Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimized Real Time Algorithm for the Inverse Kinematics of General 6R Robots

The existing real time algorithms for inverse kinematics of general 6R robots have the problems of complex procedures and ineffective roots, so they can not be applied in the online control systems where real time performance are strictly required. In this paper, we propose an optimized algorithm with strong real time performance and high accuracy. The six basic inverse kinematics equations are transformed and the target matrix is reduced from the order of 24 to 16 based on symbolic preprocessing, so the efficiency of the algorithm is improved while the ineffective roots eliminated. Eigen decomposition is utilized to extract roots from the target matrix and ensure the stability and accuracy of the algorithm. By invoking CLAPACK in the VC++ environment directly, all calculations are accomplished with the C/C++ language and OOP technique. Experiments on various general 6R robots show that, the proposed algorithm can solve the inverse kinematics problem in an average time of 1.37 ms, and ensure that the position and orientation matrixes of the end effect corresponding to the solutions have an average accuracy of 12 digits after the decimal point. All these make it a novel algorithm for online control systems of general robots where real time performance and high accuracy are strictly required.
New Public Management

8 (FOR WEB PREVIEW) New Public Management rep9 resents ‘‘an approach in public administration that 10 employs knowledge and experiences acquired in business 11 management and other disciplines to improve efficiency, 12 effectiveness, and general performance of public services 13 in modern bureaucracies. ’’ Despite contradictory views 14 about the meaning and implications of this doctrine, there 15 is no doubt that it has become extremely influential in 16 public administration theory and practice since the 1980s. 17 In the last decade, we have witnessed an ongoing 18 debate between scholars and practitioners of public ad19 ministration on the best way to revitalize and renew old 20 style bureaucracies. Since the early 1980s, much work has 21 been conducted in public administration theory and 22 practice that claimed to go beyond the conservative ap23 proach in the field. Modern public administration has 24 been urged to innovate and reform itself in a style better 25 suited to the twenty-first century and the growing de26 mands for higher efficiency and effectiveness. Drawing 27 on the experience of the business/industrialized/private 28 sector, scholars have suggested taking a more ‘‘demand29 ing’’ attitude toward the dynamics, activity, and produc30 tivity of public organizations. Similarly, a significant 31 conceptual change has transformed the ‘‘old’’ style of 32 public administration into the ‘‘new’’ approach of public 33 management. This ‘‘liberalization’’ of public administra34 tion is recognized today as the New Public Management 35 (NPM) doctrine. 36 What is the meaning of NPM? What are its roots, and 37 in what way is it actually a new arena in the study of the 38 public sector? Has it achieved enough success in recent 39 years to justify further inquiry into its principles and 40 promises? These questions, as well as many others, have 41 received considerable attention during recent years and, in 42 fact, a large number of varying opinions have emerged. 43 Proponents of this approach have provided detailed 44 explanations and a variety of examples that demonstrate 45 the enormous positive impact of the approach. Those who 46 critique NPM counter with the argument that it has 47 nothing new or notable to contribute to the field. They, 48 thus, tend to treat it as a seasonal fashion that will 49 eventually fade in face of other ‘‘new’’ doctrines. There50 fore, advocates of NPM continuously face critics who are 51 unconvinced as to the merits of NPM as an administra52 tive philosophy.
A Theoretical Framework for Strategic Dimensions of Interaction Strategy and Corporate Identity

Corporate identity as a source and an effective strategic tool is a strategic and operational plan for presenting the organization itself based on the philosophy, mission, long-term goals and the desired image to the internal and the external stakeholders. In the process of strategy, it is the source of competitive advantage, and there is a mutual interaction between these two concepts. This paper describes the corporate identity and explains its interaction with the business strategy. Then the strategic dimensions are determined by a theoretical framework. Strategic orientation, strategic action and strategic function are these dimensions.
Establishing and explaining the link between poverty and HIV/AIDS : a South African case study.

