Journal articles: 'Sequential propagation'

1

Hui, Hui, Dayou Liu, and Yafei Wang. "Sequential back-propagation." Journal of Computer Science and Technology 9, no. 3 (July 1994): 252–60. http://dx.doi.org/10.1007/bf02939506.

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Cavallera, P., C. Landrault, S. Pravossoudovitch, and P. Girard. "Delay fault propagation in synchronous sequential circuits." Electronics Letters 30, no. 10 (May 12, 1994): 765–67. http://dx.doi.org/10.1049/el:19940538.

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Papanastasiou, Yiangos. "Fake News Propagation and Detection: A Sequential Model." Management Science 66, no. 5 (May 2020): 1826–46. http://dx.doi.org/10.1287/mnsc.2019.3295.

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Kfir, Haggai, and Ido Kanter. "Parallel versus sequential updating for belief propagation decoding." Physica A: Statistical Mechanics and its Applications 330, no. 1-2 (December 2003): 259–70. http://dx.doi.org/10.1016/j.physa.2003.08.015.

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MacKernan, Donal, Raymond Kapral, and Giovanni Ciccotti. "Sequential short-time propagation of quantum classical dynamics." Journal of Physics: Condensed Matter 14, no. 40 (September 26, 2002): 9069–76. http://dx.doi.org/10.1088/0953-8984/14/40/301.

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Nagaki, Aiichiro, Yiyuan Jiang, Yosuke Ashikari, and Kaiteng Guan. "Accelerating Heat-Initiated Radical Reactions of Organic Halides with Tin Hydride Using Flow Microreactor Technologies." Synlett 31, no. 19 (October 9, 2020): 1937–41. http://dx.doi.org/10.1055/s-0040-1707307.

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AbstractWe herein report that flow microreactors can promote an efficiency of radical chain reactions. The chain reactions with a fast propagation step can be accelerated by virtue of an efficient heat-transfer character of the microreactors, whereas the yield of those reactions with a slow propagation step was increased by flow microreactors. Moreover, the yield was further increased by a sequential addition of the initiators, which was allowed by a flow-sequential-addition system.

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Zheng, Peng, Tuan Gu, Erhu Liu, Ming Zhao, and Desheng Zhou. "Simulation of Fracture Morphology during Sequential Fracturing." Processes 10, no. 5 (May 9, 2022): 937. http://dx.doi.org/10.3390/pr10050937.

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During hydraulic fracturing, the aperture of hydraulic fractures will shrink by the in-situ stress, but will not fully close because of the existence of proppant inside the fracture. In previous studies, few people noticed the existence of proppant, which has resulted in the inaccuracy of simulation results. In this study, based on the boundary element method, a numerical simulation model for sequential fracturing was established, which respectively considered the influence of proppant in staged fracturing and zipper fracturing. In addition, the influence mechanism of proppant on fracture morphology is then revealed. Simulation results show that the residual aperture of the previous hydraulic fracture, which was produced by proppant, may increase with the increase of proppant stiffness and fracture spacing and may also be shrunk by the dynamic propagation of subsequent hydraulic fracture. However, the residual aperture will rebound after hydraulic fracturing construction is finished. The shrinkage and rebound values of residual aperture of hydraulic fracture are usually less than 1 mm. In addition, at the same time, the residual aperture of previous hydraulic fracture may also influence the propagation of subsequent hydraulic fracture. These influences are represented by the bend of fractures in multistage fracturing and the intersection in zipper fracturing. With the increase of well spacing, the influence degree of residual aperture on subsequent fracture propagation is reduced. The previous hydraulic fracture cannot have a significant effect on the deflection of subsequent hydraulic fracture when fracture spacing is between 10 and 30 m. The above research has important guiding significance for controlling fracture morphology in hydraulic fracturing.

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Cai, Shuo, Fei Yu, and Yiqun Yang. "Analysis of SET pulses propagation probabilities in sequential circuits." IOP Conference Series: Materials Science and Engineering 351 (May 2018): 012010. http://dx.doi.org/10.1088/1757-899x/351/1/012010.

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Guan, J., and J. H. Graham. "Diagnostic reasoning with fault propagation digraph and sequential testing." IEEE Transactions on Systems, Man, and Cybernetics 24, no. 10 (1994): 1552–58. http://dx.doi.org/10.1109/21.310538.

