Miracle-dz/UniFAR-FTUs huggingface dataset:
Counteracting forces in implementation of IS-enabled global business processes

The international industry of engineering products and services is characterized by high complexity and competition. Corporations that expand globally have experienced that managing interdependent activities and business processes across several countries requires an effective deployment of advanced information technology. Whereas the literature has described implementation of global information systems as a means to coordinate and control the business processes, empirical studies have shown that introducing a large-scale information systems involves several managerial challenges when organizations are geographically dispersed. This paper studies deployment of a global enterprise system to support evolvement of global business processes. On the basis of a qualitative case study of a multinational corporation implementing an enterprise system across several geographical locations, we identify counteracting forces in the process of global standardization of IS and business processes and discuss how the organization try to manage these forces and challenges therein. The findings suggest that global business processes develop through diverse processes of learning and negotiation between local practices of use and infusion of the global enterprise system.
Power Risk Aversion Utility Functions

This paper introduces a new class of utility functions---the power risk aversion. It is shown that the CRRA and CARA utility functions are both in this class. The implications of the PRA utility functions are explored in the context of growth theory. In particular, it is found that economies facing a common real interest rate do not necessarily grow at the same rates if they start with different levels of capital stock. Thus diversity in growth performance across countries occurs even if these countries have access to perfect international capital markets. Potential applications of the PRA in asset pricing are considered.
Magnetic CVs in the UCT CCD CV Survey

An overview is given of all the magnetic CVs found in the UCT CCD CV Survey (Woudt & Warner 2001, 2002, 2003a). We have identified eight new candidate Intermediate Polars (IP), of which six are classical novae (RR Cha, DD Cir, AP Cru, V697 Sco, V373 Sct, and RX J1039.7-0507). The two other candidate IPs are Aqr1 (2236+0052) and RX J0944.5+0357. In addition, there are two probable Polars, namely V351 Pup (= Nova Puppis 1991) and FIRST J102347.6+003841.
Towards a robotic model of the mirror neuron system

Action understanding undoubtedly involves visual representations. However, linking the observed action with the respective motor category might facilitate processing and provide us with the mechanism to “step into the shoes” of the observed agent. Such principle might be very useful also for a cognitive robot allowing it to link the observed action with its own motor repertoire in order to understand the observed scene. A recent account on action understanding based on computational modeling methodology suggests that it depends on mutual interaction between visual and motor areas. We present a multi-layer connectionist model of action understanding circuitry and mirror neurons, emphasizing the bidirectional activation flow between visual and motor areas. To accomplish the mapping between two high-level modal representations we developed a bidirectional activation-based learning algorithm inspired by a supervised, biologically plausible GeneRec algorithm. We implemented our model in a simulated iCub robot that learns a grasping task. Within two experiments we show the function of the two topmost layers of our model. We also discuss further steps to be done to extend the functionality of our model.
A hybrid interpolation scheme for footprint-driven walking synthesis

In many constrained environments, precise control of foot placement during character locomotion is crucial to avoid collisions and to ensure a natural locomotion. In this paper, we present a new exact motion synthesis technique that generates planar parameterized stepping motions based on a combination of rotational and Cartesian interpolation. Existing stepping motions are blended in a linearized representation to guarantee exact control of foot placement. By concatenating these parameterized steps, we can generate highly-constrained stepping animations in real-time. Furthermore, because of a novel blend candidates selection strategy, soft constraints such as stance duration and foot orientation are also taken into account. We will show that our technique can generate a variety of different stepping animations efficiently, even though we impose many constraints on the animation.
̧ Xw ëÓY 5 FÛ 1⁄2 Xà 1 Generating Customizable Guided Tours For Networked Virtual Environments ì ' F è lC [

Interactive 3D graphics is becoming a popular way of publishing information on the Internet. However, most networked VR systems today are still not very intuitive to use, especially for a novice user equipped with a regular PC and a 2D mouse. One way to improve such systems is by providing the user with some guidance during the navigation of a virtual environment. In this paper, we present a software system capable of generating customizable guided tours for a networked virtual environment. The implemented system incorporates motion planning algorithms in Robotics into the latest development of network-based virtual reality standards such as VRML 2.0. A user can simply click on the locations they wish to visit and select a visiting sequence, and the system will automatically generate a tour plan and guide the user walk through the desired locations. Experimental results are presented using a 3D model of our departmental building. We expect the result of this research to be used as new navigation assistance to local or remote visitors of docentry systems located in museums or national parks.
Subthalamic, not striatal, activity correlates with basal ganglia downstream activity in normal and parkinsonian monkeys

