Robotics Institute, Carnegie Mellon University, Pittsbur gh, PA.
Char2Wav: End-to-End Speech Synthesis

We present Char2Wav, an end-to-end model for speech synthesis. Char2Wav has two components: a reader and a neural vocoder. The reader is an encoderdecoder model with attention. The encoder is a bidirectional recurrent neural network that accepts text or phonemes as inputs, while the decoder is a recurrent neural network (RNN) with attention that produces vocoder acoustic features. Neural vocoder refers to a conditional extension of SampleRNN which generates raw waveform samples from intermediate representations. Unlike traditional models for speech synthesis, Char2Wav learns to produce audio directly from text.
ZnO-Based Ultraviolet Photodetectors

Ultraviolet (UV) photodetection has drawn a great deal of attention in recent years due to a wide range of civil and military applications. Because of its wide band gap, low cost, strong radiation hardness and high chemical stability, ZnO are regarded as one of the most promising candidates for UV photodetectors. Additionally, doping in ZnO with Mg elements can adjust the bandgap largely and make it feasible to prepare UV photodetectors with different cut-off wavelengths. ZnO-based photoconductors, Schottky photodiodes, metal-semiconductor-metal photodiodes and p-n junction photodetectors have been developed. In this work, it mainly focuses on the ZnO and ZnMgO films photodetectors. We analyze the performance of ZnO-based photodetectors, discussing recent achievements, and comparing the characteristics of the various photodetector structures developed to date.
Lean UX: the next generation of user-centered agile development?

In this paper we discuss the opportunities and challenges of the recently introduced Lean UX software development philosophy. The point of view is product design and development in a software agency. Lean UX philosophy is identified by three ingredients: design thinking, Lean production and Agile development. The major challenge for an agency is the organizational readiness of the client organization to adopt a new way of working. Rather than any special tool or practice, we see that the renewal of user-centered design and development is hindered by existing purchase processes and slow decision making patterns.
Fog Computing: Principles, Architectures, and Applications

The Internet of Everything (IoE) solutions gradually bring every object online, and processing data in centralized cloud does not scale to requirements of such environment. This is because, there are applications such as health monitoring and emergency response that require low latency and delay caused by transferring data to the cloud and then back to the application can seriously impact the performance. To this end, Fog computing has emerged, where cloud computing is extended to the edge of the network to decrease the latency and network congestion. Fog computing is a paradigm for managing a highly distributed and possibly virtualized environment that provides compute and network services between sensors and cloud data centers. This chapter provides background and motivations on emergence of Fog computing and defines its key characteristics. In addition, a reference architecture for Fog computing is presented and recent related development and applications are discussed. KeywordsInternet of Things; IoT; Web of Things; Cloud of Things; Fog Computing; IoT Applications;
A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach

The bilateral filter is a nonlinear filter that smoothes a signal while preserving strong edges. It has demonstrated great effectiveness for a variety of problems in computer vision and computer graphics, and fast versions have been proposed. Unfortunately, little is known about the accuracy of such accelerations. In this paper, we propose a new signal-processing analysis of the bilateral filter which complements the recent studies that analyzed it as a PDE or as a robust statistical estimator. The key to our analysis is to express the filter in a higher-dimensional space where the signal intensity is added to the original domain dimensions. Importantly, this signal-processing perspective allows us to develop a novel bilateral filtering acceleration using downsampling in space and intensity. This affords a principled expression of accuracy in terms of bandwidth and sampling. The bilateral filter can be expressed as linear convolutions in this augmented space followed by two simple nonlinearities. This allows us to derive criteria for downsampling the key operations and achieving important acceleration of the bilateral filter. We show that, for the same running time, our method is more accurate than previous acceleration techniques. Typically, we are able to process a 2 megapixel image using our acceleration technique in less than a second, and have the result be visually similar to the exact computation that takes several tens of minutes. The acceleration is most effective with large spatial kernels. Furthermore, this approach extends naturally to color images and cross bilateral filtering.
Structured Learning for Taxonomy Induction with Belief Propagation

We present a structured learning approach to inducing hypernym taxonomies using a probabilistic graphical model formulation. Our model incorporates heterogeneous relational evidence about both hypernymy and siblinghood, captured by semantic features based on patterns and statistics from Web n-grams and Wikipedia abstracts. For efficient inference over taxonomy structures, we use loopy belief propagation along with a directed spanning tree algorithm for the core hypernymy factor. To train the system, we extract sub-structures of WordNet and discriminatively learn to reproduce them, using adaptive subgradient stochastic optimization. On the task of reproducing sub-hierarchies of WordNet, our approach achieves a 51% error reduction over a chance baseline, including a 15% error reduction due to the non-hypernym-factored sibling features. On a comparison setup, we find up to 29% relative error reduction over previous work on ancestor F1.
Path integration and the neural basis of the 'cognitive map'