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Hernandez-Fajardo, Isaac, and Leonardo Dueñas-Osorio. "Sequential Propagation of Seismic Fragility across Interdependent Lifeline Systems." Earthquake Spectra 27, no. 1 (February 2011): 23–43. http://dx.doi.org/10.1193/1.3544052.

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Realistic models of service networks must consider the evolution of interactions with external systems to evaluate emergent response effects on individual network performance. This paper introduces a new dynamic methodology for the assessment of systemic fragility propagation across interdependent networks subjected to seismic action that improves existing static methodologies. Interdependencies are discrete, unidirectional relationships between elements of distinct networks, which are able to influence response evolution from transient to steady-state stages. Comparisons of systemic fragility curves results for isolated and interdependent power and water networks display the importance of interdependence strength and density properties. For the test water network, inter-systemic failure propagation increases its connectivity loss by up to 24%, while high interdependence strengths make the median fragility rise up to 56.2%. In contrast, reductions of interdependence density improve the median water fragility up to 81.7%. Insights obtained from this model, and its associated sequential fragility algorithm, reveal complex coupling patterns and interdependence-based mitigation strategies that are essential for lifeline system management.

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Kurt, Mehmet Necip, and Xiaodong Wang. "Multisensor Sequential Change Detection With Unknown Change Propagation Pattern." IEEE Transactions on Aerospace and Electronic Systems 55, no. 3 (June 2019): 1498–518. http://dx.doi.org/10.1109/taes.2018.2873067.

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Sahimi, Muhammad, and S. Mehdi Vaez Allaei. "Numerical Simulation of Wave Propagation, Part I: Sequential Computing." Computing in Science & Engineering 10, no. 3 (May 2008): 66–75. http://dx.doi.org/10.1109/mcse.2008.77.

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Xue, Jianru, Nanning Zheng, and Xiaopin Zhong. "Sequential stratified sampling belief propagation for multiple targets tracking." Science in China Series F 49, no. 1 (January 2006): 48–62. http://dx.doi.org/10.1007/s11432-004-0140-6.

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Chen, Yadang, Chuanyan Hao, Wen Wu, and Enhua Wu. "Efficient frame-sequential label propagation for video object segmentation." Multimedia Tools and Applications 77, no. 5 (March 1, 2017): 6117–33. http://dx.doi.org/10.1007/s11042-017-4520-5.

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Zhang, Zhijun, and Hong Liu. "Social recommendation model combining trust propagation and sequential behaviors." Applied Intelligence 43, no. 3 (May 19, 2015): 695–706. http://dx.doi.org/10.1007/s10489-015-0681-y.

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Nakamura, Yatsuka, Takaya Nishiyama, and Yasushi Fuwa. "Signal propagation timing check system in multilevel sequential circuits." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 76, no. 7 (1993): 50–62. http://dx.doi.org/10.1002/ecjc.4430760705.

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Li, Xiaochen, and Kai Du. "Sequential Propagation of Chaos for Mean-Field BSDE Systems." Chinese Annals of Mathematics, Series B 45, no. 1 (January 2024): 11–40. http://dx.doi.org/10.1007/s11401-024-0002-z.

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Ivanov, Nickolay, Gennady Kurtsev, and Aleksandr Shashurin. "THE METHOD OF SEQUENTIAL TRANSFORMATION OF THE SOUND FIELDS." VOLUME 39, VOLUME 39 (2021): 141. http://dx.doi.org/10.36336/akustika202139141.

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A rule for describing the sequential transformation of the sound fields when properties of the surfaces or structural elements change due to such basic processes as sound absorption, reflection, diffraction, or sound divergence is proposed. The main assumption is that sound fields are non-coherent, i.e., resonant phenomena and sound interference are not considered. The examples show solutions to such problems: - sound propagation in space if there are artificial structures; - sound propagation in the rooms; - efficiency calculation of the noise protection structures; - calculation of the expected noise level of the machinery and separation of the contribution of noise and vibration sources to sound fields (for example, an external sound field, a sound field in the office, etc.)

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Liu, Xien, Xinxin You, Xiao Zhang, Ji Wu, and Ping Lv. "Tensor Graph Convolutional Networks for Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 8409–16. http://dx.doi.org/10.1609/aaai.v34i05.6359.

