Bach in a Box - Real-Time Harmony

We describe a system for learning J. S. Bach's rules of musical harmony. These rules are learned from examples and are expressed as rule-based neural networks. The rules are then applied in real-time to generate new accompanying harmony for a live performer. Real-time functionality imposes constraints on the learning and harmonizing processes, including limitations on the types of information the system can use as input and the amount of processing the system can perform. We demonstrate algorithms for generating and refining musical rules from examples which meet these constraints. We describe a method for including a priori knowledge into the rules which yields significant performance gains. We then describe techniques for applying these rules to generate new music in real-time. We conclude the paper with an analysis of experimental results

Gradient Harmonized Single-stage Detector

Despite the great success of two-stage detectors, single-stage detector is still a more elegant and efficient way, yet suffers from the two well-known disharmonies during training, i.e. the huge difference in quantity between positive and negative examples as well as between easy and hard examples. In this work, we first point out that the essential effect of the two disharmonies can be summarized in term of the gradient. Further, we propose a novel gradient harmonizing mechanism (GHM) to be a hedging for the disharmonies. The philosophy behind GHM can be easily embedded into both classification loss function like cross-entropy (CE) and regression loss function like smooth-$L_1$ ($SL_1$) loss. To this end, two novel loss functions called GHM-C and GHM-R are designed to balancing the gradient flow for anchor classification and bounding box refinement, respectively. Ablation study on MS COCO demonstrates that without laborious hyper-parameter tuning, both GHM-C and GHM-R can bring substantial improvement for single-stage detector. Without any whistles and bells, our model achieves 41.6 mAP on COCO test-dev set which surpasses the state-of-the-art method, Focal Loss (FL) + $SL_1$, by 0.8.Comment: To appear at AAAI 201

Scanner Invariant Representations for Diffusion MRI Harmonization

Purpose: In the present work we describe the correction of diffusion-weighted MRI for site and scanner biases using a novel method based on invariant representation. Theory and Methods: Pooled imaging data from multiple sources are subject to variation between the sources. Correcting for these biases has become very important as imaging studies increase in size and multi-site cases become more common. We propose learning an intermediate representation invariant to site/protocol variables, a technique adapted from information theory-based algorithmic fairness; by leveraging the data processing inequality, such a representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to underlying structures. To implement this, we use a deep learning method based on variational auto-encoders (VAE) to construct scanner invariant encodings of the imaging data. Results: To evaluate our method, we use training data from the 2018 MICCAI Computational Diffusion MRI (CDMRI) Challenge Harmonization dataset. Our proposed method shows improvements on independent test data relative to a recently published baseline method on each subtask, mapping data from three different scanning contexts to and from one separate target scanning context. Conclusion: As imaging studies continue to grow, the use of pooled multi-site imaging will similarly increase. Invariant representation presents a strong candidate for the harmonization of these data

Melody Harmonization

Vedci z oboru informačných technológií oddávna považovali hudbu za obzvlášť zaujímavé umenie. Pravdou je, že história hudby tvorenej počítačom je skoro tak dlhá ako história počítačovej vedy. Programy pre komponovanie, alebo tvorenie hudby" na rôznych úrovniach procesu kompozície boli vyvíjané už od 50tych rokov minulého storočia. Táto bakalárska práca uvádza hlavné prístupy v oblasti automatickej harmonizácie t.j. Problém produkovania hudobného aranžmá (nôt) z daných melódií, a sústreďuje sa na najpoužívanejšie techniky jeho riešenia. Hlavným cieľom tejto práce je návrh a implementácia softvérového systému pre automatickú harmonizáciu, ktorý by mal byť schopný naučiť sa pravidlá harmónie z databázy midi súborov. V tejto práci popíšem existujúce harmonizačné systémy a ďalej sa zameriam hlavne na princípy strojového učenia - teóriu a aplikáciu umelých neurónových sietí a ich použitie pre harmonizáciu.Computer scientists have long been considering music as a particularly interesting art Indeed, the history of computer music is almost as long as the history of computer science. Programs to compose music, or to make music" at various levels of the composition process have been designed since the 50s. This bachelor's thesis surveys the main approaches in the field of automatic harmonization, i.e. the problem of producing musical arrangements (scores) from given melodies, and focuses on the most widely used techniques to do so. The main goal of this paper is the issue of design and implementation of a software system for an automatic music harmonization which should learn the rules of harmony from the database of midi file. In the paper. In this thesis I describe existing systems for harmonization and furthermore I focus mainly on principles of machine learning - theory and application of Artificial Neural Networks and their use for harmonization.

Energy Efficiency Prediction using Artificial Neural Network

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%

ANN for Predicting Antibiotic Susceptibility

Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict efficiency of antibiotics in treating various bacteria types. Attributes that were taken in account are: organism name, specimen type, and antibiotic name as input and susceptibility as an output. A model based on one input, one hidden, and one output layers concept topology was developed and trained using a data from Queensland government's website. The evaluation shows that the ANN model is capable of correctly predicting the susceptibility of organisms to the antibiotics with 98% accuracy