Miracle-dz/UniFAR-FTUs huggingface dataset:
SPIRITUAL PSYCHOLOGY AND COUNSELING

A great deal of importance has recently been given to studies dealing with the spiritual aspect of psychological theories and approaches; the aim has been to shed light on the spiritual aspect of humanity through these conducted researches and compilations. The current study aims to explain the perspective of transactional analysis on spirituality in terms of the theory of interpersonal relations and the extent of spirituality on the understanding of transactional analysis. Mainly the views of Berne and the researchers following in his footsteps have been taken into consideration. The experience of self-transcendence has been referenced by elaborating on the concept of autonomy, which occurs in spirituality through transactional analysis. By forming a framework over the transactional analysis view on spirituality, a perspective is offered towards transactional analysis patterns and spirituality along the axis of transaction and ego states. Family prisms, which are especially effective in the process of working with families in the understanding of transactional analysis, are clarified along the axis of spirituality. In addition, brief and summary information is presented about the pilgrim model, which guides transactional analysts regarding the spiritual elements a client brings during the consultation process. This study aims to show that the understanding of transactional analysis is a theory of relations and that therapists do not exclude spirituality from the counseling process but reference spirituality, particularly when focusing on ego states, intimacy, and autonomy, although not on all the concepts discussed along the understanding axis. For this purpose, the study aims to give the point of view on spirituality in transactional analysis theory by compiling the research that has been performed.
Industrial Control Intrusion Detection Approach Based on Multiclassification GoogLeNet-LSTM Model

Intrusion detection is essential for ensuring the security of industrial control systems. However, conventional intrusion detection approaches are unable to cope with the complexity and ever-changing nature of industrial intrusion attacks. In this study, we propose an industrial control intrusion detection approach based on a combined deep learning model for communication processes that use the Modbus protocol. Initially, the network packets are classified as carrying information and noncarrying information based on key fields according to the communication protocol used. Next, a template comparison approach is employed to detect the network packets that do not carry any information. Furthermore, an approach based on a GoogLeNet-long short-term memory model is used to detect the network packets that do carry information. This approach involves network packet sequence construction, feature extraction, and time-series level detection. Subsequently, the detected intrusions are classified into multiple categories through a Softmax classifier. A gas pipeline dataset of the Modbus protocol is used to evaluate the proposed approach and compare it with existing strategies. The accuracy, false-positive rate, and miss rate are 97.56%, 2.42%, and 2.51%, respectively, thus confirming that the proposed approach is suitable for intrusion detection in industrial control systems.
Infra-humanisation of an in-group and out-group and its impact on explaining discrimination between groups based on socio-economic status

This study investigated whether people of a high socio-economic status (SES) infra-humanise people of a SES. Infra-humanisation is a process whereby people consider their in-group as fully human and out-groups as less human and more animal-like, and on this basis attribute secondary emotions to their in-group but not to the out-group (Leyens et al., 2000). An Implicit Association Task was used in order to investigate implicit beliefs that people from a high SES feel that people from a low SES group are not fully human and therefore experience fewer secondary emotions. I had hypothesized that subjects from a high SES group would categorise more words correctly in the matched (i.e. high SES group and secondary emotions) versus the mismatched (i.e. low SES with secondary emotions) task. The results from the study however demonstrate that while there is an effect for IAT score it does not show that infra-humanisation occurs, because more secondary emotions are given to the out-group as opposed to the in-group. Race was not found to be a variable that had an impact on the IAT score. Research found a low moderate correlation between the quality of contact and the IAT score. This is important research to do in South Africa, as it is a country which has great inequality based on SES and because of its history the class inequality for the most part reflects the racial divide.
Charged up? Preferences for Electric Vehicle Charging and Implications for Charging Infrastructure Planning

