Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Evaluation of Efficacy of Systemic Steroid Therapy for the Patients with Olfactory Disturbance

Objectives:This study quantifies the efficacy of a short-term systemic steroid therapy in the patients with anosmia or hyposmia using the butanol threshold test (BTT and the visual analogue scale (VAS. Materials and Method:Thirty patients with anosmia or hyposmia were included in this study from June 1996 through April 1997. The patients were divided into inflammatory and noninflammatory groups depending on the etiology. We evaluated the patients before and after the systemic steroid admi-nistration using both BTT and VAS. Results:Overall improvements of olfactory function was observed upto 70% by BTT and 53% by VAS, and the therapeutic effect of steroid on olfactory malfunction was statistically significant (p=0.0001. Improvements in the olfaction was measured by BTT, which correlated strongly with the measurements by VAS with statistical significance (r=0.77578, p=0.0001. The inflammatory group showed better steroid response (83% than the noninflammatory group (61%, but there was no statistical significance. Conclusion:The systemic steroid administration may be effective in the treatment of olfactory disturbance with a good correlation between BTT and VAS. (Korean J Otolaryngol 1998;41(1 :37-41
Learning audio-visual associations using mutual information

This paper addresses the problem of finding useful associations between audio and visual input signals. The proposed approach is based on the maximization of mutual information of audio-visual clusters. This approach results in segmentation of continuous speech signals, and finds visual categories which correspond to segmented spoken words. Such audio-visual associations may be used for modeling infant language acquisition and to dynamically personalize speech-based human-computer interfaces for various applications including catalog browsing and wearable computing. This paper describes an implemented system for learning shape names from camera and microphone input. We present results in an evaluation of the system for the domain of modeling language learning.
Balastos electrónicos no resonantes para lámparas de alta intensidad de descarga. Aportaciones en el circuito de arranque y en las etapas de calentamiento y régimen permanente

La presente tesis doctoral se enmarca dentro de una de las lineas de investigacion consolidadas por el Grupo de Electronica Industrial (GEI), perteneciente al Area de Tecnologia Electronica (ATE) de la Universidad de Oviedo. La linea de investigacion referida es la iluminacion electronica, es decir, el empleo de fuentes de alimentacion electronica en aplicaciones de iluminacion. En esta tesis doctoral, se ha realizado un estudio basico del fenomeno de resonancias acusticas en lamparas de alta intensidad de descarga. Estas lamparas, son las empleadas comunmente en aplicaciones en las que se requiere una gran cantidad de luz (alumbrado de exteriores y carreteras, alumbrado industrial, espectaculos deportivos, aeropuertos, etc.). El fenomeno de resonancias acusticas es el principal impedimento al empleo generalizado y a gran escala, de balastos electronicos en lamparas de alta intensidad de descarga. Estas resonancias acusticas provocan la inestabilidad del arco de descarga, de manera que la luz emitida por la lampara, asi como determinados parametros electricos de la misma, fluctuan enormemente y de manera caotica. Este hecho se tiene al alimentar lamparas de descarga a altas frecuencias. Por tanto es importante realizar este estudio si se pretende buscar soluciones al problema. De las conclusiones del mencionado estudio, se han obtenido una serie de caracteristicas que deben presentar las formas de onda de alta frecuencia de alimentacion de este tipo de lamparas. De este modo han podido especificarse los requerimientos que debe cumplir un sistema electronico de alimentacion en alta frecuencia, para lamparas de este tipo, que minimice el riesgo de aparicion de inestabilidad en el arco de descarga, debidas a resonancias acusticas. A continuacion, se han propuesto nuevas topologias y metodos de control en los cuales basar el diseno y construccion de balastos electronicos que eviten resonanc
PropBank: the Next Level of TreeBank

There has long been a recognition that syntactic structure alone does not provide enough information for machine understanding of human language. Various efforts under the auspices of MUC [8] have added limited-coverage semantic lexicons in order to improve the performance of the systems under evaluation. With the aim of providing data for statistical techniques several sites are investigating semantic annotation. The Prague Tectogrammatics project [3] endeavours to annotate semantic relationships at the same time as syntactic and morphological structure. The Framenet Project [4] is eschewing fine-grained syntactic structure in favor of ’chunked’ data and semantic annotation. This paper describes the PropBank project at Penn, which adds a layer of semantic annotation atop the syntactic structure already present in the Penn TreeBank [5,6].
Ascertaining Data Mining Rules Using Statistical Approaches