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Compared to sequential learning models, graph-based neural networks exhibit some excellent properties, such as ability capturing global information. In this paper, we investigate graph-based neural networks for text classification problem. A new framework TensorGCN (tensor graph convolutional networks), is presented for this task. A text graph tensor is firstly constructed to describe semantic, syntactic, and sequential contextual information. Then, two kinds of propagation learning perform on the text graph tensor. The first is intra-graph propagation used for aggregating information from neighborhood nodes in a single graph. The second is inter-graph propagation used for harmonizing heterogeneous information between graphs. Extensive experiments are conducted on benchmark datasets, and the results illustrate the effectiveness of our proposed framework. Our proposed TensorGCN presents an effective way to harmonize and integrate heterogeneous information from different kinds of graphs.

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Han, Bing, Cheng Wang, and Kaushik Roy. "Oscillatory Fourier Neural Network: A Compact and Efficient Architecture for Sequential Processing." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6838–46. http://dx.doi.org/10.1609/aaai.v36i6.20640.

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Tremendous progress has been made in sequential processing with the recent advances in recurrent neural networks. However, recurrent architectures face the challenge of exploding/vanishing gradients during training, and require significant computational resources to execute back-propagation through time. Moreover, large models are typically needed for executing complex sequential tasks. To address these challenges, we propose a novel neuron model that has cosine activation with a time varying component for sequential processing. The proposed neuron provides an efficient building block for projecting sequential inputs into spectral domain, which helps to retain long-term dependencies with minimal extra model parameters and computation. A new type of recurrent network architecture, named Oscillatory Fourier Neural Network, based on the proposed neuron is presented and applied to various types of sequential tasks. We demonstrate that recurrent neural network with the proposed neuron model is mathematically equivalent to a simplified form of discrete Fourier transform applied onto periodical activation. In particular, the computationally intensive back-propagation through time in training is eliminated, leading to faster training while achieving the state of the art inference accuracy in a diverse group of sequential tasks. For instance, applying the proposed model to sentiment analysis on IMDB review dataset reaches 89.4% test accuracy within 5 epochs, accompanied by over 35x reduction in the model size compared to LSTM. The proposed novel RNN architecture is well poised for intelligent sequential processing in resource constrained hardware.

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Park, Yongsung, Florian Meyer, and Peter Gerstoft. "Graph-based sequential beamforming." Journal of the Acoustical Society of America 153, no. 1 (January 2023): 723–37. http://dx.doi.org/10.1121/10.0016876.

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This paper presents a Bayesian estimation method for sequential direction finding. The proposed method estimates the number of directions of arrivals (DOAs) and their DOAs performing operations on the factor graph. The graph represents a statistical model for sequential beamforming. At each time step, belief propagation predicts the number of DOAs and their DOAs using posterior probability density functions (pdfs) from the previous time and a different Bernoulli-von Mises state transition model. Variational Bayesian inference then updates the number of DOAs and their DOAs. The method promotes sparse solutions through a Bernoulli-Gaussian amplitude model, is gridless, and provides marginal posterior pdfs from which DOA estimates and their uncertainties can be extracted. Compared to nonsequential approaches, the method can reduce DOA estimation errors in scenarios involving multiple time steps and time-varying DOAs. Simulation results demonstrate performance improvements compared to state-of-the-art methods. The proposed method is evaluated using ocean acoustic experimental data.

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WANG, JINGYAN, and YONGPING LI. "SEQUENTIAL LINEAR NEIGHBORHOOD PROPAGATION FOR SEMI-SUPERVISED PROTEIN FUNCTION PREDICTION." Journal of Bioinformatics and Computational Biology 09, no. 06 (December 2011): 663–79. http://dx.doi.org/10.1142/s0219720011005550.

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Predicting protein function is one of the most challenging problems of the post-genomic era. The development of experimental methods for genome scale analysis of molecular interaction networks has provided new approaches to inferring protein function. In this paper we introduce a new graph-based semi-supervised classification algorithm Sequential Linear Neighborhood Propagation (SLNP), which addresses the problem of the classification of partially labeled protein interaction networks. The proposed SLNP first constructs a sequence of node sets according to their shortest distance to the labeled nodes, and then predicts the function of the unlabel proteins from the set closer to labeled one, using Linear Neighborhood Propagation. Its performance is assessed on the Saccharomyces cerevisiae PPI network data sets, with good results compared with three current state-of-the-art algorithms, especially in settings where only a small fraction of the proteins are labeled.

