Miracle-dz/UniFAR-FTUs huggingface dataset:
Warifteine, an Alkaloid Purified from Cissampelos sympodialis, Inhibits Neutrophil Migration In Vitro and In Vivo

Cissampelos sympodialis Eichl is a plant from the Northeast and Southeast of Brazil. Its root infusion is popularly used for treatment of inflammatory and allergic diseases. We investigated whether warifteine, its main alkaloid, would have anti-inflammatory effect due to a blockage of neutrophil function. In vivo warifteine treatment inhibited casein-induced neutrophil migration to the peritoneal cavity but did not inhibit neutrophil mobilization from the bone marrow. Analysis of the direct effect of warifteine upon neutrophil adherence and migration in vitro demonstrated that the alkaloid decreased cell adhesion to P and E-selectin-transfected cells. In addition, fLMP-induced neutrophil migration in a transwell system was blocked by warifteine; this effect was mimicked by cAMP mimetic/inducing substances, and warifteine increased intracellular cAMP levels in neutrophils. The production of DNA extracellular traps (NETs) was also blocked by warifteine but there was no alteration on PMA-induced oxidative burst or LPS-stimulated TNFα secretion. Taken together, our data indicate that the alkaloid warifteine is a potent anti-inflammatory substance and that it has an effect on neutrophil migration through a decrease in both cell adhesion and migration.
Unified simulation of elastic rods, shells, and solids

We develop an accurate, unified treatment of elastica. Following the method of resultant-based formulation to its logical extreme, we derive a higher-order integration rule, or elaston, measuring stretching, shearing, bending, and twisting along any axis. The theory and accompanying implementation do not distinguish between forms of different dimension (solids, shells, rods), nor between manifold regions and non-manifold junctions. Consequently, a single code accurately models a diverse range of elastoplastic behaviors, including buckling, writhing, cutting and merging. Emphasis on convergence to the continuum sets us apart from early unification efforts.
Macroeconomic Policy and the Future of Capitalism: The Revenge of the Rentiers and the Threat to Prosperity

Prosperity and austerity the main macroeconomic trends of the twentieth century politics and economics the power of ideas inflation and the economy cause and effect in the relationship between budget defecits and the rate of interest what should be done about the balance of payments and the exchange rate? macroeconomics and the stock market concluding remarks.
Time-weighted quantitative testing of image segmentation with a genetic algorithm

Automatic parameter selection for image segmentation is accelerated by means of a genetic algorithm (GA). Issues remain in selecting the population size, number of generations, and termination of the search. As evaluation time and subsequent image batch-processing time, when the algorithm is applied in practice, are important considerations, this paper introduces a time factor into the GA cost function. It is found that this procedure while preserving the GA solution also improves interpretation and parameter selection.
A new hybrid approach to large vocabulary cursive handwriting recognition

Presents a hybrid modeling technique that is used for the first time in hidden Markov model-based handwriting recognition. This new approach combines the advantages of discrete and continuous Markov models and it is shown that this is especially suitable for modeling the features typically used in handwriting recognition. The performance of this hybrid technique is demonstrated by an extensive comparison with traditional modeling techniques for a difficult large vocabulary handwriting recognition task.
Probabilistic relational data mining

Probabilistic Relational Data Mining (PRDM) refers to the use of Knowledge discovery in database (KDD) methods of learning probabilistic statistical models from relational data that has information about several types of objects. This is usually the case when the database has more than one table. PRDM provides techniques for discovering descriptive models, including relationships, correlations and causal dependencies, embedded in a set of objects as well as their component attributes. In essence, it is a marriage of probabilistic modeling, multi-relational database modeling, and object oriented modeling. The three modeling processes are integrated together into a data mining system to fulfill the overall modeling task, in which, intuitively speaking, database modeling plays a role of input, probabilistic modeling is like an output, and object-oriented modeling provides necessary background information.
Hierarchical physical defect reasoning in digital circuits

We propose a hierarchical physical defect-oriented approach for fault diagnosis in combinational digital circuits. We present the circuit as a network of modules. As modules we consider either library components (e.g. complex gates) of digital circuits or arbitrary subcircuits. The higher level fault diagnosis is carried out in two phases. In the first phase, faulty modules are located by cause-effect analysis using high-level faulty module dictionary. The size of the dictionary depends linearly on the number of modules in the circuit. In the second phase, the set of suspected faulty modules is pruned by reasoning of the defective behaviour. At the lower level, the physical defects are directly located in suspected faulty modules using defect libraries of the modules or by effect-cause reasoning inside the module. The proposed approach to fault diagnosis helps to cope with the growing complexities of digital circuits. The experimental results show high module-level diagnostic resolution of the proposed approach.
New iterative learning identification and model based control of robots using only actual motor torque data