This study assesses respondents’ preferences for privately-used passenger electric vehicle (EV) charging with respect to the six attributes: (1) place of charging; (2) charging duration (full charge); (3) charging technology; (4) waiting time for charging spot to become available; (5) share of renewables in the electricity mix used for vehicle charging; and (6) total cost for the whole bundle of attributes per month. Due to the low number of current EV users in Germany, investigating consumers’ EV charging infrastructure preferences and their willingness to pay (WTP) for it based on real usage data is challenging. In addition, the results would not be directly transferable to the development of sound business cases since the sample size is too small. Therefore, we gathered data through a Discrete Choice Experiment (DCE) conducted in Germany (N = 4,101). Our DCE measures the preferences for certain attributes of EV charging infrastructures indirectly by confronting participants with hypothetical choice bundles. We analyze the data using conditional logit models, including fixed effects at the participant level, in order to gain actionable insights into the expected charging behavior of current and future EV drivers. We predict tendencies of consumer behavior and show that locational and time attributes are highly appreciated. Respondents are willing to pay, on average, around 22 €/month more for charging at home rather than at work and 46.26 €/month more for charging at home rather than on the roadside. For a reduction in charging time from 8 h to 7 h, respondents are willing to pay around 8 €/month; whereas from 8 h to 10 min, respondents are willing to pay around 70 €/month for all monthly charging processes. We also find WTP of five specific consumer categories (environmentalists, EV owners, EV experts, at-home charger, and home owners). Our results could be useful for charging point operators.
15.7 THUNDERSTORM CHARACTERIZATION DERIVED FROM CLOUD TO GROUND LIGHTNING FLASH DATA BASED ON INTERCOMPARISONS OF HOVMÖLLER DIAGRAMS AND SPATIAL DENSITY DATA

This paper demonstrates the potential utility of examining lightning flash spatial and temporal density variations as a means of evaluating the effects of thunderstorms on air traffic operations, particularly as they relate to an arbitrarily selected region in the midwestern part of the contiguous United States (CONUS). The region of interest is centered on Kentucky, encompassing a latitude range of 35-41o N and a longitude range of 79-92o W. This region was arbitrarily selected as being representative of an important aviation corridor within the CONUS that is subject to higher delays due to severe weather. The region is centered on Ft. Knox and Louisville, KY and covers, for example, Cleveland and Cincinnati, OH, St. Louis, MO and Memphis, TN; its northern border is just south of Chicago, IL.
The Effects of Information Quality on Trust in Vendor and Seller Uncertainty on Online Shopping-Decision

This study examines how information quality of online reviews influences trust in vendor and seller uncertainty, respectively, which subsequently influence repeat purchase intention for online shopping decision-making of undergraduate students in China. The findings of this study indicate that information quality positively influence trust in vendor and negatively influence seller uncertainty, whereas trust in vendor has a positive effect on repeat purchase intention and seller uncertainty has a negative effect on repeat purchase intention.
Prevalence and Factors Assoc iated with Depression among Rural Communities in Negeri Sembilan, Peninsular Malaysia

Aims: To determine the prevalence of depression and factors associated with depression among rural communities in Negeri Sembilan, Peninsular Malaysia. Study Design:A cross-sectional study with simple random sampling. Place and Duration of Study:Port Dickson, Negeri Sembilan, between June 2013 and July 2013. Methodology: A total of 223 respondents participated in this study. Data were collected by face-to-face interview using a structured questionnaire. The Patient Health Questionnaire (PHQ -9) was used as a screening tool to detect the presence of depression. Results:The prevalence of depression among the rural communities was 7.6%. Non bumiputra (OR = 3.90; 95% CI: 1.25, 12.18), low education level (OR = 2.65; 95% CI: 1.18, 5.94), smoking (OR = 4.69; 95% CI: 1.69, 13.05), no alcohol consumption (OR = 8.90; 95% CI: 1.71, 46.29), practice healthy diet (OR = 4.83; 95% CI: 1.07, 21.18) and did not exercise regularly (OR = 3.07; 95% CI: 1.40, 6.73) were factors associated with
DEMO Design Point Studies