The hippocampal formation can encode relative spatial location, without reference to external cues, by the integration of linear and angular self-motion (path integration). Theoretical studies, in conjunction with recent empirical discoveries, suggest that the medial entorhinal cortex (MEC) might perform some of the essential underlying computations by means of a unique, periodic synaptic matrix that could be self-organized in early development through a simple, symmetry-breaking operation. The scale at which space is represented increases systematically along the dorsoventral axis in both the hippocampus and the MEC, apparently because of systematic variation in the gain of a movement-speed signal. Convergence of spatially periodic input at multiple scales, from so-called grid cells in the entorhinal cortex, might result in non-periodic spatial firing patterns (place fields) in the hippocampus.
A Wireless Sensor Network Air Pollution Monitoring System

Sensor networks are currently an active research area mainly due to the potential of their applications. In this paper we investigate the use of Wireless Sensor Networks (WSN) for air pollution monitoring in Mauritius. With the fast growing industrial activities on the island, the problem of air pollution is becoming a major concern for the health of the population. We proposed an innovative system named Wireless Sensor Network Air Pollution Monitoring System (WAPMS) to monitor air pollution in Mauritius through the use of wireless sensors deployed in huge numbers around the island. The proposed system makes use of an Air Quality Index (AQI) which is presently not available in Mauritius. In order to improve the efficiency of WAPMS, we have designed and implemented a new data aggregation algorithm named Recursive Converging Quartiles (RCQ). The algorithm is used to merge data to eliminate duplicates, filter out invalid readings and summarise them into a simpler form which significantly reduce the amount of data to be transmitted to the sink and thus saving energy. For better power management we used a hierarchical routing protocol in WAPMS and caused the motes to sleep during idle time.
Methods for Reconstructing Causal Networks from Observed Time-Series: Granger-Causality, Transfer Entropy, and Convergent Cross-Mapping

A major question that arises in many areas of Cognitive Science is the need to distinguish true causal connections between variables from mere correlations. The most common way of addressing this distinction is the design of wellcontrolled experiments. However, in many situations, it is extremely difficult –or even outright impossible– to perform such experiments. Researchers are then forced to rely on correlational data in order to make causal inferences. This situation is especially common when one needs to analyze longitudinal data corresponding to historical time-series, symbolic sequences, or developmental data. These inferences are often very problematic. From the correlations alone it is difficult to determine the direction of the causal arrow linking two variables. Worse even, the lack of controls of observational data entail that correlations found between two variables need not reflect any causal connection between them. The possibility always remains that some third variable which the researchers were not able to measure, or were actually unaware of, is the actually driver for both measured variables, giving rise to the mirage of a direct relationship between them. In recent years, it has been shown that, under particular circumstances, one can use correlational information for making sound causal inferences (cf., Pearl, 2000). In this tutorial I will provide a hands-on introduction to the use of modern causality techniques for the analysis of observational time series. I will cover causality analyses for three types of time-series that are often encountered in Cognitive
Permeability of Cracked Steel Fiber-Reinforced Concrete

This research explores the relationship between permeability and crack width in cracked, steel fiber-reinforced con addition, it inspects the influence of steel fiber reinforcement on concrete permeability. The feedback-controlled splitting tension ~also known as the Brazilian test ! is used to induce cracks of up to 500 mm ~0.02 in.! in concrete specimens without reinforcement, and w steel fiber reinforcement volumes of both 0.5 and 1%. The cracks relax after induced cracking. The steel fibers decrease the pe of specimens with relaxed cracks larger than 100 mm.  Permeability; Cracking; Fiber reinforced materials; Concrete. ular ties ani gh an rela rtie ect ue es, bar bilto be ica tly tee de elp in ding utful ucete, ber s. 00, n then
A Re-Examination of Text Categorization Methods

This paper reports a controlled study with statistical signi cance tests on ve text categorization methods: the Support Vector Machines (SVM), a k-Nearest Neighbor (kNN) classi er, a neural network (NNet) approach, the Linear Leastsquares Fit (LLSF) mapping and a Naive Bayes (NB) classier. We focus on the robustness of these methods in dealing with a skewed category distribution, and their performance as function of the training-set category frequency. Our results show that SVM, kNN and LLSF signi cantly outperform NNet and NB when the number of positive training instances per category are small (less than ten), and that all the methods perform comparably when the categories are su ciently common (over 300 instances).
Creativity Support Tools: Report From a U.S. National Science Foundation Sponsored Workshop

The following item is made available as a courtesy to scholars by the author(s) and Drexel University Library and may contain materials and content, including computer code and tags, artwork, text, graphics, images, and illustrations (Material) which may be protected by copyright law. Unless otherwise noted, the Material is made available for non profit and educational purposes, such as research, teaching and private study. For these limited purposes, you may reproduce (print, download or make copies) the Material without prior permission. All copies must include any copyright notice originally included with the Material. You must seek permission from the authors or copyright owners for all uses that are not allowed by fair use and other provisions of the U.S. Copyright Law. The responsibility for making an independent legal assessment and securing any necessary permission rests with persons desiring to reproduce or use the Material.
Adverse health effects of marijuana use.

