Miracle-dz/UniFAR-FTUs huggingface dataset:
Satellite-based analysis of regional evapotranspiration trends in a semi-arid area

ABSTRACT A regional framework for a spatial and temporal distributed assessment of time series trends in the hydrological variable and its related ecological factors of an arid area was presented in this research. To achieve this, we tested the Surface Energy Balance System (SEBS) algorithm for estimating evapotranspiration (ET) in regional scale and the result was further validated by water budget. The ET assessment was applied for the Yinchuan and Weining (YW) Plains (China), the upstream areas of the Yellow River. Moreover, we analysed the recent trends (from year 2001 to 2014) in actual ET, normalized difference vegetation index (NDVI), farmland and wetland using a combination of remote sensing and ground observations. The results illustrated that the yearly ET of 78% areas has no change during the study period and the areas with decreasing ET are larger than the areas with increasing trend. The highest decreasing rate is observed in urban areas and the value is –20 mm year–1, while the rate of increasing trend is especially higher in the wetlands reaching around 60 mm year–1. This phenomenon can be explained by changes in NDVI, farmland and wetland. The distribution and magnitude of NDVI trends shows that the higher NDVI (NDVI > 0.4) area is occupied 56% and the farmland (NDVI > 0.55) covered about 35% of the YW Plains. The increasing trends of mean NDVI is mostly observed in farmland and shrub covers, while the decreasing NDVI areas are mainly wetlands and urban area. The emerging image showed that the greening trend of vegetation and variation of evapotranspiration in the YW Plains are related to land-cover changes and to the adjustment of crop pattern for agriculture. The increasing of wetland area is also believed as a cause related to evapotranspiration change. Such spatiotemporally distributed analysis in regional level is important for water management at this scale, which can be also applied to other similar areas.
Headroom-based pretenuring: dynamically pretenuring objects that live "long enough"

Many modern garbage collectors are generational, operating under the assumption that "most objects die young. " Such collectors allocate all objects in a frequently collected nursery and tenure only surviving objects to a less-frequently collected, older generation. But these survivors induce copying costs upon tenuring. To avoid these costs, pretenuring schemes construct classifiers to predict whether an object will be long-lived or short-lived; accordingly, it is tenured or not immediately upon allocation. Up to now, however, these predictions did not account for one important fact: the proximity of the next collection. In contrast, headroom-based pretenuring does take this into account; thus, it can dynamically pretenure objects whenever they live "long enough. " We devised two ways to estimate an object's lifetime from garbage collection traces. This led to two headroom-based pre-tenuring schemes, which we implemented on top of Jikes RVM and MMTk. Our experiments show that the dynamic, headroom-based pretenuring schemes outperform static schemes in terms of collector performance, albeit at the cost of increased mutator overhead.
A linear generalized Hough transform and its parallel implementation

A linear generalized Hough transform (LIGHT) in which a linear numeric pattern is used to replace the single peak in the original generalized Hough transform (GHT) is presented. The LIGHT is more capable of detecting partially occluded objects. Moreover, it is well-suited for parallelization, especially on SIMD array processors. Several sequential and parallel LIGHT algorithms have been developed. Preliminary results for parallel implementation were obtained from and SIMD one-dimensional array processor (the AIS-4000 vision processor with 512 processing elements). <<ETX>>
Application of machine language during the transmit in tacit knowledge of elderly technological talents

Tacit knowledge, specificity and complexity play adjustment role in the influence of knowledge transfer, the user can make human-machine interaction through the machine language, making deep mining and analysis to the data, so as to help the enterprise management personnel have an experiential learning from the elderly technological talents. Thus they can explore Application of Machine language during the transmit in tacit knowledge of elderly technological talents.
The accuracy of dependency analysis in static architecture compliance checking

Architecture compliance checking (ACC) is an approach to verify conformance of implemented program code to high‐level models of architectural design. Static ACC focuses on the modular software architecture and on the existence of rule violating dependencies between modules. Accurate tool support is essential for effective and efficient ACC. This paper presents a study on the accuracy of ACC tools regarding dependency analysis and violation reporting. Ten tools were tested and compared by means of a custom‐made benchmark. The Java code of the benchmark testware contains 34 different types of dependencies, which are based on an inventory of dependency types in object oriented program code. In a second test, the code of open source system FreeMind was used to compare the 10 tools on the number of reported rule violating dependencies and the exactness of the dependency and violation messages. On the average, 77% of the dependencies in our custom‐made test software were reported, while 72% of the dependencies within a module of FreeMind were reported. The results show that all tools in the test could improve the accuracy of the reported dependencies and violations, though large differences between the 10 tools were observed. We have identified 10 hard‐to‐detect types of dependencies and four challenges in dependency detection. The relevance of our findings is substantiated by means of a frequency analysis of the hard‐to‐detect types of dependencies in five open source systems. © 2016 The Authors. Software: Practice and Experience Published by John Wiley & Sons, Ltd.
Mandatory human participation: a new authentication scheme for building secure systems

