Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive switching control of fuzzy dynamical systems based on hybrid T-S multiple models

A new hybrid scheme which contains multiple fixed and adaptive Takagi-Sugeno (T-S) identification models is proposed in this paper in order to control efficiently a class of unknown nonlinear dynamical fuzzy systems. One state feedback fuzzy controller corresponds to each T-S model, producing its signal according to the certainty equivalence approach. A performance index and an appropriate switching rule are used to determine the T-S model which best approximates the plant and consequently to pick the best available controller at every time instant. There are two kind of adaptive models: one free running adaptive model and one reinitialized adaptive model which uses the parameters of the best fixed model at every time instant. Lyapunov stability theory is used in order to obtain the adaptive law for every adaptive model's parameters ensuring the asymptotic stability of the system. Singularity problems in the control signal are avoided by modifying the adaptive law and the Next Best Controller Logic (NBCL) ensures the controller's feasibility. A computer simulation example is given to verify the theoretical results.
Debt Defaults and Lessons from a Decade of Crises

The debt crises in emerging market countries over the past decade have given rise to renewed debate about crisis prevention and resolution. In Debt Defaults and Lessons from a Decade of Crises, Federico Sturzenegger and Jeromin Zettelmeyer examine the facts, the economic theory, and the policy implications of sovereign debt crises. They present detailed case histories of the default and debt crises in seven emerging market countries between 1998 and 2005: Russia, Ukraine, Pakistan, Ecuador, Argentina, Moldova, and Uruguay. These accounts are framed with a comprehensive overview of the history, economics, and legal issues involved and a discussion from both domestic and international perspectives of the policy lessons that can be derived from these experiences. Sturzenegger and Zettelmeyer examine how each crisis developed, what the subsequent restructuring encompassed, and how investors and the defaulting country fared. They discuss the new theoretical thinking on sovereign debt and the ultimate costs entailed, for both debtor countries and private creditors. The policy debate is considered first from the perspective of policymakers in emerging market countries and then in terms of international financial architecture. The authors' surveys of legal and economic issues associated with debt crises, and of the crises themselves, are the most comprehensive to be found in the literature on sovereign debt and default, and their theoretical analysis is detailed and nuanced. The book will be a valuable resource for investors as well as for scholars and policymakers.
From Money to Action: Strategies in U.S. Support to Education in the Developing World (SPECIAL ISSUE: The Evaluation of International Cooperation in Education)

The United States provides substantial amounts of development assistance to lowand middle-income countries, much of it administered by the United States Agency for International Development (USAID). While the motives are complex, reasons include the desire to provide humanitarian relief, promote economic and social development, encourage political values and practices compatible with those of the U.S, and build international goodwill toward the U.S. International assistance agencies, such as USAID, have tended to utilize three mechanisms for allocating development assistance funds: problem oriented funding, sector-oriented project funding, and program funding. Of these, USAID has relied most heavily on project assistance, even at a time that many other donors are emphasizing program assistance. In awarding project assistance, USAID staff have choices about the mechanisms through which project funds can be converted into on-the-ground development activities, choices that can shape the eventual impact and effectiveness of the assistance. This paper examines the advantages and challenges associated with using these different mechanisms, with particular attention to aid to education, and suggests implications for the evaluation of development assistance activities. Background U.S. aid to education starts as money, approved by Congress, and intended for the support of education activities and institutions. But money, by itself, does not improve education. To be useful, those funds need to be converted into activities, e.g., training, instructional materials, classroom teaching, data systems. An important role of USAID and government staff in the receiving countries is to convert these inputs into improved educational processes that lead to more knowledgeable and effective graduates, able to participate meaningfully in the continued development of their own country. Consequently, USAID and government staff face two decisions: (a) what development activities to support and (b) what implementation mechanism to use in getting those activities to happen. To some extent, different funding mechanisms have emerged from different philosophies about development and the role of national governments and civil society in directing development funds. Nonetheless, the choice among mechanisms also needs to be grounded in pragmatic considerations of what is most likely to yield the intended development outcomes.
Advances in variational Bayesian nonlinear blind source separation