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Zhao, Ke, Lan Huang, Rui Song, Qiang Shen, and Hao Xu. "A Sequential Graph Neural Network for Short Text Classification." Algorithms 14, no. 12 (December 1, 2021): 352. http://dx.doi.org/10.3390/a14120352.

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Short text classification is an important problem of natural language processing (NLP), and graph neural networks (GNNs) have been successfully used to solve different NLP problems. However, few studies employ GNN for short text classification, and most of the existing graph-based models ignore sequential information (e.g., word orders) in each document. In this work, we propose an improved sequence-based feature propagation scheme, which fully uses word representation and document-level word interaction and overcomes the limitations of textual features in short texts. On this basis, we utilize this propagation scheme to construct a lightweight model, sequential GNN (SGNN), and its extended model, ESGNN. Specifically, we build individual graphs for each document in the short text corpus based on word co-occurrence and use a bidirectional long short-term memory network (Bi-LSTM) to extract the sequential features of each document; therefore, word nodes in the document graph retain contextual information. Furthermore, two different simplified graph convolutional networks (GCNs) are used to learn word representations based on their local structures. Finally, word nodes combined with sequential information and local information are incorporated as the document representation. Extensive experiments on seven benchmark datasets demonstrate the effectiveness of our method.

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Camacho, Maximo, and Danilo Leiva-Leon. "THE PROPAGATION OF INDUSTRIAL BUSINESS CYCLES." Macroeconomic Dynamics 23, no. 1 (September 21, 2017): 144–77. http://dx.doi.org/10.1017/s1365100516001140.

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This paper examines the evolution of the distribution of industry-specific business cycle linkages, which are modeled through a multivariate Markov-switching model and estimated by Gibbs sampling. Using nonparametric density estimation approaches, we find that the number and location of modes in the distribution of industrial dissimilarities change over the business cycle. There is a relatively stable trimodal pattern during expansionary and recessionary phases characterized by highly, moderately, and lowly synchronized industries. However, during phase changes, the density mass spreads from moderately synchronized industries to lowly synchronized industries. This agrees with a sequential transmission of the industrial business cycle dynamics.

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Li, Yuliang, Wei Zhao, and Yongsheng Ma. "A shortest path method for sequential change propagations in complex engineering design processes." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30, no. 1 (June 9, 2015): 107–21. http://dx.doi.org/10.1017/s0890060415000311.

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AbstractEngineering design changes constantly occur in complex engineering design processes. Designers need appropriate measures to handle the numerous design changes in order to realize consistent and completely validated product models so that successful product development is assured. In this paper, a time-based mathematic model is presented to characterize the sequential change propagation process, and then the shortest path algorithm is given to find the most timesaving routes for changes to propagate to other dependent design tasks. An analysis method is introduced to compute the sensitivities of change impacts on the affected design tasks, which indicates that the more time consumed by a change to take its effect, the more sensitive the change impacts on those downstream dependent tasks. A case study of change propagations in motorcycle engine design process was presented to demonstrate the proposed method.

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Yoshimura, Masayoshi, Yusuke Akamine, and Yusuke Matsunaga. "An Exact Estimation Algorithm of Error Propagation Probability for Sequential Circuits." IPSJ Transactions on System LSI Design Methodology 5 (2012): 63–70. http://dx.doi.org/10.2197/ipsjtsldm.5.63.

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Park, Sangjoon, and Sooyong Choi. "QR Decomposition Based Sequential Belief Propagation Detection Scheme for MIMO Systems." Journal of Korean Institute of Communications and Information Sciences 40, no. 9 (September 30, 2015): 1725–27. http://dx.doi.org/10.7840/kics.2015.40.9.1725.

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Bohyung Han, Ying Zhu, D. Comaniciu, and L. S. Davis. "Visual Tracking by Continuous Density Propagation in Sequential Bayesian Filtering Framework." IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 5 (May 2009): 919–30. http://dx.doi.org/10.1109/tpami.2008.134.

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Bai, Yulong, Xin Li, and Chunlin Huang. "Handling error propagation in sequential data assimilation using an evolutionary strategy." Advances in Atmospheric Sciences 30, no. 4 (June 18, 2013): 1096–105. http://dx.doi.org/10.1007/s00376-012-2115-7.