Mandatory human participation (MHP) is a novel authentication scheme that asks the question "are you human? " (Instead of "who are you? "), and upon the correct answer to this question, can prove a principal to be a human being instead of a computer program. MHP helps solve old and new problems in computer security that existing security measures cannot address properly, including password (or PIN number) guessing attacks and application-level denial of service. A key component of this "are you human? " authentication process is a character morphing algorithm that transforms a character string into its graphical form in such a way that a human being won't have any problem recognizing the original string, while a computer program (e.g., an optical character recognition program), will not be able to decipher it or make a correct guess with nonnegligible probability. The basic idea of the MHP scheme is to ask an agent to recognize the string before its login attempts or transaction requests can be honored. Here a protocol is needed to send a puzzle to an agent, check if the answer supplied by the agent is correct, and most importantly make sure that the agent cannot cheat in the process. A number of system and security issues that relate to the protocol need to be addressed for the protocol to be secure, efficient, robust, and user-friendly. The MHP scheme contributes to the foundation of the computer security by faithfully implementing novel security semantics, "human," which existing cryptographic measures cannot express accurately. As many real-world security applications involve the interaction between a human and a computer, which naturally contains "human" as a part of its protocol semantics, we believe that the MHP scheme will find many new applications in the future.
An Efficient State-Space Realization With Minimum Roundoff Noise Gain

This paper deals with efficient digital filter structures with roundoff noise consideration. Motivated by the direct-form II transposed (DFIIt) structure in rho-operator (rhoDFIIt) an alternative structure is obtained [Li in 2005, where, instead of the first-order rho-operators in rhoDFIIt, a set of second-order polynomial operators is used. In this paper, with the rounding before multiplication implementation taken into account, the equivalent state-space realization of the proposed structure by Li is derived and its roundoff noise performance is analyzed by deriving the roundoff noise expressions without/with error feedback consideration. This state-space realization can be efficiently implemented and has more degrees of freedom than its counterpart rhoDFIIt, which can be utilized to minimize the roundoff noise gain. A genetic algorithm is proposed to efficiently solve the optimal structure problem. Extensive examples are given to illustrate the advantage of this state-space realization and support the theoretical analysis
Ordering Clone Libraries in Computational Biology

We consider a probabilistic model, due to Lander and Waterman and to Alizadeh, Karp, Newberg and Weisser, for the physical mapping of DNA molecules. Within this model, we answer precisely a question of Alizadeh et al. concerning the minimum number of probes required to reconstruct the entire ordering of a given clone library with high probability. We also examine the related problem of determining the least number of probes required to construct a "tiling" for the library. We give a fairly precise characterization for this number.
Process-based schema matching: from manual design to adaptive process construction

Mappings between complex metadata structures are needed in a number of domains such as data integration, ontology alignment or web service composition. A mapping describes how elements of one metadata structure correspond to elements of another metadata structure. Defining such mappings is a complex and time-consuming process. It is often done manually, with the help of point and click interfaces. In the last 10 to 15 years a strong effort was made in research to partly automate the mapping process. Many schemaand ontology matching systems were developed to semi-automatically compute mapping suggestions for a user. Current systems are often not robust enough to be able to cope with different mapping problems and often face performance issues when mapping large structures. This thesis investigates how to support the user in the task of configuring schema matching systems and how to improve run-time performance.
A Novel Computer Oculomotor Rehabilitation (COR) Program for Mild Traumatic Brain Injury (mTBI)

Individuals with traumatic brain injury (TBI) manifest a wide range of visual dysfunctions. One of the most prevalent involves the oculomotor system, which includes version, vergence, and accommodation. However, until recently, there has been no comprehensive, computer-based program for remediation of these oculomotor deficits. We present such an oculomotor rehabilitation program that has been tested in a clinical trial in patients having TBI with a high degree of success based on before-and-after objective system recordings, performance measures, and related visual symptomotology. The basic program components include a versatile stimulus package incorporating the attentional paradigm of rapid serial visual presentation (RSVP), the ability to add a visual and/or auditory distractor to the training to increase difficulty level (“task loading”), automated assessment of RSVP errors, and automated assessment of visual performance over the training period. Program limitations and future directions are also considered.
Online estimation and control of voltage flicker using neural network

This paper deals with the design, analysis and simulation of an online voltage envelope estimator and an online feed-forward neural network (FFNN) controller based Distributed Static Compensator (DSTATCOM) controller. Existing controllers such as PI Controller and offline neural network controller are fixed structures and provide satisfactory control only for certain problems for which they are designed. The online Neural Controller is an error correction system, designed to give better voltage control- let it be voltage sag or flicker. In order to test the system, an electric arc furnace model is developed and is used as a load. A novel voltage control technique for on line tracking of voltage envelope and controllers to generate reference current values in DSTATCOM using neural network is developed. Simulated results show better flicker mitigation as compared to traditional PI controllers.
Does Intraarticular Morphine Improve Pain Control with Femoral Nerve Block after Anterior Cruciate Ligament Reconstruction?

