542 research outputs found
Solving Multiclass Learning Problems via Error-Correcting Output Codes
Multiclass learning problems involve finding a definition for an unknown
function f(x) whose range is a discrete set containing k > 2 values (i.e., k
``classes''). The definition is acquired by studying collections of training
examples of the form [x_i, f (x_i)]. Existing approaches to multiclass learning
problems include direct application of multiclass algorithms such as the
decision-tree algorithms C4.5 and CART, application of binary concept learning
algorithms to learn individual binary functions for each of the k classes, and
application of binary concept learning algorithms with distributed output
representations. This paper compares these three approaches to a new technique
in which error-correcting codes are employed as a distributed output
representation. We show that these output representations improve the
generalization performance of both C4.5 and backpropagation on a wide range of
multiclass learning tasks. We also demonstrate that this approach is robust
with respect to changes in the size of the training sample, the assignment of
distributed representations to particular classes, and the application of
overfitting avoidance techniques such as decision-tree pruning. Finally, we
show that---like the other methods---the error-correcting code technique can
provide reliable class probability estimates. Taken together, these results
demonstrate that error-correcting output codes provide a general-purpose method
for improving the performance of inductive learning programs on multiclass
problems.Comment: See http://www.jair.org/ for any accompanying file
Gallium nitride film deposition by ionized cluster beam epitaxy
An experimental Ionized Cluster Beam System similar in design to the one developed at Kyoto University in Japan was. designed and constructed in the Microelectronics Laboratory at NJIT. This involved making the working drawings, having the parts machined and then assembling the system in a vacuum system together with the necessary variable power supplies, meters, controls, gas inlets, cooling water connections, etc.
Eleven deposition runs were then attempted to grow gallium nitride utilizing the system constructed. In a typical deposition run, gallium was vaporized from a boron nitride crucible at 1000° C and allowed to expand through a 0.5 mm nozzle into a low pressure region containing nitrogen at 1-5 X 10-4 torr in order to form clusters of about 1000 atoms each. The gallium clusters were ionized by an electron source (300 mA) and deposited on heated quartz substrates (550° C) for reactive growth with nitrogen.
For the above deposition conditions, the film growth rate was only 1 Å/min and the films were highly insulating. Photoluminescence measurements and surface analysis of the grown films revealed boron contaimination which was then traced to water vapor contamination of the boron nitride crucible and heater. The water vapor if not driven out by pre-heating the crucible in an oven, caused the boron nitride to decompose at elevated temperatures introducing reactive contaminants at the substrate surface. Further experiments showed that for GaN deposition rates greater, than 1 Å /min (and up to 1 Å /sec) 0 the crucible temperature should be at least 1100° C but preferably around 1400° C
Parasites Induced Skin Allergy: A Strategic Manipulation of the Host Immunity
The absence of a consistent link between parasitoses and skin allergic symptoms in the clinical investigations contrasts to the fact that some parasites are the most potent inducers of immunoglobulin E that exist in nature. To shed some light into this question, this review is focused on the actual knowledge regarding parasites life cycle, interactions with host immunity, the influence on host behavior, and finally the role of all these factors on the skin allergy. The collected data demonstrate that parasites could manipulate the host behavior for its own benefit in different ways, altering its (epi)genetic, biochemical, immunologic or physiologic functions as well as altering its behavior and activity. In this context, skin allergy may be associated with certain stages of the parasites' life cycle and migration into biological barriers, but not necessarily with presence of the parasitosis in the host organism. As compared to T helper (Th) 1 response, the Th2 one, the eosinophilic infiltration and the complement inhibition could assure better conditions for the development of some parasites. Taken together, the suggested hypotheses could be a plausible explanation for the epidemiological puzzle regarding urticaria occurrence, Th2 response and parasitoses, but further studies are necessary to provide better-based conclusions
QUALITY OF LIFE OF DOCTORS – FIELD STUDY UNDER SOME VARIABLES
This research aims to identify the quality of life of a sample of physicians by presenting the results of a field study conducted in the state of Setif on a sample of doctors working in public health in some state hospitals. Therefore, we will try in this intervention to answer the following questions: a. What is the quality of life of physicians? B. Do their levels differ according to the following variables: gender, age, marital status, seniority years, and hospital differences? Article visualizations
Action potential broadening in a presynaptic channelopathy
Brain development and interictal function are unaffected in many paroxysmal neurological channelopathies, possibly explained by homoeostatic plasticity of synaptic transmission. Episodic ataxia type 1 is caused by missense mutations of the potassium channel Kv1.1, which is abundantly expressed in the terminals of cerebellar basket cells. Presynaptic action potentials of small inhibitory terminals have not been characterized, and it is not known whether developmental plasticity compensates for the effects of Kv1.1 dysfunction. Here we use visually targeted patch-clamp recordings from basket cell terminals of mice harbouring an ataxia-associated mutation and their wild-type littermates. Presynaptic spikes are followed by a pronounced afterdepolarization, and are broadened by pharmacological blockade of Kv1.1 or by a dominant ataxia-associated mutation. Somatic recordings fail to detect such changes. Spike broadening leads to increased Ca2+ influx and GABA release, and decreased spontaneous Purkinje cell firing. We find no evidence for developmental compensation for inherited Kv1.1 dysfunction
Contribution à la conduite et à l'évolution des systèmes de production intégrant les domaines qualité, sécurité et environnement
Aujourd’hui les décideurs se doivent de conduire leur système de production en ayant constamment à l’esprit, la satisfaction de toutes les performances, le coût, le délai mais aussi la qualité, la sécurité et l’environnement (QSE). L’objectif de cette thèse est de montrer l’intérêt de disposer de modèles de référence pour construire, piloter et évaluer un système de production en intégrant les exigences QSE imposées par les différentes normes. Il est donc nécessaire de s’appuyer sur un modèle de référence générique de management des systèmes de production dans lequel on va intégrer les domaines Qualité, Sécurité et environnement et sur un ensemble d’indicateurs spécifiques. Dans un premier temps, après avoir expliqué l’intérêt d’une approche intégrée, nous présenterons notre référentiel QSE, la démarche d’intégration suivie puis le modèle de référence obtenu basé sur la méthode GRAI. Enfin, les indicateurs de référence seront présentés afin de mesurer et piloter la performance QSE.Nowadays, decision makers must control their production system taking into account the satisfaction of all performances in terms of cost, lead time, but also quality, security and environment. This thesis aims at showing the interest to have reference models to build, control and evaluate production management systems including Quality, Security and Environment (QSE) requirements according to standards. So, it is necessary to use generic reference models dedicated to production management within which QSE requirements will be integrated and performance indicators will be defined. First, after presenting the interest of integrated approach, the method of work will be presented and the reference model will be detailed, based on the GRAI Method. Finally, the reference performance indicators will be defined in order to measure and control the QSE performance
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Converting English text to speech : a machine learning approach
The task of mapping spelled English words into strings of phonemes and stresses ("reading aloud") has many practical applications. Several commercial systems perform this task by applying a knowledge base of expert-supplied letter-to-sound
rules. This dissertation presents a set of machine learning methods for automatically constructing letter-to-sound rules by analyzing a dictionary of words and their pronunciations. Taken together, these methods provide a substantial performance
improvement over the best commercial system-DECtalk from Digital
Equipment Corporation. In a performance test, the learning methods were trained on a dictionary of 19,002 words. Then, human subjects were asked to compare the performance of the resulting letter-to-sound rules against the dictionary for an additional
1,000 words not used during training. In a blind procedure, the subjects rated the pronunciations of both the learned rules and the DECtalk rules according to whether they were noticeably different from the dictionary pronunciation. The error rate for the learned rules was 28.8% (288 words noticeably different), while the error rate for the DECtalk rules was 44.3% (443 words noticeably different). If, instead of using human judges, we required that the pronunciations of the letter-to-sound rules exactly match the dictionary to be counted correct, then the error
rate for our learned rules is 35.2% and the error rate for DECtalk is 63.6%. Similar results were observed at the level of individual letters, phonemes, and stresses. To achieve these results, several techniques were combined. The key learning
technique represents the output classes by the codewords of an error-correcting code. Boolean concept learning methods, such as the standard ID3 decision-tree algorithm, can be applied to learn the individual bits of these codewords. This converts the multiclass learning problem into a number of boolean concept learning problems. This method is shown to be superior to several other methods: multiclass ID3, one-tree-per-class 1D3, the domain-specific distributed code employed by T. Sejnowski and C. Rosenberg in their NETtalk system, and a method developed by D. Wolpert. Similar results in the domain of isolated-letter speech recognition with the backpropagation algorithm show that error-correcting output codes provide a domain-independent, algorithm-independent approach to multiclass learning problems
An OpenCores/Opensource based Embedded System-on-Chip Platform for Voice over Internet Protocol
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Error-correcting output codes : a general method for improving multiclass inductive learning programs
Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k > 2 values (i.e., k "classes") . The definition is acquired by studying large collections of training examples of the form (xi, f(xi)) . Existing approaches to this problem include (a) direct application of multiclass algorithms such as the decision-tree algorithms ID3 and CART, (b) application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and (c) application of binary concept learning algorithms with distributed output codes such as those employed by Sejnowski and Rosenberg in the NETtalk system. This paper compares these three approaches to a new technique in which BCH error-correcting codes are employed as a distributed output representation. We show that these output representations improve the performance of ID3 on the NETtalk task and of backpropagation on an isolated-letter speech-recognition task. These results demonstrate that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multi- class problems
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