Linear data analysis methods such as factor analysis (FA), independent component analysis (ICA) and blind source separation (BSS) as well as state-space models such as the Kalman filter model are used in a wide range of applications. In many of these, linearity is just a convenient approximation while the underlying effect is nonlinear. It would therefore be more appropriate to use nonlinear methods. In this work, nonlinear generalisations of FA and ICA/BSS are presented. The methods are based on a generative model, with a multilayer perceptron (MLP) network to model the nonlinearity from the latent variables to the observations. The model is estimated using variational Bayesian learning. The variational Bayesian method is well-suited for the nonlinear data analysis problems. The approach is also theoretically interesting, as essentially the same method is used in several different fields and can be derived from several different starting points, including statistical physics, information theory, Bayesian statistics, and information geometry. These complementary views can provide benefits for interpretation of the operation of the learning method and its results. Much of the work presented in this thesis consists of improvements that make the nonlinear factor analysis and blind source separation methods faster and more stable, while being applicable to other learning problems as well. The improvements include methods to accelerate convergence of alternating optimisation algorithms such as the EM algorithm and an improved approximation of the moments of a nonlinear transform of a multivariate probability distribution. These improvements can be easily applied to other models besides FA and ICA/BSS, such as nonlinear state-space models. A specialised version of the nonlinear factor analysis method for post-nonlinear mixtures is presented as well.
T-algebras as predictors

Monads and their t-algebras have been studied as an important link between categories and universal algebra. Monads of fuzzy sets, that is, monads which are mappings from sets to complete Heyting algebras, have also been studied for some time (see, e.g., [3-4]). This paper reviews two important kinds of monads in this category, singleton monads and fuzzy theories. It is shown that fuzzy theories may have considerable value as a foundation for fuzzy time series forecasting and even directly as a forecasting method.
Compliance control based for a quadruped robot walking over rough terrain

It is well-known that the walking gait for quadruped robots has great potential to travel over rough terrain because of its higher flexibility and better environment adaptability. This paper presents a control framework for a quadruped robot walking over rough terrain, which includes such features: First, a preview controller is designed to create an appropriate body trajectory to realize the desired ZMP; Second, a new optimal force distribution is presented by minimizing the sum of the weighted torque efforts of the robot; Third, a contact force based compliance controller is designed to increase robustness in face of unknown and unanticipated disturbances. Simulations were performed with no four feet supporting period, the robot model successfully fast walking over rough terrain and going upstairs and downstairs. Then in the experiments, the quadruped robot achieved going upstairs with a stair of 0.15m. Simulations and experiments demonstrate the effectiveness of our compliance control strategy.
New nearby stars in the Liverpool-Edinburgh high proper motion survey selected by DENIS photometry ,

We present a systematic search for new neighbourhood stars among the Liverpool-Edinburgh high proper motion survey which we cross-identified with the DENIS survey. Their high proper motions ensure that they are not giant stars. The distances are estimated using DENIS photometry and we found that 100 stars probably lie within 25 parsec from the Sun. They are mostly M-dwarfs, and 10 are probably white dwarfs. This is the first distance estimate for 84 stars among them. 10 stars, L 170-14A, DENIS J0146291-533931, DENIS J0235219-240038, LP 942-107, DENIS J0320518-635148, LTT 1732, DENIS J0428054-620929, DENIS J2210200-701005, DENIS J2230096-534445, and NLTT 166-3 are estimated to be closer than 15 parsec. In addition, one star, DENIS J2343155-241047, could also lie within 15 pc if it belongs to the halo.
Cytolytic CD8 T Cells Recognizing CFP10 Are Recruited to the Lung after Mycobacterium tuberculosis Infection

Optimum immunity against Mycobacterium tuberculosis requires both CD4 (cid:1) and CD8 (cid:1) T cells. In contrast with CD4 (cid:1) T cells, few antigens are known that elicit CD8 (cid:1) T cells during infection. CD8 (cid:1) T cells specific for culture filtrate protein-10 (CFP10) are found in purified protein derivative positive donors, suggesting that CFP10 primes CD8 (cid:1) T cells in vivo. Using T cells from M. tuberculosis –infected mice, we identified CFP10 epitopes recognized by CD8 (cid:1) T cells and CD4 (cid:1) T cells. CFP10-specific T cells were detected as early as week 3 after infection and at their peak accounted for up to 30% of CD8 (cid:1) T cells in the lung. IFN (cid:2) -producing CD8 (cid:1) and CD4 (cid:1) T cells recognizing CFP10 epitopes were preferentially recruited to the lungs of M. tuberculosis –infected mice. In vivo cytolytic activity of CD8 (cid:1) T cells specific for CFP10 and TB10.3/10.4 proteins was detected in the spleen, pulmonary lymph nodes, and lungs of infected mice. The cytolytic activity persisted long term and could be detected 260 d after infection. This paper highlights the cytolytic function of antigen-specific CD8 (cid:1) T cells elicited by M. tuberculosis infection and demonstrates that large numbers of CFP10-specific cytolytic CD8 (cid:1) T cells are recruited to the lung after M. tuberculosis infection.
Design and Performance Analysis of 2.5 GHz Microstrip Bandpass Filter for LTE

