nils-herrmann/clean_scirepeval huggingface dataset:
Eight-Degrees-of-Freedom Remote Actuation of Small Magnetic Mechanisms

Magnetically-driven micrometer to millimeter-scale robotic devices have recently shown great capabilities for remote applications in medical procedures, in microfluidic tools and in microfactories. Significant effort recently has been on the creation of mobile or stationary devices with multiple independently-controllable degrees of freedom (DOF) for multiagent or complex mechanism motions. In most applications of magnetic microrobots, however, the relatively large distance from the field generation source and the microscale devices results in controlling magnetic field signals which are applied homogeneously over all agents. While some progress has been made in this area allowing up to six independent DOF to be individually commanded, there has been no rigorous effort in determining the maximum achievable number of DOF for systems with homogeneous magnetic field input. In this work, we show that this maximum is eight and we introduce the theoretical basis for this conclusion, relying on the number of independent usable components in a magnetic field at a point. In order to verify the claim experimentally, we develop a simple demonstration mechanism with 8 DOF designed specifically to show independent actuation. Using this mechanism with $500 \mu \mathrm{m}$ magnetic elements, we demonstrate eight independent motions of 0.6 mm with 8.6 % coupling using an eight coil system. These results will enable the creation of richer outputs in future microrobotic devices.
X-ray thin-film measurement techniques V . X-ray reflectivity measurement

This is the fifth article in the series of X-ray thin-film measurement techniques. The second, third and forth articles of this series, previously published in the Rigaku Journal, describe out-of-plane, high-resolution and inplane XRD measurements to obtain crystallographic information on crystal size, lattice strain and orientation relationship of a thin-film material. These measurements have been based on the premise of a crystalline thin film. On the other hand, the X-ray reflectivity (XRR) measurement is not a technique to evaluate diffraction phenomenon. The XRR measurement technique described in this article is used to analyze X-ray reflection intensity curves from grazing incident X-ray beam to determine thin-film parameters including thickness, density, and surface or interface roughness. This article will provide an overview of the principles of X-ray reflectivity, measurement procedures, and analysis methods. It also discusses the procedural flow from measurement to analysis, as well as precautions.
The design of glass crack detection system based on image preprocessing technology

This paper Put forward a glass crack detection algorithm based on digital image processing technology, obtain identification information of glass surface crack image by making use of pre-processing, image segmentation, feature extraction on the glass crack image, calculate the target area and perimeter of the roundness index to judge whether this image with a crack, make use of Visual Basic6.0 programming language to impolder the crack detection system, achieve the function of each part in crack detection process.
Smart HVAC Control in IoT: Energy Consumption Minimization with User Comfort Constraints

Smart grid is one of the main applications of the Internet of Things (IoT) paradigm. Within this context, this paper addresses the efficient energy consumption management of heating, ventilation, and air conditioning (HVAC) systems in smart grids with variable energy price. To that end, first, we propose an energy scheduling method that minimizes the energy consumption cost for a particular time interval, taking into account the energy price and a set of comfort constraints, that is, a range of temperatures according to user's preferences for a given room. Then, we propose an energy scheduler where the user may select to relax the temperature constraints to save more energy. Moreover, thanks to the IoT paradigm, the user may interact remotely with the HVAC control system. In particular, the user may decide remotely the temperature of comfort, while the temperature and energy consumption information is sent through Internet and displayed at the end user's device. The proposed algorithms have been implemented in a real testbed, highlighting the potential gains that can be achieved in terms of both energy and cost.
Neural Collective Entity Linking

Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task. However, most existing methods rely on local contexts to resolve entities independently, which may usually fail due to the data sparsity of local information. To address this issue, we propose a novel neural model for collective entity linking, named as NCEL. NCEL applies Graph Convolutional Network to integrate both local contextual features and global coherence information for entity linking. To improve the computation efficiency, we approximately perform graph convolution on a subgraph of adjacent entity mentions instead of those in the entire text. We further introduce an attention scheme to improve the robustness of NCEL to data noise and train the model on Wikipedia hyperlinks to avoid overfitting and domain bias. In experiments, we evaluate NCEL on five publicly available datasets to verify the linking performance as well as generalization ability. We also conduct an extensive analysis of time complexity, the impact of key modules, and qualitative results, which demonstrate the effectiveness and efficiency of our proposed method.
Preliminary use of Polygonum minus Linn. leaf extract on growth performance, feed utilization, and some hematological indices of Anabas testudineus (Bloch, 1792)

The main objectives of the present study were to improve the growth performance, survival rate, and hematological indices of fingerling Anabas testudineus, by using Polygonum minus plant leaf extract, sprayed at four different levels (0 as the control group, 50, 70, and 100 ml/kg fish feed), for 60 days. The growth indices and survival rate were investigated at 15-day intervals, while hematological indices were evaluated at the end of experiment (60 days). The results showed that the weight gain (WG), average daily growth rate (ADG), specific growth rate (SGR), feed efficiency (FE), and protein efficiency ratio (PER) values were significantly different, and that the highest value in the group was found in fish fed 70 ml/kg feed. Meanwhile, feed conversion rate (FCR) was shown to have the lowest value in the group that was fed 70 ml/kg feed. In addition, the survival rate was shown to be higher in all groups except for the control group: a significant difference (p < 0.05). Also, the dietary supplementation, with P. minus extract, caused a significant difference in total white blood cell count (WBC) among the treatments, the highest cell value of WBC was found in the group of fish fed 70 and 100 ml/kg feed. Although the red blood cells (RCB) and hematocrit (Htc) concentrations were not significantly different (p > 0.05), there was a trend towards higher concentrations. Overall, our results indicated that P. minus leaf extract supplementation could improve growth performance, especially with regard to feed utilization, and the survival rate was very important for the fingerling nursing step; moreover, the WBC could be improved by using herbal extracts.
Combining Deep Learning and Topic Modeling for Review Understanding in Context-Aware Recommendation

With the rise of e-commerce, people are accustomed to writing their reviews after receiving the goods. These comments are so important that a bad review can have a direct impact on others buying. Besides, the abundant information within user reviews is very useful for extracting user preferences and item properties. In this paper, we investigate the approach to effectively utilize review information for recommender systems. The proposed model is named LSTM-Topic matrix factorization (LTMF) which integrates both LSTM and Topic Modeling for review understanding. In the experiments on popular review dataset Amazon , our LTMF model outperforms previous proposed HFT model and ConvMF model in rating prediction. Furthermore, LTMF shows the better ability on making topic clustering than traditional topic model based method, which implies integrating the information from deep learning and topic modeling is a meaningful approach to make a better understanding of reviews.
Text Embeddings for Retrieval From a Large Knowledge Base

Text embedding representing natural language documents in a semantic vector space can be used for document retrieval using nearest neighbor lookup. In order to study the feasibility of neural models specialized for retrieval in a semantically meaningful way, we suggest the use of the Stanford Question Answering Dataset (SQuAD) in an open-domain question answering context, where the first task is to find paragraphs useful for answering a given question. First, we compare the quality of various text-embedding methods on the performance of retrieval and give an extensive empirical comparison on the performance of various non-augmented base embedding with, and without IDF weighting. Our main results are that by training deep residual neural models specifically for retrieval purposes can yield significant gains when it is used to augment existing embeddings. We also establish that deeper models are superior to this task. The best base baseline embeddings augmented by our learned neural approach improves the top-1 paragraph recall of the system by 14%.
Analysis of temporal noise in CMOS photodiode active pixel sensor

Temporal noise sets the fundamental limit on image sensor performance, especially under low illumination and in video applications. In a CCD image sensor, temporal noise is primarily due to the photodetector shot noise and the output amplifier thermal and 1 noise. CMOS image sensors suffer from higher noise than CCDs due to the additional pixel and column amplifier transistor thermal and 1 noise. Noise analysis is further complicated by the time-varying circuit models, the fact that the reset transistor operates in subthreshold during reset, and the nonlinearity of the charge to voltage conversion, which is becoming more pronounced as CMOS technology scales. The paper presents a detailed and rigorous analysis of temporal noise due to thermal and shot noise sources in CMOS active pixel sensor (APS) that takes into consideration these complicating factors. Performing time-domain analysis, instead of the more traditional frequency-domain analysis, we find that the reset noise power due to thermal noise is at most half of its commonly quoted value. This result is corroborated by several published experimental data including data presented in this paper. The lower reset noise, however, comes at the expense of image lag. We find that alternative reset methods such as overdriving the reset transistor gate or using a pMOS transistor can alleviate lag, but at the expense of doubling the reset noise power. We propose a new reset method that alleviates lag without increasing reset noise.
Role of Intensive Training in the Growth and Maturation of Artistic Gymnasts