Knowledge acquisition techniques have been well researched in the data mining community. Such techniques, especially when used for unsupervised learning, often generate a large quantity of rules and patterns. While many rules generated are useful and interesting, some information is not captured by those rules, such as already known patterns, coincidental patterns and patterns with no significant value for the real world applications. Sustaining the interestingness of rules generated by data mining algorithm is an active and important area of data mining research. Different methods have been proposed and have been well examined for discovering interestingness in rules. These measures often only reflect the interestingness with respect to the database being observed, and as such the rules will satisfy the constrains with respect to the sample data only, but not with respect to the whole data distribution. Therefore, one can still argue the usefulness of the rules and patterns with respect to their use in practical problems. As the data mining techniques are naturally data driven, it would benefit to affirm the generated hypothesis with a statistical methodology. In our research, we investigate how to combine data mining and statistical measurement techniques to arrive at more reliable and interesting set of rules. Such a combination is greatly essential to conquer the data overload in practical problems. A real world data set is used to explore the ways in which one can measure and verify the usefulness of rules from data mining techniques using statistical analysis.
PREMISES, a Scalable Data-Driven Service to Predict Alarms in Slowly-Degrading Multi-Cycle Industrial Processes

In recent years, the number of industry-4.0-enabled manufacturing sites has been continuously growing, and both the quantity and variety of signals and data collected in plants are increasing at an unprecedented rate. At the same time, the demand of Big Data processing platforms and analytical tools tailored to manufacturing environments has become more and more prominent. Manufacturing companies are collecting huge amounts of information during the production process through a plethora of sensors and networks. To extract value and actionable knowledge from such precious repositories, suitable data-driven approaches are required. They are expected to improve the production processes by reducing maintenance costs, reliably predicting equipment failures, and avoiding quality degradation. To this aim, Machine Learning techniques tailored for predictive maintenance analysis have been adopted in PREMISES (PREdictive Maintenance service for Industrial procesSES), an innovative framework providing a scalable Big Data service able to predict alarming conditions in slowly-degrading processes characterized by cyclic procedures. PREMISES has been experimentally tested and validated on a real industrial use case, resulting efficient and effective in predicting alarms. The framework has been designed to address the main Big Data and industrial requirements, by being developed on a solid and scalable processing framework, Apache Spark, and supporting the deployment on modularized containers, specifically upon the Docker technology stack.
Phase entrainment and perceptual cycles in audition and vision

Recent research indicates fundamental differences between the auditory and visual systems: Whereas the visual system seems to sample its environment, cycling between "snapshots" at discrete moments in time (creating perceptual cycles), most attempts at discovering discrete perception in the auditory system failed. Here, we show in two psychophysical experiments that subsampling the very input to the visual and auditory systems is indeed more disruptive for audition; however, the existence of perceptual cycles in the auditory system is possible if they operate on a relatively high level of auditory processing. Moreover, we suggest that the auditory system, due to the rapidly fluctuating nature of its input, might rely to a particularly strong degree on phase entrainment, the alignment between neural activity and the rhythmic structure of its input: By using the low and high excitability phases of neural oscillations, the auditory system might actively control the timing of its "snapshots" and thereby amplify relevant information whereas irrelevant events are suppressed. Not only do our results suggest that the oscillatory phase has important consequences on how simultaneous auditory inputs are perceived; additionally, we can show that phase entrainment to speech sound does entail an active high-level mechanism. We do so by using specifically constructed speech/noise sounds in which fluctuations in low-level features (amplitude and spectral content) of speech have been removed, but intelligibility and high-level features (including, but not restricted to phonetic information) have been conserved. We demonstrate, in several experiments, that the auditory system can entrain to these stimuli, as both perception (the detection of a click embedded in the speech/noise stimuli) and neural oscillations (measured with electroencephalography, EEG, and in intracranial recordings in primary auditory cortex of the monkey) follow the conserved "high-level" rhythm of speech. Taken together, the results presented here suggest that, not only in vision, but also in audition, neural oscillations are an important tool for the discretization and processing of the brain's input. However, there seem to be fundamental differences between the two systems: In contrast to the visual system, it is critical for the auditory system to adapt (via phase entrainment) to its environment, and input subsampling is done most likely on a hierarchically high level of stimulus processing.
Neural control systems trained by dynamic gradient methods for automotive applications

