BIOLOGICAL ASPECTS OF THE SAILFIN DORY Zenopsis conchifer (LOWE, 1852) CAUGHT BY DEEP-SEA TRAWLING FISHERY OFF SOUTHERN BRAZIL

Biological aspects of sailfin dory, Zenopsis conchifer, were studied from 839 individuals obtained from deep-sea commercial bottom trawling off southern Brazil at depths up to 526 m in 2002 and 2003. Samples included fish from 101 mm Lt and 15 g up to 640 mm Lt and 2,9 g. The sex-ratio was 50% at 150 mm Lt and between 300-350 mm Lt, with females outnumbering males in the remaining size classes. Reproductive activity seems to peak between July and August ( austral winter). Size at attainment of 50% maturity (Lt(50)) was 311 mm Lt in females. The mean length and maturity of the specimens increased with depth, suggesting that larger fish concentrate in deeper waters.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a "divide-and-conquer" effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.