41 research outputs found
LARNet:Towards Lightweight, Accurate and Real-time Salient Object Detection
Salient object detection (SOD) has rapidly developed in recent years, and detection performance has greatly improved. However, the price of these improvements is increasingly complex networks that require more computing resources and sacrifice real-time performance. This makes it difficult to deploy these approaches on devices with limited computing resources (such as mobile phones, embedded platforms, etc.). Considering recently developed lightweight SOD models, their detection and real-time performance are always compromised in demanding practical application scenarios. To solve these problems, we propose a novel lightweight SOD method called LARNet and its corresponding extremely lightweight method LARNet* according to application requirements. These methods balance the relationship between lightweight requirements, detection accuracy and real-time performance. First, we propose a saliency backbone network tailored for SOD, which removes the need for pre-training with ImageNet and effectively reduces feature redundancy. Subsequently, we propose a novel context gating module (CGM), which simulates the physiological mechanism of human brain neurons and visual information processing, and realizes the deep fusion of multilevel features at the global level. Finally, the saliency map is output after fusion of multi-level features. Extensive experiments on popular benchmark datasets demonstrate that the proposed LARNet (LARNet*) achieves 98 (113) FPS on a GPU and 3 (6) FPS on a CPU. With approximately 680K (90K) parameters, the model has significant performance advantages over (extremely) lightweight methods, even surpassing some heavyweight model
MMPL-Net: Multi-modal prototype learning for one-shot RGB-D segmentation
For one-shot segmentation, prototype learning is extensively used. However, using only one RGB prototype to represent all information in the support image may lead to ambiguities. To this end, we propose a one-shot segmentation network based on multi-modal prototype learning that uses depth information to complement RGB information. Specifically, we propose a multi-modal fusion and refinement block (MFRB) and multi-modal prototype learning block (MPLB). MFRB fuses RGB and depth features to generate multi-modal features and refined depth features, which are used by MPLB, to generate multi-modal information prototypes, depth information prototypes, and global information prototypes. Furthermore, we introduce self-attention to capture global context information in RGB and depth images. By integrating self-attention, MFRB, and MPLB, we propose the multi-modal prototype learning network (MMPL-Net), which adapts to the ambiguity of visual information in the scene. Finally, we construct a one-shot RGB-D segmentation dataset called OSS-RGB-D-5i. Experiments using OSS-RGB-D-5i show that our proposed method outperforms several state-of-the-art techniques with fewer labeled images and generalizes well to previously unseen objects.</p
Unseen-Material Few-Shot Defect Segmentation With Optimal Bilateral Feature Transport Network
Graph Wasserstein Autoencoder-Based Asymptotically Optimal Motion Planning With Kinematic Constraints for Robotic Manipulation
Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning
—The market state changes when a new piece of information arrives. It affects decisions made by investors and is considered to be an important data source that can be used for financial forecasting. Recently information derived from news articles has become a part of financial predictive systems. The usage of news articles and their forecasting potential have been extensively researched.
However, so far no attempts have been made to utilise different categories of news articles simultaneously. This paper studies how the concurrent, and appropriately weighted, usage of news articles, having different degrees of relevance to the target stock, can improve the performance of financial forecasting and support the decision-making process of investors and traders. Stock price movements are predicted using the multiple kernel learning technique which integrates information extracted from multiple news categories while separate kernels are utilised to analyse each category. News articles are partitioned according to their relevance to the target stock, its sub industry, industry, group industry and sector. The experiments are run on stocks from the Health Care sector and show that increasing the number of relevant news categories used as data sources for financial forecasting improves the performance of the predictive system in comparison with approaches based on a lower number of categories
Computational modelling of salamander retinal ganglion cells using machine learning approaches
Artificial vision using computational models that can mimic biological vision is an area of ongoing research. One of the main themes within this research is the study of the retina and in particular, retinal ganglion cells which are responsible for encoding the visual stimuli. A common approach to modelling the internal processes of retinal ganglion cells is the use of a linear - non-linear cascade model, which models the cell's response using a linear filter followed by a static non-linearity. However, the resulting model is generally restrictive as it is often a poor estimator of the neuron's response. In this paper we present an alternative to the linear - non-linear model by modelling retinal ganglion cells using a number of machine learning techniques which have a proven track record for learning complex non-linearities in many different domains. A comparison of the model predicted spike rate shows that the machine learning models perform better than the standard linear - non-linear approach in the case of temporal white noise stimuli
Mental health first aid training for nursing students: a protocol for a pragmatic randomised controlled trial in a large university
BackgroundThe impact of mental health problems and disorders in Australia is significant. Mental health problems often start early and disproportionately affect young people. Poor adolescent mental health can predict educational achievement at school and educational and occupational attainment in adulthood. Many young people attend higher education and have been found to experience a range of mental health issues. The university setting therefore presents a unique opportunity to trial interventions to reduce the burden of mental health problems. Mental Health First Aid (MHFA) aims to train participants to recognise symptoms of mental health problems and assist an individual who may be experiencing a mental health crisis. Training nursing students in MHFA may increase mental health literacy and decrease stigma in the student population. This paper presents a protocol for a trial to examine the efficacy of the MHFA training for students studying nursing at a large university in Perth, Western Australia. Methods/DesignThis randomised controlled trial will follow the CONSORT guidelines. Participants will be randomly allocated to the intervention group (receiving a MHFA training course comprising two face to face 6.5 hour sessions run over two days during the intervention period) or a waitlisted control group (not receiving MHFA training during the study). The source population will be undergraduate nursing students at a large university located in Perth, Western Australia. Efficacy of the MHFA training will be assessed by following the intention-to-treat principle and repeated measures analysis. DiscussionGiven the known burden of mental health disorders among student populations, it is important universities consider effective strategies to address mental health issues. Providing MHFA training to students offers the advantage of increasing mental health literacy, among the student population. Further, students trained in MHFA are likely to utilise these skills in the broader community, when they graduate to the workforce. It is anticipated that this trial will demonstrate the scalability of MHFA in the university environment for pre-service nurses and that implementation of MHFA courses, with comprehensive evaluation, could yield positive improvements in the mental health literacy amongst this target group as well as other tertiary student groups. Trial registrationAustralian New Zealand Clinical Trials Registry ACTRN1261400086165
Enhancing Discovery of Genetic Variants for Posttraumatic Stress Disorder Through Integration of Quantitative Phenotypes and Trauma Exposure Information
Funding Information: This work was supported by the National Institute of Mental Health / U.S. Army Medical Research and Development Command (Grant No. R01MH106595 [to CMN, IL, MBS, KJRe, and KCK], National Institutes of Health (Grant No. 5U01MH109539 to the Psychiatric Genomics Consortium ), and Brain & Behavior Research Foundation (Young Investigator Grant [to KWC]). Genotyping of samples was provided in part through the Stanley Center for Psychiatric Genetics at the Broad Institute supported by Cohen Veterans Bioscience . Statistical analyses were carried out on the LISA/Genetic Cluster Computer ( https://userinfo.surfsara.nl/systems/lisa ) hosted by SURFsara. This research has been conducted using the UK Biobank resource (Application No. 41209). This work would have not been possible without the financial support provided by Cohen Veterans Bioscience, the Stanley Center for Psychiatric Genetics at the Broad Institute, and One Mind. Funding Information: MBS has in the past 3 years received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, Bionomics, BioXcel Therapeutics, Clexio, EmpowerPharm, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals, and Roche/Genentech and has stock options in Oxeia Biopharmaceuticals and Epivario. In the past 3 years, NPD has held a part-time paid position at Cohen Veterans Bioscience, has been a consultant for Sunovion Pharmaceuticals, and is on the scientific advisory board for Sentio Solutions for unrelated work. In the past 3 years, KJRe has been a consultant for Datastat, Inc., RallyPoint Networks, Inc., Sage Pharmaceuticals, and Takeda. JLM-K has received funding and a speaking fee from COMPASS Pathways. MU has been a consultant for System Analytic. HRK is a member of the Dicerna scientific advisory board and a member of the American Society of Clinical Psychopharmacology Alcohol Clinical Trials Initiative, which during the past 3 years was supported by Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Dicerna, Ethypharm, Indivior, Lundbeck, Mitsubishi, and Otsuka. HRK and JG are named as inventors on Patent Cooperative Treaty patent application number 15/878,640, entitled “Genotype-guided dosing of opioid agonists,” filed January 24, 2018. RP and JG are paid for their editorial work on the journal Complex Psychiatry. OAA is a consultant to HealthLytix. All other authors report no biomedical financial interests or potential conflicts of interest. Funding Information: This work was supported by the National Institute of Mental Health/ U.S. Army Medical Research and Development Command (Grant No. R01MH106595 [to CMN, IL, MBS, KJRe, and KCK], National Institutes of Health (Grant No. 5U01MH109539 to the Psychiatric Genomics Consortium), and Brain & Behavior Research Foundation (Young Investigator Grant [to KWC]). Genotyping of samples was provided in part through the Stanley Center for Psychiatric Genetics at the Broad Institute supported by Cohen Veterans Bioscience. Statistical analyses were carried out on the LISA/Genetic Cluster Computer (https://userinfo.surfsara.nl/systems/lisa) hosted by SURFsara. This research has been conducted using the UK Biobank resource (Application No. 41209). This work would have not been possible without the financial support provided by Cohen Veterans Bioscience, the Stanley Center for Psychiatric Genetics at the Broad Institute, and One Mind. This material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting true views of the U.S. Department of the Army or the Department of Defense. We thank the investigators who comprise the PGC-PTSD working group and especially the more than 206,000 research participants worldwide who shared their life experiences and biological samples with PGC-PTSD investigators. We thank Mark Zervas for his critical input. Full acknowledgments are in Supplement 1. MBS has in the past 3 years received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, Bionomics, BioXcel Therapeutics, Clexio, EmpowerPharm, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals, and Roche/Genentech and has stock options in Oxeia Biopharmaceuticals and Epivario. In the past 3 years, NPD has held a part-time paid position at Cohen Veterans Bioscience, has been a consultant for Sunovion Pharmaceuticals, and is on the scientific advisory board for Sentio Solutions for unrelated work. In the past 3 years, KJRe has been a consultant for Datastat, Inc. RallyPoint Networks, Inc. Sage Pharmaceuticals, and Takeda. JLM-K has received funding and a speaking fee from COMPASS Pathways. MU has been a consultant for System Analytic. HRK is a member of the Dicerna scientific advisory board and a member of the American Society of Clinical Psychopharmacology Alcohol Clinical Trials Initiative, which during the past 3 years was supported by Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Dicerna, Ethypharm, Indivior, Lundbeck, Mitsubishi, and Otsuka. HRK and JG are named as inventors on Patent Cooperative Treaty patent application number 15/878,640, entitled ?Genotype-guided dosing of opioid agonists,? filed January 24, 2018. RP and JG are paid for their editorial work on the journal Complex Psychiatry. OAA is a consultant to HealthLytix. All other authors report no biomedical financial interests or potential conflicts of interest. Publisher Copyright: © 2021 Society of Biological PsychiatryBackground: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs). Methods: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms. Results: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program. Conclusions: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods.publishersversionpublishe
The genetic architecture of the human cerebral cortex
The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder