nils-herrmann/clean_scirepeval huggingface dataset:
Cause of Cambrian Explosion - Terrestrial or Cosmic?

We review the salient evidence consistent with or predicted by the Hoyle-Wickramasinghe (H-W) thesis of Cometary (Cosmic) Biology. Much of this physical and biological evidence is multifactorial. One particular focus are the recent studies which date the emergence of the complex retroviruses of vertebrate lines at or just before the Cambrian Explosion of ∼500 Ma. Such viruses are known to be plausibly associated with major evolutionary genomic processes. We believe this coincidence is not fortuitous but is consistent with a key prediction of H-W theory whereby major extinction-diversification evolutionary boundaries coincide with virus-bearing cometary-bolide bombardment events. A second focus is the remarkable evolution of intelligent complexity (Cephalopods) culminating in the emergence of the Octopus. A third focus concerns the micro-organism fossil evidence contained within meteorites as well as the detection in the upper atmosphere of apparent incoming life-bearing particles from space. In our view the totality of the multifactorial data and critical analyses assembled by Fred Hoyle, Chandra Wickramasinghe and their many colleagues since the 1960s leads to a very plausible conclusion - life may have been seeded here on Earth by life-bearing comets as soon as conditions on Earth allowed it to flourish (about or just before 4.1 Billion years ago); and living organisms such as space-resistant and space-hardy bacteria, viruses, more complex eukaryotic cells, fertilised ova and seeds have been continuously delivered ever since to Earth so being one important driver of further terrestrial evolution which has resulted in considerable genetic diversity and which has led to the emergence of mankind.
Resolution enhanced SAR tomography: A nonparametric iterative adaptive approach

The ground-volume separation of radar scattering plays an important role in the analysis of forested scenes. For this purpose, the data covariance matrix of multi-polarimetric (MP) multi-baseline (MB) SAR surveys can be represented thru a sum of two Kronecker products composed of the data covariance matrices and polarimetric signatures that correspond to the ground and canopy scattering mechanisms (SMs), respectively. The sum of Kronecker products (SKP) decomposition allows the use of different tomographic SAR focusing methods on the ground and canopy structural components separately, nevertheless, the main drawback of this technique relates to the rank-deficiencies of the resultant data covariance matrices, which restrict the usage of the adaptive beamforming techniques, requiring more advanced beamforming methods, such as compressed sensing (CS). This paper proposes a modification of the nonparametric iterative adaptive approach for amplitude and phase estimation (IAA-APES), which applied to MP-MB SAR data, serves as an alternative to the SKP-based techniques for ground-volume reconstruction, which main advantage relates precisely to the non-need of the SKP decomposition technique as a pre-processing step.
SCALE STRUCTURE, DEGREE MODIFICATION, AND THE SEMANTICS OF GRADABLE PREDICATES

In this article we develop a semantic typology of gradable predicates, with special emphasis on deverbal adjectives. We argue for the linguistic relevance of this typology by demonstrating that the distribution and interpretation of degreemodifiers is sensitive to its twomajor classificatory parameters: (1) whether a gradable predicate is associated with what we call an open or closed scale, and (2) whether the standard of comparison for the applicability of the predicate is absolute or relative to a context. We further show that the classification of an important subclass of adjectives within the typology is largely predictable. Specifically, the scale structure of a deverbal gradable adjective correlates either with the algebraic part structure of the event denoted by its source verb or with the part structure of the entities to which the adjective applies. These correlations underscore the fact that gradability is characteristic not only of adjectives but also of verbs and nouns, and that scalar properties are shared by categorially distinct but derivationally related expressions.
Fundamentals of Coherent Optical Fiber Communications

The recently developed digital coherent receiver enables us to employ a variety of spectrally efficient modulation formats such as M-ary phase-shift keying and quadrature-amplitude modulation. Moreover, in the digital domain, we can equalize all linear transmission impairments such as group-velocity dispersion and polarization-mode dispersion of transmission fibers, because coherent detection preserves the phase information of the optical signal. This paper reviews the history of research and development related to coherent optical communications and describes the principle of coherent detection, including its quantum-noise characteristics. In addition, it discusses the role of digital signal processing in mitigating linear transmission impairments, estimating the carrier phase, and tracking the state of polarization of the signal in coherent receivers.
Vehicle and Guard Rail Detection Using Radar and Vision Data Fusion

This paper describes a vehicle detection system fusing radar and vision data. Radar data are used to locate areas of interest on images. Vehicle search in these areas is mainly based on vertical symmetry. All the vehicles found in different image areas are mixed together, and a series of filters is applied in order to delete false detections. In order to speed up and improve system performance, guard rail detection and a method to manage overlapping areas are also included. Both methods are explained and justified in this paper. The current algorithm analyzes images on a frame-by-frame basis without any temporal correlation. Two different statistics, namely: 1) frame based and 2) event based, are computed to evaluate vehicle detection efficiency, while guard rail detection efficiency is computed in terms of time savings and correct detection rates. Results and problems are discussed, and directions for future enhancements are provided
Ant Algorithms for Discrete Optimization

This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.
White matter hyperintensities, cognitive impairment and dementia: an update

White matter hyperintensities (WMHs) in the brain are the consequence of cerebral small vessel disease, and can easily be detected on MRI. Over the past three decades, research has shown that the presence and extent of white matter hyperintense signals on MRI are important for clinical outcome, in terms of cognitive and functional impairment. Large, longitudinal population-based and hospital-based studies have confirmed a dose-dependent relationship between WMHs and clinical outcome, and have demonstrated a causal link between large confluent WMHs and dementia and disability. Adequate differential diagnostic assessment and management is of the utmost importance in any patient, but most notably those with incipient cognitive impairment. Novel imaging techniques such as diffusion tensor imaging might reveal subtle damage before it is visible on standard MRI. Even in Alzheimer disease, which is thought to be primarily caused by amyloid, vascular pathology, such as small vessel disease, may be of greater importance than amyloid itself in terms of influencing the disease course, especially in older individuals. Modification of risk factors for small vessel disease could be an important therapeutic goal, although evidence for effective interventions is still lacking. Here, we provide a timely Review on WMHs, including their relationship with cognitive decline and dementia.
Research on public opinion based on Big Data

Public opinion is the people's response for social phenomena, issues, hot topics, attitudes, emotions, and so on. It reflects the focus problems of the current time of the society. By analyzing the public opinion, we can infer what will happen in the next time, and give better decision support for governments and businesses. Big Data technology is becoming a powerful data analyzing tools for massive data in recent years.  Mahout is a data mining algorithms' set based on Hadoop, which is designed for processing large-scale and complex data. In most instances, the public opinion information contains many text messages. For many traditional text mining algorithms, it is almost impossible to handle high dimensional data concerns large-volume and complex data sets. Hence, this paper uses Mahout text mining algorithms to process public opinion information.
A Highly Parallel Multi-class Pattern Classification on GPU

Multi-class pattern classification has a variety of applications and could be achieved using artificial neural networks (ANN). There are two major system architectures for using ANNs in multi-class pattern classification: using a single ANN and using multiple ANNs. Independent of what architecture is used, one of the main concerns of using ANNs is that with increasing number of pattern classes and training datasets, the training time will increase dramatically which renders the ANN unfeasible. In this paper, the vast computational power of Graphics Processing Units (GPUs) is utilized to mitigate this problem. Different architectures and different methods of feeding pattern classes are implemented in a GPU platform. Different methods have been proposed to achieve maximum parallelism and subsequently maximize throughput. Our implementation exceeds the state-of-the-art in literature in terms of speed and the accurate use of GPU resources. As a result, the proposed approach's run time is about 75% shorter than the previous approaches. In multi-ANN architecture, due to the inherent parallelism in the proposed implementation, the execution time of a system for a digit recognition application is reduced from seven hours in CPU to about 4 seconds in GPU.
Safe Streets , Livable Streets

Beyond simply acting as thoroughfares for motor vehicles, urban streets often double as public spaces. Urban streets are places where people walk, shop, meet, and generally engage in the diverse array of social and recreational activities that, for many, are what makes urban living enjoyable. And beyond even these quality-of-life benefits, pedestrian-friendly urban streets have been increasingly linked to a host of highly desirable social outcomes, including economic growth and innovation (Florida, ), improvements in air quality (Frank et al., ), and increased physical fitness and health (Frank et al., ), to name only a few. For these reasons, many groups and individuals encourage the design of “livable” streets, or streets that seek to better integrate the needs of pedestrians and local developmental objectives into a roadway’s design. There has been a great deal of work describing the characteristics of livable streets (see Duany et al., ; Ewing, ; Jacobs, ), and there is general consensus on their characteristics: livable streets, at a minimum, seek to enhance the pedestrian character of the street by providing a continuous sidewalk network and incorporating design features that minimize the negative impacts of motor vehicle use on pedestrians. Of particular importance is the role played by roadside features such as street trees and on-street parking, which serve to buffer the pedestrian realm from potentially hazardous oncoming traffic, and to provide spatial definition to the public right-of-way. Indeed, many livability advocates assert that trees, as much as any other single feature, can play a central role in enhancing a roadway’s livability (Duany et al., ; Jacobs, ). While most would agree that the inclusion of trees and other streetscape features enhances the aesthetic quality of a roadway, there is substantive disagreement about their safety effects . Conventional engineering practice encourages the design of roadsides that will allow a vehicle leaving the travelway to safely recover before encountering a potentially hazardous fixed object. When one considers the aggregate statistics on run-off-roadway crashes, there is indeed 
Autonomous overtaking decision making of driverless bus based on deep Q-learning method