The epidemics of poverty and HIV/ AIDS, whether individually or when combined, pose major threats to development both in South Africa and elsewhere in the world. However, it is when these epidemics coexist that major devastation occurs. As such exploration of the relationship between these two epidemics has been the focal point of most research on either poverty or HIV/ AIDS. Through mainly the use of literature based research, studies have indicated how poverty and disease, most specifically HIV/ AIDS are related. Since most of these studies have been literature based, this has resulted in a lack of sufficient empirical evidence arguing for the existence of this poverty-HIV/ AIDS cycle. Providing such evidence forms the main objective of this study. This study uses data collected by the 1998 South African Demographic and Health Survey as well as existing literature on the link between poverty and disease. For purposes of this study, analysis is restricted to women in their reproductive ages (15-49) . The results indicate an increased risk of HIV infection among the poor due to poverty related characteristics of low education and low knowledge of the means of avoiding HIV infection as opposed to the non-p oor. Moreover the poor and the less educated people were found to be more likely to not use condoms than the non-poor. The results do not , however, provide reasons for these relations and as such further research is required. One possible explanation was financial dependence on their partners as it was found that women who received money from their partners as well as those who came from households where hunger was a common phenomenon were more likely to not use condoms because their partners disliked condoms than those who didn't receive any money from their partners. The results also hinted on the intricacy of the poverty-HIV/ AIDS relation whereby it was not only low socia-economic status that increased susceptibility to HIV infection but also high socio-economic status. This was indicated by the high odds of non-use of condoms due to low perceived risk of HIV infection among the non-poor and the White population (a race with the minority poor people). These results also hint at the prevailing stigmatization of HIV/ AIDS as a disease of the poor despite efforts by prevention programmes to destigrnatize this disease.
A temporal constraint network based temporal planner ?

This paper describes a preliminary work on Machine , a temporal partial order planner based on the use of temporal constraint networks to represent and propagate temporal constraints over the actions of a plan. The use of this formalism provide several advantages like the possibility of including deadline goals, makespans and imprecise temporal references able to obtain a flexible timeline for the execution of temporal plans. The expressiveness of the approach also meet the main temporal specifications of PDDL 2.1 level 3 like durative actions and invariant conditions.
Integrating symmetry of environment by designing special basis functions for value function approximation in reinforcement learning

Reinforcement learning (RL) is usually regarded as tabula rasa learning, and the agent needs to randomly explore the environment, so the time consuming and data inefficiency will hinder RL from the real application. In order to accelerate learning speed and improve data efficiency, in this paper we expand the symmetry definition from finite state space to infinite state space and then propose designing a special type of symmetric basis functions for value function approximation to integrate the prior knowledge of symmetry about the environment for large or even infinite state space. After that, as an example, this particular approximate structure is incorporated into the policy evaluation phase of Least-Square Policy Iteration (LSPI), which we call symmetric LSPI (S-LSPI) and the convergence property is analyzed. Simulation results of chain walk and inverted pendulum balancing demonstrate that in contrast with regular LSPI (R-LSPI), the convergence speed of S-LSPI increases greatly and the computational burden decreases significantly simultaneously. It can illustrate the use of symmetric basis functions to capture the property of symmetry very well, and as a case study, it shows the promise to integrate symmetry of environment into RL agent.
Efficient decode-and-forward relaying scheme for wireless communication systems

In this paper, we propose a decode-and-forward incremental relaying scheme to improve the performance of wireless communication systems. In specific, the scheme aims to enhance the spectral efficiency by introducing a policy that avoids cooperative transmission whenever it is not beneficial. We provide performance analysis in terms of average spectral efficiency and average bit error rate under Rayleigh fading channels. Numerical results show that with the scheme, the average spectral efficiency and average bit error rate of wireless communication systems can be improved significantly.
Paratus: Instantaneous Failover via Virtual Machine Replication

Business continuity is valuable to IT management in both small and large organizations. As faults, errors, and even disasters perfectly exist, instantaneous service fail over must be furnished to computer systems in order to preserve business continuity. However, current solutions concentrate either on volatile memory and CPU context or on persistent disk storage, instantaneous fail over can hardly be achieved as expected. To remedy this, we design a novel framework that encapsulates computer system in a virtual machine, and henceforth frequently replicates its state to a backup physical host. In case the primary virtual machine crashes, the virtual machine replica at the backup host can instantaneously take over the failed virtual machine to service unfinished and new tasks without user’s perception. We design and implement a prototype system, Paratus, to demonstrate this argument. Experimental results show that seamless service recovery is accomplished within seconds at the cost of no consistency check, restart, or reconfiguration required. Meanwhile performance degradation is hardly visible to users.
Relaxation Guided Variable Neighborhood Search

In this article we investigate a new variant of Variable Neighborhood Search (VNS): Relaxation Guided Variable Neighborhood Search. It is based on the general VNS scheme and a new Variable Neighborhood Descent (VND) algorithm. The ordering of the neighborhood structures in this VND is determined by solving relaxations of them. The objective values of these relaxations are used as indicators for the potential gains of searching the corresponding neighborhoods. We tested this new approach on the well-studied multidimensional knapsack problem. Computational experiments show that our approach is beneficial to the search, improving the obtained results. The concept is, in principle, more generally applicable and seems to be promising for many other combinatorial optimization problems.
Vers l'utilisation de relations de préférence pour le filtrage collaboratif

