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Статті в журналах з теми "Residual Pairwise Network"
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He, Zhi, and Dan He. "Spatial-Adaptive Siamese Residual Network for Multi-/Hyperspectral Classification." Remote Sensing 12, no. 10 (May 20, 2020): 1640. http://dx.doi.org/10.3390/rs12101640.
Повний текст джерелаАнотація:
Deep learning methods have been successfully applied for multispectral and hyperspectral images classification due to their ability to extract hierarchical abstract features. However, the performance of these methods relies heavily on large-scale training samples. In this paper, we propose a three-dimensional spatial-adaptive Siamese residual network (3D-SaSiResNet) that requires fewer samples and still enhances the performance. The proposed method consists of two main steps: construction of 3D spatial-adaptive patches and Siamese residual network for multiband images classification. In the first step, the spectral dimension of the original multiband images is reduced by a stacked autoencoder and superpixels of each band are obtained by the simple linear iterative clustering (SLIC) method. Superpixels of the original multiband image can be finally generated by majority voting. Subsequently, the 3D spatial-adaptive patch of each pixel is extracted from the original multiband image by reference to the previously generated superpixels. In the second step, a Siamese network composed of two 3D residual networks is designed to extract discriminative features for classification and we train the 3D-SaSiResNet by pairwise inputting the training samples into the networks. The testing samples are then fed into the trained 3D-SaSiResNet and the learned features of the testing samples are classified by the nearest neighbor classifier. Experimental results on three multiband image datasets show the feasibility of the proposed method in enhancing classification performance even with limited training samples.
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Li, Zhong, Yuele Lin, Arne Elofsson, and Yuhua Yao. "Protein Contact Map Prediction Based on ResNet and DenseNet." BioMed Research International 2020 (April 6, 2020): 1–12. http://dx.doi.org/10.1155/2020/7584968.
Повний текст джерелаАнотація:
Residue-residue contact prediction has become an increasingly important tool for modeling the three-dimensional structure of a protein when no homologous structure is available. Ultradeep residual neural network (ResNet) has become the most popular method for making contact predictions because it captures the contextual information between residues. In this paper, we propose a novel deep neural network framework for contact prediction which combines ResNet and DenseNet. This framework uses 1D ResNet to process sequential features, and besides PSSM, SS3, and solvent accessibility, we have introduced a new feature, position-specific frequency matrix (PSFM), as an input. Using ResNet’s residual module and identity mapping, it can effectively process sequential features after which the outer concatenation function is used for sequential and pairwise features. Prediction accuracy is improved following a final processing step using the dense connection of DenseNet. The prediction accuracy of the protein contact map shows that our method is more effective than other popular methods due to the new network architecture and the added feature input.
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Domonkos, Peter, José A. Guijarro, Victor Venema, Manola Brunet, and Javier Sigró. "Efficiency of Time Series Homogenization: Method Comparison with 12 Monthly Temperature Test Datasets." Journal of Climate 34, no. 8 (April 2021): 2877–91. http://dx.doi.org/10.1175/jcli-d-20-0611.1.
Повний текст джерелаАнотація:
AbstractThe aim of time series homogenization is to remove nonclimatic effects, such as changes in station location, instrumentation, observation practices, and so on, from observed data. Statistical homogenization usually reduces the nonclimatic effects but does not remove them completely. In the Spanish “MULTITEST” project, the efficiencies of automatic homogenization methods were tested on large benchmark datasets of a wide range of statistical properties. In this study, test results for nine versions, based on five homogenization methods—the adapted Caussinus-Mestre algorithm for the homogenization of networks of climatic time series (ACMANT), “Climatol,” multiple analysis of series for homogenization (MASH), the pairwise homogenization algorithm (PHA), and “RHtests”—are presented and evaluated. The tests were executed with 12 synthetic/surrogate monthly temperature test datasets containing 100–500 networks with 5–40 time series in each. Residual centered root-mean-square errors and residual trend biases were calculated both for individual station series and for network mean series. The results show that a larger fraction of the nonclimatic biases can be removed from station series than from network-mean series. The largest error reduction is found for the long-term linear trends of individual time series in datasets with a high signal-to-noise ratio (SNR), where the mean residual error is only 14%–36% of the raw data error. When the SNR is low, most of the results still indicate error reductions, although with smaller ratios than for large SNR. In general, ACMANT gave the most accurate homogenization results. In the accuracy of individual time series ACMANT is closely followed by Climatol, and for the accurate calculation of mean climatic trends over large geographical regions both PHA and ACMANT are recommended.