Microwave filters since a long time have been an integral part of the radar and the telecommunication systems. Designing better and more accurate filters is the major requirement for the various possible communication techniques like the LTE (Long Term Evaluation) system and WiMAX systems. This paper aims to design microstrip bandpass filters for Parallel - Edge Coupled Line Filter and Interdigital Filter configurations for the frequency of 2.5 GHz used for LTE systems. For microstrip implementation, substrate Rogers TMM10 is used for both designs. The filters have been designed on the ADS software. The final conclusions have been drawn by analysing various performance parameters such as return loss, insertion loss, 3-dB bandwidth etc and found very much acceptable for applications.
Immunity to Infection and in Reticulum-Associated Aminopeptidase 1 in The Role of Endoplasmic

Benoit Kanzler, Peter van Endert and Gabriele NiedermannJisen Huai, Simone Gaedicke, Ahmed Nil, Gilles Besin, Elke Firat, Loredana Saveanu, Peter Aichele, Peter Staeheli,http://www.jimmunol.org/content/178/4/2241J Immunol€2007; 178:2241-2248; ;Referenceshttp://www.jimmunol.org/content/178/4/2241.full#ref-list-1This article cites 37 articles, 10 of which you can access for free at: Subscriptionshttp://jimmunol.org/subscriptionsInformation about subscribing to The Journal of Immunology is online at: Permissionshttp://www.aai. org/ji/copyright.htmlSubmit copyright permission requests at: Email Alertshttp://jimmunol.org/cgi/alerts/etocReceive free email-alerts when new articles cite this article. Sign up at:
Developmental changes in children understanding robotic actions: The case of lifting

Humans develop already from the first years of life the ability to understand the actions and intentions of others and naturally use this skill to help others [1]. It would be important for the future of human-robot collaboration if children could easily generalize this understanding to robotic agents. In this paper we have investigated whether this is possible, at least in the context of inferring object weight from the observation of a humanoid action. Our results show that children of different ages need a different degree of human-likeness in robot motion to be able to infer which weight is being lifted. Indeed, from 10 years of age on, even non-humanlike trajectories can communicate the lifted load, if the lifting speed is appropriately varied as a function of weight. Conversely, younger children are significantly better at judging weight only in presence of a human-like trajectory. Hence, robots should adapt even the basic properties of their motion to their users, taking into account that children perception progressively changes with age.
Feature Discovery by Competive Learning

This paper reporis the results of our studies with an unsupervised learning paradigm which we have called “Competitive Learning. ” We have examined competitive learning using both computer simulation and formal analysis and hove found that when it is applied to parallel networks of neuron-like elements, many potentially useful learning tasks can be accomplished. We were attracted to competitive learning because it seems to provide o way to discover the salient, general features which can be used to classify o set of patterns. We show how o very simply competitive mechanism con discover a set of feature detectors which capture important aspects of the set of stimulus input patterns. We 0150 show how these feature detectors con form the basis of o multilayer system that con serve to learn categorizations of stimulus sets which ore not linearly separable. We show how the use of correlated stimuli con serve IX o kind of “teaching” input to the system to allow the development of feature detectors which would not develop otherwise. Although we find the competitive learning mechanism o very interesting and powerful learning principle, we do not, of course, imagine thot it is the only learning principle. Competitive learning is cm essentially nonassociative stotisticol learning scheme. We certainly imagine that other kinds of learning mechanisms will be involved in the building of associations among patterns of activation in o more complete neural network. We offer this analysis of these competitive learning mechanisms to further our understanding of how simple adaptive networks can discover features importont in the description of the stimulus environment in which the system finds itself.
Secure key agreement over reciprocal fading channels in the low SNR regime

We study the low SNR scaling of the non-coherent secret-key agreement capacity over a reciprocal, block-fading channel. For the restricted class of strategies, where one of the nodes is constrained to transmit pilot-only symbols, we show that the secret-key capacity scales as SNR ·log T if T ≤ 1/SNR, where T denotes the coherence period, and as SNR·log(1/SNR) otherwise. Our upper bound is inspired by the genie-aided argument of Borade and Zheng (IT-Trans 2010). Our lower bound is based on bursty communication, channel training, and secret message transmission.
A two-stage approach for interpreting line drawings of curved objects

We describe a two-stage approach for interpreting line drawings of curved objects. In the first stage, the user enters a natu-ral line drawing of a polyhedral template; this is automatically interpreted as the corresponding polyhedral object. In the second stage, the user enters freehand curves; by relating these to the template, a curved object can be constructed
Resource-Aware Dynamic Spectrum Pricing for Cognitive Radio Networks

