Miracle-dz/UniFAR-FTUs huggingface dataset:
A Six Degree-of-Freedom God-Object Method for Haptic Display of Rigid Bodies

This paper describes a generalization of the god-object method for haptic interaction between rigid bodies. Our approach separates the computation of the motion of the six degree-of-freedom god-object from the computation of the force applied to the user. The motion of the god-object is computed using continuous collision detection and constraint-based quasi-statics, which enables high-quality haptic interaction between contacting rigid bodies. The force applied to the user is computed using a novel constraint-based quasi-static approach, which allows us to suppress force artifacts typically found in previous methods. Our approach has been implemented on a 3.2 GHz Xeon bi-processor and has been successfully tested on complex benchmarks. Our results show that the separation into asynchronous processes allows us to satisfy the different update rates required by the haptic and the visual displays. The constraint-based force applied to the user, which handles any number of simultaneous contact points, is typically computed within a few microseconds, while the update of the configuration of the rigid god-object is performed within a few milliseconds for rigid bodies containing up to tens of thousands of triangles.
Multi-Stage Flow Table Caching: From Theory to Algorithm

Flow table capacity in programmable switches is constrained due to the limited on-chip hardware resource. The current mainstream approach is to cache only the popular rules in hardware. By taking advantage of traffic locality, the majority of packets can be forwarded directly after matching the rules cached in hardware and the remaining missed packets are handled by software that accommodates the full flow table. Existing works focus on selecting the cache entries for a single-stage flow table to achieve a high cache hit-rate, which cannot adapt to multi-stage flow tables. Due to hardware constraints as well as service requirements, it is often necessary to decompose a single-stage flow table to a multi-stage flow table or directly create multiple stages of tables in hardware. For the first time, we abstract and model the multi-stage flow table caching problem and prove the NP-hardness of the Optimal Multi-stage Flow table Caching (OMFC). Further, we propose a Greedy Caching Algorithm (GCA) for OMFC, which considers both the rule popularity across multiple stages of flow tables and entry popularity within the same stage of flow table when determining the content and size of the multi-stage flow tables. The simulation results show that GCA achieves a l0~30% higher cache hit-rate than the existing algorithms.
A Deep Learning Model, interpreted with an XAI technique, to simulate and optimize the remediation of oil-drilling cuttings in bubble flow reactors

A multitask deep neural network (DNN) is developed to simulate the ozonation of oil-drilling cuttings (ODC) and is interpreted through a technique of explainable artificial intelligence (XAI) to provide knowledge about the experimental conditions that will maximize the decontamination of ODC. On a semi-batch bubble flow column, ozonation experiments of ODC are carried out after pretreatment with synthetic seawater (SW) and the anionic surfactant sodium dodecyl sulphate (SDS). The performance of ozonation experiments is evaluated by measuring the removal efficiency of the total organic carbon (TOC). The experimental data are used for training and testing an DNN that can predict accurately the TOC removal efficiency of the ozonation process as well as the values of different variables such as pH, oxidation-reduction potential (ORP), temperature (T), pressure drop (ΔP), based on the values of the input variables of the model. The acquired model is interpreted through the Shapley Additive explanations (SHAP) method, an important advancement in the field of machine learning interpretation provided by XAI, regarding the significance of the models’ input variables in the TOC removal efficiency. This step aims at establishing the experimental conditions that lead to the highest remediation rate.
Investigation of Machine Learning Algorithms for Pattern Recognition in Image Processing

In order to recognize patterns in images, this study tests the performance of many “machine learning algorithms” and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machine learning methods (“deep learning, support vector machines, decision trees, and random forests”) and two feature extraction strategies (raw pixel values and Histogram of Oriented Gradients). The efficacy of each algorithm is measured in terms of its “accuracy, precision, recall, and F1 score”, among others. Our findings also demonstrate that the Histogram of Oriented Gradients feature extraction method is good at collecting local gradient information in pictures and that deep learning and support vector machines obtain the best accuracy overall. The results of our research have significant ramifications for the future of machine learning techniques used in computer vision and handwriting recognition. Research in the future may test these methods on other datasets and picture kinds, or look into alternative feature extraction strategies and machine learning algorithms.
Productivity and habitat selection of ring -necked pheasants and greater prairie -chickens in Nebraska

