237 research outputs found

    EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch

    Full text link
    Designing the structure of neural networks is considered one of the most challenging tasks in deep learning, especially when there is few prior knowledge about the task domain. In this paper, we propose an Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of succession, extinction, mimicry, and gene duplication to search neural network structure from scratch with poorly initialized simple network and few constraints forced during the evolution, as we assume no prior knowledge about the task domain. Specifically, we first use primary succession to rapidly evolve a population of poorly initialized neural network structures into a more diverse population, followed by a secondary succession stage for fine-grained searching based on the networks from the primary succession. Extinction is applied in both stages to reduce computational cost. Mimicry is employed during the entire evolution process to help the inferior networks imitate the behavior of a superior network and gene duplication is utilized to duplicate the learned blocks of novel structures, both of which help to find better network structures. Experimental results show that our proposed approach can achieve similar or better performance compared to the existing genetic approaches with dramatically reduced computation cost. For example, the network discovered by our approach on CIFAR-100 dataset achieves 78.1% test accuracy under 120 GPU hours, compared to 77.0% test accuracy in more than 65, 536 GPU hours in [35].Comment: CVPR 201

    Evidence of luminous bacterial symbionts in the light organs of myctophid and stomiiform fishes

    Full text link
    The myctophids and stomiiforms represent two common groups of luminous fishes, but the source of luminescence in these animals has remained undetermined. In this study, labeled luciferase gene fragments from luminous marine bacteria were used to probe DNA isolated from specific fish tissues. A positive signal was obtained from skin DNA in all luminous fishes examined, whereas muscle DNA gave a weaker signal and brain DNA was negative. This observation is consistent with luminous bacteria acting as the light source in myctophids and stomiiforms and argues against the genes necessary for luminescence residing on the fish chromosomes. To confirm the location of this signal, a bacterial probe was hybridized in situ to sections of a stomiiform. A strong signal was generated directly over specific regions of the fish light organs, whereas no signal was found over other internal or epidermal tissues of the fish. Taken together, these data provide the first indication that luminous bacterial symbionts exist in myctophids and stomiiforms and that these symbionts account for luminescence in these fishes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/38097/1/1402590102_ftp.pd

    Understanding energy-related regimes: A participatory approach from central Australia

    Get PDF
    AbstractFor a particular community, what energy-related innovations constitute no-regrets strategies? We present a methodology to understand how alternative energy consuming activities and policy regimes impact on current and future liveability of socio-culturally diverse communities facing climate change. Our methodology augments the energy policy literature by harnessing three concepts (collaborative governance, innovation and political economic regime of provisioning) to support dialogue around changing energy-related activities. We convened workshops in Alice Springs, Australia to build capability to identify no-regrets energy-related housing or transport activities and strategies. In preparation, we interviewed policy actors and constructed three new housing-related future scenarios. After discussing the scenarios, policy and research actors prioritised five socio-technical activities or strategies. Evaluations indicate participants enjoyed opportunities given by the methodology to have focussed discussions about activities and innovation, while requesting more socially nuanced scenario storylines. We discuss implications for theory and technique development

    Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis

    Full text link
    The role of chest X-ray (CXR) imaging, due to being more cost-effective, widely available, and having a faster acquisition time compared to CT, has evolved during the COVID-19 pandemic. To improve the diagnostic performance of CXR imaging a growing number of studies have investigated whether supervised deep learning methods can provide additional support. However, supervised methods rely on a large number of labeled radiology images, which is a time-consuming and complex procedure requiring expert clinician input. Due to the relative scarcity of COVID-19 patient data and the costly labeling process, self-supervised learning methods have gained momentum and has been proposed achieving comparable results to fully supervised learning approaches. In this work, we study the effectiveness of self-supervised learning in the context of diagnosing COVID-19 disease from CXR images. We propose a multi-feature Vision Transformer (ViT) guided architecture where we deploy a cross-attention mechanism to learn information from both original CXR images and corresponding enhanced local phase CXR images. We demonstrate the performance of the baseline self-supervised learning models can be further improved by leveraging the local phase-based enhanced CXR images. By using 10\% labeled CXR scans, the proposed model achieves 91.10\% and 96.21\% overall accuracy tested on total 35,483 CXR images of healthy (8,851), regular pneumonia (6,045), and COVID-19 (18,159) scans and shows significant improvement over state-of-the-art techniques. Code is available https://github.com/endiqq/Multi-Feature-ViTComment: Accepted to the 2022 MICCAI Workshop on Medical Image Learning with Limited and Noisy Dat

    Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis

    Full text link
    Under the global COVID-19 crisis, accurate diagnosis of COVID-19 from Chest X-ray (CXR) images is critical. To reduce intra- and inter-observer variability, during the radiological assessment, computer-aided diagnostic tools have been utilized to supplement medical decision-making and subsequent disease management. Computational methods with high accuracy and robustness are required for rapid triaging of patients and aiding radiologists in the interpretation of the collected data. In this study, we propose a novel multi-feature fusion network using parallel attention blocks to fuse the original CXR images and local-phase feature-enhanced CXR images at multi-scales. We examine our model on various COVID-19 datasets acquired from different organizations to assess the generalization ability. Our experiments demonstrate that our method achieves state-of-art performance and has improved generalization capability, which is crucial for widespread deployment.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2023:00

    Increased functional coupling between VTA and hippocampus during rest in first-episode psychosis

    Get PDF
    Animal models suggest that interactions between the hippocampus and ventral tegmental area (VTA) underlie the onset and etiology of psychosis. While a large body of research has separately characterized alterations in hippocampal and VTA function in psychosis, alterations across the VTA and hippocampus have not been characterized in first-episode psychosis (FEP). As the phase of psychosis most proximal to conversion, studies specifically focused on FEP are valuable to psychosis research. Here, we characterize alterations in VTA-hippocampal interactions across male and female human participants experiencing their first episode of psychosis using resting state functional magnetic resonance imaging (rsfMRI). In comparison to age and sex matched healthy controls (HCs), FEP individuals had significantly greater VTA-hippocampal functional coupling but significantly less VTA-striatal functional coupling. Further, increased VTA-hippocampal functional coupling in FEP correlated with individual differences in psychosis-related symptoms. Together, these findings demonstrate alterations in mesolimbic-hippocampal circuits in FEP and extend prominent animal models of psychosis
    corecore