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Zhang, Hao-yu, Jun-bin Chen, and Yang Wang. "An XFEM-based CZM Numerical Strategy for Modeling Hydraulic Fracture Propagation under Different Fracturing Schemes." Journal of Physics: Conference Series 2594, no. 1 (October 1, 2023): 012025. http://dx.doi.org/10.1088/1742-6596/2594/1/012025.

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Abstract Segmented and cluster fracturing can improve the efficiency of volume stimulation in shale reservoirs and reduce construction costs. It is important to clarify the propagation characteristics of hydraulic fractures and stress interference under different fracturing techniques to optimize the process of clustered and staged fracturing. For this purpose, we have developed a 2D XFEM-based CZM hydraulic fracturing model. The capability of this model was validated by analytical solutions and then used to study the propagation paths of hydraulic fractures and the characteristics of stress interference under simultaneous fracturing, sequential fracturing, zipper fracturing and multi-cluster fracturing. The results show that in simultaneous fracturing, the middle cluster is compressed by the external position clusters, and the opening width of hydraulic fractures is reduced. In sequential fracturing, the fracture that first initiates create an additional stress field that inhibits the propagation distance of subsequent fractures and the propagation path of hydraulic fractures is also affected in the stress shadow region. Zipper fracturing can effectively alleviate stress interference between multiple fractures, and internal fractures can also propagate a certain distance. In multi-cluster fracturing, the fluid rate into the internal fractures may be limited, and fracture propagation is also limited by stress interference. Therefore, it is necessary to optimize the parameters of clusters to ensure that all clusters can initiate fractures normally. The research results are important for the parameter optimization of clustered and staged fracturing, especially for well factory fracturing mode.

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Schumann, Andrew, Krzysztof Pancerz, and Andrzej Szelc. "The Swarm Computing Approach to Business Intelligence." Studia Humana 4, no. 3 (July 1, 2015): 41–50. http://dx.doi.org/10.1515/sh-2015-0019.

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Abstract We have proposed to use some features of swarm behaviours in modelling business processes. Due to these features we deal with a propagation of business processes in all accessible directions. This propagation is involved into our formalization instead of communicating sequential processes. As a result, we have constructed a business process diagram language based on the swarm behavior and an extension of that language in the form of reflexive management language.

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Makki, R. Z., and S. Su. "Analysis and Characterization of State Assignment Techniques for Sequential Machines." VLSI Design 2, no. 1 (January 1, 1994): 81–88. http://dx.doi.org/10.1155/1994/51798.

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In this paper, we study the problem of state assignment as it relates to silicon area, propagation delay time and testability of finite state machines. The results of a study involving various FSM benchmarks show that the simple technique of one-hot encoding often produces better results than those attained by complex state assignment algorithms.

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El-Rabbany, Ahmed, and Mohamed El-Diasty. "A New Approach to Sequential Tidal Prediction." Journal of Navigation 56, no. 2 (May 2003): 305–14. http://dx.doi.org/10.1017/s0373463303002285.

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Accurate tide prediction is required for safe marine navigation in shallow waters as well as for other marine operations. Traditionally, tide prediction was carried out using the harmonic method, which is based on the identification of the harmonic tidal constituents existing in the tidal record. Unfortunately, however, unless long tidal records are available at the tide gauges, some important tidal constituents may not be identified. This, in turn, deteriorates the accuracy of the tidal prediction. More recently, a sequential least-squares prediction method capable of using relatively short tidal records was developed. This method allows for the modifications and corrections of the original solution of the tidal constituents when new observations and parameters are included. Although it reduces the computation time significantly compared to the batch harmonic method, this method exhibits large residuals particularly when very short tidal records are used. To overcome the limitations of the sequential least-squares method, a neural network-based model is developed for sequentially predicting the tidal heights using tide data series collected at various tide gauges. A modular, three-layer feedforward neural network trained using the back-propagation algorithm is used for this purpose. Tide data from three tide gauges are used to validate the model. A comparison is made between the developed neural network model and the sequential least squares method for tidal prediction. It is shown that the accuracy level of the tidal prediction has improved by a factor of 5 when using the neural network model.

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Wong, Basil T., M. Pinar Mengüç, and R. Ryan Vallance. "Sequential Nano-Patterning Using Electron and Laser Beams: A Numerical Methodology." Journal of Computational and Theoretical Nanoscience 3, no. 2 (April 1, 2006): 219–30. http://dx.doi.org/10.1166/jctn.2006.3001.

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A methodology is presented for nanometer-size patterning of a workpiece using both an electron-beam and a laser. A Monte Carlo/Ray Tracing technique is used in modeling the electron-beam propagation inside a thin gold film. This approach is identical to that of a typical Monte Carlo simulation in radiative transfer except that proper electron scattering properties are employed. The laser propagation within the one-dimensional, non-scattering film on top of a quartz substrate is modeled using a ray-tracing approach and reflections at the boundaries are accounted for with the Fresnel-expressions. The temperature distribution inside a gold film is then predicted using the Fourier law of heat conduction, after evaluating the accuracy of the model for the range considered. A sequential nano-pattern is created using these coupled numerical simulations. The procedure we present here is the first to outline the sequential nano-machining processes and likely to guide the experimental studies to success with less trial-and-error attempts.

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Yang, Bin, Qin Shou Huang, Ping Liang, and Hua Xu. "Simulation of Reflective Crack Propagation Path in Asphalt Overlay under the Partial Wheel Load." Advanced Materials Research 189-193 (February 2011): 2001–4. http://dx.doi.org/10.4028/www.scientific.net/amr.189-193.2001.

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Partial wheel load is one of the main causes that lead to shear-type reflective crack of asphalt overlay in the old cement concrete pavement. Based on the theory of fracture mechanics and finite element method, this paper focuses on the numerical simulation of the reflective crack propagation path in asphalt overlay under the partial wheel load. Calculation result and analysis show that reflective crack partially expands upwards at the side of wheel load under the effect of partial wheel load with a sequential increase of crack propagation length. As the crack propagation length increases, stress intensity factors grow rapidly in the early period, and then increase by degrees slowly and, in the later period, the amplitude grows increasingly. Stress and strain fields enhance as the reflective crack increases upwards and the crack propagation angle expands gradually with the increase of reflective crack propagation length.

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Jung, Ki-Young, Jae-Moon Kim, and Dong Wook Kim. "Patterns of Interictal Spike Propagation across the Central Sulcus in Benign Rolandic Epilepsy." Clinical Electroencephalography 34, no. 3 (July 2003): 153–57. http://dx.doi.org/10.1177/155005940303400309.

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It has been reported that the rolandic area generating spikes is hyperexcitable, and that rolandic spikes propagate across the central area. However, the pattern of rolandic spike propagation and how the dipolar distribution of the spikes is related to the propagation pattern have not yet been studied. Thirty-nine EEGs from 27 patients with benign rolandic epilepsy (BRE) were examined. Sequential topographic mapping in 4-ms steps was used to analyze the pattern of spike propagation. The locations of maximum negative foci, the presence and distribution of the dipolar field, and the propagation pattern were examined. Dipoles were present in 23 (85.2%) out of 27 patients and in 43 (72.9%) out of 59 foci. Thirty-two foci (54.2%) in 20 patients demonstrated a propagation pattern. The typical pattern consisted of propagation from central to midtemporal locations across the central sulcus. Most spike foci exhibiting a propagation pattern had a dipolar distribution (87.5%; p=0.008). These results suggest that rolandic spikes originate from sulcal or gyral cortices on either side of the central sulcus, and that spike propagation can ensue by intracortical spreading across the central sulcus.

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Larsen, Brett W., and Shaul Druckmann. "Towards a more general understanding of the algorithmic utility of recurrent connections." PLOS Computational Biology 18, no. 6 (June 21, 2022): e1010227. http://dx.doi.org/10.1371/journal.pcbi.1010227.