Sharing of spectrum has been identified as a key requirement in cognitive radio networks(CRNs). We address the problem of spectrum sharing with spectrum pricing where the primary user wants to sell the spectrum to the secondary users. By equilibrium pricing scheme, each of the players in the spectrum sharing game aims to take the strategy which maximize its own profit under sojourn-time constraint for secondary users. In condition of spectrum sojourn-time is limited because of primary user’s channel usage pattern each of spectrum providers will choose different strategy to increase profit more. With the Bertrand model we analyze the impacts of several secondary users’ preference to spectrum product provided by primary user. (i.e., channel quality, spectrum substitutability and sojourn-time). We propose not only the static solution of the game but also practical one. We propose d-DBRSC algorithm which forces the price dynamically adjusted to the equilibrium in distributed way. The stability of the proposed scheme in terms of convergence to the Nash equilibrium is also studied.
Diet-Health Information and Nutrition: The Intake of Dietary Fats and Cholesterol

Diet-health information and nutrient intake data for a sample of U.S. household meal planners are used to estimate the effect of information on the intake of fat, saturated fat, and cholesterol. Results indicate that an awareness of health problems due to excess intake of these nutrients and the self-assessed importance of avoiding too much of these nutrients in one's diet have significant influence on nutrient intake. Results support the allocative efficiency hypothesis that states that higher human capital promotes healthier food choices through better acquisition and use of health information. Personal and household characteristics significantly affecting nutrient intake include income, schooling, age, sex, race, ethnicity, body mass index, vegetarian status, and dieting status. The informational role of exogenous variables is illustrated by computing their direct and indirect effects on intake.
New Developments in the Pathophysiology, Workup, and Diagnosis of Dural Venous Sinus Thrombosis (DVST) and a Systematic Review of Endovascular Treatments

Dural venous sinus thrombosis (DVST) is a rare cause of stroke, which typically affects young women. The importance of identifying pre-disposing factors that lead to venous stasis lies in the foundation of understanding the etiology, pathophysiology and clinical presentation. The precise therapeutic role of interventional therapies is not fully understood though the current data do suggest potential applications. The aim of the study was to perform a systematic review and meta-analysis to evaluate the utility of and short-term 30-day survival after endovascular therapy for patients with DVST. Standard PRISMA guidelines were followed. Data sources included PubMed keywords and phrases, which were also incorporated into a MeSH search to yield articles indexed in Medline over a 5-year period. All RCTs, observational cohort studies, and administrative registries comparing or reporting DVST were included. Sixty-six studies met inclusion criteria. 35 articles investigating treatment in a summation of 10,285 patients were eligible for data extraction and included in the review of treatment modalities. A total of 312 patients were included for statistical analysis. All patients included received endovascular intervention with direct thrombolysis, mechanical thrombectomy or both. 133 (42.6%) patients were documented to have a neurologic decline, which prompted endovascular intervention. All patients who had endovascular interventions were those who were started on and failed systemic anticoagulation. 44 patients were reported to have intracranial hemorrhages after intervention. Regardless of systemic anticoagulation, patients were still reported to have complications of VTE and PE. Primary outcome at 3-6 month follow up revealed mRS<1 in 224 patients. DVST presents with many diagnostic and therapeutic challenges. The utility of invasive interventions such as local thrombolysis and mechanical thrombectomy is not fully understood. It is exceedingly difficult to conduct large randomized trials for this low incidence disease process with large pathophysiological heterogeneity.
Back to Direct Style II: First-Class Continuations

[18] Robert Hieb and R. Kent Dybvig. Continuations and concurrency. In Proceedings of the Second ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming, pages 128{136, Seattle, Washington, March 1990. SIGPLAN Notices, Vol. 25, No. 3. [19] Pierre Jouvelot and David K. Gi ord. Reasoning about continuations with control e ects. In Proceedings of the ACM SIGPLAN'89 Conference on Programming Languages Design and Implementation, pages 218{226, Portland, Oregon, June 1989.
P-Codec: Parallel Compressed File Decompression Algorithm for Hadoop

The increasing amount and size of data being handled by data analytic applications running on Hadoop has created a need for faster data processing. One of the effective methods for handling big data sizes is compression. Data compression not only makes network I/O processing faster, but also provides better utilization of resources. However, this approach defeats one of Hadoop’s main purposes, which is the parallelism of map and reduce tasks. The number of map tasks created is determined by the size of the file, so by compressing a large file, the number of mappers is reduced which in turn decreases parallelism. Consequently, standard Hadoop takes longer times to process. In this paper, we propose the design and implementation of a Parallel Compressed File Decompressor (P-Codec) that improves the performance of Hadoop when processing compressed data. P-Codec includes two modules; the first module decompresses data upon retrieval by a data node during the phase of uploading the data to the Hadoop Distributed File System (HDFS). This process reduces the runtime of a job by removing the burden of decompression during the MapReduce phase. The second P-Codec module is a decompressed map task divider that increases parallelism by dynamically changing the map task split sizes based on the size of the final decompressed block. Our experimental results using five different MapReduce benchmarks show an average improvement of approximately 80% compared to standard Hadoop.