In light of the rapidly shifting landscape regarding the legalization of marijuana for medical and recreational purposes, patients may be more likely to ask physicians about its potential adverse and beneficial effects on health. The popular notion seems to be that marijuana is a harmless pleasure, access to which should not be regulated or considered illegal. Currently, marijuana is the most commonly used “illicit” drug in the United States, with about 12% of people 12 years of age or older reporting use in the past year and particularly high rates of use among young  The most common route of administration is inhalation. The greenish-gray shredded leaves and flowers of the Cannabis sativa plant are smoked (along with stems and seeds) in cigarettes, cigars, pipes, water pipes, or “blunts” (marijuana rolled in the tobacco-leaf wrapper from a cigar). Hashish is a related product created from the resin of marijuana flowers and is usually smoked (by itself or in a mixture with tobacco) but can be ingested orally. Marijuana can also be used to brew tea, and its oil-based extract can be mixed into food products. The regular use of marijuana during adolescence is of particular concern, since use by this age group is associated with an increased likelihood of deleterious consequences2 (Table 1). Although multiple studies have reported detrimental effects, others have not, and the question of whether marijuana is harmful remains the subject of heated debate. Here we review the current state of the science related to the adverse health effects of the recreational use of marijuana, focusing on those areas for which the evidence is strongest.
Realizing Strategic Value Through Center-Edge Digital Transformation in Consumer-Centric Industries

Many consumer-centric industries provide products and services to millions of consumers. These industries include healthcare and wellness, retail, hospitality and travel, sports and entertainment, legal services, financial services, residential real estate and many more. IT professionals and business executives are used to thinking about enterprise-centric ERP systems as the IT center of gravity, but increasingly the focus of IT activity is shifting from the enterprise Center to the Edge of the enterprise as consumers are digitally connected and activated. Enabling this shift requires managing both IT deployment and organizational transformation at the Center of the enterprise, as well as accommodating consumers’ digital interactions at the Edge and understanding how to realize new strategic value through the shift. This article examines the phenomenon of Center-Edge digital transformation in consumercentric industries through a case study in the healthcare industry. It provides guidelines for IT and business executives in any consumer-centric industry who would like to understand how to
VideoMocap: modeling physically realistic human motion from monocular video sequences

This paper presents a video-based motion modeling technique for capturing physically realistic human motion from monocular video sequences. We formulate the video-based motion modeling process in an image-based keyframe animation framework. The system first computes camera parameters, human skeletal size, and a small number of 3D key poses from video and then uses 2D image measurements at intermediate frames to automatically calculate the "in between" poses. During reconstruction, we leverage Newtonian physics, contact constraints, and 2D image measurements to simultaneously reconstruct full-body poses, joint torques, and contact forces. We have demonstrated the power and effectiveness of our system by generating a wide variety of physically realistic human actions from uncalibrated monocular video sequences such as sports video footage.
Music Generation from Statistical Models

This paper discusses the use of statistical models for the problem of musical style imitation. Statistical models are created from extant pieces in a stylistic corpus, and have an objective goal which is to accurately classify new pieces. The process of music generation is equated with the problem of sampling from a statistical model. In principle there is no need to make the classical distinction between analytic and synthetic models of music. This paper presents several methods for sampling from an analytic statistical model, and proposes a new approach that maintains the intra opus pattern repetition within an extant piece. A major component of creativity is the adaptation of extant art works, and this is also an efficient way to sample pieces from complex statistical models.
A robust audio classification and segmentation method

In this paper, we present a robust algorithm for audio classification that is capable of segmenting and classifying an audio stream into speech, music, environment sound and silence. Audio classification is processed in two steps, which makes it suitable for different applications. The first step of the classification is speech and non-speech discrimination. In this step, a novel algorithm based on KNN and LSP VQ is presented. The second step further divides non-speech class into music, environment sounds and silence with a rule based classification scheme. Some new features such as the noise frame ratio and band periodicity are introduced and discussed in detail. Our experiments in the context of video structure parsing have shown the algorithms produce very satisfactory results.
ADMIT- A Five Dimensional Approach towards Standardization of Network and Computer Attack Taxonomies

This paper proposed a five dimensional taxonomy ADMIT which captures five major classifiers to characterize the nature of attacks. These are classification by attack vector, classification by defense, classification by method, classification by impact and classification by attack target. The classification structure of proposed taxonomy described the nature of attacks thoroughly. The administrator can use the proposed taxonomy to locate strategies that are appropriate for securing their system against vulnerabilities that can be exploited. Use of ADMIT taxonomy in network defense strategies can improve the overall level of security.