Linear Analysis of Selected Posttonal Works of Arnold Schoenberg: Toward an Application of Schenkerian Concepts to Music of the Posttonal Era (and) String Quartet, No. 2 (Original Composition)

The analysis of posttonal music is approached through a method (which originated with Henry Weinberg) utilizing Schenker\u27s approach to tonal music as a conceptual model. This method differs from all other methods of analysis in that voice leading is combined with a unique approach to nontonal harmony. The presentation of the analytical method is given a historical perspective through the analysis of Jimbo\u27s Lullaby, by Debussy. There follows detailed analyses of Op. 15 no. 1 (The Book of the Hanging Gardens) and Six Little Piano Pieces, Op. 19 by Arnold Schoenberg. Part II of the dissertation is an original composition, String Quartet, No. 2. Three movements (Vibrant, Exalted, Recitative: Scherzo) for two violins, viola, and \u27cello

Employability skills: Profiling data scientists in the digital labour market

In the current scenario, data scientists are expected to make sense of vast stores of big data, which are becoming increasingly complex and heterogeneous in nature. In the context of today's rapid technological development and its application in a growing array of fields, this role is evolving simultaneously. The present study provides an insight into the current expectations of employers seeking to hire individuals with this job title. It is argued that gaining a better understanding of data scientists’ employability criteria and the evolution of this professional role is crucial. The focus is placed on the desired prerequisites articulated through job advertisements, thus deriving relevant means for furthering theory and practice. It was achieved by harvesting relevant data from job advertisements published on US employment websites, which currently attract the US market's highest recruitment traffic. The key contribution of this study is to have identified means of systematically mapping skills, experience, and qualifications sought by employers for their data scientists, thus providing a data-driven pathway for employability and avoiding skills gaps and mismatches in a profession that is pivotal in the Industry 4.0

Targeted treatment of recurrent platinum-resistant ovarian cancer: current and emerging therapies

With advances in surgical techniques and chemotherapeutic agents, mortality rates from epithelial ovarian cancer (EOC) have slightly decreased over the last 30 years. However, EOC still ranks as the most deadly gynecologic cancer with an overall 5-year survival rate of 45%. Prognosis is especially disappointing for women with platinum-resistant disease, where 80% of patients will fail to respond to available therapies. Emerging treatment strategies have sub-sequently focused on targets which are integral to tumor growth and metastasis. In this review, we will focus on those innovative agents currently under investigation in clinical trials

Quantum Convolutional Neural Networks for Multi-Channel Supervised Learning

As the rapidly evolving field of machine learning continues to produce incredibly useful tools and models, the potential for quantum computing to provide speed up for machine learning algorithms is becoming increasingly desirable. In particular, quantum circuits in place of classical convolutional filters for image detection-based tasks are being investigated for the ability to exploit quantum advantage. However, these attempts, referred to as quantum convolutional neural networks (QCNNs), lack the ability to efficiently process data with multiple channels and therefore are limited to relatively simple inputs. In this work, we present a variety of hardware-adaptable quantum circuit ansatzes for use as convolutional kernels, and demonstrate that the quantum neural networks we report outperform existing QCNNs on classification tasks involving multi-channel data. We envision that the ability of these implementations to effectively learn inter-channel information will allow quantum machine learning methods to operate with more complex data. This work is available as open source at https://github.com/anthonysmaldone/QCNN-Multi-Channel-Supervised-Learning

String Quartet, No. 2

Three movements (Vibrant, Exalted, Recitative: Scherzo) for two violins, viola, and \u27cello. Part I of this dissertation is an essay entitled Linear Analysis of Selected Posttonal Works of Arnold Schoenberg: Toward an Application of Schenkerian Concepts to Music of the Posttonal Era

Robust MPC-Based Gait Generation in Humanoids

We introduce a robust gait generation framework for humanoid robots based on our Intrinsically Stable Model Predictive Control (IS-MPC) scheme, which features a stability constraint to guarantee internal stability. With respect to the original version, the new framework adds multiple components addressing the robustness problem from different angles: an observer-based disturbance compensation mechanism; a ZMP constraint restriction that provides robustness with respect to bounded disturbances; and a step timing adaptation module to prevent the loss of feasibility. Simulation and experimental results are presented

ZMP Constraint Restriction for Robust Gait Generation in Humanoids

We present an extension of our previously proposed IS-MPC method for humanoid gait generation aimed at obtaining robust performance in the presence of disturbances. The considered disturbance signals vary in a range of known amplitude around a mid-range value that can change at each sampling time, but whose current value is assumed to be available. The method consists in modifying the stability constraint that is at the core of IS-MPC by incorporating the current mid-range disturbance, and performing an appropriate restriction of the ZMP constraint in the control horizon on the basis of the range amplitude of the disturbance. We derive explicit conditions for recursive feasibility and internal stability of the IS-MPC method with constraint modification. Finally, we illustrate its superior performance with respect to the nominal version by performing dynamic simulations on the NAO robot