In a prospective, randomized, double-blinded manner, we compared the effects of a preoperative intraarticular injection of morphine (5 mg) or a placebo, combined with a postoperative femoral nerve block, on postoperative pain. Sixty-two patients underwent an arthroscopically assisted anterior cruciate ligament reconstruction using patellar tendon autograft under general anesthesia. No statistical difference between the two groups was evident in terms of age, sex, weight, operative time, volume of bupivacaine received with the femoral nerve block, or tourniquet use or tourniquet time. Comparison of visual analog scale pain scores revealed no statistical difference between the groups at any point after the operation. Both groups had a significant decrease in visual analog scale scores after the femoral nerve block, with the lowest mean values 4 hours after the operation (morphine group, 1.7; placebo group, 1.4), and continuing to be significantly less through 24 hours (morphine, 2.6; placebo, 2.9). No significant difference in postoperative narcotic medication use was evident in the recovery room or at home. A post hoc power analysis revealed that the study power reached 87%, with a significance level of 5%. The postoperative femoral nerve block was effective, and intraarticular morphine provided no additional benefit.
2 A REVIEW OF TECHNIQUES IN THE VERIFIED SOLUTION OF CONSTRAINED GLOBAL OPTIMIZATION PROBLEMS

Elements and techniques of state-of-the-art automatically verified constrained global optimization algorithms are reviewed, including a description of ways of rigorously verifying feasibility for equality constraints and a careful consideration of the role of active inequality constraints. Previously developed algorithms and general work on the subject are also listed. Limitations of present knowledge are mentioned, and advice is given on which techniques to use in various contexts. Applications are discussed.
Effect of yoga practices in reducing counterproductive work behavior and its predictors

Context: Aggression and negative affectivity (NA) are known for moderating relationship between job stressors and counterproductive work behavior. Yoga has been demonstrated earlier to reduce the parameters of aggression and negative emotions. Aim: The present study examined the effectiveness of yoga practices in reducing counterproductive work behavior and its predictors such as aggression and NA. Settings and Design: A pretest–posttest randomized controlled design. Materials and Methods: The study sample included yoga group (n = 80) and control group (n = 80). Yoga module that included asanas, pranayama, meditation, and yogic theory were taught to the yoga group. Mild to moderate physical exercises and management theories were taught to the control group. Both groups received intervention for 10 weeks, covering 1 h daily, 5 days a week. Measurements of self-reported counterproductive work behaviors, aggression, and NA were taken as baseline and postintervention for assessment. Results: Yoga group reported significant reductions in aggression, NA, and counterproductive work behavior in comparison with the control group after 10 weeks. Conclusions: This study contributes by introducing a cost-effective way to prevent the heavy losses organizations are incurring due to counterproductive work behavior and its predictors. The findings support that yoga at workplace may result in positive psychological empowerment of the employees as well.
Shared and unique environmental factors determine the ecology of methanogens in humans and rats

OBJECTIVE:This study ascertains the relative contributions of genetics and environment in determining methane emission in humans and rats. There is considerable interest in the factors determining the microbial species that inhabit the colon. Methanogens, which are archaebacteria, are an easily detected colonic luminal bacteria because they respire methane. They are present in some but not all human colons and lower animal hindguts. Opinion varies on the nature of the factors influencing this ecology with some studies proposing the existence of host genetic influences.METHODS:Methane emission was measured in human twin pairs by gas chromatography, and structural equation modeling was used to determine the proportion of genetic and environmental determinants. The importance of the timing of environmental effects and rat strain on the trait of methane emission were ascertained by experiments with cohabiting methanogenic and nonmethanogenic rats.RESULTS:Analysis of breath samples from 274 adolescent twin pairs and their families indicated that the major influences on the trait of methane emission are the result of shared (53%, 95% confidence interval 39–61) and unique environmental (47%, 95% confidence interval 38–56) effects. No significant autosomal genetic effects were detected, but as observed in other studies, men (37%) were less likely to excrete methane in their breath than women (63%). Investigation of methane emission in rats indicated that environmental effects in this animal are most potent during the weaning period, with stable gut microbial ecology thereafter for some but not all rat strains.CONCLUSIONS:These results are consistent with shared and unique environmental factors being the main determinants of the ecology of this colonic microbe.
Classical simulations of Abelian-group normalizer circuits with intermediate measurements