This paper deals with a new iterative learning dynamic identification method of robot controlled with a Computed Torque Control (CTC) law. The parameters of the Inverse Dynamic Model (IDM) used to compute the CTC, are periodically calculated to minimize the quadratic error between the actual joint force/torque and a joint force/torque calculated with the Inverse Dynamic Identification Model (IDIM), linear in relation to the parameters. Usually the parameters are estimated off-line and the IDIM is calculated with the joint position and its noisy derivatives and cannot take into account on line variations of the parameters (IDIM-LS method). The new method called IDIM-ILIC (IDIM with Iterative Learning Identification and Control) overcomes these 2 drawbacks. The parameters are periodically calculated over a moving time window to update the IDM of the CTC, and the IDIM is calculated with the noise-free data of the trajectory generator, which avoids using the noisy derivatives of the actual joint position. An experimental setup on a prismatic joint validates the procedure with stationary parameters and with a variation of the payload.
Presupposition Projection as Proof

Even though Van der Sandt's presuppositions as anaphora approach is empirically successful, it fails to give a formal account of the interaction between world-knowledge and presuppositions. In this paper, an algorithm is sketched which is based on the idea of presuppositions as anaphora. It improves on this approach by employing a de-ductive system, Constructive Type Theory (CTT), to get a formal handle on the way world-knowledge innuences presupposition projection. In CTT, proofs for expressions are explicitly represented as objects. These objects can be seen as a generalization of DRT's discourse markers. They are useful in dealing with presuppositional phenomena which require world-knowledge, such as Clark's bridging examples and Beaver's conditional presuppositions.
Effectiveness of Linear Predictive Coding in Telephony based applications of Speech Recognition

Real time speech recognition is an active area of speech research. Human perception of speech is, in part, reliant on detection of formant frequencies and their transitions times. Subjects utilize formant transition length to categorize sounds. Speech recognition systems can be classified according to the type of speech, the size of the vocabulary, the basic units and the speaker dependence. The position of a speech-recognition system in these dimensions determines which algorithm can or has to be used. Speaker independence is the typical aspect of a speech-recognition application where the rate of success is independent of the user. The demand for telecommunications applications of automatic speech recognition [1] has exploded in recent years. This area seems a natural candidate for speech recognition systems, since it embraces a tremendous variety of applications that rely entirely on audio signals and serial interfaces. However, the telecommunication environment strains the capabilities of current technology, given its broad range of uncontrollable variables, from speaker characteristics to telephone handsets and line quality. Speech coding refers to the process of reducing the bit rate of digital speech representations for transmission or storage, while maintaining a speech quality that is acceptable for the application. LPC (Linear Predictive Coding) is a wellknown technique for speech analysissynthesis at low bit rates. The analysis consists in finding a time-based series of n-pole IIR filters whose coefficients better adapt to the formants of a speech signal. These computations produce a residual signal that is the exact complement to the information kept in the coefficients, if we wish to recover the original. The model of the human vocal tract mechanism assumed by LPC presupposes that speech can be reduced to a succession of voiced or unvoiced sounds. Speech recognition research and development has several goals [2]. Simplifying the interface between user and machine is one major goal. Machine speech recognition and understanding has the potential to greatly simplify the way people work with machines.
An Alternative to compactification

Conventional wisdom states that Newton's force law implies only four noncompact dimensions. We demonstrate that this is not necessarily true in the presence of a nonfactorizable background geometry. The specific example we study is a single 3-brane embedded in five dimensions. We show that even without a gap in the Kaluza-Klein spectrum, four-dimensional Newtonian and general relativistic gravity is reproduced to more than adequate precision. (c) 1999 The American Physical Society.
ffi cient Breadth-First Search Algorithms for Advanced Multi-core Processors