The striatum and the subthalamic nucleus (STN) constitute the input stage of the basal ganglia (BG) network and together innervate BG downstream structures using GABA and glutamate, respectively. Comparison of the neuronal activity in BG input and downstream structures reveals that subthalamic, not striatal, activity fluctuations correlate with modulations in the increase/decrease discharge balance of BG downstream neurons during temporal discounting classical condition task. After induction of parkinsonism with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), abnormal low beta (8-15 Hz) spiking and local field potential (LFP) oscillations resonate across the BG network. Nevertheless, LFP beta oscillations entrain spiking activity of STN, striatal cholinergic interneurons and BG downstream structures, but do not entrain spiking activity of striatal projection neurons. Our results highlight the pivotal role of STN divergent projections in BG physiology and pathophysiology and may explain why STN is such an effective site for invasive treatment of advanced Parkinson's disease and other BG-related disorders. DOI: http://dx.doi. org/10.7554/eLife.16443. 001
Research Article THE ROLE OF LINOLEIC ACID, PALMITIC ACID AND LEUCINE IN LIPSTATIN BIOSYNTHESIS BY STREPTOMYCES TOXYTRICINI

Lipstatin, a new and very potent inhibitor of pancreatic lipase, was isolated from the Streptomyces toxytricini. Lipstatin contain a beta lactone structure that probably account for the irreversible lipase inhibition. As per the biosynthetic pathway the role of linoleic acid, palmitic acid and leucine in lipstatin biosynthesis by Streptomyces toxytricini was studied. Different oils were analyzed to measure the concentration of the linoleic acid and palmitic acid in the medium, and then the selected oil was used as media ingredients. While leucine was used as a media ingredient as well as feeding nutrient in the production medium. The lipstatin production depends on linoleic acid palmitic acid and leucine concentration as per biochemical pathway. But in present study it was observed that in control media the lipstatin activity was 1.108 mg/g while when we use linoleic acid and palmitic acid as media ingredients then the activity was 0.610 mg/g. Similarly in case of leucine was used as ingredient as well as feeding solution the maximum activity was observed 0.810 mg/g at 264 h. Thus, it may conclude that their is no significant impact of linoleic acid, palmitic acid and leucine as media ingredients and as a feeding solution on lipstatin production.
Water Contamination Detection

For the detection of contaminations in urban water supply networks we introduce a continuous optimal control model governed by partial differential equations. We derive a linear mixedinteger model by discretization of the dynamics of the partial differential equations and by approximations to the cost functional. Finally, we present numerical results for artificial and real-world networks.
Upper body detection in unconstrained still images

The detection of upper bodies is fundamental to many computer vision tasks, with human pose estimation being our focus. Accurate upper body detection improves the robustness and reduces the search space for top-down as well as bottom-up approaches for pose estimation. This paper focuses on a particularly challenging task of detecting upper bodies from unconstrained still images. We propose a method that fuses the reliability of face detectors with the robustness of people detection based on HoG descriptors to improve the accuracy of upper body detection from monocular still images with cluttered background, poor illumination, motion blur and high-degree of occlusion. We compare the performance of the proposed method with six existing face and upper body detectors. Despite the relatively simple concept behind the proposed detector, it performed on par with the state of the art systems using challenging test images from the Buffy Stickmen v2.1 dataset.
A Comparative Study of Model Based Design of PV cell in MATLAB/Simulink/Simscape

 Abstract— Solar-electric-energy system has grown consistently over the years. The core of solar power generation system is a solar cell. Modeling the solar cell is required for study of photovoltaic systems. This paper focuses to show different models that simulate the IV and PV performance characteristics of the solar cell and compared. The PV models based on diodes or equation based and models in MATLAB, Simscape are considered. Three, diode based PV cell models of, Open circuit (O/C) voltage & Short Circuit (S/C) current with 5 parameters, Equivalent circuit with 5 and 8 parameters configurations are examined. These solar cell diode based models characteristics are compared with the Solar cell model in MATLAB Simscape, Simelectronics library which is modelled such that the user has a choice to select from one of the three mentioned configurations of PV cell. The comparative studies are validated with the results of Manufacturer datasheet for certain test conditions and parameters. On the basis of all these, the adequate model is selected.
Valproic Acid Increases CD133 Positive Cells that Show Low Sensitivity to Cytostatics in Neuroblastoma