To allow coherent conceptual design activities for a demonstration fusion power plant (DEMO), a self-consistent design point must first be developed. The DEMO design point is a set of parameters characterising the key features of a DEMO power plant on which evaluation of different systems can be based with confidence that there are no significant conflicts between those systems. System codes representing the full plant by capturing the interactions between (usually relatively simple) models of all the important plant subsystems are used to identify design points based on assumptions about plasma performance and technology. The purpose of using a systems code is to identify potential solution spaces without having to carry out complex analysis at every point. The EU DEMO strategy currently considers two possible operating scenarios: a conservative pulsed design, using near-ITER technology and plasma performance, termed DEMO1; and an optimistic, higher-performance, steady-state option (DEMO2). This contribution presents the work being carried out to develop these design points, including development of the systems code models. In each case the variables are subject to change as more DEMO-relevant models and experimental data become available and technological knowledge improves. However, these operating points are intended to provide a well-justified and stable foundation on which to base wider design evaluation work.
RICE HUSK AND SCRAP TIRES CO-PROCESSING AND REVERSE LOGISTICS IN CEMENT MANUFACTURING

The purpose of this article is to describe the case of a cement manufacturer that implemented and consolidated the co-processing of rice husk and scrap tires in clinker kilns, supported by reverse logistics practices. The research method was a case study. The company being studied already used rice husk as an alternative fuel and received an offer from the tire industry for burning scrap tires in clinker kilns. To take advantage of this waste, the company integrated direct and reverse flows of raw materials, fuels and industrial waste, reducing transportation costs by more than 30%. Combined co-processing partially replaced fossil fuels sourced from the oil industry. The most significant environmental gain was a reduction in the burning of about 10,000 tonnes of fossil fuel which previously generated approximately 30,000 tonnes of CO2 per year.
THE CAPACITY AND PRECISION OF VISUAL WORKING MEMORY FOR OBJECTS AND ENSEMBLES

visual impressions. Nature Communications, 7, 13186. Working Memory for Objects and Ensembles 753 Yantis, S. , & Jonides, J. (1984). Abrupt visual onsets and selective attention: Evidence from visual search. Journal o f Experimental Psychology: Human Perception and Performance, 10, 601-621. Zhang, W. , & Luck, S. J. (2008). Discrete fixed-resolution representations in visual working memory. Nature, 452, 233-235. Yuri A. Markov — research assistant, Laboratory for Cognitive Research, National Research University Higher School of Economics. Research area: cognitive psychology, visual attention, visual search, fea­ ture binding, visual working memory E-mail: ymarkov@hse.ru Natalia A. Tiurina — research fellow, Laboratory for Cognitive Research, National Research University Higher School of Economics, Ph.D. Research area: cognitive psychology, visual attention, visual search, memory, psychometrics E-mail: ntyurina@hse.ru Yulia M. Stakina — expert, Laboratory for Cognitive Research, National Research University Higher School of Economics, Ph.D. Research area: attention, individual differences E-mail: stakina@hse.ru Igor S. Utochkin — head, Laboratory for Cognitive Research; associate professor, Faculty of Social Sciences, School of Psychology, National Research University Higher School of Economics, Ph.D. Research area: visual attention, visual search, object and scene percep­ tion, ensemble summary statistics, visual working memory E-mail: isutochkin@inbox.ru 754 Ю. А. Марков, Н. А. Тюрина, Ю. М. Стакина, И. С. Уточкин Объем и точность зрительной рабочей памяти на объекты и ансамбли Ю . А. Марков", Н. А. Тюрина", Ю . М . Стакина", И . С . Уточкин" a Национальный исследовательский университет «Высшая школа экономики», 101000, Россия, Москва, ул. Мясницкая, д. 20
Electrical and Spectroscopic Properties of SiC Detectors

