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First, the optical field-free, Stark, and Zeeman spectroscopic studies have been performed on AuF and AuCl. The simple polar bonds between Au and typical halogens (i.e. F and Cl) can be well characterized by the electronic structure studies and the permanent electric dipole moments, el. The spectroscopic parameters have been precisely determined for the [17.7]1, [17.8]0+ and X1+ states of AuF, and the [17.07]1, [17.20]0+ and X1+ states of AuCl. The el have been determined for ground and excited states of AuF and AuCl. The results from the hyperfine analysis and Stark measurement support the assignments that the [17.7]1 and [17.8]0+ states of AuF are the components of a 3 state. Similarly, the analysis demonstrated the [19.07]1 and [19.20]0+ states are the components of the 3 state of AuCl.
Second, my study focused on AuO and AuS because the bonding between gold and sulfur/oxygen is a key component to numerous established and emerging technologies that have applications as far ranging as medical imaging, catalysis, electronics, and material science. The high-resolution spectra were record and analyzed to obtain the geometric and electronic structural data for the ground and excited states. The electric dipole moment, el, and the magnetic dipole moment, m, has been the precisely measured by applying external static electric and magnetic fields. el andm are used to give insight into the unusual complex bonding in these molecules.
In addition to direct studies on the gold-containing molecules, other studies of related molecules are included here as well. These works contain the pure rotation measurement of PtC, the hyperfine and Stark spectroscopic studies of PtF, and the Stark and Zeeman spectroscopic studies of MgH and MgD.
Finally, a perspective discussion and conclusion will summarize the results of AuF, AuCl, AuO, and AuS from this work (bond lengths, dipole moment, etc.). The highly quantitative information derived from this work is the foundation of a chemical description of matter and essential for kinetic energy manipulation via Stark and Zeeman interactions. This data set also establishes a synergism with computation chemists who are developing new methodologies for treating relativistic effects and electron correlation.
The purpose of this project is to create a useful tool for musicians that utilizes the harmonic content of their playing to recommend new, relevant chords to play. This is done by training various Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) on the lead sheets of 100 different jazz standards. A total of 200 unique datasets were produced and tested, resulting in the prediction of nearly 51 million chords. A note-prediction accuracy of 82.1% and a chord-prediction accuracy of 34.5% were achieved across all datasets. Methods of data representation that were rooted in valid music theory frameworks were found to increase the efficacy of harmonic prediction by up to 6%. Optimal LSTM input sizes were also determined for each method of data representation.
My proposed project is an educational application that will seek to simplify the<br/>process of internalizing the chord symbols most commonly seen by those learning<br/>musical improvisation. The application will operate like a game, encouraging the<br/>user to identify chord tones within time limits and award points for successfully<br/>doing so.