Array最新文献

{"title":"Salient object contour extraction based on pixel scales and hierarchical convolutional network","authors":"Xixi Yuan ,&nbsp;Youqing Xiao ,&nbsp;Zhanchuan Cai ,&nbsp;Leiming Wu","doi":"10.1016/j.array.2022.100270","DOIUrl":"10.1016/j.array.2022.100270","url":null,"abstract":"<div><p>Image salient object contours are helpful for many advanced computer vision tasks, such as object segmentation, action recognition, and scene understanding. We propose a new contour extraction method for salient objects based on pixel scale knowledge and hierarchical network structure, which improves the accuracy of object contours. First, a deep hierarchical network is designed to capture rich feature details. Then, a new loss function with adaptive weighted coefficients is developed, which can reduce the uneven distribution influence of contour pixels and non-contour pixels in training datasets. Next, the object contours are classified based on the scale information of contour pixels. By importing the prior knowledge of scale categories into the network structure, the model requires a small number of training samples. Finally, the regression task of contour scale prediction is added to the network, and the precise contour scales of foreground objects are performed as prior knowledge or an auxiliary task. The experimental results demonstrate that compared with related methods, the proposed method achieves satisfactory results from precision/recall curves and F-measure score estimation on three datasets.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45654210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SemanticGraph2Vec: Semantic graph embedding for text representation","authors":"Wael Etaiwi,&nbsp;Arafat Awajan","doi":"10.1016/j.array.2023.100276","DOIUrl":"10.1016/j.array.2023.100276","url":null,"abstract":"<div><p>Graph embedding is an important representational technique that aims to maintain the structure of a graph while learning low-dimensional representations of its vertices. Semantic relationships between vertices contain essential information regarding the meaning of the represented graph. However, most graph embedding methods do not consider the semantic relationships during the learning process. In this paper, we propose a novel semantic graph embedding approach, called SemanticGraph2Vec. SemanticGraph2Vec learns mappings of vertices into low-dimensional feature spaces that consider the most important semantic relationships between graph vertices. The proposed approach extends and enhances prior work based on a set of random walks of graph vertices by using semantic walks instead of random walks which provides more useful embeddings for text graphs. A set of experiments are conducted to evaluate the performance of SemanticGraph2Vec. SemanticGraph2Vec is employed on a part-of-speech tagging task. Experimental results demonstrate that SemanticGraph2Vec outperforms two state-of-the-art baselines methods in terms of precision and F1 score.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41716264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harmonizing motion and contrast vision for robust looming detection","authors":"Qinbing Fu ,&nbsp;Zhiqiang Li ,&nbsp;Jigen Peng","doi":"10.1016/j.array.2022.100272","DOIUrl":"https://doi.org/10.1016/j.array.2022.100272","url":null,"abstract":"<div><p>This paper presents a novel neural model of insect’s visual perception paradigm to address a challenging problem on detection of looming motion, particularly in extremely low-contrast, and highly variable natural scenes. Current looming detection models are greatly affected by visual contrast between moving target and cluttered background lacking robust and low-cost solutions. Considering the anatomical and physiological homology between preliminary visual systems of different insect species, this gap can be significantly reduced by coordinating motion and contrast neural processing mechanisms. The proposed model draws lessons from research progress in insect neuroscience, articulates a neural network hierarchy based upon ON/OFF channels encoding motion and contrast signals in four parallel pathways. Specifically, the two ON/OFF motion pathways react to successively expanding ON–ON and OFF–OFF edges through spatial–temporal interactions between polarity excitations and inhibitions. To formulate contrast neural computation, the instantaneous feedback normalization of preliminary motion received at starting cells of ON/OFF channels works effectively to suppress time-varying signals delivered into the ON/OFF motion pathways. Besides, another two ON/OFF contrast pathways are dedicated to neutralize high-contrast polarity optic flows when converging with motion signals. To corroborate the proposed method, we carried out systematic experiments with thousands of looming-square motions at varied grey scales, embedded in different natural moving backgrounds. The model response achieves remarkably lower variance and peaks more smoothly to looming motions in different natural scenarios, a significant enhancement upon previous works. Such robustness can be maintained against extremely low-contrast looming motion against cluttered backgrounds. The results demonstrate a parsimonious solution to stabilize looming detection against high input variability, analogous to insect’s capability.