The Conservation Reserve Program (CRP) has provided critical wildlife habitat for many species since 1985; however, the quality of this habitat for early successional species, such as ring-necked pheasant (Phasianus colchicus), may decrease with field age. These late successional fields may lack valuable vegetative and structural diversity needed by pheasants, especially during nesting and brood-rearing stages. Beginning in 2004, all new CRP contracts were required to perform some type of midcontract management. Included in the acceptable practices were discing and interseeding. During 2005-2006 we evaluated nesting and brood-rearing habitat used by radio-marked hen pheasants in areas where portions of CRP fields had been recently disced and interseeded with legumes. Pheasant hens selected managed portions of CRP fields for both nesting and brood-rearing. Forb cover and vertical cover were important variables associated with nest site placement. Hens with broods also selected vegetation with high forb composition. Discing and legume interseeding appeared to be an effective strategy for improving pheasant use of CRP fields.
Cladding Failure Modelling for Lead-Based Fast Reactors: A Review and Prospects

Lead-cooled fast reactors (LFRs) are considered one of the most promising technologies to meet the requirements introduced for advanced nuclear systems. LFRs have higher neutron doses, higher temperatures, higher burnup and an extremely corrosive environment. The failure studies of claddings play a vital role in improving the safety criteria of nuclear reactors and promoting research on advanced nuclear materials. This paper presented a comprehensive review of the extreme environment in LFRs based on the fuel performance analyses and transient analyses of reference LFRs. It provided a clear image of cladding failure, focusing on the underlying mechanisms, such as creep, rupture, fatigue, swelling, corrosion, etc., which are resulted from the motions of defects, the development of microcracks and accumulation of fission products to some extent. Some fundamental parameters and behavior models of Ferritic/Martensitic (F/M) steels and Austenitic stainless (AuS) steels were summarized in this paper. A guideline for cladding failure modelling was also provided to bridge the gap between fundamental material research and realistic demands for the application of LFRs.
SmartScore: From OMR to Auto Page Turning

Page turning is a classic problem that troubles pianists, however, with the proliferation of digital music scores and tablet computers, it presents an opportunity to solve the predicament of automatic page turning. This paper proposes a “turning music score by listening” solution. It utilizes a stave-aware Optical Music Recognition (OMR) and a Dynamic Time Warping-oriented (DTW) music sequence modeling method to extract the musical sequence from both paper and PDF music scores. Then, classic transcription and alignment methods enable real-time music score following. Finally, all these features are elegantly integrated into an application called SmartScore, which provides intelligent page turning services for performers.
Comprehensive autoantibody profiling in systemic autoimmunity by a highly-sensitive multiplex protein array

Comprehensive autoantibody evaluation is essential for the management of autoimmune disorders. However, conventional methods suffer from poor sensitivity, low throughput, or limited availability. Here, using a proteome-wide human cDNA library, we developed a novel multiplex protein assay (autoantibody array assay; A-Cube) covering 65 antigens of 43 autoantibodies that are associated with systemic sclerosis (SSc) and polymyositis/dermatomyositis (PM/DM). The performance of A-Cube was validated against immunoprecipitation and established enzyme-linked immunosorbent assay. Further, through an evaluation of serum samples from 357 SSc and 172 PM/DM patients, A-Cube meticulously illustrated a diverse autoantibody landscape in these diseases. The wide coverage and high sensitivity of A-Cube also allowed the overlap and correlation analysis between multiple autoantibodies. Lastly, reviewing the cases with distinct autoantibody profiles by A-Cube underscored the importance of thorough autoantibody detection. Together, these data highlighted the utility of A-Cube as well as the clinical relevance of autoantibody profiles in SSc and PM/DM.
A complex in Morse theory computing intersection homology

