Miracle-dz/UniFAR-FTUs huggingface dataset:
Web Ecology: Recycling HTML Pages as XML Documents Using W4F

In this paper we present the World-Wide WebWrapper Factory (W4F), a Java toolkit to generate wrappers for Web data sources. Some key features of W4F are an expressive language to extract information from HTML pages in a structured way, a mapping to export it as XML documents and some visual tools to assist the user during wrapper creation. Moreover, the entire description of wrappers is fully declarative. As an illustration, we demonstrate how to use W4F to create XML gateways, that serve transparently and onthe-fly HTML pages as XML documents with their DTDs. Comments Postprint version. Copyright ACM, 1999. This is the author’s version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in WebDB '99. This conference paper is available at ScholarlyCommons: http://repository.upenn. edu/db_research/24
Historical Tales and National Identity: An introduction to narrative social psychology

Social psychologists argue that people’s past weighs on their present. Consistent with this view, Historical Tales and National Identity outlines a theory and a methodology which provide tools for better understanding the relation between the present psychological condition of a society and representations of its past. Author Janos Laszlo argues that various kinds of historical texts including historical textbooks, texts derived from public memory (e.g. media or oral history), novels, and folk narratives play a central part in constructing national identity. Consequently, with a proper methodology, it is possible to expose the characteristic features and contours of national identities. In this book Laszlo enhances our understanding of narrative psychology and further elaborates his narrative theory of history and identity. He offers a conceptual model that draws on diverse areas of psychology - social, political, cognitive and psychodynamics - and integrates them into a coherent whole. In addition to this conceptual contribution, he also provides a major methodological innovation: a content analytic framework and software package that can be used to analyse various kinds of historical texts and shed new light on national identity. In the second part of the book, the potential of this approach is empirically illustrated, using Hungarian national identity as the focus. The author also extends his scope to consider the potential generalizations of the approach employed. Historical Tales and National Identity will be of great interest to a broad range of student and academic readers across the social sciences and humanities: in psychology, history, cultural studies, literature, anthropology, political science, media studies, sociology and memory studies.
The Virtual Physiological Human ToolKit

The Virtual Physiological Human (VPH) is a major European e-Science initiative intended to support the development of patient-specific computer models and their application in personalized and predictive healthcare. The VPH Network of Excellence (VPH-NoE) project is tasked with facilitating interaction between the various VPH projects and addressing issues of common concern. A key deliverable is the ‘VPH ToolKit’—a collection of tools, methodologies and services to support and enable VPH research, integrating and extending existing work across Europe towards greater interoperability and sustainability. Owing to the diverse nature of the field, a single monolithic ‘toolkit’ is incapable of addressing the needs of the VPH. Rather, the VPH ToolKit should be considered more as a ‘toolbox’ of relevant technologies, interacting around a common set of standards. The latter apply to the information used by tools, including any data and the VPH models themselves, and also to the naming and categorizing of entities and concepts involved. Furthermore, the technologies and methodologies available need to be widely disseminated, and relevant tools and services easily found by researchers. The VPH-NoE has thus created an online resource for the VPH community to meet this need. It consists of a database of tools, methods and services for VPH research, with a Web front-end. This has facilities for searching the database, for adding or updating entries, and for providing user feedback on entries. Anyone is welcome to contribute.
Short-term load forecasting using PSO-based phase space neural networks

The nonlinear theories of load forecasting, such as the applications of neural network and chaos, have recently made considerable progress. Generally, it is an effective method to combine phase space restructures theory with artificial neural networks (ANN) model for load forecasting. But, they are not so effective to forecast attractors with higher embedded dimension. The paper proposes a new idea based on incidence-degree to determine the nearest point in phase space. In the mean time, an artificial neural networks model based on particle swarm optimization (PSO) learning algorithm is presented for load forecasting. The proposed method has been examined and tested on a practical power system. The test result shows that the precision of load forecasting is improved by means of the new method when the embedded dimension is higher.
A High-Voltage Output Driver for Implantable Biomedical Stimulators and I/O Applications

This work presents a high voltage output driver stage for implantable microelectronic stimulators implemented in AMI 1.5-¿m standard CMOS technology. Stacked device topology is used to implement a high voltage output driver capable of delivering output voltages up to three times the nominal supply voltage. Oxide stress and hot carrier effects are minimized as the voltage across any two terminals of all devices in the output stage are kept below the nominal supply voltage for the utilized technology. Novel techniques are used for level shifting and delay equalization of the complementary inputs to the output driver. The unloaded driver has negligible static power consumption. The output stage is capable of delivering 8.6 V output with a 9 V supply to a 1 k¿ load at an operating frequency of 100 kHz. This corresponds to a driver efficiency of 95.6%. Further improvements and experiments are underway.
Agent-based partner selection and simulation in Collaborative Product Development process

