Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the use of ray tracing for future games

Rasterization hardware and computer games have always been tightly connected: The hardware implementation of rasterization has made complex interactive 3D games possible while requirements for future games drive the development of increasingly parallel GPUs and CPUs. Interestingly, this development - together with important algorithmic improvements - also enabled ray tracing to achieve realtime performance recently.In this paper we explore the opportunities offered by ray tracing based game technology in the context of current and expected future performance levels. In particular, we are interested in simulation-based graphics that avoids pre-computations and thus enables the interactive production of advanced visual effects and increased realism necessary for future games. In this context we analyze the advantages of ray tracing and demonstrate first results from several ray tracing based game projects. We also discuss ray tracing API issues and present recent developments that support interactions and dynamic scene content. We end with an outlook on the different options for hardware acceleration of ray tracing.
Estimating the Impacts of Special Events on an Economy

Special events are typically regarded as major generators of economic activity and jobs. Despite its continued use in event assessment, Input-Output (I-O) analysis has been rejected in other areas of economic impact evaluation in favor of more rigorous evaluation techniques that recognize resource constraints in the economy and interindustry effects of demand shocks. This article discusses traditional and alternative perspectives on event assessment and the assumptions on which each approach are based. Illustrative results are then provided from a study that contrasted the economic impacts from a selected event analyzed by use of a traditional I-O approach, with estimates provided through the use of a more comprehensive computable general equilibrium (CGE) model. Issues involved in the wider use of CGE models for event assessment are discussed together with the need for further development in event evaluation techniques.
An improved method of constructing binned luminosity functions

We show that binned differential luminosity functions constructed using the 1/Va method have a significant systematic error for objects close to their parent sample’s flux limit(s). This is particularly noticeable when luminosity functions are produced for a number of different redshift ranges as is common in the study of AGN or galaxy evolution. We present a simple method of constructing a binned luminosity function which overcomes this problem and has a number of other advantages over the traditional 1/Va method. We also describe a practical method for comparing binned and model luminosity functions, by calculating the expectation values of the binned luminosity function from the model. Binned luminosity functions produced by the two methods are compared for simulated data and for the Large Bright QSO Survey (LBQS). It is shown that the 1/Va method produces a very misleading picture of evolution in the LBQS. The binned luminosity function of the LBQS is then compared to a model two power law luminosity function undergoing pure luminosity evolution from Boyle et al. (1991). The comparison is made using a model luminosity function averaged over each redshift shell, and using the expectation values for the binned luminosity function calculated from the model. The luminosity function averaged in each redshift shell gives a misleading impression that the model over predicts the number of QSOs at low luminosity even when model and data are consistent. The expectation values show that there are significant differences between model and data: the model overpredicts the number of low luminosity sources at both low and high redshift. The luminosity function does not appear to steepen relative to the model as redshift increases.
Location prediction for large scale urban vehicular mobility

Knowledge of where vehicles will be in near future helps users in daily planning, traffic monitors in vehicles scheduling, advertisers in fixed point advertising, and especially helps in communication network source provisioning. In this paper, we analyze the predictability of taxi mobility based on their locations and time period records and we present a prediction method of taxis for their next locations in 15 seconds using Markov predictor. The historical location trace of each taxi is used to train the transition probability matrix of next location for our predictor, and we use 3 different scenarios to predict. Based on records from over 2,000 taxis in Shanghai, and over 14,000 taxis in Beijing, we are able to predict the next vehicular location with an accuracy of 82%.
P2P Traffic Classification Method Based on the PNN

A method of classifying the P2P traffic based on the Probabilistic Neural Network is proposed. Firstly, we utilize "WinPcap" to capture the network packets and then the core characteristics of the obtained data are analyzed with the help of the tool. Based on the above work, the P2P traffic classification system was realized. It makes full use of the advantage of the PNN which has a high convergence rate and strong classification ability. Meanwhile, the complexity of the system is reduced efficiently. The verification results show that the scheme is better than others in the flexibility, adaptability and accuracy.
Automatic discovery of cell types and microcircuitry from neural connectomics

