We propose a novel progressive parameter-sharing strategy (MPPS) in this paper for effectively training multitask learning models on diverse computer vision tasks simultaneously. Specifically, we propose to parameterize distributions for different tasks to control the sharings, based on the concept of Exclusive Capacity that we introduce. A scheduling mechanism following the concept of curriculum learning is also designed to progressively change the sharing strategy to increase the level of sharing during the learning process. We further propose a novel loss function to regularize the optimization of network parameters as well as the sharing probabilities of each neuron for each task. Our approach can be combined with many state-of-the-art multitask learning solutions to achieve better joint task performance. Comprehensive experiments show that it has competitive performance on three challenging datasets (Multi-CIFAR100, NYUv2, and Cityscapes) using various convolution neural network architectures.

Large-scale graph-structured computation is becoming increasingly important for various data analytics applications. However, most distributed graph processing frameworks do not directly support efficient implementation of sophisticated algorithms requiring massive graph traversals. In this paper, we propose concurrent hybrid breadth-first-search (BFS) algorithm on a popular distributed graph processing framework (Power-Graph and its optimized version PowerLyra). It leverages the small-world property of skewed graphs which apply to most realworld data sets. Hybrid BFS algorithm changes graph traversal direction to save computational workload and message transmissions. Concurrent BFS algorithm enables running multiple BFS simultaneously while sharing vertex explorations efficiently with bit operations. Extensive experiments are conducted to evaluate performance and scalability on a multi-core computer and a cluster of AWS instances. Concurrent hybrid BFS algorithm dramatically increases graph traversal efficiency with respect to the increasing number of concurrent BFS traversals. Compared with PowerGraph, PowerLyra uses fewer resources, while achieving significantly higher performance and better scalability.

Graph Processing has been widely used to capture complex data dependency and uncover relationship insights. Due to the ever-growing graph scale and algorithm complexity, distributed graph processing has become more and more popular. In this paper, we investigate how to balance performance and cost for large scale graph processing on configurable virtual machines (VMs). We analyze the system architecture and implementation details of a Pregel-like distributed graph processing framework and develop a system-aware model to predict the execution time. Consequently, cost effective execution scenarios are recommended by selecting a certain number of VMs with specified capability subject to the predefined resource price and user preference. Experiments using synthetic and real world graphs have verified that system-aware model can achieve much higher prediction accuracy than popular machine-learning models which treat graph processing framework as a black box. As a result, the recommended execution scenarios have comparable cost efficiency to the optimal scenarios.

This paper presents an EEG-based interactive genetic algorithm framework, with the goal of leveraging EEG signals collected from a human expert involved in the evaluation of interactive genetic algorithm as inputs for genetic parameter control. We explain the framework of the system and our cognitive model constructed based on a 19 channel EEG system. An experiment has been performed to test the effectiveness of our framework and our cognitive model. Our work is the first attempt to combine brain-computer interaction with interactive evolutionary computation and parameter control.