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Lateral and recurrent connections are ubiquitous in biological neural circuits. Yet while the strong computational abilities of feedforward networks have been extensively studied, our understanding of the role and advantages of recurrent computations that might explain their prevalence remains an important open challenge. Foundational studies by Minsky and Roelfsema argued that computations that require propagation of global information for local computation to take place would particularly benefit from the sequential, parallel nature of processing in recurrent networks. Such “tag propagation” algorithms perform repeated, local propagation of information and were originally introduced in the context of detecting connectedness, a task that is challenging for feedforward networks. Here, we advance the understanding of the utility of lateral and recurrent computation by first performing a large-scale empirical study of neural architectures for the computation of connectedness to explore feedforward solutions more fully and establish robustly the importance of recurrent architectures. In addition, we highlight a tradeoff between computation time and performance and construct hybrid feedforward/recurrent models that perform well even in the presence of varying computational time limitations. We then generalize tag propagation architectures to propagating multiple interacting tags and demonstrate that these are efficient computational substrates for more general computations of connectedness by introducing and solving an abstracted biologically inspired decision-making task. Our work thus clarifies and expands the set of computational tasks that can be solved efficiently by recurrent computation, yielding hypotheses for structure in population activity that may be present in such tasks.

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Takata, Taiga, Masayoshi Yoshimura, and Yusuke Matsunaga. "Efficient Fault Simulation Algorithms for Analyzing Soft Error Propagation in Sequential Circuits." IPSJ Transactions on System LSI Design Methodology 6 (2013): 127–34. http://dx.doi.org/10.2197/ipsjtsldm.6.127.

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Xudong Luo and Chengqi Zhang. "Proof of the correctness of EMYCIN sequential propagation under conditional independence assumptions." IEEE Transactions on Knowledge and Data Engineering 11, no. 2 (1999): 355–59. http://dx.doi.org/10.1109/69.761668.

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Lee, Guesuk, Hyejeong Son, and Byeng D. Youn. "Sequential optimization and uncertainty propagation method for efficient optimization-based model calibration." Structural and Multidisciplinary Optimization 60, no. 4 (August 10, 2019): 1355–72. http://dx.doi.org/10.1007/s00158-019-02351-2.

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Hao, Huijuan, and Zhansheng Duan. "Sequential centralized fusion of multiple passive acoustic sensors with unknown propagation delays." Digital Signal Processing 146 (March 2024): 104388. http://dx.doi.org/10.1016/j.dsp.2024.104388.

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Li, Guohui, Xinyu Qi, Zhaoyang Hu, and Qi Tang. "Mechanisms Mediating Nuclear Trafficking Involved in Viral Propagation by DNA Viruses." Viruses 11, no. 11 (November 7, 2019): 1035. http://dx.doi.org/10.3390/v11111035.

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Typical viral propagation involves sequential viral entry, uncoating, replication, gene transcription and protein synthesis, and virion assembly and release. Some viral proteins must be transported into host nucleus to facilitate viral propagation, which is essential for the production of mature virions. During the transport process, nuclear localization signals (NLSs) play an important role in guiding target proteins into nucleus through the nuclear pore. To date, some classical nuclear localization signals (cNLSs) and non-classical NLSs (ncNLSs) have been identified in a number of viral proteins. These proteins are involved in viral replication, expression regulation of viral genes and virion assembly. Moreover, other proteins are transported into nucleus with unknown mechanisms. This review highlights our current knowledge about the nuclear trafficking of cellular proteins associated with viral propagation.

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Lisitsyna, Liubov, Marina Senchilo, and Sergei Teleshev. "CREATING RLCP-COMPATIBLE VIRTUAL LABORATORIES FOR TRAINING BASIC ALGORITHMS ON NEURAL NETWORKS." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2021, no. 3 (July 30, 2021): 134–42. http://dx.doi.org/10.24143/2072-9502-2021-3-134-142.

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The article describes the principles of developing RLCP-compatible virtual laboratories. There are build two virtual laboratories based on these principles for mastering the basic algo-rithms on neural networks: Algorithm for Sequential Signal Propagation in Perceptron and Algorithm for Training Perceptron Using Method of Backward Error Propagation. Virtual laboratories consist of two independent modules – a virtual stand and an RLCP server. The virtual stand implements a visual display of the task's data and provides the listener with tools for forming and editing intermediate solutions and responses. Since the virtual laboratories were assumed for the first acquaintance with neural networks, the simplest neural network architectures in the form of single-layer perceptrons were used as the initial data. And the algorithm of sequential propagation of signals in a neural network (VL1) and the algorithm of training a neural network with a teacher based on the method of inverse error propagation (VL2) are used as the basic algorithms. For automatic generation of equally complex and valid tasks there have been proposed algorithms with high efficiency (the average time for generating an individual task on the VL2 stand for a student was no longer than 3 seconds). It was found out experimentally that such virtual laboratories should be created in two modes: the mode of training and mode of certification. The training shop works for solving problems using the studied algorithms on the stands of virtual laboratories in the training mode with the diagnosis of admitted errors significantly increase the effectiveness of students' results

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Klein, R. I., C. F. McKee, M. T. Sandford, R. Whitaker, and P. T. P. Ho. "Formation of OB Stars by Radiatively-Driven Implosion." Symposium - International Astronomical Union 115 (1987): 435–37. http://dx.doi.org/10.1017/s0074180900096145.

