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Journal articles on the topic 'Dual-stream'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Wang, Xi, Malcolm Crowe, and Colin Fyfe. "Dual stream data exploration." International Journal of Data Mining, Modelling and Management 4, no. 2 (2012): 188. http://dx.doi.org/10.1504/ijdmmm.2012.046810.

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2

Loui, Psyche. "A Dual-Stream Neuroanatomy of Singing." Music Perception 32, no. 3 (February 1, 2015): 232–41. http://dx.doi.org/10.1525/mp.2015.32.3.232.

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Singing requires effortless and efficient use of auditory and motor systems that center around the perception and production of the human voice. Although perception and production are usually tightly coupled functions, occasional mismatches between the two systems inform us of dissociable pathways in the brain systems that enable singing. Here I review the literature on perception and production in the auditory modality, and propose a dual-stream neuroanatomical model that subserves singing. I will discuss studies surrounding the neural functions of feedforward, feedback, and efference systems that control vocal monitoring, as well as the white matter pathways that connect frontal and temporal regions that are involved in perception and production. I will also consider disruptions of the perception-production network that are evident in tone-deaf individuals and poor pitch singers. Finally, by comparing expert singers against other musicians and nonmusicians, I will evaluate the possibility that singing training might offer rehabilitation from these disruptions through neuroplasticity of the perception-production network. Taken together, the best available evidence supports a model of dorsal and ventral pathways in auditory-motor integration that enables singing and is shared with language, music, speech, and human interactions in the auditory environment.

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3

Wu, Xuecheng, Qimeng Lv, Yingchun Wu, Can Li, and Kefa Cen. "Dual-stream of monodisperse droplet generator." Chemical Engineering Science 223 (September 2020): 115645. http://dx.doi.org/10.1016/j.ces.2020.115645.

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4

Holländer, Antje, Michael C. Corballis, and Jeff P. Hamm. "Visual-field asymmetry in dual-stream RSVP." Neuropsychologia 43, no. 1 (January 2005): 35–40. http://dx.doi.org/10.1016/j.neuropsychologia.2004.06.006.

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5

Murakami, Erina, and Dimitri Papamoschou. "Mean Flow Development in Dual-Stream Compressible Jets." AIAA Journal 40, no. 6 (June 2002): 1131–38. http://dx.doi.org/10.2514/2.1762.

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6

Viswanathan, K. "True Farfield for Dual-Stream Jet Noise Measurements." AIAA Journal 49, no. 2 (February 2011): 443–47. http://dx.doi.org/10.2514/1.j050771.

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7

VISWANATHAN, K. "Parametric study of noise from dual-stream nozzles." Journal of Fluid Mechanics 521 (December 25, 2004): 35–68. http://dx.doi.org/10.1017/s0022112004000813.

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8

Tam, Christopher K. W., N. N. Pastouchenko, and K. Viswanathan. "Broadband shock-cell noise from dual stream jets." Journal of Sound and Vibration 324, no. 3-5 (July 2009): 861–91. http://dx.doi.org/10.1016/j.jsv.2009.02.012.

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9

Upton, Emily, and Thomas M. H. Hope. "Moving beyond the dual stream account of language." Brain 143, no. 8 (August 1, 2020): 2336–38. http://dx.doi.org/10.1093/brain/awaa197.

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10

Xu, Ning, An-An Liu, Yongkang Wong, Yongdong Zhang, Weizhi Nie, Yuting Su, and Mohan Kankanhalli. "Dual-Stream Recurrent Neural Network for Video Captioning." IEEE Transactions on Circuits and Systems for Video Technology 29, no. 8 (August 2019): 2482–93. http://dx.doi.org/10.1109/tcsvt.2018.2867286.

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11

Murakami, E., and D. Papamoschou. "Mean flow development in dual-stream compressible jets." AIAA Journal 40 (January 2002): 1131–38. http://dx.doi.org/10.2514/3.15173.

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12

Knight, D., and G. Mallinson. "Visualizing unstructured flow data using dual stream functions." IEEE Transactions on Visualization and Computer Graphics 2, no. 4 (1996): 355–63. http://dx.doi.org/10.1109/2945.556503.

