Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconfigurable Parallel VLSI Co-Processor for Space Robot Using FPGA

This paper proposes hardware solutions to the computation for the trigonometric and square root functions of inverse kinematics. They are based on an existing pipeline arithmetic which employs the CORDIC(Coordinate Rotation Digital Computer) algorithm. This integrated approach enhances computational efficiency by reducing the duplicate calculations of this functions and maximizing the parallel/pipelining processing for real-time robot control. The reliability of an onboard computer for space robot mostly depends on the reliability of the memory module. According to the fault mode in aerospace, the reconfigurable EDAC (Error Detect and Correct) system including Hamming coding and TMR(Triple Modular Redundancy) function on a FPGA (Field Programmable Gate Array) is implemented. The chip also implements peripheral controller such as AD interface and power control module. The whole system can be implemented in a single chip and the hardware system is more flexible and compact. Characteristics and performance analysis of the architecture are discussed through experiments.
Performance Analysis of Concurrent Red-Black Trees on HTM Platforms

In this paper we analyze the performance of concurrent redblack trees using two HTM implementations, Intel’s Transactional Synchronization Extensions (TSX) on Haswell processors and IBM’s Power8 HTM. We parallelize bottom-up and top-down red-black trees using coarse-grained transactions and evaluate their performance. Our experimental results show that HTM can outperform lock-based implementations with minimal programming effort. Furthermore, they reveal that scalability depends on limits imposed by the underlying hardware, indicating that a programmer needs to take them into account in order to utilize HTM efficiently.
Fast thermal profiling of power semiconductor devices using Fourier techniques

Accurate prediction of temperature variation of power semiconductor devices in power electronic circuits is important to obtain optimum designs and estimate reliability levels. Temperature estimation of power electronic devices has generally been performed using transient thermal equivalent circuits. In the presence of varying load cycles, it has been typical to resort to a time-domain electrical simulation tool such as P-Spice or SABER to obtain a time series of the temperature profiles. However, for complex and periodic load cycles, time-series simulation is time consuming. In this paper, a fast Fourier analysis-based approach is presented for obtaining temperature profiles for power semiconductors. The model can be implemented readily into a spreadsheet or simple mathematical algebraic calculation software. The technique can be used for predicting lifetime and reliability level of power circuits easily. Details of the analytical approach and illustrative examples are presented in this paper.
Building Mobile Applications with Java - Using the Google Web Toolkit and PhoneGap

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Understanding news stories through SVO triplets

With the exponential growth in data, we often find ourselves struggling to deal with information overload. Techniques such as timeline summarization tackle this problem by generating short summaries for each time stamp on a timeline. However, we argue that rather than reading a set of blocks of texts, it is easier and quicker for a reader to observe dynamically changing relations between important entities that are illustrated through graphs. It enables her/him to grasp the major events of the whole story quickly. We develop an experimental system to evaluate on the 2014 FIFA World Cup Brazil news articles, and show that the result covers major events of the World Cup and can be understood in a short span of time.
Bayesian Learning of Gaussian Mixture Densities for Hidden Markov Models

An investigation into the use of Bayesian learning of the parameters of a multivariate Gaussian mixture density has been carried out. In a continuous density hidden Markov model (CDHMM) framework, Bayesian learning serves as a unified approach for parameter smoothing, speaker adaptation, speaker clustering, and corrective training. The goal of this study is to enhance model robustness in a CDHMM-based speech recognition system so as to improve performance. Our approach is to use Bayesian learning to incorporate prior knowledge into the CDHMM training process in the form of prior densities of the HMM parameters. The theoretical basis for this procedure is presented and preliminary results applying to HMM parameter smoothing, speaker adaptation, and speaker clustering are given.Performance improvements were observed on tests using the DARPA RM task. For speaker adaptation, under a supervised learning mode with 2 minutes of speaker-specific training data, a 31% reduction in word error rate was obtained compared to speaker-independent results. Using Baysesian learning for HMM parameter smoothing and sex-dependent modeling, a 21% error reduction was observed on the FEB91 test.
Colloidal Stability of a Pharmaceutical Latex: Experimental Determinations and Theoretical Predictions