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49753140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LFR-Net: Local feature residual network for single image dehazing","authors":"Xinjie Xiao,&nbsp;Zhiwei Li,&nbsp;Wenle Ning,&nbsp;Nannan Zhang,&nbsp;Xudong Teng","doi":"10.1016/j.array.2023.100278","DOIUrl":"https://doi.org/10.1016/j.array.2023.100278","url":null,"abstract":"<div><p>Previous learning-based methods only employ clear images to train the dehazing network, but some useful information such as hazy images, media transmission maps and atmospheric light values in datasets were ignored. Here, we propose a local feature residual network (LFR-Net) for single image dehazing, which is aimed at improving the quality of dehazed images by fully utilizing the information in the training dataset. The backbone of LFR-Net is structured by feature residual block and adaptive feature fusion model. Furthermore, to preserve more details for the recovered clear images, we design an adaptive feature fusion model that adaptively fuses shallow and deep features at each scale of the encoder and decoder. Extended experiments show that the performance of our LFR-Net outperforms the state-of-the-art methods.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49865134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When quantum annealing meets multitasking: Potentials, challenges and opportunities","authors":"Tian Huang ,&nbsp;Yongxin Zhu ,&nbsp;Rick Siow Mong Goh ,&nbsp;Tao Luo","doi":"10.1016/j.array.2023.100282","DOIUrl":"https://doi.org/10.1016/j.array.2023.100282","url":null,"abstract":"<div><p>Quantum computers have provided a promising tool for tackling NP hard problems. However, most of the existing work on quantum annealers assumes exclusive access to all resources available in a quantum annealer. This is not resource efficient if a task consumes only a small part of an annealer and leaves the rest wasted. We ask if we can run multiple tasks in parallel or concurrently on an annealer, just like the multitasking capability of a classical general-purpose processor. By far, multitasking is not natively supported by any of the existing annealers. In this paper, we explore Multitasking in Quantum Annealer (QAMT) by identifying the parallelism in a quantum annealer from the aspect of space and time. Based on commercialised quantum annealers from D-Wave, we propose a realisation scheme for QAMT, which packs multiple tasks into a quantum machine instruction (QMI) and uses predefined sampling time to emulate task preemption. We enumerate a few scheduling algorithms that match well with QAMT and discuss the challenges in QAMT. To demonstrate the potential of QAMT, we simulate a quantum annealing system, implement a demo QAMT scheduling algorithm, and evaluate the algorithm. Experimental results suggest that there is great potential in multitasking in quantum annealing.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49865135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Organically distributed sustainable storage clusters","authors":"Paul W. Poteete","doi":"10.2139/ssrn.4266638","DOIUrl":"https://doi.org/10.2139/ssrn.4266638","url":null,"abstract":"","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44264754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic optimization model of transmission line based on GIS and genetic algorithm","authors":"Yuan Qin, Zhao Li, Jieyu Ding, Fei Zhao, Mingmeng Meng","doi":"10.2139/ssrn.4220612","DOIUrl":"https://doi.org/10.2139/ssrn.4220612","url":null,"abstract":"","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47928015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonlinear anisotropic diffusion methods for image denoising problems: Challenges and future research opportunities","authors":"Baraka Maiseli","doi":"10.1016/j.array.2022.100265","DOIUrl":"https://doi.org/10.1016/j.array.2022.100265","url":null,"abstract":"<div><p>Nonlinear anisotropic diffusion has attracted a great deal of attention for its ability to simultaneously remove noise and preserve semantic image features. This ability favors several image processing and computer vision applications, including noise removal in medical and scientific images that contain critical features (textures, edges, and contours). Despite their promising performance, methods based on nonlinear anisotropic diffusion suffer from practical limitations that have been lightly discussed in the literature. Our work surfaces these limitations as an attempt to create future research opportunities. In addition, we have proposed a diffusion-driven method that generates superior results compared with classical methods, including the popular Perona–Malik formulation. The proposed method embeds a kernel that properly guides the diffusion process across image regions. Experimental results show that our kernel encourages effective noise removal and ensures preservation of significant image features. We have provided potential research problems to further expand the current results.