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13

Puurula, Antti, and Dirk Van Compernolle. "Dual stream speech recognition using articulatory syllable models." International Journal of Speech Technology 13, no. 4 (November 4, 2010): 219–30. http://dx.doi.org/10.1007/s10772-010-9080-2.

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14

Fan, Xitian, Di Wu, Wei Cao, Wayne Luk, and Lingli Wang. "Stream Processing Dual-Track CGRA for Object Inference." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 26, no. 6 (June 2018): 1098–111. http://dx.doi.org/10.1109/tvlsi.2018.2797600.

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15

Tam, Christopher K. W., and Nikolai N. Pastouchenko. "Fine-Scale Turbulence Noise from Dual-Stream Jets." AIAA Journal 44, no. 1 (January 2006): 90–101. http://dx.doi.org/10.2514/1.18018.

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16

Fitzgerald, Garrett C., Jonathan S. Krones, and Nickolas J. Themelis. "Greenhouse gas impact of dual stream and single stream collection and separation of recyclables." Resources, Conservation and Recycling 69 (December 2012): 50–56. http://dx.doi.org/10.1016/j.resconrec.2012.08.006.

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17

Viswanathan, K., M. J. Czech, and I. C. Lee. "Towards Prediction of Dual-Stream Jet Noise: Database Generation." AIAA Journal 49, no. 12 (December 2011): 2695–712. http://dx.doi.org/10.2514/1.j051124.

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18

Verrière, Jonas, Fabien Gand, and Sébastien Deck. "Zonal Detached-Eddy Simulations of a Dual-Stream Jet." AIAA Journal 54, no. 10 (October 2016): 3176–90. http://dx.doi.org/10.2514/1.j054896.

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19

Aksakal, B., S. Sezek, and Y. Can. "Forging of polygonal discs using the dual stream functions." Materials & Design 26, no. 8 (January 2005): 643–54. http://dx.doi.org/10.1016/j.matdes.2004.09.005.

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20

Luo, Shangjun, Junwei Luo, Wei Lu, Yanmei Fang, Jinhua Zeng, Shaopei Shi, and Yue Zhang. "Resampling Factor Estimation via Dual-stream Convolutional Neural Network." Computers, Materials & Continua 66, no. 1 (2020): 647–57. http://dx.doi.org/10.32604/cmc.2020.012869.

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21

Baig, Mohammed Salman, Ahmad Fauzi Abas, Mohammed Thamer Alresheedi, and Mohd Adzir Mahdi. "IM/DD dual stream asymmetrically clipped optical OFDM system." Optical Engineering 57, no. 08 (August 6, 2018): 1. http://dx.doi.org/10.1117/1.oe.57.8.086103.

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22

Macoviak, John A., Jimmy Hwang, Kelly L. Boerjan, and Dwight D. Deal. "Comparing dual-stream and standard cardiopulmonary bypass in pigs." Annals of Thoracic Surgery 73, no. 1 (January 2002): 203–8. http://dx.doi.org/10.1016/s0003-4975(01)03271-4.

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23

Makarevich, R. A., and P. L. Dyson. "Dual HF radar study of the subauroral polarization stream." Annales Geophysicae 25, no. 12 (January 2, 2007): 2579–91. http://dx.doi.org/10.5194/angeo-25-2579-2007.

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Abstract. The dual HF radars comprising the Tasman International Geophysical Environment Radar (TIGER) system often observe localized high-velocity F-region plasma flows (≥1500 m/s) in the midnight sector (20:00–02:00 MLT) at magnetic latitudes as low as Λ=60° S. The flow channels exhibit large variability in the latitudinal extent and electric field strength, and are similar to the subauroral polarization stream or SAPS, a plasma convection feature thought to be related to the polarization electric field due to the charge separation during substorm and storm development. In this study, the 2-D plasma drift velocity within the channel is derived for each of the two TIGER radars from the maximum velocities measured in all 16 radar beams within the latitudinally narrow channel, and the time variation of the subauroral electric field is examined near substorm onset. It is demonstrated that the flow channel often does not have a clear onset, rather it manifests differently in different phases of its evolution and can persist for at least two substorm cycles. During the growth phase the electric fields within the flow channel are difficult to distinguish from those of the background auroral convection but they start to increase near substorm onset and peak during the recovery phase, in contrast to what has been reported previously for auroral convection which peaks just before the substorm onset and falls sharply at the substorm onset. The response times to substorm onset range from −5 to +40 min and show some dependence on the substorm location with longer delays observed for substorms eastward of the radars' viewing area. The propagation velocity of the high-velocity region is also investigated by comparing the observations from the two closely-spaced TIGER radars. The observations are consistent with the notion that the polarization electric field is established with the energetic ions drifting westward and equatorward from the initial substorm injection. The ion injection front can precede that of the electrons and hence substorm onset resulting in a negative response time of a few minutes.