Neural connectomics has begun producing massive amounts of data, necessitating new analysis methods to discover the biological and computational structure. It has long been assumed that discovering neuron types and their relation to microcircuitry is crucial to understanding neural function. Here we developed a non-parametric Bayesian technique that identifies neuron types and microcircuitry patterns in connectomics data. It combines the information traditionally used by biologists in a principled and probabilistically coherent manner, including connectivity, cell body location, and the spatial distribution of synapses. We show that the approach recovers known neuron types in the retina and enables predictions of connectivity, better than simpler algorithms. It also can reveal interesting structure in the nervous system of Caenorhabditis elegans and an old man-made microprocessor. Our approach extracts structural meaning from connectomics, enabling new approaches of automatically deriving anatomical insights from these emerging datasets. DOI: http://dx.doi. org/10.7554/eLife.04250. 001
The observed behavioural effects of a universal social-emotional learning program in an elementary school setting

In this thesis, I identified and addressed the need for social-emotional skill development in schools by creating and implementing a tailored social-emotional learning program in an inner-city, faith-based school. Evaluation of the program was done by using quantitative and qualitative methods through a questionnaire, survey, and interviews. This thesis aimed to answer the question: In what ways does implementing the prosocial, leadership and self-regulation-based ROAR program affect elementary school student behaviour? The program was found to have positive effects on the school community across various domains including community building, leadership and social-emotional awareness.
Automatic Recognition and Classification of Cerebral Microemboli in Ultrasound Images 1

The aim of this work was the definition of a method devoted to the automated recognition of differ- ent composition of cerebral microemboli. The developed diagnostic procedure made use of a feature-based analysis of ultrasonographic images containing the characteristic microembolic signals. The images were acquired with a transcranial Doppler and classified using a hierarchical neural network. The proposed procedure was tested on clinical cases selected by expert neurologists for their relevance, and experimental results showed its reliability.
State of Art Method and Advanced Computerized Technology For Assessment of Mental States

Objective: Understanding states of consciousness is a major scientific challenge of our times and it requires the exploration and application of new methods, technologies to assess and to evaluate mental states, which are further developed for clinical applications. Methods: Exploration of new state-of-the-art methods and computerized technologies to assess neural pathways and evaluate mental states. MRIT scan of 30 subjects with the purpose to evaluate and qualify the neural signaling channels (quantitative and qualitative data for analysis of mental states in real-time). Results: Application of state-of - the-art method and advanced computerized technology allows to quickly assess the functional network of mental states that refers to the awareness, vitality, somatic perception, ego, self-esteem, decision making, involuntary movements, and other chrono-biological reactions. The method was initiated by the research of children’s health conditions born by Preterm Caesarean Section (PCS). In all researched PCS cases we revealed the retardation of innate and adaptive immune signaling that caused lifelong adverse effects on children’ vitality, awareness, self-perception, decision making and other major capacities and abilities of survival. The cases were compared with MRI or other conventional results. Further development allowed us to investigate the mental states of autistic children, pre stroke and after stroke incidences, depression types, Alzheimer disorders, etc. New method introduces 8 neural signatures (pathways) at three chrono-biological levels representing neural signaling at optimum level (synchronization of neural functions in real time), at retarded level (vector to past is associated with retardation of neural control) and accelerated level (vector to future is associated with confused mental states). Conclusion: The state-of-the-art method and technology is based upon best of MRIT, Doppler Effect of sound waves, is easy to use, non-invasive, allows to study and understand networking and dynamics of different mental states, cognitive processes as well as it helps experts to search for practical optimized performance for each individual case.
The Impact of Hopscotch Game towards the Growth of Kinesthetic Intelligence on 3-4 Year Old Children

This research aims to understand the impact of traditional game hopscotch towards the growth of kinesthetic intelligence on children enrolled at PAUD ALIF Kalinegoro, Kecamatan Mertoyudan, Kabupaten Magelang. The research employs experimental research design with One Group Pre-Postest Design. The experiment is conducted to one group without comparison group by using initial and final measurement. The variables in this research include children within 3-4 age group, amounting to 15 children enrolled at PAUD ALIF Kecamatan Mertoyudan Kabupaten Magelang. The samples are acquired using Total Sampling technique. The method for data collection is by using observation sheet and work method. The data analysis technique used is Wilcoxon Signed Rank Test with the help of computer program SPSS for Windows version 19.00. The results find asymp.sig. column (2-tailed)/asymptotic significance for two-tailed test is 0,001. Since the studied case is one-tailed test, the acquired probability is 0.001 / 2 = 0.0005. Based on this data, the probability is that 0.05 (0.0005<0.05), in which Ho is rejected, or in other words, there is a difference of kinesthetic intelligence on the initial and final measurement after the implementation of learning activity through traditional game hopscotch.
Evidence-based guidelines for the pharmacological treatment of anxiety disorders: recommendations from the British Association for Psychopharmacology