Collaborative Product Development (CPD) process is characterized by autonomous task control, dynamic task sequence, and frequent team collaboration, which endow the process with high flexibility and uncertainty. In the CPD process, designers are the one of the important elements with prominent characteristics of autonomy, collaboration and initiative, the process is knowledge-based simultaneously. Therefore, partner selection is essential problem. Aimed the problem, a multi-agent negotiation-arbitration framework of partner selection is proposed firstly; secondly, partner selection process and rules based on the framework are given; thirdly, 3 optimal strategies for conflict resolution are approached, including suboptimization strategy, waiting strategy and utility strategy; lastly, various contrastive experiments are carried for some CPD process based on the former simulation platform by add new functions. The simulation results show that the selection rules and conflict resolution strategies can shorten project duration on the basis of quantitative analysis; it indicates that the method of agent-based simulation is a valid way to improve the efficiency of process management.
Adaptive visual target detection and tracking using incremental appearance learning

Multiple visual target tracking is a challenging problem due to various uncertainties including noise, clutter, miss-detection and occlusion. In this paper, we propose an adaptive keypoint-based appearance model to represent the appearance of visual targets independent of their shape or type. We also develop an incremental learning algorithm to learn the appearance of targets in time. The proposed method utilizes a simple background subtraction method to prune insignificant keypoints and to adapt the target appearances in different frames. The experimental results presented on several video datasets show the effectiveness of our proposed method.
COL-MOD: A New Module to Quantify the Weight of Damage Incurred by Collision Attacks

The wireless technology is inherently susceptible to many types of attacks. There are few studies that have investigated the collision attacks. However, to the best of our knowledge, there is no prior work on investigating the consequences of the collision attacks on performance of the wireless networks. Hence, this work proposes a new module speciﬁcally for NS2 simulator which is capable of implementing and measuring the impact of the collision attacks on performance of the functional wireless networks. With the same conditions, we further design a testbed to measure the eﬀectiveness of the collision attacks in real networks. Finally, in order to prove the accuracy of the proposed module, its results are compared with the results from the testbed. The purpose is to accurately determine the impact of the collision attacks which is essential for evaluation and testing the potential defense appliances against the attack. The results signify the extensive impact of the attack on disrupting the normal operation of the wireless networks.
Exploration of multiple roots for a polynomial system

Several problems from engineering, chemistry, medicine, etc. can be formulated as a system of equations. Finding a solution for such a system sometimes requires high computational efforts. There are situations when these systems have multiple solutions. For such problems, the task is to find as many solutions as possible. In this paper, we deal with such systems of equations, which have multiple solutions and we attempt to solve them using two different approaches. Both approaches transform the problem into an optimization problem. The two approaches proposed in are (1) a modified line search and (2) an evolutionary algorithm. Several experiments are performed in order to emphasize the advantages and disadvantages of the two methods.
A Gauss-Newton Method for Markov Decision Processes

Approximate Newton methods are a standard optimization tool which aim to maintain the benefits of Newton's method, such as a fast rate of convergence, whilst alleviating its drawbacks, such as computationally expensive calculation or estimation of the inverse Hessian. In this work we investigate approximate Newton methods for policy optimization in Markov Decision Processes (MDPs). We first analyse the structure of the Hessian of the objective function for MDPs. We show that, like the gradient, the Hessian exhibits useful structure in the context of MDPs and we use this analysis to motivate two Gauss-Newton Methods for MDPs. Like the Gauss-Newton method for non-linear least squares, these methods involve approximating the Hessian by ignoring certain terms in the Hessian which are difficult to estimate. The approximate Hessians possess desirable properties, such as negative definiteness, and we demonstrate several important performance guarantees including guaranteed ascent directions, invariance to affine transformation of the parameter space, and convergence guarantees. We finally provide a unifying perspective of key policy search algorithms, demonstrating that our second Gauss-Newton algorithm is closely related to both the EM-algorithm and natural gradient ascent applied to MDPs, but performs significantly better in practice on a range of challenging domains.
Can an Educational Handout Enhance Placebo Analgesia for Experimentally-Induced Pain?

The placebo effect is an interesting phenomenon whereby a dummy treatment can produce therapeutic benefit, such as, pain relief. While evidence for the placebo effect is growing, relatively few studies have explored ways of enhancing placebo effects. To address this, the current study tested whether placebo-induced analgesia could be enhanced by providing an educational handout about the efficacy of analgesics. Fifty university students were allocated to receive placebo treatment under the guise of a new analgesic formula, either with or without an educational handout, or to a no treatment control group before undergoing electrical and cold pressor pain tests. There was a placebo effect for electrically-induced pain with those receiving placebo treatment reporting significantly less pain compared with those who received no treatment. There was also some evidence of enhancement of this placebo-induced analgesia for electrically-induced pain as a result of the educational handout. No differences were found on cold pressor-induced pain. These findings suggest that providing educational information about a treatment could enhance its efficacy via the placebo effect. Future studies should test different methods of providing educational information in order to determine which elicit the strongest effects.
Prognostic Significance of Combination of Preoperative Platelet Count and Neutrophil-Lymphocyte Ratio (COP-NLR) in Patients with Non-Small Cell Lung Cancer: Based on a Large Cohort Study

Introduction The aim of this study was to investigate the prognostic significance of the combination of the preoperative platelet count and neutrophil-lymphocyte ratio (COP-NLR) for predicting postoperative survival of patients undergoing complete resection for non-small cell lung cancer (NSCLC). Methods The preoperative COP-NLR was calculated on the basis of data obtained.Patients with both an increased platelet count (>30.0×104 mm-3) and an elevated NLR (>2.3) were assigned a score of 2, and patients with one or neither were assigned as a score of 1 or 0, respectively. Results A total of 1238 NSCLC patients were enrolled in this analysis. Multivariate analysis using the 15 clinicolaboratory variables selected by univariate analyses demonstrated that the preoperative COP-NLR was an independent prognostic factor for DFS (HR: 1.834, 95%CI: 1.536 to 2.200, P<0.001) and OS (HR: 1.810, 95%CI: 1.587 to 2.056, P<0.001). In sub-analyses by tumor stage (I, II, IIIA), a significant association was found between DFS and OS and level of COP-NLR in each subgroup (P<0.001, P=0.002, P<0.001 for DFS, respectively; P<0.001, P=0.001, P<0.001 for OS). When the subgroup of patients with high-risk COP-NLR (score of 2) was analyzed, no benefit of adjuvant chemotherapy could be found (P=0.237 for DFS and P=0.165 for OS). Conclusions The preoperative COP-NLR is able to predict the prognosis of patients with NSCLC and divide these patients into three independent groups before surgery. Our results also demonstrate that high-risk patients based on the COP-NLR do not benefit from adjuvant chemotherapy. Independent validation of our findings is warranted.
Nintendo Super Smash Bros. Melee: An "Untouchable" Agent

Nintendo's Super Smash Bros. Melee fighting game can be emulated on modern hardware allowing us to inspect internal memory states, such as character positions. We created an AI that avoids being hit by training using these internal memory states and outputting controller button presses. After training on a month's worth of Melee matches, our best agent learned to avoid the toughest AI built into the game for a full minute 74.6% of the time.
Un modèle de bilan hydrique simplifié à deux utilisable en agrométéorologie

EnglishThe authors present a personal-computer model of water balance, that allows the moisture balance of crops to be monitored at county resolution and at 10-day time step. The soil, crop and meteorological data needed for the calculation are listed. Also described is the calculation of potential evapotranspiration, needed for the model. This simple model monitors the soil moisture between two reservoirs and, calibrated against a more complete model, satisfactorily simulates the variation of real evapotranspiration of a crop, particularly during alternating wet and dry spells. The examples of the model's graphitai output have been chosen to show its sensitivity to the parameters "available reserve" and "type of crop"; and this during years of very different rainfall distribution. The display of the variables ETM-ETR and RU-R(t) allows an objective approach to irrigation needs and soil-moisture depletion. Finally some different uses for the results are proposed. francaisLes auteurs presentent un modele de bilan hydrique simplifie au pas de temps decadaire, implante sur micro-ordinateur et permettant un suivi operationnel du bilan hydrique des cultures au niveau departemental. Les donnees d'entree pedologiques, culturales et meteorologiques necessaires au calcul sont repertoriees. Le mode de calcul de l'evapotranspiration potentielle, une donnee d'entree du modele, est egalement rappele. Ce modele simplifie, gerant l'etat hydrique du sol au travers de deux reservoirs et etalonne par rapport a un modele plus complet, simule de facon satisfaisante la regulation de l'evapotranspiration reelle d'une culture, en particulier lors d'alternances de periodes seches et pluvieuses. Des exemples choisis de sorties graphiques du modele permettent de montrer la sensibilite de celui-ci aux parametres «reserve utile» et «type de culture», et ceci pour des annees climatiquement tres differentes sur le plan de la pluviometrie. La visualisation des variables ETM-EIR et RU-R(t) permet une approche synthetique du profil annuel des besoins en eau d'irrigation et du destockage du sol. Enfin, les auteurs proposent differentes utilisations possibles des resultats du modele.
The Generalized Subgraph Problem: Complexity, Approximability and Polyhedra