Les systemes de recommandation a base de filtrage collaboratif exploitent les preferences d'utilisateurs sur des ressources pour effectuer des recommandations. Ces preferences se presentent generalement sous la forme de votes quantitatifs. Cependant, fixer un vote pour un utilisateur n'est pas une tâche simple ; elle peut etre influencee par de nombreux facteurs et les votes obtenus ne sont donc pas totalement fiables. Dans cet article nous proposons une nouvelle approche pour exprimer les preferences des utilisateurs sous forme de relations de preference au lieu de votes. Nous utilisons les memes etapes que le filtrage collaboratif classique pour effectuer les recommandations et nous proposons dans ce cadre des mesures alternatives adaptees a l'exploitation de relations de preference. Des premieres experimentations de cette nouvelle approche ont ete effectuees sur un corpus de l'etat de l'art des systemes de recommandation.
Pancreatic Acinar Cells Employ miRNAs as Mediators of Intercellular Communication to Participate in the Regulation of Pancreatitis-Associated Macrophage Activation

Macrophage activation plays an important role in the inflammatory response in acute pancreatitis. In the present study, the activation of AR42J pancreatic acinar cells was induced by taurolithocholate treatment. The results showed that the culture medium from the activated AR42J cells significantly enhanced NFκB activation in the macrophages compared to that without taurolithocholate treatment. Additionally, the precipitates obtained from ultracentrifugation of the culture media that were rich in exosomes were markedly more potent in activating macrophages compared with the supernatant fraction lacking exosomes. The results indicated that the mediators carried by the exosomes played important roles in macrophage activation. Exosomal miRNAs were extracted and examined using microarrays. A total of 115 differentially expressed miRNAs were identified, and 30 showed upregulated expression, while 85 displayed downregulated expression. Target genes of the differentially expressed miRNAs were predicted using TargetScan, MiRanda, and PicTar software programs. The putative target genes were subjected to KEGG functional analysis. The functions of the target genes were primarily enriched in MAPK pathways. Specifically, the target genes regulated macrophage activation through the TRAF6-TAB2-TAK1-NIK/IKK-NFκB pathway. As the mediators of signal transduction, miRNAs and their predicted target mRNAs regulate every step in the MAPK pathway.
Functional Variation of the Dopamine D2 Receptor Gene Is Associated with Emotional Control as well as Brain Activity and Connectivity during Emotion Processing in Humans

Personality traits related to emotion processing are, at least in part, heritable and genetically determined. Dopamine D2 receptor signaling is involved in modulation of emotional behavior and activity of associated brain regions such as the amygdala and the prefrontal cortex. An intronic single nucleotide polymorphism within the D2 receptor gene (DRD2) (rs1076560, guanine > thymine or G > T) shifts splicing of the two protein isoforms (D2 short, mainly presynaptic, and D2 long) and has been associated with modulation of memory performance and brain activity. Here, our aim was to investigate the association of DRD2 rs1076560 genotype with personality traits of emotional stability and with brain physiology during processing of emotionally relevant stimuli. DRD2 genotype and Big Five Questionnaire scores were evaluated in 134 healthy subjects demonstrating that GG subjects have reduced “emotion control” compared with GT subjects. Functional magnetic resonance imaging in a sample of 24 individuals indicated greater amygdala activity during implicit processing and greater dorsolateral prefrontal cortex (DLPFC) response during explicit processing of facial emotional stimuli in GG subjects compared with GT. Other results also demonstrate an interaction between DRD2 genotype and facial emotional expression on functional connectivity of both amygdala and dorsolateral prefrontal regions with overlapping medial prefrontal areas. Moreover, rs1076560 genotype is associated with differential relationships between amygdala/DLPFC functional connectivity and emotion control scores. These results suggest that genetically determined D2 signaling may explain part of personality traits related to emotion processing and individual variability in specific brain responses to emotionally relevant inputs.
On the intrinsic disorder status of the major players in programmed cell death pathways

Earlier computational and bioinformatics analysis of several large protein datasets across 28 species showed that proteins involved in regulation and execution of programmed cell death (PCD) possess substantial amounts of intrinsic disorder. Based on the comprehensive analysis of these datasets by a wide array of modern bioinformatics tools it was concluded that disordered regions of PCD-related proteins are involved in a multitude of biological functions and interactions with various partners, possess numerous posttranslational modification sites, and have specific evolutionary patterns (Peng et al. 2013). This study extends our previous work by providing information on the intrinsic disorder status of some of the major players of the three major PCD pathways: apoptosis, autophagy, and necroptosis. We also present a detailed description of the disorder status and interactomes of selected proteins that are involved in the p53-mediated apoptotic signaling pathways.
Improving deep neural network acoustic models using unlabeled data