— Let X be a space with isolated conical singularities. The aim of this article is to establish, using anti-radial Morse functions on X, a combinatorial complex which computes the intersection homology of X. The complex constructed here, is generated by the smooth critical points of the Morse function and representatives of the de Rham cohomology (in low degree) of the link manifolds of the singularities of X. It can be seen as an analogue of the famous Thom-Smale complex for smooth Morse functions and singular homology on a compact manifold. The article also discusses the homotopy principle familiar in smooth Morse homology in this singular context. Résumé. — Dans cet article on associe à une fonction de Morse f anti-radiale sur un espace singulier X à singularités coniques un complexe généré par les points critiques de f et par certaines formes sur le link de la singularité. Ce complexe calcule de façon canonique l’homologie d’intersection. Également on discute le comportement de ce complexe par rapport aux homotopies. Le complexe construit dans cet article est un analogue du complexe de Thom-Smale sur une variété lisse pour une fonction de Morse lisse et l’homologie singulière.
Confidence Interval Estimation of the Conditional Reliability Function for Time Domain Data

The function of conditional reliability gives the probability of successfully implementing another operation following the successful implementation of a previous operation. The prediction of this function can help software developers in determining optimal release times. In this paper, the Maximum Likelihood Estimation (MLE) method is used to estimate the Non-Homogeneous Poisson Process Log-Logistic (NHPP LL) model’s parameters. The upper and the lower bounds of the parameters and conditional reliability function of time domain data are obtained. Real data application is conducted using the coefficient of multiple determination criteria and observed interval length to evaluate the performance of the NHPP LL model and the constructed confidence intervals, respectively. Our results encourage for more assessment of confidence intervals of other measures of reliability of the NHPP models.
Privacy Online: Perspectives on Privacy and Self-Disclosure in the Social Web

Communications and personal information that are posted online are usually accessible to a vast number of people. Yet when personal data exist online, they may be searched, reproduced and mined by advertisers, merchants, service providers or even stalkers. Many users know what may happen to their information, while at the same time they act as though their data are private or intimate. They expect their privacy will not be infringed while they willingly share personal information with the world via social network sites, blogs, and in online communities.The chapters collected by Trepte and Reinecke address questions arising from this disparity that has often been referred to as the privacy paradox. Works by renowned researchers from various disciplines including psychology, communication, sociology, and information science, offer new theoretical models on the functioning of online intimacy and public accessibility, and propose novel ideas on the how and why of online privacy. The contributing authors offer intriguing solutions for some of the most pressing issues and problems in the field of online privacy. They investigate how users abandon privacy to enhance social capital and to generate different kinds of benefits. They argue that trust and authenticity characterize the uses of social network sites. They explore how privacy needs affect users virtual identities. Ethical issues of privacy online are discussed as well as its gratifications and users concerns. The contributors of this volume focus on the privacy needs and behaviors of a variety of different groups of social media users such as young adults, older users, and genders. They also examine privacy in the context of particular online services such as social network sites, mobile internet access, online journalism, blogs, and micro-blogs.In sum, this book offers researchers and students working on issues related to internet communication not only a thorough and up-to-date treatment of online privacy and the social web. It also presents a glimpse of the future by exploring emergent issues concerning new technological applications and by suggesting theory-based research agendas that can guide inquiry beyond the current forms of social technologies.
Reconstructing a history of Spanish immigration in West Virginia: Implications for multicultural education