Multi-core processors are a shift of paradigm in computer architecture that promises a dramatic increase in performance. But multi-core processors also bring an unprecedented level of complexity in algorithmic design and software development. In this paper we describe the challenges involved in designing a breadth-first search (BFS) for advanced multi-core processors. The proposed methodology combines a high-level algorithmic design that captures the machine-independent aspects, to guarantee portability with performance to future processors, with a low-level implementation that embeds processor-specific optimizations. Using a fine-graind global coordination strategy derived by the Bulk-Synchronous Parallel (BSP) model we have determined an accurate performance model that has guided the implementation and the optimization of our algorithms. To validate our approach, we use a state-of-the-art multicore processor, the Cell Broadband Engine (Cell BE). Our experiments, obtained on a pre-production Cell BE board running at 3.2 GHz, show almost linear speedups when using multiple synergistic processing elements, and an impressive level of performance when compared to other processors. The Cell BE is typically an order of magnitude faster than conventional processors, such as the AMD Opteron and the Intel Pentium 4 and Woodcrest, an order of magnitude faster than the MTA-2 multi-threaded processor, and two orders of magnitude faster than a BlueGene/L processor.
Novel signal extraction algorithm for communication signals by combining independent component analysis and spatial constraints

Signal of interest (SOI) extraction is a vital issue in communication signal processing. In this paper, we propose a novel iterative algorithm for extracting SOIs from instantaneous mixtures, which explores the spatial constraint corresponding to the Directions of Arrival (DOAs) of the SOIs as prior information into the constrained Independent Component Analysis (cICA) framework. Compared with the FastICA algorithm and beamforming techniques, i.e., MVDR, numerical simulation results demonstrate the effectiveness, robustness and higher performance of the proposed algorithms.
Reconsidering the Avian Nature of the Oviraptorosaur Brain (Dinosauria: Theropoda)

The high degree of encephalization characterizing modern birds is the product of a long evolutionary history, our understanding of which is still largely in its infancy. Here we provide a redescription of the endocranial space of the oviraptorosaurian dinosaur Conchoraptor gracilis with the goal of assessing the hypothesis that it shares uniquely derived endocranial characters with crown-group avians. The existence of such features has implications for the transformational history of avian neuroanatomy and suggests that the oviraptorosaur radiation is a product of the immediate stem lineage of birds—after the divergence of Archaeopteryx lithographica. Results derived from an expanded comparative sample indicate that the strong endocranial similarity between Conchoraptor and modern birds largely reflects shared conservation of plesiomorphic features. The few characters that are maintained as being uniquely expressed in these two taxa are more likely products of convergence than homology but still indicate that the oviraptorosaur endocranial cavity has much to teach us about the complex history of avian brain evolution.
RIP Technique for Frequent Itemset Mining

Data mining is a rapidly expanding field being applied in many disciplines, ranging from remote sensing to geographical information systems, computer cartography, environmental assessment and planning. Rule mining is a powerful technique used to discover interesting associations between attributes contained in a database (Han et al., 2006). Association rules can have one or several output attributes. An output attribute from one rule can be used as the input of another rule. Association rules are thus useful, both for obtaining an idea of what concept structures exist in the data (as with unsupervised clustering) and for model creation. In the second instance, the generated rules provide the underlying concepts used in the construction of decision trees and even neural networks, although this is carried out by the automated learning process. Sequential Pattern Mining also comes in Association rule mining For a given transaction database D, an association rule is an expression of the form X →Y, where X and Y are subsets of attributed set A. The rule X →Y holds with confidence t, if t% of transactions in D that support X also support Y. The rule X →Y has support δ in the transaction set D if δ% of transactions in D support X ∪ Y. Association rule mining can be divided into two steps. In first step, frequent patterns with respect to support threshold (known as min sup) are mined. In second step, association rules are generated with respect to minimum confidence. Many variants of the ARM based algorithm have been developed. Proposed technique is termed as Relative Item Path (RIP) based ARM. It creates an innovative graphical structure that is dynamically updated for each transaction in order to determine associated frequent itemset. This technique scores over existing efficient techniques, which had been proposed in recently year. In this proposed technique, each transaction updates the existing graph created by previous transactions, modifying the RIP value associated with the link. The number of scans of database that are required for determining the association of frequent itemset is reduced, saving a great deal of time consumed in database access. The unique feature of the created RIP graph is that it contains nodes equal to total number of items only. This significantly reduces the processing time and memory space required for ARM. The technique works optimally for small and moderate size database. Large databases give rise to enhanced RIP, which are cumbersome in updating. Still, saving in number of access of database and efficient handling of generated RIP graph achieved by the proposed technique make it a strong candidate for determining ARM.
Fish Species Classification Using Graph Embedding Discriminant Analysis

Fish Species classification is of great utility to marine biologists for the understanding of underwater ecology and fish behavior as well as to keep a log of endangered species, which assists in fisheries management. Traditional methods being either tedious or too computationally intensive lead to the requirement of an automated method of analysis and counting. Generally, the classification of underwater images is challenging due to difficulties in camera calibrations, which lead to distortion, background noise, occlusion and image quality to name a few. This paper proposes a novel method based on an improved image-set matching approach, which makes use of Graph-Embedding Discriminant Analysis. In contrast to the state-of-the-art methods, which operate on single input images, our method makes, use of explicit image set matching which renders it robust computationally. In comparison with the previous classification methods, this method results in considerable discrimination accuracy improvements.
Evolving artificial datasets to improve interpretable classifiers

