nils-herrmann/clean_scirepeval huggingface dataset:
Suffocation using plastic bags: a retrospective study of suicides in Ontario, Canada.

One hundred and ten cases of suicidal suffocation using a plastic bag were identified in the files of the Office of the Chief Coroner of Ontario, Canada, between 1993 and 1997. The records were reviewed to determine the demographic characteristics of this group compared with all cases of suicide in Ontario, the scene information, autopsy findings and toxicology results. Most suicides occurred in people over 60 years of age, with older women making up a considerable proportion of cases as compared with other methods of suicide. In 40% of cases the deceased was suffering from a serious illness. Autopsy findings were usually minimal, with facial, conjunctival and visceral petechiae present in a minority of cases. One or more drugs were detected in the blood in 92.6% of cases where toxicologic testing was performed. Benzodiazepines, diphenhydramine and antidepressants were the most common drugs found, with diphenhydramine the most common drug present at an elevated concentration. Information at the scene from "right to die" societies was uncommon. One quarter of decedents took additional measures, besides the use of drugs or alcohol, to ensure the rapidity, certainty or comfort of their death. This study further elucidates the characteristics of this uncommon method of suicide. It emphasizes additional scene findings, such as the presence of dust masks, physical restraints and modification of the plastic bag that may be of use to death investigators in determining the correct manner of death.
Grounded Theory

Grounded theory was originally expounded by Barney Glaser and Anselm Strauss in their 1967 book The Discovery of Grounded Theory (Glaser and Strauss 1967). Reacting against what they saw as the dominance of hypothetico-deductive, theory-testing approaches, Glaser and Strauss proposed grounded theory as a way of building theory systematically using data obtained from social research. Since its first appearance, grounded theory has gone on to become ‘currently the most widely used and popular qualitative research method across a wide range of disciplines and subject areas’ (Bryant and Charmaz 2007: 1).
Improving deep neural network acoustic models using generalized maxout networks

Recently, maxout networks have brought significant improvements to various speech recognition and computer vision tasks. In this paper we introduce two new types of generalized maxout units, which we call p-norm and soft-maxout. We investigate their performance in Large Vocabulary Continuous Speech Recognition (LVCSR) tasks in various languages with 10 hours and 60 hours of data, and find that the p-norm generalization of maxout consistently performs well. Because, in our training setup, we sometimes see instability during training when training unbounded-output nonlinearities such as these, we also present a method to control that instability. This is the “normalization layer”, which is a nonlinearity that scales down all dimensions of its input in order to stop the average squared output from exceeding one. The performance of our proposed nonlinearities are compared with maxout, rectified linear units (ReLU), tanh units, and also with a discriminatively trained SGMM/HMM system, and our p-norm units with p equal to 2 are found to perform best.
The Design and Use of Steerable Filters

Oriented lters are useful in many early vision and image processing tasks. One often needs to apply the same lter, rotated to di erent angles under adaptive control, or wishes to calculate the lter response at various orientations. We present an e cient architecture to synthesize lters of arbitrary orientations from linear combinations of basis lters, allowing one to adaptively \steer" a lter to any orientation, and to determine analytically the lter output as a function of orientation. Steerable lters may be designed in quadrature pairs to allow adaptive control over phase as well as orientation. We show how to design and steer the lters, and present examples of their use in several tasks: the analysis of orientation and phase, angularly adaptive ltering, edge detection, and shape-from-shading. One can also build a self-similar steerable pyramid representation which may be used to implement a steerable \wavelet" decomposition. The same concepts can be generalized to the design of 3-D steerable lters, which should be useful in the analysis of image sequences and volumetric data.
Digital Video Segmentation

The data driven, bottom up approach to video segmentation has ignored the inherent structure that exists in video. This work uses the model driven approach to digital video segmentation. Mathematical models of video based on video production techniques are formulated. These models are used to classify the edit effects used in video and film production. The classes and models are used to systematically design the feature detectors for detecting edit effects in digital video. Digital video segmentation is formulated as a feature based classification problem. Experimental results from segmenting cable television programming with cuts, fades, dissolves and page translate edits are presented.
RPA Antagonizes Microhomology-Mediated Repair of DNA Double-Strand Breaks

Microhomology-mediated end joining (MMEJ) is a Ku- and ligase IV–independent mechanism for the repair of DNA double-strand breaks that contributes to chromosome rearrangements. Here we used a chromosomal end-joining assay to determine the genetic requirements for MMEJ in Saccharomyces cerevisiae. We found that end resection influences the ability to expose microhomologies; however, it is not rate limiting for MMEJ in wild-type cells. The frequency of MMEJ increased by up to 350-fold in rfa1 hypomorphic mutants, suggesting that replication protein A (RPA) bound to the single-stranded DNA (ssDNA) overhangs formed by resection prevents spontaneous annealing between microhomologies. In vitro, the mutant RPA complexes were unable to fully extend ssDNA and were compromised in their ability to prevent spontaneous annealing. We propose that the helix-destabilizing activity of RPA channels ssDNA intermediates from mutagenic MMEJ to error-free homologous recombination, thus preserving genome integrity.
Perceptual quality assessment of 3D dynamic meshes: Subjective and objective studies