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24

Strutwolf, Jörg, Grégoire Herzog, Alexandra Homsy, Alfonso Berduque, Courtney J. Collins, and Damien W. M. Arrigan. "Potentiometric characterisation of a dual-stream electrochemical microfluidic device." Microfluidics and Nanofluidics 6, no. 2 (June 20, 2008): 231–40. http://dx.doi.org/10.1007/s10404-008-0319-z.

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25

Hobloss, Nour, Lu Zhang, Stéphane Lathuilière, Marco Cagnazzo, and Attilio Fiandrotti. "Hybrid dual stream blender for wide baseline view synthesis." Signal Processing: Image Communication 97 (September 2021): 116366. http://dx.doi.org/10.1016/j.image.2021.116366.

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26

Xu, Chenchu, Zhifan Gao, Heye Zhang, Shuo Li, and Victor Hugo C. de Albuquerque. "Video salient object detection using dual-stream spatiotemporal attention." Applied Soft Computing 108 (September 2021): 107433. http://dx.doi.org/10.1016/j.asoc.2021.107433.

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27

Wang, Yan, Yikun Huang, Can Liu, Xiaoying Gu, Dandan Yang, Shuopeng Wang, and Bo Zhang. "Micro Expression Recognition via Dual-Stream Spatiotemporal Attention Network." Journal of Healthcare Engineering 2021 (August 17, 2021): 1–10. http://dx.doi.org/10.1155/2021/7799100.

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Microexpression can manifest the real mood of humans, which has been widely concerned in clinical diagnosis and depression analysis. To solve the problem of missing discriminative spatiotemporal features in a small data set caused by the short duration and subtle movement changes of microexpression, we present a dual-stream spatiotemporal attention network (DSTAN) that integrates dual-stream spatiotemporal network and attention mechanism to capture the deformation features and spatiotemporal features of microexpression in the case of small samples. The Spatiotemporal networks in DSTAN are based on two lightweight networks, namely, the spatiotemporal appearance network (STAN) learning the appearance features from the microexpression sequences and the spatiotemporal motion network (STMN) learning the motion features from optical flow sequences. To focus on the discriminative motion areas of microexpression, we construct a novel attention mechanism for the spatial model of STAN and STMN, including a multiscale kernel spatial attention mechanism and global dual-pool channel attention mechanism. To obtain the importance of each frame in the microexpression sequence, we design a temporal attention mechanism for the temporal model of STAN and STMN to form spatiotemporal appearance network-attention (STAN-A) and spatiotemporal motion network-attention (STMN-A), which can adaptively perform dynamic feature refinement. Finally, the feature concatenate-SVM method is used to integrate STAN-A and STMN-A to a novel network, DSTAN. The extensive experiments on three small spontaneous microexpression data sets of SMIC, CASME, and CASME II demonstrate the proposed DSTAN can effectively cope with the recognition of microexpressions.

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28

Zhu, Weijie, and Ying Chen. "Micro-Expression Recognition Based on Dual-Stream Networks InformationInteraction." Journal of Computer-Aided Design & Computer Graphics 33, no. 4 (April 1, 2021): 545–51. http://dx.doi.org/10.3724/sp.j.1089.2021.18456.

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29

Zhao, Xiaoyan, Wenjing Zhang, Tianyao Zhang, and Zhaohui Zhang. "Cross-View Gait Recognition Based on Dual-Stream Network." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 5 (September 20, 2021): 671–78. http://dx.doi.org/10.20965/jaciii.2021.p0671.