Differential Evolution can be used to construct effective and compact artificial training datasets for machine learning algorithms. In this paper, a series of comparative experiments are performed in which two simple interpretable supervised classifiers (specifically, Naive Bayes and linear Support Vector Machines) are trained (i) directly on “real” data, as would be the normal case, and (ii) indirectly, using special artificial datasets derived from real data via evolutionary optimization. The results across several challenging test problems show that supervised classifiers trained indirectly using our novel evolution-based approach produce models with superior predictive classification performance. Besides presenting the accuracy of the learned models, we also analyze the sensitivity of our artificial data optimization process to Differential Evolution's parameters, and then we examine the statistical characteristics of the artificial data that is evolved.
Specification and Analysis of Data for Safety-Critical Systems

Data-driven systems are those that make use of large quantities of data to perform their function. For a data-driven system that must also be dependable, ensuring the validity of data is just as crucial as ensuring that of code. Data for such systems are often developed separately from the systems themselves and, if not subjected to careful scrutiny, might not adhere to all of the assumptions made by the systems’ developers. This paper describes a specification and verification technique for data that employs both formal and informal specification methods. This technique is illustrated using part of a real-world system: the Minimum Safe Altitude Warning (MSAW) system, a data-driven system that is used by the U.S. Federal Aviation Administration to detect and alert air traffic controllers to low-flying aircraft.
An Algorithm for Vector Quantizer Design

An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data. The basic properties of the algorithm are discussed and demonstrated by examples. Quite general distortion measures and long blocklengths are allowed, as exemplified by the design of parameter vector quantizers of ten-dimensional vectors arising in Linear Predictive Coded (LPC) speech compression with a complicated distortion measure arising in LPC analysis that does not depend only on the error vector.
Techniques for 3D scalar and vector field visualization with error evaluation

Computational advances over the past few decades have enabled scientists to create ever larger and more complex scientific data sets, which have become increasingly difficult to understand, analyze, compare and communicate. Scientific visualization has become an important tool for presenting the data in graphic form in order to convey insight and understanding of the data. A variety of technologies have been introduced that allow useful graphical presentation of scientific data, however there are limitations. To address this, in this thesis, we introduce four techniques for solving some challenges of scientific visualization. First, we propose a novel scheme to interactively visualize time-varying scalar fields defined on a curvilinear grid. Previous methods either resampled a scalar field on a curvilinear grid into a regular grid and used texture mapping, or cast rays from the image plane through volume, integrating all scalar values along the ray to determine the pixel color, or project grid cells onto image plane and add all cells’ contributions together. All of these approaches have limitations: resampling field data into a regular grid is expensive in both computation and storage, especially for time-varying data sets with a large number of time
Flashing characters with famous faces improves ERP-based brain–computer interface performance

Currently, the event-related potential (ERP)-based spelling device, often referred to as P300-Speller, is the most commonly used brain–computer interface (BCI) for enhancing communication of patients with impaired speech or motor function. Among numerous improvements, a most central feature has received little attention, namely optimizing the stimulus used for eliciting ERPs. Therefore we compared P300-Speller performance with the standard stimulus (flashing characters) against performance with stimuli known for eliciting particularly strong ERPs due to their psychological salience, i.e. flashing familiar faces transparently superimposed on characters. Our results not only indicate remarkably increased ERPs in response to familiar faces but also improved P300-Speller performance due to a significant reduction of stimulus sequences needed for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-Speller.
Women and credit : researching the past, refiguring the future

Credit can be instrumental in equalizing opportunity and alleviating poverty, yet historically men and women have not had the same access. Partly because of this, women have been excluded from many previous economic histories. This book fills a significant gap in exploring the vexed relationship between the women and credit across time and space.Providing examples of credit agencies and initiatives in both the developing and developed world, Women and Credit raises important policy issues and makes valuable suggestions for reconfiguring the relationship between women and credit. It also answers questions previously ignored by scholars, yet of vital significance to women's studies and economic history. What contribution did women make to the development of industrial capitalism? How does women's access to credit vary across time and cultures? How has the development of mico-credit initiatives affected women's economic position and what role will such initiatives play in the future? This book is an invaluable resource for anyone in the fields of Women's studies, economic history, anthropology or development.