RECONSTRUCTING A HISTORY OF SPANISH IMMIGRANTS IN WEST VIRGINIA: IMPLICATIONS FOR MULTICULTURAL EDUCATION FEBRUARY 1999 THOMAS G. HIDALGO, B.S. WEST VIRGINIA UNIVERSITY M.S., SPRINGFIELD COLLEGE Ed.D. , UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professor Sonia Nieto Spain has had a significant influence on the Americas since Christopher Columbus landed at Hispanola in 1492, sailing in a Spanish ship with a Spanish crew. That event began a period of conquest that left Spain in control of much of Latin America and dominant in the territory that would become much of the southwestern and western United States. Much has been written about the conquistadors and explorers who came to the "New World," but after this period there is little mention of Spanish immigrants in the United States. This country experienced a period of mass immigration during the end of the 19th century and the first quarter of this century. Spaniards were among them, but accounts of this immigration rarely mention this fact. Several thousand Spaniards immigrated to West Virginia, drawn primarily by jobs in the coal mines. However, this story is virtually unknown because no one has documented it, like so much of America’s past that is ignored in the "official history" of the country. This study fills a gap in knowledge about Spaniards who came to West Virginia while addressing the broader question of who is included and who is excluded in our
Children’s Informal Learning in Mathematics through Parental Involvement with Play-Based Activities: A Nonformal Training Program

The present study examines the role of everyday play-based activities, planned and implemented by parents, on the development of children’s mathematical thinking at the age of up to 3 years old. Parents’ knowledge and beliefs were measured by using an online questionnaire, while a nonformal training program was developed and implemented with the participation of a group of parents using technological tools. The aim was to improve their knowledge on how to use relevant play-based activities and the understanding of their role. Results indicated that parents asked for the contribution of experts in order to acquire the relevant knowledge and skills and develop positive self-efficacy beliefs about their role. They found the training practical, through the use of digital tools, as well as useful for collaboration and communication. The shared examples by them indicated that the training has to concentrate further on the use of daily activities outside of the school-based context, which is more known to the parents.
ESTIMATION OF RELIABILITY IN MULTICOMPONENT STRESS-STRENGTH BASED ON GENERALIZED INVERTED EXPONENTIAL DISTRIBUTION -

In this paper, we are mainly interested in estimating multicomponent stress- strength reliability. The system is regarded as alive only if at least s out of k (s<k) strengths exceed the stress. The reliability of such a system is obtained when strength, stress variates are given by generalized inverted exponential distribution with different shape parameters and common scale parameter. The research methodology adopted here is to estimate the parameters by using maximum likelihood estimation. The reliability is estimated using the maximum likelihood method of estimation when samples are drawn from strength and stress distributions. The reliability estimators are compared asymptotically. The results of small sample comparison of the reliability estimates are made through Monte-Carlo simulation. The simulation results indicates that the average bias and average MSE are decreases as sample size increases for both methods of estimation in reliability. The length of the confidence interval is also decreases as the sample size increases and coverage probability is close to the nominal value in all sets of parameters considered here. By using real data sets we well illustrate the procedure.
Propionic acid enhances the virulence of Crohn ’ s disease-associated 2 adherent-invasive Escherichia coli

4 Authors 5 Michael J. Ormsby, Síle A. Johnson, Lynsey M. Meikle, Robert J. Goldstone, Anne McIntosh, 6 Hannah M. Wessel, Heather E. Hulme, Ceilidh C. McConnachie, James P. R. Connolly, Andrew J. 7 Roe, Eamonn Fitzgerald, Konstantinos Gerasimidis, Douglas Morrison, David G.E. Smith and Daniel 8 M. Wall 9 10 Affiliations 11 Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, Sir 12 Graeme Davies Building, University of Glasgow, Glasgow G12 8TA, United Kingdom 13 Institute of Biological Chemistry, Biophysics and Bioengineering, Heriot-Watt University, Edinburgh, 14 EH14 4AS, United Kingdom 15 Human Nutrition, School of Medicine, College of Medical Veterinary and Life Sciences, University of 16 Glasgow, Glasgow Royal Infirmary, Glasgow G31 2ER, United Kingdom. 17 Scottish Universities Environmental Research Centre, University of Glasgow, Glasgow G75 0QF, United 18 Kingdom. 19 20 *Corresponding author email: Donal.Wall@glasgow.ac. uk 21 22 *Corresponding author address: 23 Dr. Daniel (Dónal) M. Wall 24 Institute of Infection, Immunity and Inflammation 25 College of Medical, Veterinary and Life Sciences 26 Sir Graeme Davies Building 27 University of Glasgow 28 120 University Place 29 Glasgow G12 8TA 30 31 32 33
Associations of Positive and Negative Perceptions of Outdoor Artificial Light at Night with Nighttime Outdoor Behaviors and Health: Self-Reported Data Analyses on Urban and Suburban Residents in Japan