Nowadays, 3D mesh animations have been increasingly used in various applications, e.g., in digital entertainment and physically-based simulation. In many applications, it is possible that a surface animation undergoes some lossy operations which can impair its perceptual quality. Since the end users of mesh animations are often human beings, the perceptual quality assessment of 3D dynamic meshes has recently emerged as an interesting and important research problem. In this paper, we will first of all present the results of a large-scale subjective experimental study carried out to collect human opinion scores of the perceptual quality of distorted dynamic meshes. We integrate in our subjective study a large set of 276 impaired mesh sequences with different distortion intensities and categories, such as spatial and temporal visual masking simulation, lossy compression and network transmission error. The constructed database is publicly available and can serve as ground-truth data for the performance evaluation of objective metrics of dynamic mesh perceptual quality. A comparative study is then conducted to benchmark existing objective metrics. The main finding of this objective study is that in general model-based quality metrics outperform imageand video-based quality metrics, but none of the existing metrics can achieve high correlation with human opinion scores on the whole subjective database and for all kinds of distortions. Finally, a simple but effective full-reference objective metric is proposed for the perceptual quality assessment of 3D dynamic meshes, which incorporates both spatial and temporal features and which provides a satisfactory correlation value when tested on the subjective database.
Evaluation of alternative glyph designs for time series data in a small multiple setting

We present the results of a controlled experiment to investigate the performance of different temporal glyph designs in a small multiple setting. Analyzing many time series at once is a common yet difficult task in many domains, for example in network monitoring. Several visualization techniques have, thus, been proposed in the literature. Among these, iconic displays or glyphs are an appropriate choice because of their expressiveness and effective use of screen space. Through a controlled experiment, we compare the performance of four glyphs that use different combinations of visual variables to encode two properties of temporal data: a) the position of a data point in time and b) the quantitative value of this data point. Our results show that depending on tasks and data density, the chosen glyphs performed differently. Line Glyphs are generally a good choice for peak and trend detection tasks but radial encodings are more effective for reading values at specific temporal locations. From our qualitative analysis we also contribute implications for designing temporal glyphs for small multiple settings.
System Design for Geosynchronous Synthetic Aperture Radar Missions

Geosynchronous synthetic aperture radar (GEO SAR) has been studied for several decades but has not yet been implemented. This paper provides an overview of mission design, describing significant constraints (atmosphere, orbit, temporal stability of the surface and atmosphere, measurement physics, and radar performance) and then uses these to propose an approach to initial system design. The methodology encompasses all GEO SAR mission concepts proposed to date. Important classifications of missions are: 1) those that require atmospheric phase compensation to achieve their design spatial resolution; and 2) those that achieve full spatial resolution without phase compensation. Means of estimating the atmospheric phase screen are noted, including a novel measurement of the mean rate of change of the atmospheric phase delay, which GEO SAR enables. Candidate mission concepts are described. It seems likely that GEO SAR will be feasible in a wide range of situations, although extreme weather and unstable surfaces (e.g., water, tall vegetation) prevent 100% coverage. GEO SAR offers an exciting imaging capability that powerfully complements existing systems.
Internet-based mindfulness treatment for anxiety disorders: a randomized controlled trial.

Mindfulness-based interventions have proven effective for the transdiagnostic treatment of heterogeneous anxiety disorders. So far, no study has investigated the potential of mindfulness-based treatments when delivered remotely via the Internet. The current trial aims at evaluating the efficacy of a stand-alone, unguided, Internet-based mindfulness treatment program for anxiety. Ninety-one participants diagnosed with social anxiety disorder, generalized anxiety disorder, panic disorder, or anxiety disorder not otherwise specified were randomly assigned to a mindfulness treatment group (MTG) or to an online discussion forum control group (CG). Mindfulness treatment consisted of 96 audio files with instructions for various mindfulness meditation exercises. Primary and secondary outcome measures were assessed at pre-, posttreatment, and at 6-months follow-up. Participants of the MTG showed a larger decrease of symptoms of anxiety, depression, and insomnia from pre- to postassessment than participants of the CG (Cohen's d(between)=0.36-0.99). Within effect sizes were large in the MTG (d=0.82-1.58) and small to moderate in the CG (d=0.45-0.76). In contrast to participants of the CG, participants of the MTG also achieved a moderate improvement in their quality of life. The study provided encouraging results for an Internet-based mindfulness protocol in the treatment of primary anxiety disorders. Future replications of these results will show whether Web-based mindfulness meditation can constitute a valid alternative to existing, evidence-based cognitive-behavioural Internet treatments.
A model for concentric tube continuum robots under applied wrenches