Short stature and later maturation of youth artistic gymnasts are often attributed to the effects of intensive training from a young age. Given limitations of available data, inadequate specification of training, failure to consider other factors affecting growth and maturation, and failure to address epidemiological criteria for causality, it has not been possible thus far to establish cause-effect relationships between training and the growth and maturation of young artistic gymnasts. In response to this ongoing debate, the Scientific Commission of the International Gymnastics Federation (FIG) convened a committee to review the current literature and address four questions: (1) Is there a negative effect of training on attained adult stature? (2) Is there a negative effect of training on growth of body segments? (3) Does training attenuate pubertal growth and maturation, specifically, the rate of growth and/or the timing and tempo of maturation? (4) Does training negatively influence the endocrine system, specifically hormones related to growth and pubertal maturation? The basic information for the review was derived from the active involvement of committee members in research on normal variation and clinical aspects of growth and maturation, and on the growth and maturation of artistic gymnasts and other youth athletes. The committee was thus thoroughly familiar with the literature on growth and maturation in general and of gymnasts and young athletes. Relevant data were more available for females than males. Youth who persisted in the sport were a highly select sample, who tended to be shorter for chronological age but who had appropriate weight-for-height. Data for secondary sex characteristics, skeletal age and age at peak height velocity indicated later maturation, but the maturity status of gymnasts overlapped the normal range of variability observed in the general population. Gymnasts as a group demonstrated a pattern of growth and maturation similar to that observed among short-, normal-, late-maturing individuals who were not athletes. Evidence for endocrine changes in gymnasts was inadequate for inferences relative to potential training effects. Allowing for noted limitations, the following conclusions were deemed acceptable: (1) Adult height or near adult height of female and male artistic gymnasts is not compromised by intensive gymnastics training. (2) Gymnastics training does not appear to attenuate growth of upper (sitting height) or lower (legs) body segment lengths. (3) Gymnastics training does not appear to attenuate pubertal growth and maturation, neither rate of growth nor the timing and tempo of the growth spurt. (4) Available data are inadequate to address the issue of intensive gymnastics training and alterations within the endocrine system.
OntBot: Ontology based chatbot

A new ontology based approach is proposed to model and operate chatbots (OntBot). OntBot uses appropriate mapping technique to transform ontologies and knowledge into relational database and then use that knowledge to drive its chats. The proposed approach overcomes a number of traditional chatbots drawbacks including: the need to learn and use chatbot specific language such as AIML, high botmaster interference, and the use of non-matured technology. OntBot has the additional power of easy users interactions using their natural language, and the seamless support of different application domains. This gives the proposed approach a number of unique scalability and interoperability properties that are going to be evaluated in future phases of this research project.
A new classification of the gold deposits of Egypt

Gold deposits and occurrences located in the Nubian Shield have been known in Egypt since Predynastic times. Despite the fact that these deposits were long under exploitation and investigated many times, they are still insufficiently classified in harmony with the crustal evolution models suggested for the evolution of the Nubian Shield. Several plate tectonic models were proposed for the development of the Nubian Shield and the present classification relies heavily on the model that implies collision of arc-inferred continent through subduction and obduction of oceanic lithosphere. A three-fold classification of gold deposits of Egypt is offered here in harmony with this evolutionary model. These are stratabound deposits and non-stratabound deposits hosted in igneous and metamorphic rocks, as well as placer gold deposits. The stratabound deposits are hosted in island arc volcanic and volcaniclastic rocks of comparable composition formed in ensimatic island arcs. They are thought to have formed by exhalative hydrothermal processes during the waning phases of sub-marine volcanic activity. Stratabound deposits are sub-divided into three main types: gold-bearing Algoma-type Banded Iron Formation, gold-bearing tuffaceous sediments and gold-bearing volcanogenic massive sulphide deposits. Non-stratabound deposits occur in a wide range of igneous and metamorphic rocks. They were formed during orogenic and post-cratonization periods by mineralizing fluids of different sources. Non-stratabound deposits are divided into veintype mineralization, which constituted the main target for gold in Egypt since Pharaonic times, and disseminated-type mineralization hosted in hydrothermally altered rocks (alteration zones) which are taken recently into consideration as a new target for gold in Egypt. Placer gold deposits are divided into modern placers and lithified placers. The former are sub-divided into alluvial placers and beach placers. Conglomerates occurring on or near ancient eroded surfaces represent lithified placers. D 2003
Can Cybersecurity Be Proactive? A Big Data Approach and Challenges