The autonomous overtaking maneuver is a valuable technology in unmanned vehicle field. However, overtaking is always perplexed by its security and time cost. Now, an autonomous overtaking decision making method based on deep Q-learning network is proposed in this paper, which employs a deep neural network(DNN) to learn Q function from action chosen to state transition. Based on the trained DNN, appropriate action is adopted in different environments for higher reward state. A series of experiments are performed to verify the effectiveness and robustness of our proposed approach for overtaking decision making based on deep Q-learning method. The results support that our approach achieves better security and lower time cost compared with traditional reinforcement learning methods.
A guidance strategy for multi-player pursuit and evasion game in maneuvering target interception

To increase the probability of destroying a maneuvering target (e.g. ballistic missile), a framework of multi-missiles interception is presented in this paper. Each intercepting missile is equipped with an IR Image seeker which can provide excellent stealth ability during its course of tracking the ballistic missile. Such intelligent ranging system integrates the Interacting Multiple Model (IMM) technique and the concept of reachable set to find the optimal interception results by minimizing the energy of pursuing the maneuvering target. The proposed guidance law of every missile interceptor is designed based on pursuit and evasion game theory while considering the motion of the target in 3-D space such that the distance between the missiles and the target is minimized. Finally, extensive computer simulations have been conducted to validate the performance of the proposed system.
Beyond pleasure and pain.

People approach pleasure and avoid pain. To discover the true nature of approach-avoidance motivation, psychologists need to move beyond this hedonic principle to the principles that underlie the different ways that it operates. One such principle is regulatory focus, which distinguishes self-regulation with a promotion focus (accomplishments and aspirations) from self-regulation with a prevention focus (safety and responsibilities). This principle is used to reconsider the fundamental nature of approach-avoidance, expectancy-value relations, and emotional and evaluative sensitivities. Both types of regulatory focus are applied to phenomena that have been treated in terms of either promotion (e.g., well-being) or prevention (e.g., cognitive dissonance). Then, regulatory focus is distinguished from regulatory anticipation and regulatory reference, 2 other principles underlying the different ways that people approach pleasure and avoid pain.
Application of Remote Sensing and Gis

oastal areas, the place where the waters of the seas meet the land are indeed unique places in our global geography. They are endowed with a very wide range of coastal ecosystems like mangroves, coral reefs, lagoons, sea grass, salt marsh, estuary etc. They are unique in a very real economic sense as sites for port and harbour facilities that capture the large monetary benefits associated with waterborne commerce and are highly valued and greatly attractive as sites for resorts and as vacation destinations. The combination of freshwater and salt water in coastal estuaries creates some of the most productive and richest habitats on earth; the resulting bounty in fishes and other marine life can be of great value to coastal nations. In many locations, the coastal topography formed over the millennia provides significant protection from hurricanes, typhoons, and other ocean related disturbances. But these values could diminish or even be lost, if they are not managed. Pollution of coastal waters can greatly reduce the production of fish, as can degradation of coastal nursery grounds and other valuable wetland habitats. The storm protection afforded by fringing reefs and mangrove forests can be lost if the corals die or the mangroves removed. Inappropriate development and accompanying despoilment can reduce the attractiveness of the coastal environment, greatly affecting tourism potential. Even ports and harbours require active management if they are to remain productive and successful over the long term. Coastal ecosystem management is thus immensely important for the sustainable use, development and protection of the coastal and marine areas and resources. To achieve this, an understanding of the coastal processes that influence the coastal environments and the ways in which they interact is necessary. It is advantageous to adopt a holistic or systematic approach for solving the coastal problems, since understanding the processes and products of interaction in coastal environments is very complicated. A careful assessment of changes that occur in the coastal C
Recognition of Static Hand Gestures of Alphabet in Bangla Sign Language