Continuum robots made from telescoping precurved elastic tubes enable base-mounted actuators to specify the curved shapes of robots as thin as standard surgical needles. While free space beam mechanics-based models of the shape of these ‘active cannulas’ exist, current models cannot account for external forces and torques applied to the cannula by the environment. In this paper we apply geometrically exact beam theory to solve the statics problem for concentric-tube continuum robots. This yields the equivalent of forward kinematics for an active cannula with general tube precurvature functions and arbitrarily many tubes, under loading from a general wrench distribution. The model achieves average experimental tip errors of less than 3 mm over the workspace of a prototype active cannula subject to various tip forces.
A Self-Supervised Decision Fusion Framework for Building Detection

In this study, a new building detection framework for monocular satellite images, called self-supervised decision fusion (SSDF) is proposed. The model is based on the idea of self-supervision, which aims to generate training data automatically from each individual test image, without human interaction. This approach allows us to use the advantages of the supervised classifiers in a fully automated framework. We combine our previous supervised and unsupervised building detection frameworks to suggest a self-supervised learning architecture. Hence, we borrow the major strength of the unsupervised approach to obtain one of the most important clues, the relation of a building, and its cast shadow. This important information is, then, used in order to satisfy the requirement of training sample selection. Finally, an ensemble learning algorithm, called fuzzy stacked generalization (FSG), fuses a set of supervised classifiers trained on the automatically generated dataset with various shape, color, and texture features. We assessed the building detection performance of the proposed approach over 19 test sites and compare our results with the state of the art algorithms. Our experiments show that the supervised building detection method requires more than 30% of the ground truth (GT) training data to reach the performance of the proposed SSDF method. Furthermore, the SSDF method increases the F-score by 2 percentage points (p.p.) on the average compared to performance of the unsupervised method.
Speed Estimation and Abnormality Detection from Surveillance Cameras

Motivated by the increasing industry trends towards autonomous driving, vehicles, and transportation we focus on developing a traffic analysis framework for the automatic exploitation of a large pool of available data relative to traffic applications. We propose a cooperative detection and tracking algorithm for the retrieval of vehicle trajectories in video surveillance footage based on deep CNN features that is ultimately used for two separate traffic analysis modalities: (a) vehicle speed estimation based on a state of the art fully automatic camera calibration algorithm and (b) the detection of possibly abnormal events in the scene using robust optical flow descriptors of the detected vehicles and Fisher vector representations of spatiotemporal visual volumes. Finally we measure the performance of our proposed methods in the NVIDIA AI CITY challenge evaluation dataset.
Dynamic Pricing in the Presence of Strategic Consumers and Oligopolistic Competition

W present a dynamic pricing model for oligopolistic firms selling differentiated perishable goods to multiple finite segments of strategic consumers who are aware that pricing is dynamic and may time their purchases accordingly. This model encompasses strategic behavior by both firms and consumers in a unified stochastic dynamic game in which each firm’s objective is to maximize its total expected revenues, and each consumer responds according to a shopping-intensity-allocation consumer choice model. We prove the existence of a unique subgame-perfect equilibrium, provide equilibrium optimality conditions, and prove monotonicity results for special cases. The model provides insights about equilibrium price dynamics under different levels of competition, asymmetry between firms, and multiple market segments with varying properties. We demonstrate that strategic behavior by consumers can have serious impacts on revenues if firms ignore that behavior in their dynamic pricing policies. Moreover, ideal equilibrium responses to consumer strategic behavior can recover only a portion of the lost revenues. A key conclusion is that firms may benefit more from limiting the information available to consumers than from allowing full information and responding to the resulting strategic behavior in an optimal fashion.
Islands in the Stream: A Study of Item Recommendation within an Enterprise Social Stream

Social streams allow users to receive updates from their network by syndicating social media activity. These streams have become a popular way to share and consume information both on the web and in the enterprise. With so much activity going on, filtering and personalizing the stream for individual users is a key challenge. In this work, we study the recommendation of enterprise social stream items through a user survey with 510 participants, conducted within a globally distributed organization. In the survey, participants rated their level of interest and surprise for different items from the stream and could also indicate whether they were already familiar with the item. Thus, our evaluation goes beyond the common accuracy measure and examines aspects of serendipity and novelty. We also inspect how various features of the recommended item, its author, and reader, influence its ratings. Our results shed light on the key factors that make a stream item valuable to its reader within the enterprise.
Trichoscopy Simplified