The cybersecurity community typically reacts to attacks after they occur. Being reactive is costly and can be fatal where attacks threaten lives, important data, or mission success. But can cybersecurity be done proactively? Our research capitalizes on the Germination Period—the time lag between hacker communities discussing software flaw types and flaws actually being exploited—where proactive measures can be taken. We argue for a novel proactive approach, utilizing big data, for (I) identifying potential attacks before they come to fruition; and based on this identification, (II) developing preventive countermeasures. The big data approach resulted in our vision of the Proactive Cybersecurity System (PCS), a layered, modular service platform that applies big data collection and processing tools to a wide variety of unstructured data sources to predict vulnerabilities and develop countermeasures. Our exploratory study is the first to show the promise of this novel proactive approach and illuminates challenges that need to be addressed.
Self-folding origami at any energy scale

Programmable stiff sheets with a single low-energy folding motion have been sought in fields ranging from the ancient art of origami to modern meta-materials research. Despite such attention, only two extreme classes of crease patterns are usually studied; special Miura-Ori-based zero-energy patterns, in which crease folding requires no sheet bending, and random patterns with high-energy folding, in which the sheet bends as much as creases fold. We present a physical approach that allows systematic exploration of the entire space of crease patterns as a function of the folding energy. Consequently, we uncover statistical results in origami, finding the entropy of crease patterns of given folding energy. Notably, we identify three classes of Mountain-Valley choices that have widely varying 'typical' folding energies. Our work opens up a wealth of experimentally relevant self-folding origami designs not reliant on Miura-Ori, the Kawasaki condition or any special symmetry in space.
Quasi-newton methods for real-time simulation of hyperelastic materials

We present a new method for real-time physics-based simulation supporting many different types of hyperelastic materials. Previous methods such as Position Based or Projective Dynamics are fast, but support only limited selection of materials; even classical materials such as the Neo-Hookean elasticity are not supported. Recently, Xu et al. [2015] introduced new “splinebased materials” which can be easily controlled by artists to achieve desired animation effects. Simulation of these types of materials currently relies on Newton’s method, which is slow, even with only one iteration per timestep. In this paper, we show that Projective Dynamics can be interpreted as a quasi-Newton method. This insight enables very efficient simulation of a large class of hyperelastic materials, including the Neo-Hookean, splinebased materials, and others. The quasi-Newton interpretation also allows us to leverage ideas from numerical optimization. In particular, we show that our solver can be further accelerated using L-BFGS updates (Limitedmemory Broyden-Fletcher-Goldfarb-Shanno algorithm). Our final method is typically more than 10 times faster than one iteration of Newton’s method without compromising quality. In fact, our result is often more accurate than the result obtained with one iteration of Newton’s method. Our method is also easier to implement, implying reduced software development costs.
Clinical Perspectives on Neurobiological Effects of Psychological Trauma

Physical trauma to the brain has always been known to affect brain functions and subsequent neurobiological development. Research primarily since the early 1990s has shown that psychological trauma can have detrimental effects on brain function that are not only lasting but that may alter patterns of subsequent neurodevelopment, particularly in children although developmental effects may be seen in adults as well. Childhood trauma produces a diverse range of symptoms and defining the brain's response to trauma and the factors that mediate the body's stress response systems is at the forefront of scientific investigation. This paper reviews the current evidence relating psychological trauma to anatomical and functional changes in the brain and discusses the need for accurate diagnosis and treatment to minimize such effects and to recognize their existence in developing treatment programs.
DETECTION AND PREDICTION OF MANAGERIAL FRAUD IN THE FINANCIAL STATEMENTS OF TUNISIAN BANKS

This article models the detection and prediction of managerial fraud in the financial statements of Tunisian banks. The methodology used consist of examining a battery of financial ratios used by the Federal Deposit Insurance Corporation (FDIC) as indicators of the financial situation of a bank. We test the predictive power of these ratios using logistic regression. The results show that we can detect managerial fraud in the financial statements of Tunisian banks using performance ratios three years before its occurrence with a classification rate of 71.1%.