What Dreams May Come - Ambient Suite for Jazz Orchestra

<p>What Dreams May Come is a five-movement suite for jazz orchestra, intended to create a calm and relaxing listening experience. The project is inspired by the mystery of dreaming, and it attempts to communicate musical ideas which reflect the relaxed state one is in when sleeping. The aims of What Dreams May Come are to highlight the timbral combinations available in a jazz orchestra and to draw on characteristics of ambient music to give the listener a relaxing atmosphere. This exegesis explores timbre both in music that served as inspiration for this composition and in the composition itself, and it describes how emphasising timbre in my compositional process affected other musical elements of the piece. Chapter 1 explores Brian Eno’s ambient album Ambient 1: Music for Airports, specifically looking at the role of timbre and texture in the album, and at the overall structuring techniques used by Eno on the album to create coherency. Chapter 2 analyses two compositions for jazz orchestra by Maria Schneider, “Nocturne” and “Sea of Tranquility”, examining the role of timbre in the compositions, as well as the ways Schneider uses soft dynamics and harmonic techniques to structure the pieces. These two chapters look into how Eno and Schneider, in different ways, both highlight timbre in their compositional approaches and processes. Each chapter dives deep into timbral and textural analysis, with additional analysis of form and harmony. Chapter 3 reflects on the ways these two composers informed What Dreams May Come, focussing on how I used techniques from Eno and Schneider to challenge myself in composing for jazz orchestra. In the course of the project, I strove to tap into music’s therapeutic qualities, putting this idea at the forefront of my intentions as a composer. Using dreaming as aesthetic and conceptual influence, Brian Eno’s ambient music as inspiration, and Maria Schneider’s compositions as a musical guide, I have been able to produce a work which not only challenges traditional jazz orchestra techniques but also relaxes listeners by complementing their environments.</p>

What Dreams May Come - Ambient Suite for Jazz Orchestra

<p>What Dreams May Come is a five-movement suite for jazz orchestra, intended to create a calm and relaxing listening experience. The project is inspired by the mystery of dreaming, and it attempts to communicate musical ideas which reflect the relaxed state one is in when sleeping. The aims of What Dreams May Come are to highlight the timbral combinations available in a jazz orchestra and to draw on characteristics of ambient music to give the listener a relaxing atmosphere. This exegesis explores timbre both in music that served as inspiration for this composition and in the composition itself, and it describes how emphasising timbre in my compositional process affected other musical elements of the piece. Chapter 1 explores Brian Eno’s ambient album Ambient 1: Music for Airports, specifically looking at the role of timbre and texture in the album, and at the overall structuring techniques used by Eno on the album to create coherency. Chapter 2 analyses two compositions for jazz orchestra by Maria Schneider, “Nocturne” and “Sea of Tranquility”, examining the role of timbre in the compositions, as well as the ways Schneider uses soft dynamics and harmonic techniques to structure the pieces. These two chapters look into how Eno and Schneider, in different ways, both highlight timbre in their compositional approaches and processes. Each chapter dives deep into timbral and textural analysis, with additional analysis of form and harmony. Chapter 3 reflects on the ways these two composers informed What Dreams May Come, focussing on how I used techniques from Eno and Schneider to challenge myself in composing for jazz orchestra. In the course of the project, I strove to tap into music’s therapeutic qualities, putting this idea at the forefront of my intentions as a composer. Using dreaming as aesthetic and conceptual influence, Brian Eno’s ambient music as inspiration, and Maria Schneider’s compositions as a musical guide, I have been able to produce a work which not only challenges traditional jazz orchestra techniques but also relaxes listeners by complementing their environments.</p>

Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches

The word radiomics, like all domains of type omics, assumes the existence of a large amount of data. Using artificial intelligence, in particular, different machine learning techniques, is a necessary step for better data exploitation. Classically, researchers in this field of radiomics have used conventional machine learning techniques (random forest, for example). More recently, deep learning, a subdomain of machine learning, has emerged. Its applications are increasing, and the results obtained so far have demonstrated their remarkable effectiveness. Several previous studies have explored the potential applications of radiomics in colorectal cancer. These potential applications can be grouped into several categories like evaluation of the reproducibility of texture data, prediction of response to treatment, prediction of the occurrence of metastases, and prediction of survival. Few studies, however, have explored the potential of radiomics in predicting recurrence-free survival. In this study, we evaluated and compared six conventional learning models and a deep learning model, based on MRI textural analysis of patients with locally advanced rectal tumours, correlated with the risk of recidivism; in traditional learning, we compared 2D image analysis models vs. 3D image analysis models, models based on a textural analysis of the tumour versus models taking into account the peritumoural environment in addition to the tumour itself. In deep learning, we built a 16-layer convolutional neural network model, driven by a 2D MRI image database comprising both the native images and the bounding box corresponding to each image.

The Impact of Fiber from Buckwheat Hulls Waste on the Pasting, Rheological and Textural Properties of Normal and Waxy Potato Starch Gels

The aim of this study was to investigate the impact of fiber from buckwheat hull waste (BH) on the pasting, rheological, and textural properties of 4% and 5% (w/w) pastes and gels based on the potato starches with different amylose/amylopectin contents. The starch and starch/fiber mixtures were characterized by pasting and flow measurements as well as by viscoelastic and textural analysis. The pasting properties showed a greater BH effect (0.2%) on the gelatinization of PS than WPS. The starch gels and starch fiber mixtures showed biopolymer gel behavior. In the WPS/BH pastes, a smaller increase in hardness was noted compared to PS/BH.

Neural network texture segmentation of satellite images of woodlands using the U-net model

With the advent of space equipment that allows obtaining panchromatic images of ultra-high spatial resolution (< 1 m) there was a tendency to develop methods of thematic processing of aerospace images in the direction of joint use of textural and spectral features of the objects under study. In this paper, we consider the problem of classification of forest canopy structures based on textural analysis of multispectral and panchromatic images of Worldview-2. Traditionally, a statistical approach is used to solve this problem, based on the construction of distributions of the common occurrence of gray gradations and the calculation of statistical moments that have significant regression relationships with the structural parameters of stands. An alternative approach to solving the problem of extracting texture features is based on frequency analysis of images. To date, one of the most promising methods of this kind is based on wavelet scattering. In comparison with the traditionally applied approaches based on the Fourier transform, in addition to the characteristic signal frequencies, the wavelet analysis allows us to identify characteristic spatial scales, which is fundamentally important for the textural analysis of spatially inhomogeneous images. This paper uses a more general approach to solving the problem of texture segmentation using the convolutional neural network U-net. This architecture is a sequence of convolution-pooling layers. At the first stage, the sampling of the original image is lowered and the content is captured. At the second stage, the exact localization of the recognized classes is carried out, while the discretization is increased to the original one. The RMSProp optimizer was used to train the network. At the preprocessing stage, the contrast of fragments is increased using the global contrast normalization algorithm. Numerical experiments using expert information have shown that the proposed method allows segmenting the structural classes of the forest canopy with high accuracy.

Differential Diagnosis of Cysts and Granulomas Supported by Texture Analysis of Intraoral Radiographs

The aim of this study was to evaluate whether textural analysis could differentiate between the two common types of lytic lesions imaged with use of radiography. Sixty-two patients were enrolled in the study with intraoral radiograph images and a histological reference study. Full textural analysis was performed using MaZda software. For over 10,000 features, logistic regression models were applied. Fragments containing lesion edges were characterized by significant correlation of structural information. Although the input images were stored using lossy compression and their scale was not preserved, the obtained results confirmed the possibility of distinguishing between cysts and granulomas with use of textural analysis of intraoral radiographs. It was shown that the important information distinguishing the aforementioned types of lesions is located at the edges and not within the lesion.