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Gait recognition is a biometric identification method that can be realized under long-distance and no-contact conditions. Its applications in criminal investigations and security inspections are thus broad. Most existing gait recognition methods adopted the gait energy image (GEI) for feature extraction. However, the GEI method ignores the dynamic information of gait, which causes the recognition performance to be greatly affected by viewing angle changes and the subject’s belongings and clothes. To solve these problems, in this paper a cross-view gait recognition method that uses a dual-stream network based on the fusion of dynamic and static features (FDSN) is proposed. First, the static features are extracted from the GEI and the dynamic features are extracted from the image sequence of the human’s lower limbs. Then, the two features are fused, and finally, a nearest neighbor classifier is used for classification. Comparative experiments on the CASIA-B dataset created by the Automation Institute of the Chinese Academy of Sciences showed that the FDSN achieves a higher recognition rate than a convolutional neural network (CNN) and Gaitset under changes in viewing angle or clothing. To meet our requirements, in this study a gait image dataset was collected and produced in a campus setting. The experimental results on this dataset show the effectiveness of the FDSN in terms of eliminating the effects of disruptive changes.

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30

Tonjes, David J., Omkar Aphale, Lori Clark, and Krista L. Thyberg. "Conversion from dual stream to single stream recycling results in nuanced effects on revenues and waste stream amounts and composition." Resources, Conservation and Recycling 138 (November 2018): 151–59. http://dx.doi.org/10.1016/j.resconrec.2018.07.020.

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31

Lee, K.-H., T. Setoguchi, S. Matsuo, and H.-D. Kim. "An experimental study of underexpanded sonic, coaxial, swirl jets." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 218, no. 1 (January 1, 2004): 93–103. http://dx.doi.org/10.1243/095440604322786974.

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The present study addresses experimental investigations of the near-field flow structures of an underexpanded sonic, dual, coaxial, swirl jet. The swirl stream is discharged from the secondary annular nozzle and the primary inner nozzle provides the underexpanded free jets. The interactions between the secondary swirl and primary underexpanded jets are quantified by a fine pitot impact and static pressure measurements and are visualized using a shadowgraph optical method. The pressure ratios of the secondary swirl and primary underexpanded jets are varied below 7.0. Experiments are conducted to investigate the effects of the secondary swirl stream on the primary underexpanded jets, compared with the secondary stream of no swirl. The results show that the presence of an annular swirl stream causes the Mach disc to move further downstream, with an increased diameter, and remarkably reduces the fluctuations of the impact pressures in the underexpanded sonic dual coaxial jet, compared with the case of the secondary annular stream with no swirl.

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32

Ray, Dipanjan, Nilambari Hajare, Dipanjan Roy, and Arpan Banerjee. "Large-scale Functional Integration, Rather than Functional Dissociation along Dorsal and Ventral Streams, Underlies Visual Perception and Action." Journal of Cognitive Neuroscience 32, no. 5 (May 2020): 847–61. http://dx.doi.org/10.1162/jocn_a_01527.

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Visual dual-stream theory posits that two distinct neural pathways of specific functional significance originate from primary visual areas and reach the inferior temporal (ventral) and posterior parietal areas (dorsal). However, there are several unresolved questions concerning the fundamental aspects of this theory. For example, is the functional dissociation between ventral and dorsal stream driven by features in input stimuli or is it driven by categorical differences between visuoperceptual and visuomotor functions? Is the dual stream rigid or flexible? What is the nature of the interactions between the two streams? We addressed these questions using fMRI recordings on healthy human volunteers and employing stimuli and tasks that can tease out the divergence between visuoperceptual and visuomotor variants of dual-stream theory. fMRI scans were repeated after seven practice sessions that were conducted in a non-MRI environment to investigate the effects of neuroplasticity. Brain activation analysis supports an input-based functional dissociation and existence of context-dependent neuroplasticity in dual-stream areas. Intriguingly, premotor cortex activation was observed in the position perception task and distributed deactivated regions were observed in all perception tasks, thus warranting a network-level analysis. Dynamic causal modeling analysis incorporating activated and deactivated brain areas during perception tasks indicates that the brain dynamics during visual perception and actions could be interpreted within the framework of predictive coding. Effectively, the network-level findings point toward the existence of more intricate context-driven functional networks selective of “what” and “where” information rather than segregated streams of processing along ventral and dorsal brain regions.

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33

Shi, Dongmei, and Hongyu Tang. "Research on Safe Driving Evaluation Method Based on Machine Vision and Long Short-Term Memory Network." Journal of Electrical and Computer Engineering 2021 (April 14, 2021): 1–13. http://dx.doi.org/10.1155/2021/9955079.

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The rapid development of transportation industry has brought some potential safety hazards. Aiming at the problem of driving safety, the application of artificial intelligence technology in safe driving behavior recognition can effectively reduce the accident rate and economic losses. Based on the presence of interference signals such as spatiotemporal background mixed signals in the driving monitoring video sequence, the recognition accuracy of small targets such as human eyes is low. In this paper, an improved dual-stream convolutional network is proposed to recognize the safe driving behavior. Based on convolutional neural networks (CNNs), attention mechanism (AM) is integrated into a long short-term memory (LSTM) neural network structure, and the hybrid dual-stream AM-LSTM convolutional network channel is designed. The spatial stream channel uses the CNN method to extract the spatial characteristic value of video image and uses pyramid pooling instead of traditional pooling, normalizing the scale transformation. The time stream channel uses a single-shot multibox detector (SSD) algorithm to calculate the adjacent two frames of video sequence for the detection of small objects such as face and eyes. Then, AM-LSTM is used to fuse and classify dual-stream information. The self-built driving behavior video image set is built. ROC, accuracy rate, and loss function experiments are carried out in the FDDB database, VOT100 data set, and self-built video image set, respectively. Compared with CNN, SSD, IDT, and dual-stream recognition methods, the accuracy rate of this method can be improved by at least 1.4%, and the average absolute error in four video sequences can be improved by more than 2%. On the contrary, in the self-built image set, the recognition rate of doze reaches 68.3%, which is higher than other methods. The experimental results show that this method has good recognition accuracy and practical application value.

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34

Jiang, Fengqing, and Xiao Chen. "An Action Recognition Algorithm for Sprinters Using Machine Learning." Mobile Information Systems 2021 (May 19, 2021): 1–10. http://dx.doi.org/10.1155/2021/9919992.

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The advancements in modern science and technology have greatly promoted the progress of sports science. Advanced technological methods have been widely used in sports training, which have not only improved the scientific level of training but also promoted the continuous growth of sports technology and competition results. With the development of sports science and the gradual deepening of sport practices, the use of scientific training methods and monitoring approaches has improved the effect of sports training and athletes’ performance. This paper takes sprint as the research problem and constructs the image of sprinter’s action recognition based on machine learning. In view of the shortcomings of traditional dual-stream convolutional neural network for processing long-term video information, the time-segmented dual-stream network, based on sparse sampling, is used to better express the characteristics of long-term motion. First, the continuous video frame data is divided into multiple segments, and a short sequence of data containing user actions is formed by randomly sampling each segment of the video frame sequence. Next, it is applied to the dual-stream network for feature extraction. The optical flow image extraction involved in the dual-stream network is implemented by the system using the Lucas–Kanade algorithm. The system in this paper has been tested in actual scenarios, and the results show that the system design meets the expected requirements of the sprinters.

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35

Lee, Chien-Cheng, and Zhongjian Gao. "Sign Language Recognition Using Two-Stream Convolutional Neural Networks with Wi-Fi Signals." Applied Sciences 10, no. 24 (December 16, 2020): 9005. http://dx.doi.org/10.3390/app10249005.

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Sign language is an important way for deaf people to understand and communicate with others. Many researchers use Wi-Fi signals to recognize hand and finger gestures in a non-invasive manner. However, Wi-Fi signals usually contain signal interference, background noise, and mixed multipath noise. In this study, Wi-Fi Channel State Information (CSI) is preprocessed by singular value decomposition (SVD) to obtain the essential signals. Sign language includes the positional relationship of gestures in space and the changes of actions over time. We propose a novel dual-output two-stream convolutional neural network. It not only combines the spatial-stream network and the motion-stream network, but also effectively alleviates the backpropagation problem of the two-stream convolutional neural network (CNN) and improves its recognition accuracy. After the two stream networks are fused, an attention mechanism is applied to select the important features learned by the two-stream networks. Our method has been validated by the public dataset SignFi and adopted five-fold cross-validation. Experimental results show that SVD preprocessing can improve the performance of our dual-output two-stream network. For home, lab, and lab + home environment, the average recognition accuracy rates are 99.13%, 96.79%, and 97.08%, respectively. Compared with other methods, our method has good performance and better generalization capability.

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36

Belovich, V. M., M. Samimy, and M. F. Reeder. "Dual stream axisymmetric mixing in the presence of axial vorticity." Journal of Propulsion and Power 12, no. 1 (January 1996): 178–85. http://dx.doi.org/10.2514/3.24008.

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37

Viswanathan, K. "Noise of Dual-Stream Beveled Nozzles at Supercritical Pressure Ratios." Journal of Aircraft 43, no. 3 (May 2006): 627–38. http://dx.doi.org/10.2514/1.11433.

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38

Zhang, Yong, Wenzhe Liu, Xuezhen Ren, and Yonggong Ren. "Dual weighted extreme learning machine for imbalanced data stream classification." Journal of Intelligent & Fuzzy Systems 33, no. 2 (July 21, 2017): 1143–54. http://dx.doi.org/10.3233/jifs-16724.

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39

Zhang, Zufan, Hao Luo, Chun Wang, Chenquan Gan, and Yong Xiang. "Automatic Modulation Classification Using CNN-LSTM Based Dual-Stream Structure." IEEE Transactions on Vehicular Technology 69, no. 11 (November 2020): 13521–31. http://dx.doi.org/10.1109/tvt.2020.3030018.

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40

Giddings, J. Calvin. "Optimized field-flow fractionation system based on dual stream splitters." Analytical Chemistry 57, no. 4 (April 1985): 945–47. http://dx.doi.org/10.1021/ac00281a037.

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41

Nie, Xuan, Madhumita A. Takalkar, Mengyang Duan, Haimin Zhang, and Min Xu. "GEME: Dual-stream multi-task GEnder-based micro-expression recognition." Neurocomputing 427 (February 2021): 13–28. http://dx.doi.org/10.1016/j.neucom.2020.10.082.

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42

Aguilar-Rodriguez, E., X. Blanco-Cano, C. T. Russell, J. G. Luhmann, L. K. Jian, and J. C. Ramírez Vélez. "Dual observations of interplanetary shocks associated with stream interaction regions." Journal of Geophysical Research: Space Physics 116, A12 (December 2011): n/a. http://dx.doi.org/10.1029/2011ja016559.

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43

Ding, Yujuan, Wai Keung Wong, Zhihui Lai, and Zheng Zhang. "Discriminative dual-stream deep hashing for large-scale image retrieval." Information Processing & Management 57, no. 6 (November 2020): 102288. http://dx.doi.org/10.1016/j.ipm.2020.102288.

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44

Fridriksson, Julius, Grigori Yourganov, Leonardo Bonilha, Alexandra Basilakos, Dirk-Bart Den Ouden, and Christopher Rorden. "Revealing the dual streams of speech processing." Proceedings of the National Academy of Sciences 113, no. 52 (December 12, 2016): 15108–13. http://dx.doi.org/10.1073/pnas.1614038114.

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Several dual route models of human speech processing have been proposed suggesting a large-scale anatomical division between cortical regions that support motor–phonological aspects vs. lexical–semantic aspects of speech processing. However, to date, there is no complete agreement on what areas subserve each route or the nature of interactions across these routes that enables human speech processing. Relying on an extensive behavioral and neuroimaging assessment of a large sample of stroke survivors, we used a data-driven approach using principal components analysis of lesion-symptom mapping to identify brain regions crucial for performance on clusters of behavioral tasks without a priori separation into task types. Distinct anatomical boundaries were revealed between a dorsal frontoparietal stream and a ventral temporal–frontal stream associated with separate components. Collapsing over the tasks primarily supported by these streams, we characterize the dorsal stream as a form-to-articulation pathway and the ventral stream as a form-to-meaning pathway. This characterization of the division in the data reflects both the overlap between tasks supported by the two streams as well as the observation that there is a bias for phonological production tasks supported by the dorsal stream and lexical–semantic comprehension tasks supported by the ventral stream. As such, our findings show a division between two processing routes that underlie human speech processing and provide an empirical foundation for studying potential computational differences that distinguish between the two routes.