In this paper we compare suitability of three different semi-insulating bulk silicon carbide (SiC) materials for fabrication of radiation detectors. We prepared planar sensors with various metal contact combination and characterized detector quality by the alpha spectroscopy and I-V characteristic measurements. We observed that 4H-SiC material from the II-VI company is not suitable for radiation detector fabrication due to high concentration of vanadium doping. We also present a poor charge collection efficiency of the 4H-SiC Norstel material due to high concentration of residual impurities and we evaluated low mobility-lifetime product µ<inf>e</inf>τ<inf>e</inf><sup>alpha</sup> = 3.5×10<sup>-7</sup> cm<sup>2</sup>/V from the alpha spectroscopy. We demonstrate that the 4H-SiC material from the Cree company is the best candidate for the production of radiation detectors. We evaluated µ<inf>e</inf> τ<inf>e</inf><sup>alpha</sup> = 5.4×10<sup>-6</sup> cm<sup>2</sup>/V using AuTi/SiC/TiAu contact structure.
Multiplex PCR for Screening of Microdeletions on the Y Chromosome in Male Infertility Patients

To develop a multiplex PCR protocol, suitable for routine screening of microdeletions on the Y chromosome in male infertility patients undergoing intracytoplasmic sperm injection (ICSI). Two multiplex sets, each consisting of five primer pairs, were established. 26 azoospermic men and 61 oligozoospermic men undergoing ICSI in the center were screened for microdeletions of Y chromosome. One patient with AZFc/DAZ microdeletions was found in 26 azoospermic men, and four patients with AZFc/DAZ microdeletions were found in 61 oligozoospermic men. [Conclusion] The multiplex PCR protocol established in this study is an easy and reliable method for detection of microdeletions on the Y chromosome.
3 per cent-accurate predictions for the clustering of dark matter, haloes, and subhaloes, over a wide range of cosmologies and scales

Predicting the spatial distribution of objects as a function of cosmology is an essential ingredient for the exploitation of future galaxy surveys. In this paper we show that a specially-designed suite of gravity-only simulations together with cosmology-rescaling algorithms can provide the clustering of dark matter, haloes, and subhaloes with high precision. Specifically, with only 3 $N$-body simulations we obtain the power spectrum of dark matter at $z=0$ and $z=1$ to better than 3\% precision for essentially all currently viable values of 8 cosmological parameters, including massive neutrinos and dynamical dark energy, and over the whole range of scales explored, 0.03 < $k/h^{-1}Mpc$ < 5. This precision holds at the same level for mass-selected haloes and for subhaloes selected according to their peak maximum circular velocity. As an initial application of these predictions, we successfully constrain $\Omega_{\rm m}$, $\sigma_8$, and the scatter in subhalo-abundance-matching employing the projected correlation function of mock SDSS galaxies.
Probabilistic K-means Clustering via Nonlinear Programming

K-means is a classical clustering algorithm with wide applications. However, soft K-means, or fuzzy c-means at m=1, remains unsolved since 1981. To address this challenging open problem, we propose a novel clustering model, i.e. Probabilistic K-Means (PKM), which is also a nonlinear programming model constrained on linear equalities and linear inequalities. In theory, we can solve the model by active gradient projection, while inefficiently. Thus, we further propose maximum-step active gradient projection and fast maximum-step active gradient projection to solve it more efficiently. By experiments, we evaluate the performance of PKM and how well the proposed methods solve it in five aspects: initialization robustness, clustering performance, descending stability, iteration number, and convergence speed.
Assignment-based Path Choice Estimation for Metro Systems Using Smart Card Data

Urban rail services are the principal means of public transportation in many cities. To understand the crowding patterns and develop efficient operation strategies in the system, obtaining path choices is important. This paper proposed an assignment-based path choice estimation framework using automated fare collection (AFC) data. The framework captures the inherent correlation of crowding among stations, as well as the interaction between path choice and left behind. The path choice estimation is formulated as an optimization problem. The original problem is intractable because of a non-analytical constraint and a non-linear equation constraint. A solution procedure is proposed to decompose the original problem into three tractable sub-problems, which can be solved efficiently. The model is validated using both synthetic data and real-world AFC data in Hong Kong Mass Transit Railway (MTR) system. The synthetic data test validates the model's effectiveness in estimating path choice parameters, which can outperform the purely simulation-based optimization methods in both accuracy and efficiency. The test results using actual data show that the estimated path shares are more reasonable than survey-derived path shares and uniform path shares. Model robustness in terms of different initial values and different case study dates are also verified.
AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs

Recent breakthroughs in Deep Neural Networks (DNNs) have fueled a growing demand for domain-specific hardware accelerators (i.e., DNN chips). However, designing DNN chips is non-trivial because: (1) mainstream DNNs have millions of parameters and billions of operations; (2) the design space is large due to numerous design choices of dataflows, processing elements, memory hierarchy, etc.; and (3) there is an algorithm/hardware co-design need for the same DNN functionality to have a different decomposition that would require different hardware IPs and thus correspond to dramatically different performance/energy/area tradeoffs. Therefore, DNN chips often take months to years to design and require a large team of cross-disciplinary experts. To enable fast and effective DNN chip design, we propose AutoDNNchip - a DNN chip generator that can automatically produce both FPGA- and ASIC-based DNN chip implementation (i.e., synthesizable RTL code with optimized algorithm-to-hardware mapping) from DNNs developed by machine learning frameworks (e.g., PyTorch) for a designated application and dataset without humans in the loop. Specifically, AutoDNNchip consists of 2 integrated enablers: (1) a Chip Predictor, which can accurately and efficiently predict a DNN accelerator's energy, throughput, latency, and area based on the DNN model parameters, hardware configurations, technology-based IPs, and platform constraints; and (2) a Chip Builder, which can automatically explore the design space of DNN chips (including IP selections, block configurations, resource balancing, etc.), optimize chip designs via the Chip Predictor, and then generate synthesizable RTL code with optimized dataflows to achieve the target design metrics. Experimental results show that our Chip Predictor's predicted performance differs from real-measured ones by <10% when validated using 15 DNN models and 4 platforms (edge-FPGA/TPU/GPU and ASIC). Furthermore, DNN accelerators generated by our AutoDNNchip can achieve better (up to 3.86X improvement) performance than that of expert-crafted state-of-the-art FPGA- and ASIC-based accelerators, showing the effectiveness of AutoDNNchip. Our open-source code can be found at https://github.com/RICE-EIC/AutoDNNchip.git.
Seismic horizon detection with neural networks

Over the last few years, Convolutional Neural Networks (CNNs) were successfully adopted in numerous domains to solve various image-related tasks, ranging from simple classification to fine borders annotation. Tracking seismic horizons is no different, and there are a lot of papers proposing the usage of such models to avoid time-consuming hand-picking. Unfortunately, most of them are (i) either trained on synthetic data, which can't fully represent the complexity of subterranean structures, (ii) trained and tested on the same cube, or (iii) lack reproducibility and precise descriptions of the model-building process. With all that in mind, the main contribution of this paper is an open-sourced research of applying binary segmentation approach to the task of horizon detection on multiple real seismic cubes with a focus on inter-cube generalization of the predictive model.
Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs

We propose a novel method for fact-checking on knowledge graphs based on debate dynamics. The underlying idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to justify the fact being true (thesis) or the fact being false (antithesis), respectively. Based on these arguments, a binary classifier, referred to as the judge, decides whether the fact is true or false. The two agents can be considered as sparse feature extractors that present interpretable evidence for either the thesis or the antithesis. In contrast to black-box methods, the arguments enable the user to gain an understanding for the decision of the judge. Moreover, our method allows for interactive reasoning on knowledge graphs where the users can raise additional arguments or evaluate the debate taking common sense reasoning and external information into account. Such interactive systems can increase the acceptance of various AI applications based on knowledge graphs and can further lead to higher efficiency, robustness, and fairness.