These British Association for Psychopharmacology guidelines cover the range and aims of treatment for anxiety disorders. They are based explicitly on the available evidence and are presented as recommendations to aid clinical decision making in primary and secondary medical care. They may also serve as a source of information for patients and their carers. The recommendations are presented together with a more detailed review of the available evidence. A consensus meeting involving experts in anxiety disorders reviewed the main subject areas and considered the strength of evidence and its clinical implications. The guidelines were constructed after extensive feedback from participants and interested parties. The strength of supporting evidence for recommendations was rated. The guidelines cover the diagnosis of anxiety disorders and key steps in clinical management, including acute treatment, relapse prevention and approaches for patients who do not respond to first-line treatments.
Wave equations for graphs and the edge-based Laplacian

In this paper we develop a wave equation for graphs that has many of the properties of the classical Laplacian wave equation. This wave equation is based on a type of graph Laplacian we call the edge-based Laplacian. We give some applications of this wave equation to eigenvalue/geometry inequalities on graphs.
N-gram Opcode Analysis for Android Malware Detection

Android malware has been on the rise in recent years due to the increasing popularity of Android and the proliferation of third party application markets. Emerging Android malware families are increasingly adopting sophisticated detection avoidance techniques and this calls for more effective approaches for Android malware detection. Hence, in this paper we present and evaluate an n-gram opcode features based approach that utilizes machine learning to identify and categorize Android malware. This approach enables automated feature discovery without relying on prior expert or domain knowledge for pre-determined features. Furthermore, by using a data segmentation technique for feature selection, our analysis is able to scale up to 10-gram opcodes. Our experiments on a dataset of 2520 samples showed an f-measure of 98% using the n-gram opcode based approach. We also provide empirical findings that illustrate factors that have probable impact on the overall n-gram opcodes performance trends.
On the Impact of Entropy Estimation on Transcriptional Regulatory Network Inference Based on Mutual Information

The reverse engineering of transcription regulatory networks from expression data is gaining large interest in the bioinformatics community. An important family of inference techniques is represented by algorithms based on information theoretic measures which rely on the computation of pairwise mutual information. This paper aims to study the impact of the entropy estimator on the quality of the inferred networks. This is done by means of a comprehensive study which takes into consideration three state-of-the-art mutual information algorithms: ARACNE, CLR, and MRNET. Two different setups are considered in this work. The first one considers a set of 12 synthetically generated datasets to compare 8 different entropy estimators and three network inference algorithms. The two methods emerging as the most accurate ones from the first set of experiments are the MRNET method combined with the newly applied Spearman correlation and the CLR method combined with the Pearson correlation. The validation of these two techniques is then carried out on a set of 10 public domain microarray datasets measuring the transcriptional regulatory activity in the yeast organism.
Constrained Global Optimization for Estimating Molecular Structure from Atomic Distances

Finding optimal three-dimensional molecular configurations based on a limited amount of experimental and/or theoretical data requires efficient nonlinear optimization algorithms. Optimization methods must be able to find atomic configurations that are close to the absolute, or global, minimum error and also satisfy known physical constraints such as minimum separation distances between atoms (based on van der Waals interactions). The most difficult obstacles in these types of problems are that 1) using a limited amount of input data leads to many possible local optima and 2) introducing physical constraints, such as minimum separation distances, helps to limit the search space but often makes convergence to a global minimum more difficult. We introduce a constrained global optimization algorithm that is robust and efficient in yielding near-optimal three-dimensional configurations that are guaranteed to satisfy known separation constraints. The algorithm uses an atom-based approach that reduces the dimensionality and allows for tractable enforcement of constraints while maintaining good global convergence properties. We evaluate the new optimization algorithm using synthetic data from the yeast phenylalanine tRNA and several proteins, all with known crystal structure taken from the Protein Data Bank. We compare the results to commonly applied optimization methods, such as distance geometry, simulated annealing, continuation, and smoothing. We show that compared to other optimization approaches, our algorithm is able combine sparse input data with physical constraints in an efficient manner to yield structures with lower root mean squared deviation.
The optimal event-based control of parachute opening problem II: A case study

We propose an finite time optimal event-based controller associated with an optimized strategy of parachute air drop in a rescue mission in order to minimize the drop interval to ensure a fast and accurate landing while a safe landing speed must be guaranteed. The idea is to determine an optimal instant to trigger the event-based controller. The design takes several steps to simplify the optimization via the tools of integration, penalty function and steepest descent algorithm. The result shows that the proposed controller yields the best performance among the non-optimized ones.
Circumpolar assessment of permafrost C quality and its vulnerability over time using long‐term incubation data

High‐latitude ecosystems store approximately 1700 Pg of soil carbon (C), which is twice as much C as is currently contained in the atmosphere. Permafrost thaw and subsequent microbial decomposition of permafrost organic matter could add large amounts of C to the atmosphere, thereby influencing the global C cycle. The rates at which C is being released from the permafrost zone at different soil depths and across different physiographic regions are poorly understood but crucial in understanding future changes in permafrost C storage with climate change. We assessed the inherent decomposability of C from the permafrost zone by assembling a database of long‐term (>1 year) aerobic soil incubations from 121 individual samples from 23 high‐latitude ecosystems located across the northern circumpolar permafrost zone. Using a three‐pool (i.e., fast, slow and passive) decomposition model, we estimated pool sizes for C fractions with different turnover times and their inherent decomposition rates using a reference temperature of 5 °C. Fast cycling C accounted for less than 5% of all C in both organic and mineral soils whereas the pool size of slow cycling C increased with C : N. Turnover time at 5 °C of fast cycling C typically was below 1 year, between 5 and 15 years for slow turning over C, and more than 500 years for passive C. We project that between 20 and 90% of the organic C could potentially be mineralized to CO2 within 50 incubation years at a constant temperature of 5 °C, with vulnerability to loss increasing in soils with higher C : N. These results demonstrate the variation in the vulnerability of C stored in permafrost soils based on inherent differences in organic matter decomposability, and point toward C : N as an index of decomposability that has the potential to be used to scale permafrost C loss across landscapes.
Illumimote: Multimodal and High-Fidelity Light Sensor Module for Wireless Sensor Networks

We describe the system requirements, design, system integration, and performance evaluation of the Illumimote, a new light-sensing module for wireless sensor networks. The Illumimote supports three different light-sensing modalities: incident light intensity, color intensities, and incident light angle (the angle of ray arrival from the strongest source); and two situational sensing modalities: attitude and temperature. The Illumimote achieves high performance, comparable to commercial light meters, while conforming to the size and energy constraints imposed by its application in wireless sensor networks. We evaluated the performance of our Illumimote for light intensity, color temperature, and incident light angle measurements and verified the function of the attitude sensor. The Illumimote consumes about 90 mW when all features on board are activated. We describe our design and the experiment design for the performance evaluation.
Pro-npff a and Pro-npff B and G Protein-coupled Receptors, Npff 1 and Npff 2 , Have

Our main purpose was to evaluate the influence of cancer pain on the rewarding properties of morphine. Opioids are very addictive when used by healthy persons, conversely the occurrence of an opioid addiction seems very low when patients suffering from cancer are treated with morphine. We investigated the reinforcing properties of morphine in the place preference paradigm on a new model of mice suffering from a cancer pain induced by syngenic melanoma cells injected in the hind paw. These data were compared with mice suffering either from a short-termor a chronicinflammatory pain induced respectively by injection of carrageenan or Complete Freund’s Adjuvant. Remarkably, mice suffering from cancer pain or chronic inflammatory pain did not develop any preference for the environment associated with the injection of morphine. In mice injected with melanoma cells, the specific binding of [I]EYF, an agonist of NPFF2 receptors, was increased in several brain areas involved in the rewarding properties of opiates, including the shell of the nucleus accumbens, the major islands of Calleja, the ventral endopiriform nucleus and the amygdaloid area. Our study is the first to reveal a modification of morphine rewarding properties under cancer pain in rodents. We postulate that anti-opioid neuropeptides might contribute to the suppression of morphine rewarding effects in this murine model of cancer pain.
Research on Vector-Control System of PMSM Based on Internal Model Control of Current Loop

The current regulation based on the internal model control is applied to vector control of PMSM. It avoids the adjustment of two PI-parameters; moreover the IMC structure improves the characteristic of the current loop. Generally, the flux of PMSM is changing with the load current (torque). Simulation results under flux saturation considered and not considered show that the current internal model controller can provide good performance with matched model and unmatched model. And the system has good features of the robustness to disturbances, and parameter variations. Satisfactory performance can be achieved.