Valproic acid (VPA) is a well-known antiepileptic drug that exhibits antitumor activities through its action as a histone deacetylase inhibitor. CD133 is considered to be a cancer stem cell marker in several tumors including neuroblastoma. CD133 transcription is strictly regulated by epigenetic modifications. We evaluated the epigenetic effects of treatment with 1mM VPA and its influence on the expression of CD133 in four human neuroblastoma cell lines. Chemoresistance and cell cycle of CD133+ and CD133− populations were examined by flow cytometry. We performed bisulfite conversion followed by methylation-sensitive high resolution melting analysis to assess the methylation status of CD133 promoters P1 and P3. Our results revealed that VPA induced CD133 expression that was associated with increased acetylation of histones H3 and H4. On treatment with VPA and cytostatics, CD133+ cells were mainly detected in the S and G2/M phases of the cell cycle and they showed less activated caspase-3 compared to CD133− cells. UKF-NB-3 neuroblastoma cells which express CD133 displayed higher colony and neurosphere formation capacities when treated with VPA, unlike IMR-32 which lacks for CD133 protein. Induction of CD133 in UKF-NB-3 was associated with increased expression of phosphorylated Akt and pluripotency transcription factors Nanog, Oct-4 and Sox2. VPA did not induce CD133 expression in cell lines with methylated P1 and P3 promoters, where the CD133 protein was not detected. Applying the demethylating agent 5-aza-2’-deoxycytidine to the cell lines with methylated promoters resulted in CD133 re-expression that was associated with a drop in P1 and P3 methylation level. In conclusion, CD133 expression in neuroblastoma can be regulated by histone acetylation and/or methylation of its CpG promoters. VPA can induce CD133+ cells which display high proliferation potential and low sensitivity to cytostatics in neuroblastoma. These results give new insight into the possible limitations to use VPA in cancer therapy.
Medical Entity Recognition: A Comparaison of Semantic and Statistical Methods

Medical Entity Recognition is a crucial step towards efficient medical texts analysis. In this paper we present and compare three methods based on domain-knowledge and machine-learning techniques. We study two research directions through these approaches: (i) a first direction where noun phrases are extracted in a first step with a chunker before the final classification step and (ii) a second direction where machine learning techniques are used to identify simultaneously entities boundaries and categories. Each of the presented approaches is tested on a standard corpus of clinical texts. The obtained results show that the hybrid approach based on both machine learning and domain knowledge obtains the best performance.
A Compound Framework for Sports Prediction: The Case Study of Football

Many sports fans are interested in predicting the results of sports matches. But predicting the result of a match is difficult because there are many factors to be considered. Bayesian inference and rule-based reasoning are widely used scientific techniques to predict future information including sports results, stock prices, etc. In this paper we propose a novel framework for sports prediction using these techniques together with an in-game time-series approach. Our framework is novel and unique in three ways. Firstly, the framework consists of two major components: a rule-based reasoner and a Bayesian network component. This is a compound one in the sense that two different methods cooperate in predicting the results of sports matches. The concept of combining two different techniques into one framework is motivated by the fact that most sports results are highly stochastic, but at the same time, the strategies of a team (or a player) can be determined by certain crisp logic rules. Furthermore, because of the rule-based component, our framework can give reasonably good predictions even when statistical data are scanty, so it can be used to predict the result of a match between two teams (or players) which have had few or no previous encounters; while most machine learning techniques may not be used because they can suffer from insufficient data. Secondly, our framework considers many factors like current scores, morale, fatigue, skills, etc. when it predicts the results of sports matches; while most previous works considered only one factor, usually score, or few factors. Thirdly, contrary to most previous works on sports prediction we use a knowledge-based in-game time-series approach to predict sports matches. This approach enables our framework to reflect the tides/flows of a sports match and makes our prediction certainly more realistic, and somewhat more accurate. We have implemented a football results predictor called FRES (Football Result Expert System) based on our framework, and show that it gives reasonable and stable predictions.
A graphical restricted instructional programming environment

Following a decision not to expose our beginning students to the full Unix environment, we began with a hastily-constructed X-window environment that ooered the simpliication and protection that we required. Not surprisingly, this environment had many faults. In this paper we describe the rationale, design, and implementation of the environment that was written to replace the rst one. The design started from rst principles by establishing the target users' expectations and background skills. Surveys were conducted with all the groups interacting with the environment { the students, the system and teaching staa { to establish the audience and the system requirements and later to evaluate the new environment. Sixty percent of the student respondents believed the original X-window environment slowed their progression through the course while one third of the students who had previously failed felt the environment was a signiicant factor in their poor performance. All criticisms and requirements were addressed by the replacement environment, with further evaluation surveys endorsing the design with a 90% student user preference.
Rate Models ’ Implied Volatility Function Stochastic Movements

Abstract: This paper presents a one factor and a two factor arbitrage-free interest rate models with parsimonious implied volatility functions. The models are empirically tested on the entire swaption surface in three currencies (U. S. dollar, Euro and Japanese yen) over a five year period. They are shown to be robust in explaining the swaption values, and the implied volatility functions are shown to exhibit a three factor movement in all three currencies.
Spiral waves of spreading depression in the isolated chicken retina.