This paper presents a system for recognizing static hand gestures of alphabet in Bangla Sign Language (BSL). A BSL finger spelling and an alphabet gesture recognition system was designed with Artificial Neural Network (ANN) and constructed in order to translate the BSL alphabet into the corresponding printed Bangla letters. The proposed ANN is trained with features of sign alphabet using feed-forward backpropagation learning algorithm. Logarithmic sigmoid (logsig) function is chosen as transfer function. This ANN model demonstrated a good recognition performance with the mean square error values in this training function. This recognition system does not use any gloves or visual marking systems. This system only requires the images of the bare hand for the recognition. The Simulation results show that this system is able to recognize 36 selected letters of BSL alphabet with an average accuracy of 80.902%.
Reduced DC-Link Voltage Active Power Filter Using Modified PUC5 Converter

In this letter, the five-level packed U-cell (PUC5) inverter is reconfigured with two identical dc links operating as an active power filter (APF). Generally, the peak voltage of an APF should be greater than the ac voltage at the point-of-common coupling (PCC) to ensure the boost operation of the converter in order to inject harmonic current into the system effectively; therefore, full compensation can be obtained. The proposed modified PUC5 (MPUC5) converter has two equally regulated separated dc links, which can operate at no load condition useful for APF application. Those divided dc terminals amplitudes are added at the input of the MPUC5 converter to generate a boosted voltage that is higher than the PCC voltage. Consequently, the reduced dc-links voltages are achieved since they do not individually need to be higher than the PCC voltage due to the mentioned fact that their summation has to be higher than PCC voltage. The voltage balancing unit is integrated into the modulation technique to be decoupled from the APF controller. The proposed APF is practically tested to validate its good dynamic performance in harmonic elimination, ac-side power factor correction, reactive power compensation, and power quality improvement.
How did the discussion go: Discourse act classification in social media conversations

Within last two decades, social media has emerged as almost an alternate world where people communicate with each other and express opinions about almost anything. This makes platforms like Facebook, Reddit, Twitter, Myspace etc. a rich bank of heterogeneous data, primarily expressed via text but reflecting all textual and non-textual data that human interaction can produce. We propose a novel attention based hierarchical LSTM model to classify discourse act sequences in social media conversations, aimed at mining data from online discussion using textual meanings beyond sentence level. The very uniqueness of the task is the complete categorization of possible pragmatic roles in informal textual discussions, contrary to extraction of question-answers, stance detection or sarcasm identification which are very much role specific tasks. Early attempt was made on a Reddit discussion dataset. We train our model on the same data, and present test results on two different datasets, one from Reddit and one from Facebook. Our proposed model outperformed the previous one in terms of domain independence; without using platformdependent structural features, our hierarchical LSTM with word relevance attention mechanism achieved F1-scores of 71% and 66% respectively to predict discourse roles of comments in Reddit and Facebook discussions. Efficiency of recurrent and convolutional architectures in order to learn discursive representation on the same task has been presented and analyzed, with different word and comment embedding schemes. Our attention mechanism enables us to inquire into relevance ordering of text segments according to their roles in discourse. We present a human annotator experiment to unveil important observations about modeling and data annotation. Equipped with our text-based discourse identification model, we inquire into how Subhabrata Dutta Jadavpur University, Kolkata, India,  subha0009@gmail.com Tanmoy Chakraborty IIIT Delhi, India, e-mail: tanmoy@iiitd.ac.in Dipankar Das Jadavpur University, Kolkata, India, e-mail: dipankar.dipnil2005@gmail.com 1 ar X iv :1 80 8. 02 29 0v 1 [ cs .C L ] 7 A ug 2 01 8 2 Subhabrata Dutta, Tanmoy Chakraborty and Dipankar Das heterogeneous non-textual features like location, time, leaning of information etc. play their roles in charaterizing online discussions on Facebook.
Title page for the paper : Discourses of gender identities and gender roles in Pakistan : women and non-domestic work in political representations Author name and affiliation

This paper aims to explore some of the manifold and changing links that official Pakistani state discourses forged between women and work from the 1940s to the late 2000s. The focus of the analysis is on discursive spaces that have been created for women engaged in non-domestic work. Starting from an interpretation of the existing academic literature, this paper argues that Pakistani women’s non-domestic work has been conceptualised in three major ways: as a contribution to national development, as a danger to the nation, and as non-existent. The paper concludes that although some conceptualisations of work have been more powerful than others and, at specific historical junctures, have become part of concrete state policies, alternative conceptualisations have always existed alongside them. Disclosing the state’s implication in the discursive construction of working women’s identities might contribute to the destabilisation of hegemonic concepts of gendered divisions of labour in Pakistan.   Originally published at: Grünenfelder, Julia (2013). Discourses of gender identities and gender roles in Pakistan: Women and non-domestic work in political representations. Women’s Studies International Forum, 40:68-77.