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Rogge, Ségolène, Ionut Schiopu, and Adrian Munteanu. "Depth Estimation for Light-Field Images Using Stereo Matching and Convolutional Neural Networks." Sensors 20, no. 21 (October 30, 2020): 6188. http://dx.doi.org/10.3390/s20216188.
Повний текст джерелаАнотація:
The paper presents a novel depth-estimation method for light-field (LF) images based on innovative multi-stereo matching and machine-learning techniques. In the first stage, a novel block-based stereo matching algorithm is employed to compute the initial estimation. The proposed algorithm is specifically designed to operate on any pair of sub-aperture images (SAIs) in the LF image and to compute the pair’s corresponding disparity map. For the central SAI, a disparity fusion technique is proposed to compute the initial disparity map based on all available pairwise disparities. In the second stage, a novel pixel-wise deep-learning (DL)-based method for residual error prediction is employed to further refine the disparity estimation. A novel neural network architecture is proposed based on a new structure of layers. The proposed DL-based method is employed to predict the residual error of the initial estimation and to refine the final disparity map. The experimental results demonstrate the superiority of the proposed framework and reveal that the proposed method achieves an average improvement of 15.65% in root mean squared error (RMSE), 43.62% in mean absolute error (MAE), and 5.03% in structural similarity index (SSIM) over machine-learning-based state-of-the-art methods.
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Adewopo, Julius B., and Alexandra Felix Locher. "Network-Based Resource-Proximity Analysis of Primary Wood Processing Mills in Arkansas." Southern Journal of Applied Forestry 35, no. 3 (August 1, 2011): 109–14. http://dx.doi.org/10.1093/sjaf/35.3.109.
Повний текст джерелаАнотація:
Abstract Primary wood processing mills in Arkansas play a vital role in both the state and the national economy, as evidenced by Arkansas' high national ranking in lumber productivity. Log acquisition from forestlands is invariably constrained by suitability of terrain and road networks; hence, an accurate assessment of the sufficiency of timberlands in servicing mills based on the existing road network and cost-effective log-truck travel time is essential for planning for the future. Many different analyses were carried out on an ArcInfo 9.3.1 workstation to delineate cost-effective sawmill service areas (SSA), timber supply areas (TSA), agricultural lands, and the overlaps that exist between these land patches. Zonal area summation of the land patches was analyzed with a two-sample paired t-test. Results indicated that there were significant pairwise differences (P < 0.0001) in the extent of SSA and TSA, SSA and SSA within TSA, SSA without agricultural lands and SSA within TSA, agricultural lands with SSA, and agricultural lands without SSA. This study indicated that a significant portion (10%) of agricultural lands must be used for optimal stocking of the delineated cost-effective SSA. Furthermore, this study revealed the suitable areas in Arkansas where there are clusters of residual timberlands that can serve as a raw material supply base for new mills.
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Nolte, Wietje, Rosemarie Weikard, Ronald M. Brunner, Elke Albrecht, Harald M. Hammon, Antonio Reverter, and Christa Kühn. "Identification and Annotation of Potential Function of Regulatory Antisense Long Non-Coding RNAs Related to Feed Efficiency in Bos taurus Bulls." International Journal of Molecular Sciences 21, no. 9 (May 6, 2020): 3292. http://dx.doi.org/10.3390/ijms21093292.
Повний текст джерелаАнотація:
Long non-coding RNAs (lncRNAs) can influence transcriptional and translational processes in mammalian cells and are associated with various developmental, physiological and phenotypic conditions. However, they remain poorly understood and annotated in livestock species. We combined phenotypic, metabolomics and liver transcriptomic data of bulls divergent for residual feed intake (RFI) and fat accretion. Based on a project-specific transcriptome annotation for the bovine reference genome ARS-UCD.1.2 and multiple-tissue total RNA sequencing data, we predicted 3590 loci to be lncRNAs. To identify lncRNAs with potential regulatory influence on phenotype and gene expression, we applied the regulatory impact factor algorithm on a functionally prioritized set of loci (n = 4666). Applying the algorithm of partial correlation and information theory, significant and independent pairwise correlations were calculated and co-expression networks were established, including plasma metabolites correlated with lncRNAs. The network hub lncRNAs were assessed for potential cis-actions and subjected to biological pathway enrichment analyses. Our results reveal a prevalence of antisense lncRNAs positively correlated with adjacent protein-coding genes and suggest their participation in mitochondrial function, acute phase response signalling, TCA-cycle, fatty acid β-oxidation and presumably gluconeogenesis. These antisense lncRNAs indicate a stabilizing function for their cis-correlated genes and a putative regulatory role in gene expression.
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Yang, Liang, Chuan Wang, Junhua Gu, Xiaochun Cao, and Bingxin Niu. "Why Do Attributes Propagate in Graph Convolutional Neural Networks?" Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4590–98. http://dx.doi.org/10.1609/aaai.v35i5.16588.