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A theory for the sequential formation of OB stars in dense molecular clouds was first quantitatively investigated by Elmegreen and Lada (1976). The model was one-dimensional in nature and assumed shock propagation into homogeneous clouds. This picture has provided a successful explanation of the morphology of the Ori OB1 association.

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Combettes, L., D. Tran, T. Tordjmann, M. Laurent, B. Berthon, and M. Claret. "Ca2+-mobilizing hormones induce sequentially ordered Ca2+ signals in multicellular systems of rat hepatocytes." Biochemical Journal 304, no. 2 (December 1, 1994): 585–94. http://dx.doi.org/10.1042/bj3040585.

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The development of hormone-mediated Ca2+ signals was analysed in polarized doublets, triplets and quadruplets of rat hepatocytes by video imaging of fura2 fluorescence. These multicellular models showed dilated bile canaliculi, and gap junctions were observed by using an anti-connexin-32 antibody. They also showed highly organized Ca2+ signals in response to vasopressin or noradrenaline. Surprisingly, the primary rises in intracellular Ca2+ concentration ([Ca2+]i) did not start randomly from any cell of the multiplet. It originated invariably in the same hepatocyte (first-responding cell), and then was propagated in a sequential manner to the nearest connected cells (cell 2, then 3, in triplets; cell 2, 3, then 4 in quadruplets). The sequential activation of the cells appeared to be an intrinsic property of multiplets of rat hepatocytes. (1) In the continued presence of hormones, the same sequential order was observed up to six times, i.e. at each train of oscillations occurring between the cells. (2) The order of [Ca2+]i responses was modified neither by the repeated addition of hormones nor by the hormonal dose. (3) The mechanical disruption of an intermediate cell slowed down the speed of the propagation, suggesting a role of gap junctions in the rapidity of the sequential activation of cells. (4) The same multiplet could have a different first-responding cell for vasopressin or noradrenaline, suggesting a role of the hormonal receptors in the sequentiality of cell responses. It is postulated that a functional heterogeneity of hormonal receptors, and the presence of functional gap junctions, are involved in the existence of sequentially ordered hormone-mediated [Ca2+]i rises in the multiplets of rat hepatocytes.

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Hu, Bing, Zhixin Sun, and Jian Liu. "Distributed time synchronization algorithm based on sequential belief propagation in wireless sensor networks." Computer Communications 176 (August 2021): 119–27. http://dx.doi.org/10.1016/j.comcom.2021.05.018.

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Wu, Tianqi, Xuefeng Yin, and Juyul Lee. "A Novel Power Spectrum-Based Sequential Tracker for Time-Variant Radio Propagation Channel." IEEE Access 8 (2020): 151267–78. http://dx.doi.org/10.1109/access.2020.3017482.

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Saejoon Kim, K. Ko, Jun Heo, and Ji-hwan Kim. "Two-staged informed dynamic scheduling for sequential belief propagation decoding of LDPC codes." IEEE Communications Letters 13, no. 3 (March 2009): 193–95. http://dx.doi.org/10.1109/lcomm.2009.081938.

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Jameson, A. R., and E. A. Mueller. "Estimation of Propagation-Differential Phase Shift from Sequential Orthogonal Linear Polarization Radar Measurements." Journal of Atmospheric and Oceanic Technology 2, no. 2 (June 1985): 133–37. http://dx.doi.org/10.1175/1520-0426(1985)002<0133:eopdps>2.0.co;2.

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Yacov, N., H. Efraim, H. Kfir, I. Kanter, and O. Shental. "Parallel vs. sequential belief propagation decoding of LDPC codes over and Markov sources." Physica A: Statistical Mechanics and its Applications 378, no. 2 (May 2007): 329–35. http://dx.doi.org/10.1016/j.physa.2006.12.009.

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Journal articles on the topic 'Sequential propagation'

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