Existence of the theoretically predicted spiral waves of excitation in intact two-dimensional networks of excitable elements has been experimentally confirmed in the isolated chicken retina. The preparation supports the waves of Leão's spreading depression (SD) the concentric propagation of which from the point of origin can be directly observed as a change of the optical properties of the retinal tissue. The propagation rate of 3.7 mm/min (35 degrees C) decreased to 1.5 mm/min for SD waves elicited during relative refractory period. When a several-mm long segment of the SD wave had been blocked by anodal polarization, the laterally opened ends of the wavefront started to spread after termination of polarization into the previously blocked tissue, gradually turning around and penetrating into the region recovering from the original SD. One or two simultaneously generated spiral waves of SD continued to rotate for several cycles. Spiral SD could also be elicited by punctiform cathodal polarization (1 mA) applied to the SD wave-rear. Since the new SD wave could only spread into the recovering tissue it formed a laterally open wavefront, the free ends of which eventually turned around and started spiral SD. With continued reverberation the nucleus of the spiral SD wave gradually migrated across the retina until it approached an obstacle (e.g., pecten) which stopped further spiral propagation. Spiral SD waves were elicited in 31 retinal preparations and lasted for 4.5 cycles on the average. Average cycle duration was 4.7 min. Spontaneous spiral SD waves were observed in preparations incubated in Mg2+-free media. The spiral SD waves in retina are compared with mathematical models of analogous phenomena. It is argued that spiral SD waves probably exist in the cerebral cortex of rats and account for generation of repetitive SD waves sometimes elicited by overlapping stimulation of two cortical regions.
Pseudo Cold Start Link Prediction with Multiple Sources in Social Networks

Link prediction is an important task in social networks and data mining for understanding the mechanisms by which the social networks form and evolve. In most link prediction researches, it is assumed either a snapshot of the social network or a social network with some missing links is available. Most existing researches therefore approach this problem by exploring the topological structure of the social network using only one source of information. However, in many application domains, in addition to the social network of interest, there are a number of auxiliary information available. In this work, we introduce the pseudo cold start link prediction with multiple sources as the problem of predicting the structure of a social network when only a small subgraph of the social network is known and multiple heterogeneous sources are available. We propose a two-phase supervised method: the first phase generates an efficient feature selection scheme to find the best feature from multiple sources that is used for predicting the structure in the social network. In the second phase, we propose a regularization method to control the risk of over-fitting induced by the first phase. We assess our method empirically over a large data collection obtained from Youtube. The extensive experimental evaluations confirm the effectiveness of our approach.
Semantical Analysis of Modal Logic I Normal Modal Propositional Calculi

Publisher Summary This chapter discusses semantical analysis of modal logic ii and non-normal modal propositional calculi. The proof of sufficiency, which is omitted by many, proceeds by constructing a normal characteristic matrix by Lindenbaum's method. The tableaux that leads to a decision procedure for the propositional calculi is considered.
The Pricing of Initial Public Offerings When IPO Size and Investment Choice are Endogenous

This paper shows that the underpricing of a one-shot equity offering can be explained as a direct result of personal wealth maximization by the original shareholders. When a firm is taken public to raise capital for investment the original shareholders have to trade-off how much capital to raise and invest against how much fractional firm ownership to retain. Asymmetric information therefore creates a dilemma for the capital constrained owners of a firm with above-average investment opportunities. On one hand, their higher return on investment translates into a greater marginal gain from raising and investing more capital than for the owners of a lower quality firm. On the other hand, their firm's value is increasing more rapidly with investment than the value of a lower quality firm. This makes raising additional outside equity capital increasingly costly. The inherent relationship between the returns on investment and the firm values results in a violation of the single-crossing property. An investment policy exists that optimally trades-off an above-average firm's higher marginal costs of outside equity against its greater marginal losses from foregoing investment. At this investment policy issuing underpriced equity is shown to dominate all possible further changes in the investment policy. Several new empirical implications are derived.