A Fully Convolutional Deep Auditory Model for Musical Chord Recognition

Chord recognition systems depend on robust feature extraction pipelines. While these pipelines are traditionally hand-crafted, recent advances in end-to-end machine learning have begun to inspire researchers to explore data-driven methods for such tasks. In this paper, we present a chord recognition system that uses a fully convolutional deep auditory model for feature extraction. The extracted features are processed by a Conditional Random Field that decodes the final chord sequence. Both processing stages are trained automatically and do not require expert knowledge for optimising parameters. We show that the learned auditory system extracts musically interpretable features, and that the proposed chord recognition system achieves results on par or better than state-of-the-art algorithms.Comment: In Proceedings of the 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), Vietro sul Mare, Ital

A Discrete Time Counterpart of the Black-Scholes Bond Replication Portfolio

We construct a discrete time self-financing portfolio comprised of call options short and stock shares long which is riskless and grows at a fixed rate of return. It is also shown that when shorting periods tend to zero then so devised portfolio turns into the Black-Scholes bond replication. Unlike in standard approach the analysis presented here requires neither Ito Calculus nor solving the Heat Equation for option pricing

Dynamic sharing of a multiple access channel

In this paper we consider the mutual exclusion problem on a multiple access channel. Mutual exclusion is one of the fundamental problems in distributed computing. In the classic version of this problem, n processes perform a concurrent program which occasionally triggers some of them to use shared resources, such as memory, communication channel, device, etc. The goal is to design a distributed algorithm to control entries and exits to/from the shared resource in such a way that in any time there is at most one process accessing it. We consider both the classic and a slightly weaker version of mutual exclusion, called ep-mutual-exclusion, where for each period of a process staying in the critical section the probability that there is some other process in the critical section is at most ep. We show that there are channel settings, where the classic mutual exclusion is not feasible even for randomized algorithms, while ep-mutual-exclusion is. In more relaxed channel settings, we prove an exponential gap between the makespan complexity of the classic mutual exclusion problem and its weaker ep-exclusion version. We also show how to guarantee fairness of mutual exclusion algorithms, i.e., that each process that wants to enter the critical section will eventually succeed

Dynamic load balancing in Peer-to-Peer networks

by Miroslaw KorzeniowskiPaderborn, Univ., Diss., 200

Group cognitive intervention targeted to the strengthening of executive functions in children at social risk

El presente trabajo se propuso evaluar la efectividad de una intervencion cognitiva grupal destinada a promover las funciones ejecutivas en ninos en riesgo social. Se utilizo un diseño cuasi-experimental pretest–postest congrupo control. La muestra estuvo compuesta por 178 ninos argentinos (52% varones) de 6 a 10 anos de edad. Se empleo una batería de tests neuropsicologicos y una escala de funcionamiento ejecutivo versión docente. La intervencion incluyó 30 sesiones grupales, de dificultad creciente y se inserto dentro de la currícula escolar. Los niños entrenados evidenciaron un mejor desempeño en flexibilidad cognitiva, planificación, metacognición y control inhibitorio en comparacion con su desempeño basal y sus controles. Estos resultados aportan nueva evidencia sobre la efectividad de las intervenciones cognitivas infantiles y su capacidad para transferir las mejoras cognitivas a las actividades cotidianas de los ninos en el ámbito escolar.The present study set out to evaluate the effectiveness of a group cognitive intervention aimed at promoting executive functions in children at social risk. The quasi-experimental, pretest-posttest design included a control group. The sample was made up of 178 children (52% boys), aged 6-10. The children were evaluated by means of a battery of neuropsychological EF tests and a teacher-rated behavioral EF scale. The intervention program included 30 group cognitive stimulation sessions that increased in difficulty and were embedded into school curricula. Trained children performed better in terms of cognitive flexibility, planning, metacognition and inhibitory control, as compared to their baseline values and to children in the control group. This study provides new evidence of the effectiveness of cognitive interventions for children and of children's capability to transfer cognitive improvements to daily school activities.Fil: Korzeniowski, Celina Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; Argentina. Universidad del Aconcagua; ArgentinaFil: Ison, Mirta Susana. Universidad del Aconcagua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; ArgentinaFil: Difabio, Hilda Emilia. Centro de Investigaciones de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin