nils-herrmann/clean_scirepeval huggingface dataset:
DESIGN OF INDEPENDENT SUSPENSION MECHANISM FOR A TERRAIN VEHICLE WITH FOUR WHEELS DRIVE AND FOUR WHEELS STEERING

In this paper a terrain vehicle with four wheels drive and four wheels steer intended to use for recreational purpose is presented. The main purpose is to design the suspension mechanism that fulfills requirements about stability, safety and maneuverability. Nowadays, as well as in the past, the development of the suspension systems of the vehicle has shown greater interest by designers and manufacturers of the vehicles. Research is focused to do a comprehensive study of different available independent suspension system (MacPherson, double wishbone, multi-link) and hence forth develop a methodology to design the suspension system for a terrain vehicle. Few chosen suspension systems are analyzed into the very details in order to find out the optimal design of it. During development process of the suspension system should be considered design constraints and requirements provided in the check list. Afterwards the simulation results for kinematics analyses of suspension mechanism are performed in Working Model 2D and MATLAB environments. Achieved results are discussed in detail in order to find the best solution that will fulfill pretentious requirement from developed suspension system. All these investigations and reviews related to the suspension system will be exploited in order to obtain the optimal suspension geometry to equip a terrain vehicle, with such system.
Information Aggregation via Dynamic Routing for Sequence Encoding

While much progress has been made in how to encode a text sequence into a sequence of vectors, less attention has been paid to how to aggregate these preceding vectors (outputs of RNN/CNN) into fixed-size encoding vector. Usually, a simple max or average pooling is used, which is a bottom-up and passive way of aggregation and lack of guidance by task information. In this paper, we propose an aggregation mechanism to obtain a fixed-size encoding with a dynamic routing policy. The dynamic routing policy is dynamically deciding that what and how much information need be transferred from each word to the final encoding of the text sequence. Following the work of Capsule Network, we design two dynamic routing policies to aggregate the outputs of RNN/CNN encoding layer into a final encoding vector. Compared to the other aggregation methods, dynamic routing can refine the messages according to the state of final encoding vector. Experimental results on five text classification tasks show that our method outperforms other aggregating models by a significant margin. Related source code is released on our github page1.
Fibre Optic Sensors for Structural Health Monitoring of Aircraft Composite Structures: Recent Advances and Applications

In-service structural health monitoring of composite aircraft structures plays a key role in the assessment of their performance and integrity. In recent years, Fibre Optic Sensors (FOS) have proved to be a potentially excellent technique for real-time in-situ monitoring of these structures due to their numerous advantages, such as immunity to electromagnetic interference, small size, light weight, durability, and high bandwidth, which allows a great number of sensors to operate in the same system, and the possibility to be integrated within the material. However, more effort is still needed to bring the technology to a fully mature readiness level. In this paper, recent research and applications in structural health monitoring of composite aircraft structures using FOS have been critically reviewed, considering both the multi-point and distributed sensing techniques.
Depression and pain comorbidity: a literature review.

Because depression and painful symptoms commonly occur together, we conducted a literature review to determine the prevalence of both conditions and the effects of comorbidity on diagnosis, clinical outcomes, and treatment. The prevalences of pain in depressed cohorts and depression in pain cohorts are higher than when these conditions are individually examined. The presence of pain negatively affects the recognition and treatment of depression. When pain is moderate to severe, impairs function, and/or is refractory to treatment, it is associated with more depressive symptoms and worse depression outcomes (eg, lower quality of life, decreased work function, and increased health care utilization). Similarly, depression in patients with pain is associated with more pain complaints and greater impairment. Depression and pain share biological pathways and neurotransmitters, which has implications for the treatment of both concurrently. A model that incorporates assessment and treatment of depression and pain simultaneously is necessary for improved outcomes.
The Process of Successful Ageing

As increasingly more people experience old age as a time of growth and productivity, theoretical attention to successful ageing is needed. In this paper, we overview historical, societal and philosophical evidence for a deep, long-standing ambivalence about human ageing that has influenced even scientific views of old age. In recent years, however, discussion of the psychological and behavioural processes people use to maintain and reach new goals in late life has gained momentum. We contribute to this discussion the metamodel of selective optimisation with compensation, developed by Baltes and Baltes. The model is a metamodel that attempts to represent scientific knowledge about the nature of development and ageing with the focus on successful adaptation. The model takes gains and losses jointly into account, pays attention to the great heterogeneity in ageing and successful ageing, and views successful mastery of goals in the face of losses endemic to advanced age as the result of the interplay of the three processes, selection, compensation, and optimisation. We review evidence from the biological and social science literatures for each component and discuss new research avenues to study the interaction of the three processes.
Geometry of Polysemy

Vector representations of words have heralded a transformational approach to classical problems in NLP; the most popular example is word2vec. However, a single vector does not suffice to model the polysemous nature of many (frequent) words, i.e., words with multiple meanings. In this paper, we propose a three-fold approach for unsupervised polysemy modeling: (a) context representations, (b) sense induction and disambiguation and (c) lexeme (as a word and sense pair) representations. A key feature of our work is the finding that a sentence containing a target word is well represented by a low rank subspace, instead of a point in a vector space. We then show that the subspaces associated with a particular sense of the target word tend to intersect over a line (one-dimensional subspace), which we use to disambiguate senses using a clustering algorithm that harnesses the Grassmannian geometry of the representations. The disambiguation algorithm, which we call K-Grassmeans, leads to a procedure to label the different senses of the target word in the corpus – yielding lexeme vector representations, all in an unsupervised manner starting from a large (Wikipedia) corpus in English. Apart from several prototypical target (word,sense) examples and a host of empirical studies to intuit and justify the various geometric representations, we validate our algorithms on standard sense induction and disambiguation datasets and present new state-of-the-art results.
Experimental fault-tolerant control of a PMSM drive

The paper describes a study and an experimental verification of remedial strategies against failures occurring in the inverter power devices of a permanent-magnet synchronous motor drive. The basic idea of this design consists in incorporating a fourth inverter pole, with the same topology and capabilities of the other conventional three poles. This minimal redundant hardware, appropriately connected and controlled, allows the drive to face a variety of power device fault conditions while maintaining a smooth torque production. The achieved results also show the industrial feasibility of the proposed fault-tolerant control, that could fit many practical applications.
Mathematical Creativity for the youngest school children : Kindergarten to third grade teachers ’ interpretations of what it is and how to promote it

Creativity is important for young children learning mathematics. However, much literature has claimed creativity in the learning of mathematics for young children is not adequately supported by teachers in the classroom due to such reasons as teachers’ poor college preparation in mathematics content knowledge, teachers’ negativity toward creative students, teachers’ occupational pressure, and low quality curriculum. The purpose of this grounded theory study was to generate a model that describes and explains how a particular group of early childhood teachers make sense of creativity in the learning of mathematics and how they think they can promote or fail to promote creativity in the classroom. In-depth interviews with 30 Kto Grade-3 teachers, participating in a graduate mathematics specialist certificate program in a medium-sized Midwestern city, were conducted. In contrast to previous findings, these teachers did view mathematics in young children (age 5 to 9) as requiring creativity, in ways that aligned with Sternberg and Lubart’s (1995) investment theory of creativity. Teachers felt they could support creativity in student learning and knew strategies for how to promote creativity in their practices.
Vaccine Hesitancy: Causes, Consequences, and a Call to Action.

Vaccine hesitancy reflects concerns about the decision to vaccinate oneself or one's children. There is a broad range of factors contributing to vaccine hesitancy, including the compulsory nature of vaccines, their coincidental temporal relationships to adverse health outcomes, unfamiliarity with vaccine-preventable diseases, and lack of trust in corporations and public health agencies. Although vaccination is a norm in the U.S. and the majority of parents vaccinate their children, many do so amid concerns. The proportion of parents claiming non-medical exemptions to school immunization requirements has been increasing over the past decade. Vaccine refusal has been associated with outbreaks of invasive Haemophilus influenzae type b disease, varicella, pneumococcal disease, measles, and pertussis, resulting in the unnecessary suffering of young children and waste of limited public health resources. Vaccine hesitancy is an extremely important issue that needs to be addressed because effective control of vaccine-preventable diseases generally requires indefinite maintenance of extremely high rates of timely vaccination. The multifactorial and complex causes of vaccine hesitancy require a broad range of approaches on the individual, provider, health system, and national levels. These include standardized measurement tools to quantify and locate clustering of vaccine hesitancy and better understand issues of trust; rapid, independent, and transparent review of an enhanced and appropriately funded vaccine safety system; adequate reimbursement for vaccine risk communication in doctors' offices; and individually tailored messages for parents who have vaccine concerns, especially first-time pregnant women. The potential of vaccines to prevent illness and save lives has never been greater. Yet, that potential is directly dependent on parental acceptance of vaccines, which requires confidence in vaccines, healthcare providers who recommend and administer vaccines, and the systems to make sure vaccines are safe.
A Survey of Adversarial Machine Learning in Cyber Warfare