Unless particular assumptions are verified, the solution of T-stage stochastic optimal control problems by dynamic programming requires approximation of the optimal cost-to-go functions and discretization of the state space. When parametric approximators are used, as it is typically done in applications, this determines two sources of curse of dimensionality with respect to the dimension of the state: 1) The rate of growth of the number of parameters to be optimized necessary to obtain a sufficiently accurate model of the optimal cost-to-go (model complexity); 2) The rate of growth of the number of sample points coming from discretization, required in order to estimate the parameters in such a model (sample complexity). In this paper, we cope with these drawbacks via smart approximating structures for each optimal cost-to-go function and smart sampling techniques for the state space at each stage. We face the curse of dimensionality for model complexity by nonlinear fixed-structure parametric architectures, for which, under suitable hypotheses on the optimal cost-to-go functions, the number of parameters necessary to guarantee a desired approximation accuracy grows at most polynomially with the dimension of the state vector [1,2,3]. As regards the curse of dimensionality in sample complexity, we cope with it by means of deterministic sampling techniques used in Statistics for design of experiments and in highdimensional integration, called low-discrepancy sequences. Using such sequences, under suitable hypotheses on the optimal cost-to-go-functions the rate of decrease of the estimation error is almost linear in the number of sampling points, mildly depending on the dimension of the state [4]. As an application, an inventory forecasting problem is considered. The model represents the monthly evolution of an inventory, which has to be controlled in size by means of orders of items, on the basis of the available forecasts for items demand. We consider a T=3 stages, three-product, two-forecast instance (at the beginning of each decisional stage only the forecasts corresponding to the current month and to the month after are considered). At each stage, the inventory level of each item and its two forecasts represent a 9dimensional state vector. Numerical results are presented. References [1] P.C. Kainen, V. Kůrková, M. Sanguineti, “Minimization of error functionals over variable-basis functions”. SIAM J. on Optimization, 2003 (to appear). [2] V. Kůrková, M. Sanguineti, “Comparison of worst case errors in linear and neural network approximation”. IEEE Trans. on Information Theory, vol. 48, no.2, pp. 264-275, 2002. [3] R. Zoppoli, M. Sanguineti, and T. Parisini, “Approximating networks and Extended Ritz Method for the solution of functional optimization problems”. J. of Optimization Theory and Applications, vol. 112, pp. 403-440, 2002. [4] C. Cervellera, M. Muselli, Deterministic design for neural network learning: An approach based on discrepancy”. IEEE Trans. on Neural Networks, 2003 (to appear).
Resource allocation and utilization in the Blue Gene/L supercomputer

This paper describes partition allocation for parallel jobs in the Blue Gene®/L supercomputer. It describes the novel network architecture of the Blue Gene/L (BG/L) three-dimensional (3D) computational core and presents a preliminary analysis of its properties and advantages compared those of with more traditional systems. The scalability challenge is solved in BG/L by sacrificing granularity of system management. The system is treated as a collection of composite allocation units that contain both processing and communication resources. We discuss the ensuing algorithmic framework for computational and communication resource allocation and present results of simulations that explore resource utilization of BG/L for different workloads. We find that utilization depends strongly on both the predominant partition topology (mesh or torus) and the 3D shapes requested by the running jobs. When communication links are treated as dedicated resources, it is much more difficult to allocate toroidal partitions than mesh ones, especially for jobs of more than one allocation unit in each dimension. We show that in these difficult cases, the advantage of BG/L compared with a 3D toroidal machine of the same size is very significant, with resource utilization better by a factor of 2. In the easier cases (e.g., predominantly mesh partitions), there are no disadvantages. The advantage is primarily due to the BG/L novel multi-toroidal topology that permits coallocation of multiple toroidal partitions at negligible additional cost.
Experimental evidence of asymmetrical competition between two species of parasitic copepods

Lepeophtheirus thompsoni and Lepeophtheirus europaensis are two parasitic copepods naturally isolated on their sympatric hosts, i.e. turbot (Psetta maxima L.) and brill (Scophthalmus rhombus L.), respectively. They are able to meet, mate and hybridize on turbot experimentally, but they are naturally prevented from doing so by a strong host preference when given a choice. Theory suggests that such a pattern is possible, but only under conditions of competition for the resource. In the present study, the attachment rates of the two copepods were studied experimentally under various conditions of competition, infectious dose and number of available hosts. The results suggest a greater sensitivity to competition for the generalist species L. europaensis than for the specialist L. thompsoni, which is in agreement with theoretical predictions.