It has been a long while since skin surfaces and skin lesions have been examined by dermoscopy. However examining the hair and the scalp was done again recently and gained attention and slight popularity by the practical tool, namely trichoscopy, which can be called in a simplified way as a dermoscopy of the hair and the scalp. Trichoscopy is a great tool to examine and asses an active scalp disease and hair and other signs can be specific for some scalp and hair diseases. These signs include yellow dots, dystrophic hairs, cadaverized (black dots), white dots and exclamation mark hairs. Trichoscopy magnifies hair shafts at higher resolution to enable detailed examinations with measurements that a naked eye cannot distinguish nor see. Trichoscope is considered recently the newest frontier for the diagnosis of hair and scalp disease.  The aim of this paper is to simplify and sum up the main trichoscopic readings and findings of hair and scalp disorders that are commonly encountered at clinic dermatology settings.
A DUAL-BAND CIRCULARLY POLARIZED STUB LOADED MICROSTRIP PATCH ANTENNA FOR GPS APPLICATIONS

A single-feed low-profile and easy to fabricate circularly polarized microstrip patch antenna has been developed for GPS applications. For dual frequency operation, four slots are etched near edges of the patch and a crossed slot etched in the center for generating circular polarization. In order to reducing the frequency ratio of two frequency bands of the antenna, the patch is loaded by four short circuit microstrip stubs. The paper reports several simulation results that confirm the desired characteristics of the antenna. Using stub loading, the frequency ratio of two bands of the antenna can be, even, reduced to 1.1.
SimICS/Sun4m: A Virtual Workstation

System level simulators allow computer architects and system software designers to recreate an accurate and complete replica of the program behavior of a target system, regardless of the availability, existence, or instrumentation support of such a system. Applications include evaluation of architectural design alternatives as well as software engineering tasks such as traditional debugging and performance tuning. We present an implementation of a simulator acting as a virtual workstation fully compatible with the sun4m architecture from Sun Microsystems. Built using the system-level SPARC V8 simulator SimICS, SimICS/sun4m models one or more SPARC V8 processors, supports user-developed modules for data cache and instruction cache simulation and execution profiling of all code, and provides a symbolic and performance debugging environment for operating systems. SimICS/sun4m can boot unmodified operating systems, including Linux 2.0.30 and Solaris 2.6, directly from snapshots of disk partitions. To support essentially arbitrary code, we implemented binary-compatible simulators for several devices, including SCSI, console, interrupt, timers, EEPROM, and Ethernet. The Ethernet simulation hooks into the host and allows the virtual workstation to appear on the local network with full services available ( NFS, NIS, rsh, etc). Ethernet and console traffic can be recorded for future playback. The performance of SimICS/sun4m is sufficient to run realistic workloads, such as the database benchmark TPC-D, scaling factor 1/100, or an interactive network application such as Mozilla. The slowdown in relation to native hardware is in the range of 25 to 75 (measured using SPECint95). We also demonstrate some applications, including modeling an 8-processor sun4m version (which does not exist), modeling future memory hierarchies, and debugging an operating system.
A novel low-power full-adder cell for low voltage

This paper presents a novel low-power majority function-based 1-bit full adder that uses MOS capacitors (MOSCAP) in its structure. It can work reliably at low supply voltage. In this design, the timeconsuming XOR gates are eliminated. The circuits being studied are optimized for energy efficiency at 0.18-mm CMOS process technology. The adder cell is compared with seven widely used adders based on power consumption, speed, power-delay product (PDP) and area efficiency. Intensive simulation runs on a Cadence environment and HSPICE show that the new adder has more than 11% in power savings over a conventional 28-transistor CMOS adder. In addition, it consumes 30% less power than transmission function adder (TFA) and is 1.11 times faster.
Trojaning Attack on Neural Networks

With the fast spread of machine learning techniques, sharing and adopting public machine learning models become very popular. This gives attackers many new opportunities. In this paper, we propose a trojaning attack on neural networks. As the models are not intuitive for human to understand, the attack features stealthiness. Deploying trojaned models can cause various severe consequences including endangering human lives (in applications like autonomous driving). We first inverse the neural network to generate a general trojan trigger, and then retrain the model with reversed engineered training data to inject malicious behaviors to the model. The malicious behaviors are only activated by inputs stamped with the trojan trigger. In our attack, we do not need to tamper with the original training process, which usually takes weeks to months. Instead, it takes minutes to hours to apply our attack. Also, we do not require the datasets that are used to train the model. In practice, the datasets are usually not shared due to privacy or copyright concerns. We use five different applications to demonstrate the power of our attack, and perform a deep analysis on the possible factors that affect the attack. The results show that our attack is highly effective and efficient. The trojaned behaviors can be successfully triggered (with nearly 100% possibility) without affecting its test accuracy for normal input and even with better accuracy on public dataset. Also, it only takes a small amount of time to attack a complex neuron network model. In the end, we also discuss possible defense against such attacks.