extended versions of the DLVO theory of the stability of colloids; candidates to be used as drug transport substrates. One of the Hamaker constant, A, characteristic of the van der Waals interaction between the particles in suspension, is calculated from them appears to be Aquacoat, an ethylcellulose latex, commeasurements of the surface free energy of the polymer, based on mercially available and often used in pharmaceutical applicontact angle measurements of selected liquids on polymer pellets. cations; we have studied some of its surface properties in Concerning the electrostatic contribution to the total interaction previous works (5‐7). energy between the polymer spheres, it is calculated from zeta In this work, we will focus on the stability of Aquacoat potential data deduced from electrophoretic mobility of the latex latex. Among the various forms in which pharmaceutical suspensions. The application of the so-called extended DLVO the- products can be prepared for their administration, suspenory includes not only van der Waals and electrostatic interactions, sions are the most problematic as to their stabilization; even but also acid‐base or structural ones, estimated using a recent the measurement of the stability of pharmaceutical dispermodel of the surface free energy of solids. Using the well-known sions is one of the most difficult problems faced by medicine Fuchs equation, we have computed the stability ratio W of the designers. Such measurements should allow one to predict suspensions, using both the classical and extended DLVO theories, for the different experimental conditions. The results show that, the useful shelf-life of the preparation and to specify the when the electrolyte is KCl, the classical model correctly predicts design requirements to achieve that stability. the c.c.c. values, although the slopes of the log W concentration However, in addition to these practical reasons, there are trends are different from the experimental ones. The extended also fundamental justifications for the need of a deep undertheory underestimates the c.c.c. ’s and gives a better approximation standing of the thermodynamics and the interfacial properto the slopes. In the case of both CaCl2 and LaCl3, the best ties of these systems. Hence, the aim of this work is to agreement is always found with the extended theory. It is sug- characterize the stability of Aquacoat suspensions, both exgested that consideration of ionic strength effects on the acid‐ perimentally and theoretically, in a wide range of ionic combase characteristics of the solid could improve the extended DLVO positions of the dispersion medium, taking into account the results. q 1996 Academic Press, Inc. interfacial properties of the particles and the different inter
A rehabilitation device to improve the hand grasp function of stroke patients using a patient-driven approach

This paper proposes a robotic hand rehabilitation device for grasp training. The device is designed for stroke patients to train and recover their hand grasp function in order to undertake activities of daily living (ADL). The device consists of a control unit, two small actuators, an infrared (IR) sensor, and pressure sensors in the grasp handle. The advantages of this device are that it is small in size, inexpensive, and available for use at home without specialist's supervision. In addition, a novel patient-driven strategy based on the patient's movement intention detected by the pressure sensors without bio-signals is introduced. Once the system detects a patient's movement intention, it triggers the robotic device to move the patient's hand to form the normal grasping behavior. This strategy may encourage stroke patients to participate in rehabilitation training to recover their hand grasp function and it may also enhance neural plasticity. A user study was conducted in order to investigate the usability, acceptability, satisfaction, and suggestions for improvement of the proposed device. The results of this survey included positive reviews from therapists and a stroke patient. In particular, therapists expected that the proposed patient-driven mode can motivate patients for their rehabilitation training and it can be effective to prevent a compensational strategy in active movements. It is expected that the proposed device will assist stroke patients in restoring their grasp function efficiently.
Influence of Shade Depth on the Effectiveness of Selected Ultraviolet Absorbers in Reducing Fading

This study evaluates the ability of seven ultraviolet (UV) absorbers to reduce fading of nylon colored with nine synthetic acid dyes applied at 0.5% and 0.05% owf con centrations. By including two concentrations, we could evaluate the influence of depth of shade on the absorbers' effectiveness. Specimens were treated with UV absorbers using one of two application methods: an exhaust bath at 100°C or an immersion treatment at room temperature. After treatment, specimens were exposed to 160 AFUs of light in a xenon-arc Weather-Ometer, then evaluated instrumentally to determine the amount of color change. The UV absorbers examined here provided only very limited beneficial effects. Only two had no detrimental effects on all dye/shade com binations to which they were applied, and their beneficial effects were limited to four to seven of the eighteen dye/shade combinations evaluated. Based on this research, we cannot make general recommendations regarding UV absorber use because some of the acid-dyed nylon specimens treated with UV absorbers exhibited no improvement in lightfastness, while other treated specimens actually exhibited greater fading or color change than their respective untreated controls. Further testing is needed to determine the effects of specific absorbers on specific dyes not examined in this study, because the effectiveness of each absorber appears to be dye and shade specific.
Black hole entropy in canonical quantum gravity and superstring theory.