Повний текст джерелаАнотація:
Many efforts have been paid to enhance Graph Convolutional Network from the perspective of propagation under the philosophy that ``Propagation is the essence of the GCNNs". Unfortunately, its adverse effect is over-smoothing, which makes the performance dramatically drop. To prevent the over-smoothing, many variants are presented. However, the perspective of propagation can't provide an intuitive and unified interpretation to their effect on prevent over-smoothing. In this paper, we aim at providing a novel explanation to the question of "Why do attributes propagate in GCNNs?''. which not only gives the essence of the oversmoothing, but also illustrates why the GCN extensions, including multi-scale GCN and GCN with initial residual, can improve the performance. To this end, an intuitive Graph Representation Learning (GRL) framework is presented. GRL simply constrains the node representation similar with the original attribute, and encourages the connected nodes possess similar representations (pairwise constraint). Based on the proposed GRL, exiting GCN and its extensions can be proved as different numerical optimization algorithms, such as gradient descent, of our proposed GRL framework. Inspired by the superiority of conjugate gradient descent compared to common gradient descent, a novel Graph Conjugate Convolutional (GCC) network is presented to approximate the solution to GRL with fast convergence. Specifically, GCC adopts the obtained information of the last layer, which can be represented as the difference between the input and output of the last layer, as the input to the next layer. Extensive experiments demonstrate the superior performance of GCC.
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Comba, Allegra, Andrea Baldi, Massimo Carossa, Riccardo Michelotto Tempesta, Eric Garino, Xhuliano Llubani, Davide Rozzi, Julius Mikonis, Gaetano Paolone, and Nicola Scotti. "Post-Fatigue Fracture Resistance of Lithium Disilicate and Polymer-Infiltrated Ceramic Network Indirect Restorations over Endodontically-Treated Molars with Different Preparation Designs: An In-Vitro Study." Polymers 14, no. 23 (November 23, 2022): 5084. http://dx.doi.org/10.3390/polym14235084.
Повний текст джерелаАнотація:
The aim of the present study was to evaluate the fatigue to cyclic and static resistance of indirect restorations with different preparation designs made either of lithium disilicate (LS) or polymer-infiltrated ceramic network (PICN). Eighty-four (n = 84) molars were chosen, endodontically treated, and prepared with standardized MOD cavities. The molars were randomly divided into 6 study groups (n = 14) taking into account the “preparation design’’ (occlusal veneer with 1.2 mm occlusal thickness; overlay with 1.6 mm occlusal thickness; adhesive crown with 2 mm occlusal thickness) and the “CAD/CAM material’’ (E-max CAD, Ivoclar vivadent; Vita Enamic, Vita). A fatigue test was conducted with a chewing simulator set at 50 N for 1,500,000 cycles. Fracture resistance was assessed using a universal testing machine with a 6 mm diameter steel sphere applied to the specimens at a constant speed of 1 mm/min. A SEM analysis before the fracture test was performed to visually analyze the tooth-restoration margins. A statistical analysis was performed with a two-way ANOVA and a post-hoc pairwise comparison was performed using the Tukey test. The two-way ANOVA test showed that both the preparation design factor (p = 0.0429) and the CAD/CAM material factor (p = 0.0002) had a significant influence on the fracture resistance of the adhesive indirect restorations. The interaction between the two variables did not show any significance (p = 0.8218). The occlusal veneer had a lower fracture resistance than the adhesive crown (p = 0.042) but not lower than the overlay preparation (p = 0.095). LS was more resistant than PICN (p = 0.002). In conclusion, in the case of endodontically treated teeth, overlay preparation seems to be a valid alternative to the traditional full crown preparation, while occlusal veneers should be avoided in restoring non-vital molars with a high loss of residual tooth structure. LS material is more resistant compared to PICN.
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Luu, Dai Chu Nguyen, Rizvan Mamet, Carrie C. Zornosa, Joyce C. Niland, Thomas A. D'Amico, Gregory Peter Kalemkerian, Marianna Koczywas, Katherine Pisters, Michael S. Rabin, and Gregory Alan Otterson. "Retrospective analysis of the impact of age on overall survival in patients with non-small cell lung cancer." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): e18018-e18018. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.e18018.