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49752695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative study of supervised machine learning approaches to predict patient triage outcomes in hospital emergency departments","authors":"Hamza Elhaj ,&nbsp;Nebil Achour ,&nbsp;Marzia Hoque Tania ,&nbsp;Kurtulus Aciksari","doi":"10.1016/j.array.2023.100281","DOIUrl":"10.1016/j.array.2023.100281","url":null,"abstract":"<div><h3>Background</h3><p>The inconsistency in triage evaluation in emergency departments (EDs) and the limitations in practice within the standard triage tools among triage nurses have led researchers to seek more accurate and robust triage evaluation that provides better patient prioritization based on their medical conditions. This study aspires to establish the best methodological practices for applying machine learning (ML) techniques to build an automated triage model for more accurate evaluation.</p></div><div><h3>Methods</h3><p>A comparative study of selected supervised ML models was conducted to determine the best-performing approach to evaluate patient triage outcomes in hospital emergency departments. A retrospective dataset of 2688 patients who visited the ED between April 1, 2020 and June 9, 2020 was collected. Data included patient demographics (age and gender), Vital signs (body temperature, respiratory rate, heart rate, blood pressure and oxygen saturation), chief complaints, and chronic illness. Nine supervised ML techniques were investigated in this study. Models were trained based on patient disposition outcomes and then validated to evaluate their performance.</p></div><div><h3>Findings</h3><p>ML models show high capabilities in predicting patient disposition outcomes in ED settings. Four models (KNN, GBDT, XGBoost, and RF) performed better than the rest. RF was selected as the optimal model as it demonstrated a slight advantage over the other models with 89.1% micro accuracy, 89.0% precision, 89.1% recall, and 89.0% F1-score, exhibiting outstanding performance in differentiation between patients with critical outcomes (e.g., Mortality and ICU admission) from those patients with less critical outcomes (e.g., discharged and hospitalized) in ED settings.</p></div><div><h3>Conclusion</h3><p>Machine learning techniques demonstrate high promise in improving predictive abilities in emergency medicine and providing robust decision-making tools that can enhance the patient triage process, assist triage personnel in their decision and thus reduce the effects of ED overcrowding and enhance patient outcomes.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44500110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Random projection tree similarity metric for SpectralNet","authors":"Mashaan Alshammari ,&nbsp;John Stavrakakis ,&nbsp;Adel F. Ahmed ,&nbsp;Masahiro Takatsuka","doi":"10.1016/j.array.2022.100274","DOIUrl":"10.1016/j.array.2022.100274","url":null,"abstract":"<div><p>SpectralNet is a graph clustering method that uses neural network to find an embedding that separates the data. So far it was only used with <span><math><mi>k</mi></math></span>-nn graphs, which are usually constructed using a distance metric (e.g., Euclidean distance). <span><math><mi>k</mi></math></span>-nn graphs restrict the points to have a fixed number of neighbors regardless of the local statistics around them. We proposed a new SpectralNet similarity metric based on random projection trees (rpTrees). Our experiments revealed that SpectralNet produces better clustering accuracy using rpTree similarity metric compared to <span><math><mi>k</mi></math></span>-nn graph with a distance metric. Also, we found out that rpTree parameters do not affect the clustering accuracy. These parameters include the leaf size and the selection of projection direction. It is computationally efficient to keep the leaf size in order of <span><math><mrow><mo>log</mo><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow></math></span>, and project the points onto a random direction instead of trying to find the direction with the maximum dispersion.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44984319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}