The changing nature of warfare has seen a paradigm shift from the conventional to asymmetric, contactless warfare such as information and cyber warfare. Excessive dependence on information and communication technologies, cloud infrastructures, big data analytics, data-mining and automation in decision making poses grave threats to business and economy in adversarial environments. Adversarial machine learning is a fast growing area of research which studies the design of Machine Learning algorithms that are robust in adversarial environments. This paper presents a comprehensive survey of this emerging area and the various techniques of adversary modelling. We explore the threat models for Machine Learning systems and describe the various techniques to attack and defend them. We present privacy issues in these models and describe a cyber-warfare test-bed to test the effectiveness of the various attack-defence strategies and conclude with some open problems in this area of research.
A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation

Approximate Bayesian computation (ABC) methods make use of comparisons between simulated and observed summary statistics to overcome the problem of computationally intractable likelihood functions. As the practical implementation of ABC requires computations based on vectors of summary statistics, rather than full datasets, a central question is how to derive low dimensional summary statistics from the observed data with minimal loss of information. In this article we provide a comprehensive review and comparison of the performance of the principal methods of dimension reduction proposed in the ABC literature. The methods are split into three non-mutually exclusive classes consisting of best subset selection methods, projection techniques and regularisation. In addition, we introduce two new methods of dimension reduction. The first is a best subset selection method based on Akaike and Bayesian information criteria, and the second uses ridge regression as a regularisation procedure. We illustrate the performance of these dimension reduction techniques through the analysis of three challenging models and datasets.
The Jacobian Analysis of a Parallel Manipulator Using the Theory of Reciprocal Screws

In this paper, the theory of reciprocal screws is reviewed. Reciprocal screw systems associated with some frequently used kinematic pairs and chains are developed. Then, the application of reciprocal screw systems for the Jacobian analysis of parallel manipulators is described. The Jacobian and singular conditions of a six-dof parallel manipulator are analyzed.
Scaling Deep Learning Workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing

Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors — including NVIDIA, Intel, AMD and IBM — have architectural road-maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. This paper provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path. Our evaluation consists of a cross section of convolutional neural net workloads: CifarNet, CaffeNet, AlexNet and GoogleNet topologies using the Cifar10 and ImageNet datasets. The workloads are vendor optimized for each architecture. GPUs provide the highest overall raw performance. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and KNL can be competitive when considering performance/watt. Furthermore, NVLink is critical to GPU scaling.
Using a Digital Services Capability Model to Assess Readiness for the Digital Consumer

The increasing proliferation of digital technologies is transforming economies in many ways. This is particularly true in consumer-facing industries where the emergence of digital services is enabling novel value propositions, closer consumer relationships and greater automation of consumer-facing business  These digital services are providing value-creating consumer interactions. For instance, an Italian auto insurer uses a telematics device installed in customers’ vehicles to capture driving behavior and uses this data to create novel value propositions via personalized insurance services.3 The Finnish airline Finnair harnesses Facebook as a platform to create a customer community and a sense of collective identity with the company.4 The City of Boston introduced an iPhone app that senses potholes on city roads and allows citizens to contribute to road management in a highly automated
Application of Blockchain Technology in Smart City Infrastructure

With the rapid growth of urbanization, smart city needs efficient and sustainable intelligent solutions in the transportation, environment, energy, government affairs. The smart city infrastructure, which combines the IoT, Big Data and Energy Internet, is one of the most effective solutions. It’s facing many problems such as poor security of IoT, equipment maintenance and upgrade difficulty, high operating costs of large data center construction, poor resistance to damage, the difficulty of establishing the energy Internet users trust , user privacy is easy to leak, market model is not applicable and so on. The Blockchain is a kind of distributed P2P network common intelligence accounting technology based on cryptography. The features of open, transparent, interconnection and peer-to-peer, storage and sharing in the Blockchain are relevant to the openness, interconnection, peer-to-peer and Shared characteristics of the energy Internet. Its features of redundant storage and distribution also make up for the disadvantages of large data center’s high operation cost, poor recovery ability and the difficulties for the maintenance of IoT equipment. This paper, first introduces the role of the big data, IoT, and the energy Internet in the construction of smart city and Blockchain technology. By analyzing their respective characteristics and comparing their similarities, corresponding solutions are put forward to aim at the problems such as poor security of IoT, upgrading of equipment maintenance and upgrading, construction and operation cost of large data center, poor flexibility in anti-attack, difficulty in establishing trust in energy Internet users, user privacy leakage and inapplicability of trading market mode. A kind of architecture of P2P light-heavy backup is put forward to overcome the high cost of Blockchain data storage.
Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis

The advent of the Social Web has provided netizens with new tools for creating and sharing, in a time- and cost-efficient way, their contents, ideas, and opinions with virtually the millions of people connected to the World Wide Web. This huge amount of information, however, is mainly unstructured as specifically produced for human consumption and, hence, it is not directly machine-processable. In order to enable a more efficient passage from unstructured information to structured data, aspect-based opinion mining models the relations between opinion targets contained in a document and the polarity values associated with these. Because aspects are often implicit, however, spotting them and calculating their respective polarity is an extremely difficult task, which is closer to natural language understanding rather than natural language processing. To this end, Sentic LDA exploits common-sense reasoning to shift LDA clustering from a syntactic to a semantic level. Rather than looking at word co-occurrence frequencies, Sentic LDA leverages on the semantics associated with words and multi-word expressions to improve clustering and, hence, outperform state-of-the-art techniques for aspect extraction.
Risk prediction models for cardiovascular disease and overall mortality

Prediction or prognostication is at the core of modern evidence-based medicine. Prediction of overall mortality and cardiovascular disease can be improved by a systematic evaluation of measurements from large-scale epidemiological studies or by using nested sampling designs to discover new markers from omics technologies. In study I, we investigated if prediction measures such as calibration, discrimination and reclassification could be calculated within traditional sampling designs and which of these designs were the most efficient. We found that is possible to calculate prediction measures by using a proper weighting system and that a stratified casecohort design is a reasonable choice both in terms of efficiency and simplicity. In study II, we investigated the clinical utility of several genetic scores for incident coronary heart disease. We found that genetic information could be of clinical value in improving the allocation of patients to correct risk strata and that the assessment of a genetic risk score among intermediate risk subjects could help to prevent about one coronary heart disease event every 318 people screened. In study III, we explored the association between circulating metabolites and incident coronary heart disease. We found four new metabolites associated with coronary heart disease independently of established cardiovascular risk factors and with evidence of clinical utility. By using genetic information we determined a potential causal effect on coronary heart disease of one of these novel metabolites. In study IV, we compared a large number of demographics, health and lifestyle measurements for association with all-cause and cause-specific mortality. By ranking measurements in terms of their predictive abilities we could provide new insights about their relative importance, as well as reveal some unexpected associations. Moreover we developed and validated a prediction score for five-year mortality with good discrimination ability and calibrated it for the entire UK population. In conclusion, we applied a translational approach spanning from the discovery of novel biomarkers to their evaluation in terms of clinical utility. We combined this effort with methodological improvements aimed to expand prediction measures in settings that were not previously explored. We identified promising novel metabolomics markers for cardiovascular disease and supported the potential clinical utility of a genetic score in primary prevention. Our results might fuel future studies aimed to implement these findings in clinical practice.
ALGEBRAIC FORMULAS FOR THE COEFFICIENTS OF HALF-INTEGRAL WEIGHT HARMONIC WEAK MAASS FORMS

We prove that the coefficients of certain weight −1/2 harmonic Maass forms are “traces” of singular moduli for weak Maass forms. To prove this theorem, we construct a theta lift from spaces of weight −2 harmonic weak Maass forms to spaces of weight −1/2 vectorvalued harmonic weak Maass forms on Mp2(Z), a result which is of independent interest. We then prove a general theorem which guarantees (with bounded denominator) when such Maass singular moduli are algebraic. As an example of these results, we derive a formula for the partition function p(n) as a finite sum of algebraic numbers which lie in the usual discriminant −24n + 1 ring class field.
Exploring goal-setting, rewards, self-monitoring, and sharing to motivate physical activity

Many people have turned to technological tools to help them be physically active. To better understand how goal-setting, rewards, self-monitoring, and sharing can encourage physical activity, we designed a mobile phone application and deployed it in a four-week field study (n=23). Participants found it beneficial to have secondary and primary weekly goals and to receive non-judgmental reminders. However, participants had problems with some features that are commonly used in practice and suggested in the literature. For example, trophies and ribbons failed to motivate most participants, which raises questions about how such rewards should be designed. A feature to post updates to a subset of their Facebook NewsFeed created some benefits, but barriers remained for most participants.