Nighttime light guarantees outdoor work, leisure, and other activities, and its convenience and security greatly improve the quality of human life. However, the negative effects of outdoor artificial light at night (ALAN) on humans are also being proven. This study aimed to examine the relationships between outdoor nighttime lighting and human behaviors (i.e., outdoor nighttime behaviors such as jogging and meeting friends) and health (i.e., physical and mental health, including sleep quality and stress) through how individuals feel about outdoor ALAN (i.e., ALAN perceptions) in their living environment. Two online questionnaire surveys were conducted among residents of Tokyo (Survey 1, N = 2000) and Tsukuba City (Survey 2, N = 500), Japan. Structural equation modeling results show that both positive and negative ALAN perceptions increase nighttime outdoor behavior in both surveys. In Survey 1, this association led to a deterioration in sleep quality and physical and mental health. The current findings contribute to understanding the controversial relationships between the pros and cons of nighttime light perceptions and behaviors and health. They also contribute to designing better nighttime lighting in outdoor public and private spaces with relevant restrictions by balancing the benefits and harms of outdoor ALAN.
Individual differences in frequency and saliency of speech-accompanying gestures : The role of cognitive abilities and empathy

The present study concerns individual differences in gesture production. We used correlational and multiple regression analyses to examine the relationship between individuals’ cognitive abilities and empathy levels and their gesture frequency and saliency. We chose predictor variables according to experimental evidence of the functions of gesture in speech production and communication. We examined three types of gestures: representational gestures, conduit gestures, and palm-revealing gestures. Higher frequency of representational gestures was related to poorer visual and spatial working memory, spatial transformation ability, and conceptualization ability; higher frequency of conduit gestures was related to poorer visual working memory, conceptualization ability, and higher levels of empathy; and higher frequency of palm-revealing gestures was related to higher levels of empathy. The saliency of all gestures was positively related to level of empathy. These results demonstrate that cognitive abilities and empathy levels are related to individual differences in gesture frequency and saliency.
A Case Study of Machine Learning Comparisons for Predicting Apartment Prices in Astana

Astana, the capital city of Kazakhstan, has undergone rapid urbanization and economic development in recent years. With this growth comes an increased demand for housing, leading to the need for more accurate and efficient methods for predicting house prices. In this paper, we aim to compare the performance of five popular regression algorithms for predicting house prices in Astana: linear regression, decision trees, random forest, XGBoosting, and CatBoosting. We collected a dataset of housing prices in Astana, including information on the location, size, age, and other relevant features of the properties. We then applied each of the six algorithms to the dataset and evaluated their performance using various metrics such as mean squared error (MSE), mean absolute error (MAE), and R2 score. Our results showed that XGBoosting and CatBoosting algorithms outperformed the other methods in terms of prediction score but it overfits a data. The decision trees and random forest algorithms performed well on training set but there did not generalize the data. Linear regression performed the lowest score comparing with other algorithms, but it generalizes the data. This study provides valuable insights for policymakers and real estate developers in Astana, as well as for researchers and practitioners in the field of machine learning. Our findings suggest that advanced machine learning techniques, such as XGBoosting and CatBoosting, can significantly improve the accuracy of house price prediction and aid in informed decision-making.