The use of dynamic gradient-based training of neural controllers for automotive systems is illustrated. The authors use a recurrent structure that embeds an identification network and a neural controller and that properly treats both short- and long-term effects of controller weight changes. This results in an approximately optimal control strategy. Feedforward and hybrid feedforward-feedback neural controllers trained by dynamic backpropagation and a dynamic decoupled extended Kalman filter (DDEKF) are investigated. A quarter-car active suspension model is considered in both linear and nonlinear forms, and representative results are presented. Methods using higher-order information, e.g., DDEKF are very effective in comparison to methods based exclusively upon gradient descent, e.g., dynamic backpropagation (DBP). The use of a recurrent structure for obtaining derivatives for controller training is illustrated. <<ETX>>
Cognitive and Value Correlates of Health‐Related Behavior in Brazilian Middle‐class Women

The prevalence of several health-related behaviors was studied in a random sample of 294 Brazilian middle-class women between 25 and 45 years of age. The interrelationships among these behaviors confirmed previous findings in North America and provided the basis for the computation of a composite index of health behavior. The sociodemographic, cognitive, and value correlates of scores on the Health Behavior Index (HBI) were investigated using regression analysis. Women who behave in favor of good health were mothers of more children, had a definition of health as higher level functioning and self-realization, believed that care outweighs chance as a determinant of health, and valued inner harmony as opposed to comfort and independence. These findings verified most aspects of Pender's health promotion model (Pender, Walker, Sechrist, & Stromberg, 1990).
Energy-efficient hardware design based on high-level synthesis

This dissertation describes research activities broadly concerning the area of High-level synthesis (HLS), but more specifically, regarding the HLS-based design of energy-efficient hardware (HW) accelerators. HW accelerators, mostly implemented on FPGAs, are integral to the heterogeneous architectures employed in modern high performance computing (HPC) systems due to their ability to speed up the execution while dramatically reducing the energy consumption of computationally challenging portions of complex applications. Hence, the first activity was regarding an HLS-based approach to directly execute an OpenCL code on an FPGA instead of its traditional GPU-based counterpart. Modern FPGAs offer considerable computational capabilities while consuming significantly smaller power as compared to high-end GPUs. Several different implementations of the K-Nearest Neighbor algorithm were considered on both FPGA- and GPU-based platforms and their performance was compared. FPGAs were generally more energy-efficient than the GPUs in all the test cases. Eventually, we were also able to get a faster (in terms of execution time) FPGA implementation by using an FPGA-specific OpenCL coding style and utilizing suitable HLS directives. The second activity was targeted towards the development of a methodology complementing HLS to automatically derive power optimization directives (also known as "power intent") from a system-level design description and use it to drive the design steps after HLS, by producing a directive file written using the common power format (CPF) to achieve power shut-off (PSO) in case of an ASIC design. The proposed LP-HLS methodology reduces the design effort by enabling designers to infer low power information from the system-level description of a design rather than at the RTL. This methodology required a SystemC description of a generic power management module to describe the design context of a HW module also modeled in SystemC, along with the development of a tool to automatically produce the CPF file to accomplish PSO. Several test cases were considered to validate the proposed methodology and the results demonstrated its ability to correctly extract the low power information and apply it to achieve power optimization in the backend flow.
A Method for Studying Natural Language Dialogue.

Abstract : This report describes progress on an experimental technique for studying application-specific dialogues. In this technique, a user interacts via typed messages with a second person who is simulating a computerized natural language interface. The dialogues are all concerned with order-handling and invoicing; however, they are collected in three different situations. The user is variously attempting to describe, understand, or diagnose an order-handling and invoicing system. Preliminary results indicate some of the ways in which the specific user task (pragmatics) influences dialogue features. The importance and compexity of having a natural language interface understand comments about the communicators and the communication itself as illustrated. (Author)
The Elements Of Law Natural And Politic

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Predicting perceptual quality of images in realistic scenario using deep filter banks

Abstract. Classical image perceptual quality assessment models usually resort to natural scene statistic methods, which are based on an assumption that certain reliable statistical regularities hold on undistorted images and will be corrupted by introduced distortions. However, these models usually fail to accurately predict degradation severity of images in realistic scenarios since complex, multiple, and interactive authentic distortions usually appear on them. We propose a quality prediction model based on convolutional neural network. Quality-aware features extracted from filter banks of multiple convolutional layers are aggregated into the image representation. Furthermore, an easy-to-implement and effective feature selection strategy is used to further refine the image representation and finally a linear support vector regression model is trained to map image representation into images’ subjective perceptual quality scores. The experimental results on benchmark databases present the effectiveness and generalizability of the proposed model.
Combining Qualitative and Quantitative Computation in a Ventilator Therapy Planner

