Guitar Recital Document

This document, a supplement to a recital; contains biographical, historical, analytical, and performance-related information about the works that are on the program. Chapters are arranged chronologically according to their historical period, and feature the following composers/works: Alonso de Mudarra’s Fantasía que contrahaze la harpa en la manera de Ludovico from Tres libros de musica en cifras para vihuela; François Couperin’s La Convalescente from Pièces de clavecin; Johann Sebastian Bach’s Preludio, Fuga, y Allegro BWV 998; Isaac Albéniz’s Granada from Suite Española Op. 47; Manuel de Falla’s Homenaje: Pièce de Guitare Écrite pour le Tombeau de Claude Debussy; William Walton’s Bagatelles II and III from 5 Bagatelles for Guitar; and Joseph Breznikar’s Etude 3 Free-Form Fugue, Etude 8 Reflective Repetitions, and Etude 11 Shimmering Streams from Twelve American Etudes for guitar. The works feature a variety of musical elements, and their technical aspects of performance include: tremolo, tambora, right hand arpeggio formula, natural and artificial harmonics, glissando, and upper-register playing

Participation in Physical Education Activities by Athletes and Non-Athletes after their Graduation from College

Educators and physical educators are very much concerned about the degree of carry-over of physical education activities into the leisure experiences of pupils. They also seem to be cognizant of the need for noting the degree of interest and skill developed by pupils in these activities for carry-over to adult life. These points are invariably included in all materials encountered which refer to the objectives of physical education or evaluative criteria which have been established for raising the standards of our present programs. Obviously these groups are immensely concerned with this vital factor of carry-over value of activities for the pupils because they realize how essential it is for effective adjustment of the individual in society today. Supervisors and instructors of physical education may often be confronted by the following questions of physical education students: What good will I get out of this activity? What types of activities do people follow most in adult life? What chances are there that I can do the same thing when I get out of college? The purpose of this study is to determine the extent of participation of athletes and non-athletes in physical education activities during high school, to determine the extent of their participation during college, and to determine the frequency of participation in physical education activities by these same athletes and non-athletes during specific periods of time after graduation from college. We also wish to determine the most common physical education activities in which athlete and non-athlete graduates of the various decades from 1900-1954 participated during specific periods of time after graduation from college

Anatomic and physiologic changes in lower extremity venous hemodynamics associated with pregnancy

AbstractPurpose: The purpose of this study was to describe the physiologic effects of pregnancy on lower extremity venous hemodynamics.Methods: Eight pregnant women, six with no known venous disease (NVD) and two with documented deep venous obstruction (DVO), were identified in the first trimester (TM) and studied monthly until delivery and once postpartum (pp) by air plethysmography and duplex scan.Results: None of six women in the NVD group (12 extremities) had obstruction or elevated ambulatory venous pressures as estimated by air plethysmography. In addition, despite significant increases in common femoral vein and saphenofemoral junction diameters, no woman in the NVD group had reflux by either test. Venous filling index increased significantly during pregnancy and decreased significantly pp, but all values remained within the normal range (0.55 ± 0.2 ml/sec first TM, 1.01 ± 0.38 ml/sec late third TM, 0.58 ± 0.08 ml/sec pp; p < 0.03 both comparisons). Common femoral vein diameters increased and decreased in similar fashion (0.99 ± 0.25 cm first TM, 1.21 ± 0.25 cm late third TM, 0.80 ± 0.11 cm pp; p < 0.0005 first vs late third TM, p < 0.005 late third TM vs pp). Saphenofemoral junction vein diameters similarly increased and decreased in size (0.46 ± 0.07 cm first TM, 0.68 ± 0.19 cm late third TM, 0.50 ± 0.10 cm pp; p < 0.01 first vs late third TM, p < 0.03 late third TM vs pp). Neither of the two women in the DVO group showed deterioration of outflow fraction or venous filling index as pregnancy progressed, and neither had thromboembolic complications despite moderate to severe preexisting obstruction. Both women in the DVO group delivered uneventfully. No woman in either group developed varicose veins.Conclusion: Pregnancy-induced changes in lower extremity venous hemodynamics in the NVD and DVO groups were detected but were small. Hormonal or other systemic factors must play a significant role in the development of postpartum varicose veins. (J Vasc Surg 1996;24:763-7.

The Cityscapes Dataset for Semantic Urban Scene Understanding

Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations; 20000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.Comment: Includes supplemental materia

Understanding Cityscapes: Efficient Urban Semantic Scene Understanding

Semantic scene understanding plays a prominent role in the environment perception of autonomous vehicles. The car needs to be aware of the semantics of its surroundings. In particular it needs to sense other vehicles, bicycles, or pedestrians in order to predict their behavior. Knowledge of the drivable space is required for safe navigation and landmarks, such as poles, or static infrastructure such as buildings, form the basis for precise localization. In this work, we focus on visual scene understanding since cameras offer great potential for perceiving semantics while being comparably cheap; we also focus on urban scenarios as fully autonomous vehicles are expected to appear first in inner-city traffic. However, this task also comes with significant challenges. While images are rich in information, the semantics are not readily available and need to be extracted by means of computer vision, typically via machine learning methods. Furthermore, modern cameras have high resolution sensors as needed for high sensing ranges. As a consequence, large amounts of data need to be processed, while the processing simultaneously requires real-time speeds with low latency. In addition, the resulting semantic environment representation needs to be compressed to allow for fast transmission and down-stream processing. Additional challenges for the perception system arise from the scene type as urban scenes are typically highly cluttered, containing many objects at various scales that are often significantly occluded. In this dissertation, we address efficient urban semantic scene understanding for autonomous driving under three major perspectives. First, we start with an analysis of the potential of exploiting multiple input modalities, such as depth, motion, or object detectors, for semantic labeling as these cues are typically available in autonomous vehicles. Our goal is to integrate such data holistically throughout all processing stages and we show that our system outperforms comparable baseline methods, which confirms the value of multiple input modalities. Second, we aim to leverage modern deep learning methods requiring large amounts of supervised training data for street scene understanding. Therefore, we introduce Cityscapes, the first large-scale dataset and benchmark for urban scene understanding in terms of pixel- and instance-level semantic labeling. Based on this work, we compare various deep learning methods in terms of their performance on inner-city scenarios facing the challenges introduced above. Leveraging these insights, we combine suitable methods to obtain a real-time capable neural network for pixel-level semantic labeling with high classification accuracy. Third, we combine our previous results and aim for an integration of depth data from stereo vision and semantic information from deep learning methods by means of the Stixel World (Pfeiffer and Franke, 2011). To this end, we reformulate the Stixel World as a graphical model that provides a clear formalism, based on which we extend the formulation to multiple input modalities. We obtain a compact representation of the environment at real-time speeds that carries semantic as well as 3D information