Quantum normalizer circuits were recently introduced as generalizations of Clifford circuits [1]: a normalizer circuit over a finite Abelian group G is composed of the quantum Fourier transform (QFT) over G, together with gates which compute quadratic functions and automorphisms. In [1] it was shown that every normalizer circuit can be simulated efficiently classically. This result provides a nontrivial example of a family of quantum circuits that cannot yield exponential speed-ups in spite of usage of the QFT, the latter being a central quantum algorithmic primitive. Here we extend the aforementioned result in several ways. Most importantly, we show that normalizer circuits supplemented with intermediate measurements can also be simulated efficiently classically, even when the computation proceeds adaptively. This yields a generalization of the Gottesman-Knill theorem (valid for n-qubit Clifford operations [2, 3]) to quantum circuits described by arbitrary finite Abelian groups. Moreover, our simulations are twofold: we present efficient classical algorithms to sample the measurement probability distribution of any adaptive-normalizer computation, as well as to compute the amplitudes of the state vector in every step of it. Finally we develop a generalization of the stabilizer formalism [2, 3] relative to arbitrary finite Abelian groups: for example we characterize how to update stabilizers under generalized Pauli measurements and provide a normal form of the amplitudes of generalized stabilizer states using quadratic functions and subgroup cosets.
Hierarchical model predictive control of UAVs performing multitarget-multisensor tracking

Detection and tracking of enemy emitters such as radar-carrying platforms is a task of considerable military significance. In the work presented here, the problem of adaptively controlling the trajectories of an autonomous team of unmanned aerial vehicles (UAVs) performing this task, in order to minimize emitter localization error, while simultaneously avoiding no-fly zones, is considered and a solution developed. Because of the computational complexity of the problem when long-term goals are considered, an optimal solution cannot be found in practice by any physically implementable method. Hence, in this paper an approach is developed that enables implementation of a computationally feasible, suboptimal solution that takes into account both short-term and long-term goals. To this end, the problem is addressed by developing a new hierarchical model predictive control (MPC) algorithm. To evaluate the effectiveness of the approach, first a controller is developed using an idealized UAV model and simulations are performed. Its performance is compared with a commonly used “myopic” control approach and found to give important improvements. Subsequently an improved planner is incorporated and tested, and then a version of the controller using a fixed-wing aircraft model for the UAVs is implemented. This version is also tested by simulation and found to perform successfully. Finally, a brief discussion on system stability is provided as part of the evaluation.
A Semantic Relatedness Approach to Classifying Opinion from Web Reviews

espanolLos ultimos anos han marcado el inicio y la rapida expansion de la web social, donde cada persona puede expresar su libre opinion sobre diferentes "objetos", tales como productos, personas, topicos de politica etc. en blogs, foros o portales Web de comercio electronico. A su vez, el rapido crecimiento del volumen de informacion en la web ha ido permitiendo a los usuarios la toma de decisiones mejores y mas informadas. A raiz de esta expansion ha surgido la necesidad de desarrollar sistemas especializados de PLN que automaticamente escaneen la web en busca de las opiniones expuestas (que recuperen, extraigan y clasifiquen las opiniones existentes dada una consulta). La mineria de opiniones (analisis de sentimientos) ha demostrado ser un problema dificil debido a la gran variabilidad semantica del texto libre. En este articulo se propone un metodo para extraer, clasificar y resumir opiniones sobre productos concretos utilizando criticas realizadas en la Web. El metodo se basa en una taxonomia de caracteristicas de productos previamente construida, el calculo de la proximidad semantica entre conceptos por medio de la Distancia Normalizada de Google y el metodo de aprendizaje automatico SVM. Finalmente, demostramos que nuestro enfoque supera los resultados base de la tarea y ofrece una alta precision y una alta confianza en las clasificaciones obtenidas. EnglishRecent years have marked the beginning and rapid expansion of the social web, where people can freely express their opinion on different “objects”, such as products, persons, topics etc. on blogs, forums or e-commerce sites. While the rapid growth of the information volume on the web allowed for better and more informed decisions from users, its expansion led to the need to develop specialized NLP systems that automatically mine the web for opinions (retrieve, extract and classify opinions of a query object). Opinion mining (sentiment analysis) has been proven to be a difficult problem, due to the large semantic variability of free text. In this article, we propose a method to extract, classify and summarize opinions on products from web reviews, based on the prior building of product characteristics taxonomy and on the semantic relatedness given by the Normalized Google Distance and SVM learning. We prove that our approach outperforms the baselines and has a high precision and classification confidence.