nils-herrmann/clean_scirepeval huggingface dataset:
Walking on partial footholds including line contacts with the humanoid robot atlas

We present a method for humanoid robot walking on partial footholds such as small stepping stones and rocks with sharp surfaces. Our algorithm does not rely on prior knowledge of the foothold, but information about an expected foothold can be used to improve the stepping performance. After a step is taken, the robot explores the new contact surface by attempting to shift the center of pressure around the foot. The available foothold is inferred by the way in which the foot rotates about contact edges and/or by the achieved center of pressure locations on the foot during exploration. This estimated contact area is then used by a whole body momentum-based control algorithm. To walk and balance on partial footholds, we combine fast, dynamic stepping with the use of upper body angular momentum to regain balance. We applied this method to the Atlas humanoid designed by Boston Dynamics to walk over small contact surfaces, such as line and point contacts. We present experimental results and discuss performance limitations.
Refining Word Embeddings for Sentiment Analysis

Word embeddings that can capture semantic and syntactic information from contexts have been extensively used for various natural language processing tasks. However, existing methods for learning contextbased word embeddings typically fail to capture sufficient sentiment information. This may result in words with similar vector representations having an opposite sentiment polarity (e.g., good and bad), thus degrading sentiment analysis performance. Therefore, this study proposes a word vector refinement model that can be applied to any pre-trained word vectors (e.g., Word2vec and GloVe). The refinement model is based on adjusting the vector representations of words such that they can be closer to both semantically and sentimentally similar words and further away from sentimentally dissimilar words. Experimental results show that the proposed method can improve conventional word embeddings and outperform previously proposed sentiment embeddings for both binary and fine-grained classification on Stanford Sentiment Treebank (SST).
Kindness is Contagious: Exploring Engagement in a Gamified Persuasive Intervention for Wellbeing

Students are continually exposed to a variety of stressors during their academic career, and this can have significant negative effects on their mental health and subjective wellbeing. In this paper we explore how gamified persuasive interventions can promote engagement in performing random acts of kindness to improve wellbeing and help students manage stressors more effectively. In a pilot study we investigated how participation levels in a gamified persuasive intervention that promotes random acts of kindness at University, are influenced by (1) different persuasive message types, and (2) different game challenge categories. Furthermore, we analysed the impact on behavioural intention by comparing pre-intention and post-intention to perform random acts of kindness. Participants were assigned 5 different quests each morning, for two days, and asked to complete as many as possible by the end of each day. Participants were divided into 2 groups and received different types of persuasive notifications during the day: Group A received messages that set out group goals and used the social comparison strategy, while Group B received messages that set out individual goals and used the self-monitoring strategy. The findings from the pilot study will inform the design of a larger study to investigate persuasive game-based interventions for subjective wellbeing.
An Analysis of Relationship between Total Quality Management and Kaizen

Total Quality Management (TQM) and Kaizena continuous change toward betterment are two fundamental concepts directly dealing with continuous improvement of quality of processes and performance of an organization to achieve positive transformation in mindset and action of employees and management. For clear understanding and to get maximum benefit from both of these concepts, as such it becomes mandatory to precisely differentiate between TQM and Kaizen. TQM features primarily focus on customer’s satisfaction through improvement of quality. It is both a top down and bottom up approach whereas kaizen is processes focused and a bottom up approach of small incremental changes. Implementation of TQM is more costly as compared to Kaizen. Through kaizen, improvements are made using organization’s available resources. For the effective implementation of kaizen, the culture of the organization must be supportive and the result of continuous improvement should be communicated to the whole organization for motivation of all employees and for the success of continuous improvement program in the organization. This paper focuses on analyzing the minute differences between TQM and Kaizen. It also discusses the different tools and techniques under the umbrella of kaizen and TQM Philosophy. This paper will elucidate the differences in both these concepts as far as their inherent characteristics and practical implementations are concerned. In spite of differences in methodology, focus and scale of operation in both the concept, it can be simply concluded that Kaizen is one of the Technique of the T QM for continuous improvement of quality, process and performance of the organization.  Total Quality Management, Kaizen Technique, Continuous Improvement (CI), Tools & Techniques
A full X-band high-efficiency 12-watt GaAs MMIC power amplifier with harmonic tuning

In this paper, the design and measurements of a 8-12GHz high-efficiency MMIC high power amplifier (HPA) implemented in a 0.25μm GaAS pHEMT process is described. The 3-stage amplifier has demonstrated from 37% to 54% power-added efficiency (PAE) with 12W of output power and up to 27dB of small signal gain range from 8-12GHz. In particular, over the frequency band of 9-11 GHz, the circuit achieved above 45% PAE. The key to this design is determining and matching the optimum source and load impedance for PAE at the first two harmonics in output stage.
On Combining Crowdsourcing, Sensing and Open Data for an Accessible Smart City

This work presents a novel geospatial mapping service, based on , which has been designed and developed in order to provide personalized path to users with special needs. This system gathers data related to barriers and facilities of the urban environment via crowd sourcing and sensing done by users. It also considers open data provided by bus operating companies to identify the actual accessibility feature and the real time of arrival at the stops of the buses. The resulting service supports citizens with reduced mobility (users with disabilities and/or elderly people) suggesting urban paths accessible to them and providing information related to travelling time, which are tailored to their abilities to move and to the bus arrival time. The manuscript demonstrates the effectiveness of the approach by means of a case study focusing on the differences between the solutions provided by our system and the ones computed by main stream geospatial mapping services.
Expressiveness of Logic Programs under the General Stable Model Semantics

Stable model semantics had been recently generalized to non-Herbrand structures by several works, which provides a unified framework and solid logical foundations for answer set programming. This article focuses on the expressiveness of normal and disjunctive logic programs under general stable model semantics. A translation from disjunctive logic programs to normal logic programs is proposed for infinite structures. Over finite structures, some disjunctive logic programs are proved to be intranslatable to normal logic programs if the arities of auxiliary predicates and functions are bounded in a certain way. The equivalence of the expressiveness of normal logic programs and disjunctive logic programs over arbitrary structures is also shown to coincide with that over finite structures and coincide with whether the complexity class NP is closed under complement. Moreover, to obtain a more explicit picture of the expressiveness, some intertranslatability results between logic program classes, and fragments of second-order logic are established.
Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions.

In 2011, Lake Erie experienced the largest harmful algal bloom in its recorded history, with a peak intensity over three times greater than any previously observed bloom. Here we show that long-term trends in agricultural practices are consistent with increasing phosphorus loading to the western basin of the lake, and that these trends, coupled with meteorological conditions in spring 2011, produced record-breaking nutrient loads. An extended period of weak lake circulation then led to abnormally long residence times that incubated the bloom, and warm and quiescent conditions after bloom onset allowed algae to remain near the top of the water column and prevented flushing of nutrients from the system. We further find that all of these factors are consistent with expected future conditions. If a scientifically guided management plan to mitigate these impacts is not implemented, we can therefore expect this bloom to be a harbinger of future blooms in Lake Erie.
Children, Ethics, and the Law: Professional Issues and Cases♦

The intended readership of this volume is the full range of behavioral scientists, mental health professionals, and students aspiring to such roles who work with children. This includes psychologists (applied, clinical, counseling, developmental, school, including academics, researchers, and practitioners), family counselors, psychiatrists, social workers, psychiatric nurses, child protection workers, and any other mental health professionals who work with children, adolescents, and their families.
Defining Structural Similarity *

There is general agreement that structural similarity — a match in relational structure — is crucial in analogical processing. However, theories differ in their definitions of structural similarity: in particular, in whether there must be conceptual similarity between the relations in the two domains or whether parallel graph structure is sufficient. In two studies, we demonstrate, first, that people draw analogical correspondences based on matches in conceptual relations, rather than on purely structural graph matches; and, second, that people draw analogical inferences between passages that have matching conceptual relations, but not between passages with purely structural graph matches.
Empirical comparison of network sampling techniques

In the past few years, the storage and analysis of large-scale and fast evolving networks present a great challenge. Therefore, a number of different techniques have been proposed for sampling large networks. In general, network exploration techniques approximate the original networks more accurately than random node and link selection. Yet, link selection with additional subgraph induction step outperforms most other techniques. In this paper, we apply subgraph induction also to random walk and forest-fire sampling. We analyze different real-world networks and the changes of their properties introduced by sampling. We compare several sampling techniques based on the match between the original networks and their sampled variants. The results reveal that the techniques with subgraph induction underestimate the degree and clustering distribution, while overestimate average degree and density of the original networks. Techniques without subgraph induction step exhibit exactly the opposite behavior. Hence, the performance of the sampling techniques from random selection category compared to network exploration sampling does not differ significantly, while clear differences exist between the techniques with subgraph induction step and the ones without it.
Adaptive Physically Based Models in Computer Graphics

One of the major challenges in physically-based modeling is making simulations efficient. Adaptive models provide an essential solution to these efficiency goals. These models are able to self-adapt in space and time, attempting to provide the best possible compromise between accuracy and speed. This survey reviews the adaptive solutions proposed so far in computer graphics. Models are classified according to the strategy they use for adaptation, from time-stepping and freezing techniques to geometric adaptivity in the form of structured grids, meshes, and particles. Applications range from fluids, through deformable bodies, to articulated solids.
Speech emotion recognition with acoustic and lexical features

In this paper we explore one of the key aspects in building an emotion recognition system: generating suitable feature representations. We generate feature representations from both acoustic and lexical levels. At the acoustic level, we first extract low-level features such as intensity, F0, jitter, shimmer and spectral contours etc. We then generate different acoustic feature representations based on these low-level features, including statistics over these features, a new representation derived from a set of low-level acoustic codewords, and a new representation from Gaussian Supervectors. At the lexical level, we propose a new feature representation named emotion vector (eVector). We also use the traditional Bag-of-Words (BoW) feature. We apply these feature representations for emotion recognition and compare their performance on the USC-IEMOCAP database. We also combine these different feature representations via early fusion and late fusion. Our experimental results show that late fusion of both acoustic and lexical features achieves four-class emotion recognition accuracy of 69.2%.
Childhood trauma, psychosis and schizophrenia: a literature review with theoretical and clinical implications.

To review the research addressing the relationship of childhood trauma to psychosis and schizophrenia, and to discuss the theoretical and clinical implications.



Relevant studies and previous review papers were identified via computer literature searches.



Symptoms considered indicative of psychosis and schizophrenia, particularly hallucinations, are at least as strongly related to childhood abuse and neglect as many other mental health problems. Recent large-scale general population studies indicate the relationship is a causal one, with a dose-effect.



Several psychological and biological mechanisms by which childhood trauma increases risk for psychosis merit attention. Integration of these different levels of analysis may stimulate a more genuinely integrated bio-psycho-social model of psychosis than currently prevails. Clinical implications include the need for staff training in asking about abuse and the need to offer appropriate psychosocial treatments to patients who have been abused or neglected as children. Prevention issues are also identified.
DETECTION OF ABNORMAL BLOOD CELLS USING IMAGE PROCESSING TECHNIQUE

Image processing techniques are widely used in the domain of medical sciences for detecting various diseases, infections, tumors, cell abnormalities and various cancers. Detecting and curing a dise ase on time is very important in the field of medicine for protecting and saving human life. Mostly in case of high severity diseases where the mortality rates are more, the waiting time of patients for their reports such as blood test, MRI is more. The time taken for generation of any of the test is from 1-10 days. In high risk diseases like Hepatitis B, it is recommended that the patient’s waiting time should be as less as possible and the treatment should be started immediately. The current system used by the pathologists for identification of blood parameters is costly and the time involved in generation of the reports is also more sometimes leading to loss of patient’s life. Also the pathological tests are expensive, which are sometimes not affordable by the patient. This paper deals with an image processing technique used for detecting the abnormalities of blood cells in less time. The proposed technique also helps in segregating the blood cells in different categories based on the form factor.
Molecular Neurobiology of Drug Addiction

The purpose of this review is to illustrate the ways in which molecular neurobiological investigations will contribute to an improved understanding of drug addiction and, ultimately, to the development of more effective treatments. Such molecular studies of drug addiction are needed to establish two general types of information: (1) mechanisms of pathophysiology, identification of the changes that drugs of abuse produce in the brain that lead to addiction; and (2) mechanisms of individual risk, identification of specific genetic and environmental factors that increase or decrease an individual's vulnerability for addiction. This information will one day lead to fundamentally new approaches to the treatment and prevention of addictive disorders.
Piezoelectric properties of ScAlN thin films for piezo-MEMS devices

This paper reports the piezoelectric properties of ScAlN thin films. We evaluated the piezoelectric coefficients d<sub>33</sub> and d<sub>31</sub> of Sc<sub>x</sub>Al<sub>1-x</sub>N thin films directly deposited onto silicon wafers, as well the radio frequency (RF) electrical characteristics of Sc<sub>0.35</sub>Al<sub>0.65</sub>N bulk acoustic wave (BAW) resonators at around 2 GHz, and found a maximum value for d<sub>33</sub> of 28 pC/N and a maximum -d<sub>31</sub> of 13 pm/V at 40% scandium concentration. In BAW resonators that use Sc<sub>0.35</sub>Al<sub>0.65</sub>N as a piezoelectric film, the electromechanical coupling coefficient k<sup>2</sup> (=15.5%) was found to be 2.6 times that of resonators with AlN films. These experimental results are in very close agreement with first-principles calculations. The large electromechanical coupling coefficient and high sound velocity of these films should make them suitable for high frequency applications.
BendFields: Regularized Curvature Fields from Rough Concept Sketches