Повний текст джерелаАнотація:
e18018 Background: Clinical trials have failed to demonstrate that age is a significant prognostic indicator among patients treated for non-small cell lung cancer (NSCLC). Clinical trials do not necessarily represent real-world experience, however. We sought to analyze the impact of age on survival in patients in the National Comprehensive Cancer Network (NCCN) NSCLC Outcomes Database. Methods: We performed a retrospective analysis of 6,834 NSCLC patients from the NCCN NSCLC Database representing 8 NCCN institutions. Of this population, 4,943 patients were eligible for our analysis. Exclusion criteria included the following: alive patients with < 180 days of follow-up, patients with incomplete staging, and patients with a prior cancer diagnosis. The study population was separated into five age quintiles with equal number of patients in each group. Variables included institution, smoking status, gender, race, Charlson comorbidity score, ECOG performance status (PS), histology, stage, and receipt of resection, drug and radiation therapy. Multivariable Cox model was performed for the effect of age on survival after adjusting for the above variables. Model assumptions were evaluated via graphs and residual tests. Results: Across the five quintiles (< 54, 54-60, 61-66, 67-72 and ≥ 73) there was a trend towards lower stage and higher Charlson score with increasing quintile. In addition, there was an increased proportion of patients with squamous cancer in the older age group. In the adjusted Cox model, there was a statistically significant longer survival in each of four younger quintiles compared to the reference group of ≥ 73 years of age (p=0.01). The adjusted hazard ratio of death for patients < 54 was .82 (95% CI = .72 to .94), for patients 54-60 was .86 (95% CI = .76 to .97), for patients 61-66 was .84 (95% CI = .74 to .95), and for patients 67-72 was .84 (95% CI = .74 to .95). There were no statistically significant pairwise interactions among age, smoking status and stage. Conclusions: Even after adjusting for institution, comorbidity scores, smoking status, race, gender, ECOG PS, histology, stage and treatment, NSCLC patients who were ≥ 73 years of age had a worse survival when compared to younger age groups.
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Gilson, M., A. Tauste Campo, X. Chen, A. Thiele, and G. Deco. "Nonparametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data." Network Neuroscience 1, no. 4 (December 2017): 357–80. http://dx.doi.org/10.1162/netn_a_00019.
Повний текст джерелаАнотація:
Directed connectivity inference has become a cornerstone in neuroscience to analyze multivariate data from neuroimaging and electrophysiological techniques. Here we propose a nonparametric significance method to test the nonzero values of multivariate autoregressive model to infer interactions in recurrent networks. We use random permutations or circular shifts of the original time series to generate the null-hypothesis distributions. The underlying network model is the same as used in multivariate Granger causality, but our test relies on the autoregressive coefficients instead of error residuals. By means of numerical simulation over multiple network configurations, we show that this method achieves a good control of false positives (type 1 error) and detects existing pairwise connections more accurately than using the standard parametric test for the ratio of error residuals. In practice, our method aims to detect temporal interactions in real neuronal networks with nodes possibly exhibiting redundant activity. As a proof of concept, we apply our method to multiunit activity (MUA) recorded from Utah electrode arrays in a monkey and examine detected interactions between 25 channels. We show that during stimulus presentation our method detects a large number of interactions that cannot be solely explained by the increase in the MUA level.
Частини книг з теми "Residual Pairwise Network"
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Ugurlu, Onur, Nusin Akram, and Vahid Khalilpour Akram. "Critical Nodes Detection in IoT-Based Cyber-Physical Systems." In Emerging Trends in IoT and Integration with Data Science, Cloud Computing, and Big Data Analytics, 226–39. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-4186-9.ch012.
Повний текст джерелаАнотація:
The new generation of fast, small, and energy-efficient devices that can connect to the internet are already used for different purposes in healthcare, smart homes, smart cities, industrial automation, and entertainment. One of the main requirements in all kinds of cyber-physical systems is a reliable communication platform. In a wired or wireless network, losing some special nodes may disconnect the communication paths between other nodes. Generally, these nodes, which are called critical nodes, have many undesired effects on the network. The authors focus on three different problems. The first problem is finding the nodes whose removal minimizes the pairwise connectivity in the residual network. The second problem is finding the nodes whose removal maximizes the number of connected components. Finally, the third problem is finding the nodes whose removal minimizes the size of the largest connected component. All three problems are NP-Complete, and the authors provide a brief survey about the existing approximated algorithms for these problems.
Тези доповідей конференцій з теми "Residual Pairwise Network"
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Mehrotra, Akshay, and Ambedkar Dukkipati. "Skip Residual Pairwise Networks With Learnable Comparative Functions for Few-Shot Learning." In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2019. http://dx.doi.org/10.1109/wacv.2019.00099.
Повний текст джерела
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Zeng, Haitian, Yuchao Dai, Xin Yu, Xiaohan Wang, and Yi Yang. "PR-RRN: Pairwise-Regularized Residual-Recursive Networks for Non-rigid Structure-from-Motion." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00555.
Повний текст джерела