In this paper the entropy of an eternal Schwarzschild black hole is studied in the limit of an infinite black hole mass. The problem is addressed from the point of view of both canonical quantum gravity and superstring theory. The entropy per unit area of a free scalar field propagating in a fixed black hole background is shown to be quadratically divergent near the horizon. It is shown that such quantum corrections to the entropy per unit area are equivalent to the quantum corrections to the gravitational coupling. Unlike field theory, superstring theory provides a set of identifiable configurations which give rise to the classical contribution to the entropy per unit area. These configurations can be understood as open superstrings with both ends attached to the horizon. The entropy per unit area is shown to be finite to all orders in superstring perturbation theory. The importance of these conclusions to the resolution of the problem of black hole information loss is reiterated.
Using Head-Mounted Displays and In-Situ Projection for Assistive Systems: A Comparison

The increasing demand to customize products affects production workers in many industries, as assembly tasks become more complex due to higher product variety. Assistive systems providing instructions at the workplace have been proposed to overcome increasing cognitive demand during assembly tasks. Commercially available assistive systems provide spatially registered instructions, either by using in-situ projections or head-mounted displays (HMDs). As there is little empirical knowledge about the individual advantages and disadvantages of both approaches, we are interested in comparing both types of systems. Through a user study at a manual assembly workplace, we compare both approaches to a paper baseline. Our results reveal that both in-situ instructions and paper instructions lead to significantly faster task completion times and significantly fewer errors than HMDs. Using additional questionnaires and interviews, we are able to identify the shortcomings of HMD-based instructions and discuss the possibilities of using flexible in-situ instructions for worker assistance.
Global associations between terrestrial producer and vertebrate consumer diversity

In both ecology and conservation, often a strong positive association is assumed between the diversity of plants as primary producers and that of animals, specifically primary consumers. Such a relationship has been observed at small spatial scales, and a begetting of diversity by diversity is expected under various scenarios of co-evolution and co-adaptation. But positive producer–consumer richness relationships may also arise from similar associations with past opportunities for diversification or contemporary environmental conditions, or from emerging properties of plant diversity such as vegetation complexity or productivity. Here we assess whether the producer–consumer richness relationship generalizes from plot to regional scale and provide a first global test of its strength for vascular plants and endothermic vertebrates. We find strong positive richness associations, but only limited congruence of the most diverse regions. The richness of both primary and higher-level consumers increases with plant richness at similar strength and rate. Environmental conditions emerge as much stronger predictors of consumer richness, and after accounting for environmental differences little variation is explained by plant diversity. We conclude that biotic interactions and strong local associations between plants and consumers only relatively weakly scale up to broad geographical scales and to functionally diverse taxa, for which environmental constraints on richness dominate.
Access control and cell association in two-tier femtocell networks

Two-tier femtocell networks, which comprise macro-cells underlaid with short range femtocells, are under intensive study due to their improved indoor coverage and data rate advantages as compared to conventional single-tier macrocellular networks. Nevertheless, the implementation of two-tier networks with universal frequency reuse causes severe cross-tier interference from the femtocells to the macrocellular users, exacerbated by dense femtocell deployment. In this paper, we investigate the effect of access control and cell association in two-tier networks, where the macrocells employ closed access, whereas the femtocells can operate in either open or closed access. By introducing a tractable model, we derive the success probability for each tier under different femtocell access schemes using stochastic geometric tools. For the case of open access, we derive the enhanced success probability for a macrocell user, and we show that the effect of inter-tier interference could be eliminated through optimal cell association policy for the open access.
Quality Improvement of Liver Ultrasound Images Using Fuzzy Techniques

Background: Liver ultrasound images are so common and are applied so often to diagnose diffuse liver diseases like fatty liver. However, the low quality of such images makes it difficult to analyze them and diagnose diseases. The purpose of this study, therefore, is to improve the contrast and quality of liver ultrasound images. Methods: In this study, a number of image contrast enhancement algorithms which are based on fuzzy logic were applied to liver ultrasound images - in which the view of kidney is observable - using Matlab2013b to improve the image contrast and quality which has a fuzzy definition; just like image contrast improvement algorithms using a fuzzy intensification operator, contrast improvement algorithms applying fuzzy image histogram hyperbolization, and contrast improvement algorithms by fuzzy IF-THEN rules. Results: With the measurement of Mean Squared Error and Peak Signal to Noise Ratio obtained from different images, fuzzy methods provided better results, and their implementation - compared with histogram equalization method - led both to the improvement of contrast and visual quality of images and to the improvement of liver segmentation algorithms results in images. Conclusion: Comparison of the four algorithms revealed the power of fuzzy logic in improving image contrast compared with traditional image processing algorithms. Moreover, contrast improvement algorithm based on a fuzzy intensification operator was selected as the strongest algorithm considering the measured indicators. This method can also be used in future studies on other ultrasound images for quality improvement and other image processing and analysis applications.
Composite lightweight traffic classification system for network management

Accurate and real‐time classification of network traffic is significant to a number of network operation and management tasks such as quality of service differentiation, traffic shaping and security surveillance. However, with emerging P2P applications using dynamic port numbers, IP masquerading techniques and payload encryption, accurate and intelligent traffic classification continues to be a big challenge despite a wide range of research work on the topic. Since each classification method has its disadvantages and hardly could meet the specific requirement of Internet traffic classification, this paper innovatively presents a composite traffic classification system. The proposed lightweight system can accurately and effectively identify Internet traffic with good scalability to accommodate both known and unknown/encrypted applications. Furthermore, It promises to satisfy various Internet uses and is feasible for use in real‐time line speed applications. Our experimental results show the distinct advantages of the proposed classification system. Copyright © 2009 John Wiley & Sons, Ltd.
Long noncoding RNA MIAT promotes non-small cell lung cancer proliferation and metastasis through MMP9 activation

Long noncoding RNAs (lncRNAs) play crucial roles in carcinogenesis. Myocardial infarction-associated transcript (MIAT), originally isolated as a candidate gene for myocardial infarction, has been found to act as an oncogene in chronic lymphocytic leukaemias and neuroendocrine prostate cancer (NEPC); however, little is known about its expression pattern, biological function, and underlying mechanism in non-small cell lung cancer (NSCLC). In this study, we observed that MIAT expression was upregulated in NSCLC, and its overexpression was associated with advanced tumor stage. Moreover, MIAT knockdown decreased cell proliferation, migration, invasion, and cell cycle arrested in G1 phase. Mechanistic investigation revealed that MIAT could interact with histone methyltransferase mixed-lineage leukemia (MLL). MIAT silencing impeded the binding of MLL on the matrix metalloproteinase 9 (MMP9) promoter region and epigenetically reduced MMP9 transcriptional activity. Overall, our findings suggest that MIAT expression is associated with NSCLC and may be one of the critical targets in progression and metastasis in NSCLC.
Discovering signal transduction networks using signaling domain-domain interactions.

The objective of this paper is twofold. One objective is to present a method of predicting signaling domain-domain interactions (signaling DDI) using inductive logic programming (ILP), and the other is to present a method of discovering signal transduction networks (STN) using signaling DDI. The research on computational methods for discovering signal transduction networks (STN) has received much attention because of the importance of STN to transmit inter- and intra-cellular signals. Unlike previous STN works functioning at the protein/gene levels, our STN method functions at the protein domain level, on signal domain interactions, which allows discovering more reliable and stable STN. We can mostly reconstruct the STN of yeast MAPK pathways from the inferred signaling domain interactions, with coverage of 85%. For the problem of prediction of signaling DDI, we have successfully constructed a database of more than twenty four thousand ground facts from five popular genomic and proteomic databases. We also showed the advantage of ILP in signaling DDI prediction from the constructed database, with high sensitivity (88%) and accuracy (83%). Studying yeast MAPK STN, we found some new signaling domain interactions that do not exist in the well-known InterDom database. Supplementary materials are now available from http://www.jaist. ac.jp/s0560205/STP_DDI/.
Fast contextual preference scoring of database tuples

To provide users with only relevant data from the huge amount of available information, personalization systems utilize preferences to allow users to express their interest on specific pieces of data. Most often, user preferences vary depending on the circumstances. For instance, when with friends, users may like to watch thrillers, whereas, when with their kids, they may prefer to watch cartoons. Contextual preference systems address this challenge by supporting preferences that depend on the values of contextual attributes such as the surrounding environment, time or location. In this paper, we address the problem of finding interesting data items based on contextual preferences that assign interest scores to pieces of data based on context. To this end, we propose a number of pre-processing steps. Instead of pre-computing scores for all data items under all potential context states, we exploit the hierarchical nature of context attributes to identify representative context states. Furthermore, we introduce a method for grouping preferences based on the similarity of the scores that they produce. This method uses a bitmap representation of preferences and scores with various levels of precision that lead to approximate rankings with different degrees of accuracy. We evaluate our approach using both real and synthetic data sets and present experimental results showing the quality of the scores attained using our methods.