VentPlan is an implementation of an architecture for combining qualitative and quantitative computation in a therapy planner. VentPlan interprets patient data measurements, ventilator settings, and clinical diagnoses, then incorporates these data in a physiologic model. A quantitative modeling system, a belief network and a multiattribute value model work together to refine a patient-specific physiologic model and to calculate recommended treatment plans (settings of the ventilator). Communication among these modules occurs through a shared meaning and probabilistic representation for a common set of physiologic parameters.
A Contribution to Isolated and Grid-Connected Photovoltaic Systems under Shadow Conditions

The power supplied by photovoltaic panel is sensitive to radiation incident on itself. Both isolated and grid connected applications are very important to keep constant output voltage. The load usually works in constant voltage. Variation on radiation levels may lead to not constant output voltage. This work presents the use of a PID controller in a boost converter able to provide constant voltage for an isolated system, even during radiation variation in solar panel. This work also presents a PI controller to a three-phase inverter CC/CA in grid connected system. The controller is designed to inject the whole power produced by the photovoltaic panels on the grid.
Grammar for IELTS

Improve your grammar with Collins Grammar for IELTS and get the score you need. IELTS is the world's leading test of English for higher education and migration and is recognised by 6000 institutions in over 135 countries. Collins Grammar for IELTS is a self-study book for learners of English who plan to take the Academic module of the IELTS test. This book is an ideal tool for learners studying at CEF level B1 or above or with a band score 5-5.5 who are aiming for a band score of 6 or higher. Twenty 4-page units present key areas of grammar with practice exercises similar to the ones in the IELTS test. Practical exam strategies and tips help you improve your IELTS score. Practice exam sections at the end of each unit allow you to familiarise yourself with the test questions. The Audio CD contains texts for Listening practice as well as model answers showing you how to use certain grammatical structures in the IELTS Speaking test. * Also available in the 'Collins English for Exams' series: - Listening for IELTS - Speaking for IELTS - Writing for IELTS - Reading for IELTS - Vocabulary for IELTS
A theory of the effects of advanced information technologies on organizational design, intelligence

This article sets forth a theory of the effects that computer-assisted communication and decision-aiding technologies have on organizational design, intelligence, and decision making. Several components of the theory are controversial and in need of critical empirical investigation. The article focuses on those technology-prompted changes in organizational design that affect the quality and timeliness of intelligence and decision making, as contrasted with those that affect the production of goods and services.
Proposta de um protocolo para reabilitação vestibular em vestibulopatias periféricas

INTRODUCTION: Vestibular rehabilitation has been recognized as a treatment of choice for patients with persistent vertigo due to peripheral vestibular dysfunction, providing significant improvements in quality of life. Among the treatments listed have been exercises Cawthorne and Cooksey, sensory integration and body-moving platforms. OBJECTIVE: The research objective was to invent the protocol of exercises in balance with the exercises of Cawthorne and Cooksey associated to the Sensory Integrative. METHOD: It was developed a pilot case, where was evaluated in the patient the balance for the Scale of Balance of Berg and Dynamic Gait Index (DGI); Five Times Sit-to-Stand (FTSTS), that evaluate the activity to sit down and to get up; Dizziness Handicap Inventory (DHI) that evaluate symptoms of vertigo; Scale of Symptoms after Physiotherapeutic Treatment (ESATF), graduates the symptoms after exercises. It was developed a protocol associating 2 techniques, which the ocular exercises, cephalic and of trunk were associated to the swinging. 10 of 20 sessions were accomplished, due to the patient's little regular attendance. RESULTS: Patient obtained improvement from 19 to 22 in DGI; maintained the points of the Berg; she improved from 74 to 67 in DHI. In scale FTSTS she decreased the time from 15 to 14 seconds to accomplish activities of to sit down and to get up. In ESATF it happened oscillate in punctuation. CONCLUSION: After the treatment, the patient obtained an improvement in the appraised balance and improvement of the functional capacity, and she reduced the time spend to accomplish the activity of to sit down and to get up five times of a chair with the crossed arms. Additional studies with the proposed protocol should be accomplished, with a larger number of patients and regular attendance to have a faster adaptation.
Evolutionary algorithm based neural network controller optimization for autonomous mobile robot navigation

A neural network based navigation algorithm is proposed for mobile robots using ultrasonic sensors. The neural network has a dynamically reconfigurable structure which can not only optimize the weights but also the input sensory connectivity in order to meet any user-defined objective. Further, in order to enhance generalization capability of a single network, a modular network is used in which each network module is optimized for a specific local environment based on environment classification. Both computer simulation and real experiments show the effective performance of the algorithm.