Designers frequently draw curvature lines to convey bending of smooth surfaces in concept sketches. We present a method to extrapolate curvature lines in a rough concept sketch, recovering the intended 3D curvature field and surface normal at each pixel of the sketch. This 3D information allows to enrich the sketch with 3D-looking shading and texturing.
 We first introduce the concept of regularized curvature lines that model the lines designers draw over curved surfaces, encompassing curvature lines and their extension as geodesics over flat or umbilical regions. We build on this concept to define the orthogonal cross field that assigns two regularized curvature lines to each point of a 3D surface. Our algorithm first estimates the projection of this cross field in the drawing, which is nonorthogonal due to foreshortening. We formulate this estimation as a scattered interpolation of the strokes drawn in the sketch, which makes our method robust to sketchy lines that are typical for design sketches. Our interpolation relies on a novel smoothness energy that we derive from our definition of regularized curvature lines. Optimizing this energy subject to the stroke constraints produces a dense nonorthogonal 2D cross field which we then lift to 3D by imposing orthogonality. Thus, one central concept of our approach is the generalization of existing cross field algorithms to the nonorthogonal case.
 We demonstrate our algorithm on a variety of concept sketches with various levels of sketchiness. We also compare our approach with existing work that takes clean vector drawings as input.
A Smart Irrigation and Monitoring System

Internet of Things, commonly known as IoT is a promising area in technology that is growing day by day. It is a concept whereby devices connect with each other or to living things. Internet of Things has shown its great benefits in today’s life. Agriculture is one amongst the sectors which contributes a lot to the economy of Mauritius and to get quality products, proper irrigation has to be performed. Hence proper water management is a must because Mauritius is a tropical island that has gone through water crisis since the past few years. With the concept of Internet of Things and the power of the cloud, it is possible to use low cost devices to monitor and be informed about the status of an agricultural area in real time. Thus, this paper provides the design and implementation of a Smart Irrigation and Monitoring System which makes use of Microsoft Azure machine learning to process data received from sensors in the farm and weather forecasting data to better inform the farmers on the appropriate moment to start irrigation. The Smart Irrigation and Monitoring System is made up of sensors which collect data such as air humidity, air temperature, and most importantly soil moisture data. These data are used to monitor the air quality and water content of the soil. The raw data are transmitted to the
Study on the fast charging method of lead-acid battery with negative pulse discharge

In order to improve electric vehicle lead-acid battery charging speed, analysis the feasibility of shortening the charging time used the charge method with negative pulse discharge, presenting the negative pulse parameters determined method for the fast charging with pulse discharge, determined the negative pulse amplitude and negative pulse duration in the pulse charge with negative pulse. Experiments show that the determined parameters with this method has some Advantages such as short charging time, small temperature rise etc, and the method of negative pulse parameters determined can used for different capacity of lead-acid batteries.
Regularization and completion of tomosar point clouds in a projected height map domain

TomoSAR (Tomographic SAR) is a technique allowing to extend SAR imaging to the third dimension by using several images of a scene acquired from different sensor positions. 3D point clouds extracted thanks to tomographic processing methods are often corrupted by noise and artifacts which need to be corrected. In this paper, we propose a simple convex optimization formulation that exploits the geometric constraint that the line of sight between a sensor and a surface measurement must be unobscured. We demonstrate the ability of our method to denoise point clouds and fill holes on both synthetic and experimental DLR E-SAR data.
Deep Multimodal Embedding Model for Fine-grained Sketch-based Image Retrieval

Fine-grained Sketch-based Image Retrieval (Fine-grained SBIR), which uses hand-drawn sketches to search the target object images, has been an emerging topic over the last few years. The difficulties of this task not only come from the ambiguous and abstract characteristics of sketches with less useful information, but also the cross-modal gap at both visual and semantic level. However, images on the web are always exhibited with multimodal contents. In this paper, we consider Fine-grained SBIR as a cross-modal retrieval problem and propose a deep multimodal embedding model that exploits all the beneficial multimodal information sources in sketches and images. In our experiment with large quantity of public data, we show that the proposed method outperforms the state-of-the-art methods for Fine-grained SBIR.