The impact of fire on habitat use by the short-snouted elephant shrew ('Elephantulus brachyrhynchus') in North West Province, South Africa

Several studies have investigated the response of small mammal populations to fire, but few have investigated behavioural responses to habitat modification. In this study we investigated the impact of fire on home range, habitat use and activity patterns of the short-snouted elephant shrew (Elephantulus brachyrhynchus) by radio-tracking individuals before and after a fire event. All animals survived the passage of fire in termite mound refugia. Before the fire, grassland was used more than thickets, but habitat utilization shifted to thickets after fire had removed the grass cover. Thickets were an important refuge both pre- and post-fire, but the proportion of thicket within the home range was greater post-fire. We conclude that fire-induced habitat modification resulted in a restriction of E. brachyrhynchus movements to patches of unburned vegetation. This may be a behavioural response to an increase in predation pressure associated with a reduction in cover, rather than a lack of food. This study highlights the importance of considering the landscape mosaic in fire management and allowing sufficient island patches to remain post-fire ensures the persistence of the small mammal fauna

Structured Dropout for Weak Label and Multi-Instance Learning and Its Application to Score-Informed Source Separation

Many success stories involving deep neural networks are instances of supervised learning, where available labels power gradient-based learning methods. Creating such labels, however, can be expensive and thus there is increasing interest in weak labels which only provide coarse information, with uncertainty regarding time, location or value. Using such labels often leads to considerable challenges for the learning process. Current methods for weak-label training often employ standard supervised approaches that additionally reassign or prune labels during the learning process. The information gain, however, is often limited as only the importance of labels where the network already yields reasonable results is boosted. We propose treating weak-label training as an unsupervised problem and use the labels to guide the representation learning to induce structure. To this end, we propose two autoencoder extensions: class activity penalties and structured dropout. We demonstrate the capabilities of our approach in the context of score-informed source separation of music

Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data

This paper presents a new method for shadow removal using unpaired data, enabling us to avoid tedious annotations and obtain more diverse training samples. However, directly employing adversarial learning and cycle-consistency constraints is insufficient to learn the underlying relationship between the shadow and shadow-free domains, since the mapping between shadow and shadow-free images is not simply one-to-one. To address the problem, we formulate Mask-ShadowGAN, a new deep framework that automatically learns to produce a shadow mask from the input shadow image and then takes the mask to guide the shadow generation via re-formulated cycle-consistency constraints. Particularly, the framework simultaneously learns to produce shadow masks and learns to remove shadows, to maximize the overall performance. Also, we prepared an unpaired dataset for shadow removal and demonstrated the effectiveness of Mask-ShadowGAN on various experiments, even it was trained on unpaired data.Comment: Accepted to ICCV 201

Improving Whole Slide Segmentation Through Visual Context - A Systematic Study

While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology. Although specific convolutional neural network architectures have been applied with great success to the problem, few effectively incorporate visual context information from multiple scales. With this paper, we present a systematic comparison of different architectures to assess how including multi-scale information affects segmentation performance. A publicly available breast cancer and a locally collected prostate cancer datasets are being utilised for this study. The results support our hypothesis that visual context and scale play a crucial role in histology image classification problems