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45

Zhang, Zichen, Xinggang Luo, Chenghao Liu, Liqiang Zhao, and Hao Jing. "Single stream or dual stream? A game-theoretic approach to the sustainable evolution in waste recycling systems." Journal of Cleaner Production 269 (October 2020): 121847. http://dx.doi.org/10.1016/j.jclepro.2020.121847.

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46

Speechley, W. J., C. B. Murray, R. M. McKay, M. T. Munz, and E. T. C. Ngan. "A failure of conflict to modulate dual-stream processing may underlie the formation and maintenance of delusions." European Psychiatry 25, no. 2 (March 2010): 80–86. http://dx.doi.org/10.1016/j.eurpsy.2009.05.012.

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AbstractBackgroundDual-stream information processing proposes that reasoning is composed of two interacting processes: a fast, intuitive system (Stream 1) and a slower, more logical process (Stream 2). In non-patient controls, divergence of these streams may result in the experience of conflict, modulating decision-making towards Stream 2, and initiating a more thorough examination of the available evidence. In delusional schizophrenia patients, a failure of conflict to modulate decision-making towards Stream 2 may reduce the influence of contradictory evidence, resulting in a failure to correct erroneous beliefs.MethodDelusional schizophrenia patients and non-patient controls completed a deductive reasoning task requiring logical validity judgments of two-part conditional statements. Half of the statements were characterized by a conflict between logical validity (Stream 2) and content believability (Stream 1).ResultsPatients were significantly worse than controls in determining the logical validity of both conflict and non-conflict conditional statements. This between groups difference was significantly greater for the conflict condition.ConclusionsThe results are consistent with the hypothesis that delusional schizophrenia patients fail to use conflict to modulate towards Stream 2 when the two streams of reasoning arrive at incompatible judgments. This finding provides encouraging preliminary support for the Dual-Stream Modulation Failure model of delusion formation and maintenance.

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Li, Jiangnan, and Howard W. Barker. "Perturbation Solution for Four-Stream Infrared Radiative Transfer." Journal of the Atmospheric Sciences 77, no. 4 (April 1, 2020): 1497–507. http://dx.doi.org/10.1175/jas-d-19-0179.1.

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Abstract A four-stream solution of the longwave radiative transfer is proposed. It is based on the exact perturbation method utilizing the absorption approximation equation as the zero-order solution. Scattering is handled by the first-order perturbation equation. The two- and four-stream approximations are compared both offline and using data from CALIPSO’s dual-wavelength lidar.

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Zhu, Ye, Xiaoqian Shen, Shikun Liu, Xiaoli Zhang, and Gang Yan. "Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network." Applied Sciences 11, no. 18 (September 11, 2021): 8437. http://dx.doi.org/10.3390/app11188437.

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Splicing is the most common operation in image forgery, where the tampered background regions are imported from different images. Illumination maps are inherent attribute of images and provide significant clues when searching for splicing locations. This paper proposes an end-to-end dual-stream network for splicing location, where the illumination stream, which includes Grey-Edge (GE) and Inverse-Intensity Chromaticity (IIC), extract the inconsistent features, and the image stream extracts the global unnatural tampered features. The dual-stream feature in our network is fused through Multiple Feature Pyramid Network (MFPN), which contains richer context information. Finally, a Cluster Region Proposal Network (C-RPN) with spatial attention and an adaptive cluster anchor are proposed to generate potential tampered regions with greater retention of location information. Extensive experiments, which were evaluated on the NIST16 and CASIA standard datasets, show that our proposed algorithm is superior to some state-of-the-art algorithms, because it achieves accurate tampered locations at the pixel level, and has great robustness in post-processing operations, such as noise, blur and JPEG recompression.

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Lara-Benítez, Pedro, Manuel Carranza-García, Jorge García-Gutiérrez, and José C. Riquelme. "Asynchronous dual-pipeline deep learning framework for online data stream classification." Integrated Computer-Aided Engineering 27, no. 2 (February 27, 2020): 101–19. http://dx.doi.org/10.3233/ica-200617.

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Cao, Gang, Antao Zhou, Xianglin Huang, Gege Song, Lifang Yang, and Yonggui Zhu. "Resampling detection of recompressed images via dual-stream convolutional neural network." Mathematical Biosciences and Engineering 16, no. 5 (2019): 5022–40. http://dx.doi.org/10.3934/mbe.2019253.

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