The Context-Dependent Deep-Neural-Network HMM, or CD-DNN-HMM, is a powerful acoustic modeling technique. Its training process typically involves unsupervised pre-training and supervised fine-tuning. In the paper, we demonstrate that the performance of DNNs can be improved by utilizing a large amount of unlabeled data in the training procedure. In our method, CD-DNN-HMM trained using 309 hours of unlabeled data and 24 hours of labeled data achieved word-error rate of 23.7% on the Hub5'00-SWB phone-call transcription task, compared to word-error rate of 24.3% obtained by a CD-DNN-HMM trained without using unlabeled data. We also applied a priori probability smoothing algorithm that further reduced the error rate to 23.2%. On RT03S-FSH benchmark corpus, our experimental results show that similar performance gains can be obtained by the use of unlabeled data.
PERSISTENT STORAGE AND QUERYING OF COMPRESSED XML DOCUMENTS ON THE WEB

We describe the design and implementation of a Web-based distributed system called TREESTORE, intended for storing compressed XML documents in a relational database. The use of a database is fully portable, requiring minimal changes to application code to substitute one database management system for another. In TREESTORE, compressed XML documents are shredded into a fixed number of relational tables. Besides the storage capability, TREESTORE provides online querying of compressed XML documents without requiring full decompression. Therefore, it is a useful component of any Web application as it combines space-efficient, database-portable access to persistent storage with online querying.
Shadow Economy and Tax Evasion: A Panel VAR approach. The Case of E.U.27

The paper investigates the biunivoque relationship between shadow economy (s) and tax evasion (t), considering a panel data-series, from 1997 to 2005, in the case of European Union 27 (E.U.27) countries. The empirical results show that: (1) A positive 1% impulse in s determines a strong descendent reaction of t on short term (first 2-3 years), becoming “aggressive descendent” on the medium and long term, and (2) A positive 1% impulse in t determines a very low ascendant reaction of s’s level on medium and long term. The main finding reveals that the relationship between shadow economy and tax evasion has different amplitude and signs.
EMBRYONIC STEM CELLS / INDUCED PLURIPOTENT STEM CELLS Long-Term Safety and Function of RPE from Human Embryonic Stem Cells in Preclinical Models of Macular Degeneration

Assessments of safety and efficacy are crucial before human ESC (hESC) therapies can move into the clinic. Two important early potential hESC applications are the use of retinal pigment epithelium (RPE) for the treatment of age-related macular degeneration and Stargardt disease, an untreatable form of macular dystrophy that leads to early-onset blindness. Here we show long-term functional rescue using hESC-derived RPE in both the RCS rat and Elov14 mouse, which are animal models of retinal degeneration and Stargardt, respectively. Good Manufacturing Practice-compliant hESC-RPE survived subretinal transplantation in RCS rats for prolonged periods (>220 days). The cells sustained visual function and photoreceptor integrity in a dose-dependent fashion without teratoma formation or untoward pathological reactions. Near-normal functional measurements were recorded at >60 days survival in RCS rats. To further address safety concerns, a Good Laboratory Practice-compliant study was carried out in the NIH III immune-deficient mouse model. Long-term data (spanning the life of the animals) showed no gross or microscopic evidence of teratoma/ tumor formation after subretinal hESC-RPE transplantation. These results suggest that hESCs could serve as a potentially safe and inexhaustible source of RPE for the efficacious treatment of a range of retinal degenerative diseases. STEM CELLS 2009;27:2126–2135 Disclosure of potential conflicts of interest is found at the end of this article.
Stock Index Modeling using EDA based Local Linear Wavelet Neural Network

The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using local linear wavelet neural network (LLWNN) technique. To this end, we considered the Nasdaq-100 index of Nasdaq Stock MarketSM and the S&P CNX NIFTY stock index. We analyzed 7-year Nasdaq-100 main index values and 4-year NIFTY index values. This paper investigates the development of novel reliable and efficient techniques to model the seemingly chaotic behavior of stock markets. The LLWNN are optimized by using estimation of distribution algorithm (EDA). This paper investigates whether the proposed method can provide the required level of performance, which is sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results shown that the model considered could represent the stock indices behavior very accurately