scholarly journals SPAN: SPIKE PATTERN ASSOCIATION NEURON FOR LEARNING SPATIO-TEMPORAL SPIKE PATTERNS

2012 ◽  
Vol 22 (04) ◽  
pp. 1250012 ◽  
Author(s):  
AMMAR MOHEMMED ◽  
STEFAN SCHLIEBS ◽  
SATOSHI MATSUDA ◽  
NIKOLA KASABOV
Keyword(s):  
Learning Algorithm ◽  
Research Area ◽  
Spike Trains ◽  
Classification Performance ◽  
Temporal Information ◽  
Output Spike ◽  
Supervised Learning Algorithms ◽  
Learning Capabilities ◽  
Spatio Temporal ◽  
Mathematical Operations

Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN — a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the precise timing of spikes. The idea of the proposed algorithm is to transform spike trains during the learning phase into analog signals so that common mathematical operations can be performed on them. Using this conversion, it is possible to apply the well-known Widrow–Hoff rule directly to the transformed spike trains in order to adjust the synaptic weights and to achieve a desired input/output spike behavior of the neuron. In the presented experimental analysis, the proposed learning algorithm is evaluated regarding its learning capabilities, its memory capacity, its robustness to noisy stimuli and its classification performance. Differences and similarities of SPAN regarding two related algorithms, ReSuMe and Chronotron, are discussed.

Spatio-Temporal Indexing Techniques

2009 ◽  
pp. 260-268
Author(s):  
Michael Vassilakopoulos ◽  
Antonio Corral
Keyword(s):  
Information Systems ◽  
Traffic Control ◽  
Spatial Information ◽  
Research Area ◽  
Temporal Information ◽  
Recent Developments ◽  
Spatio Temporal ◽  
To Come ◽  
Changes Over Time ◽  
Web Research

Time and space are ubiquitous aspects of reality. Temporal and Spatial information appear together in many everyday activities, and many information systems of modern life should be able to handle such information. For example, information systems for traffic control, fleet management, environmental management, military applications, local and public administration, and academic institutions need to manage information with spatial characteristics that changes over time, or in other words, Spatio-temporal Information. The need for Spatio-temporal applications has been strengthened by recent developments in mobile telephony technology, mobile computing, positioning technology, and the evolution of the World Wide Web. Research and technology that aim at the development of Database Management Systems (DBMSs) that can handle Spatial, Temporal, and Spatio-temporal information have been developed over the last few decades. The embedding of spatio-temporal capabilities in DBMSs and GISs is a hot research area that will continue to attract researchers and the informatics industry in the years to come.

scholarly journals mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Prabina Kumar Meher ◽  
Anil Rai ◽  
Atmakuri Ramakrishna Rao
Keyword(s):  
Random Forest ◽  
Cellular Localization ◽  
Learning Algorithm ◽  
Elastic Net ◽  
Messenger Rnas ◽  
Computational Tool ◽  
Temporal Gene Expression ◽  
Single Location ◽  
Spatio Temporal ◽  
Localization Prediction

Abstract Background Localization of messenger RNAs (mRNAs) plays a crucial role in the growth and development of cells. Particularly, it plays a major role in regulating spatio-temporal gene expression. The in situ hybridization is a promising experimental technique used to determine the localization of mRNAs but it is costly and laborious. It is also a known fact that a single mRNA can be present in more than one location, whereas the existing computational tools are capable of predicting only a single location for such mRNAs. Thus, the development of high-end computational tool is required for reliable and timely prediction of multiple subcellular locations of mRNAs. Hence, we develop the present computational model to predict the multiple localizations of mRNAs. Results The mRNA sequences from 9 different localizations were considered. Each sequence was first transformed to a numeric feature vector of size 5460, based on the k-mer features of sizes 1–6. Out of 5460 k-mer features, 1812 important features were selected by the Elastic Net statistical model. The Random Forest supervised learning algorithm was then employed for predicting the localizations with the selected features. Five-fold cross-validation accuracies of 70.87, 68.32, 68.36, 68.79, 96.46, 73.44, 70.94, 97.42 and 71.77% were obtained for the cytoplasm, cytosol, endoplasmic reticulum, exosome, mitochondrion, nucleus, pseudopodium, posterior and ribosome respectively. With an independent test set, accuracies of 65.33, 73.37, 75.86, 72.99, 94.26, 70.91, 65.53, 93.60 and 73.45% were obtained for the respective localizations. The developed approach also achieved higher accuracies than the existing localization prediction tools. Conclusions This study presents a novel computational tool for predicting the multiple localization of mRNAs. Based on the proposed approach, an online prediction server “mLoc-mRNA” is accessible at http://cabgrid.res.in:8080/mlocmrna/. The developed approach is believed to supplement the existing tools and techniques for the localization prediction of mRNAs.

scholarly journals Optimization of Rocky Desertification Classification Model Based on Vegetation Type and Seasonal Characteristic

2021 ◽  
Vol 13 (15) ◽  
pp. 2935
Author(s):  
Chunhua Qian ◽  
Hequn Qiang ◽  
Feng Wang ◽  
Mingyang Li
Keyword(s):  
Learning Algorithm ◽  
Vegetation Type ◽  
Research Area ◽  
Classification Model ◽  
Guizhou Province ◽  
Model Accuracy ◽  
Spatiotemporal Evolution ◽  
Rocky Desertification ◽  
The North ◽  
Svm Model

Building a high-precision, stable, and universal automatic extraction model of the rocky desertification information is the premise for exploring the spatiotemporal evolution of rocky desertification. Taking Guizhou province as the research area and based on MODIS and continuous forest inventory data in China, we used a machine learning algorithm to build a rocky desertification model with bedrock exposure rate, temperature difference, humidity, and other characteristic factors and considered improving the model accuracy from the spatial and temporal dimensions. The results showed the following: (1) The supervised classification method was used to build a rocky desertification model, and the logical model, RF model, and SVM model were constructed separately. The accuracies of the models were 73.8%, 78.2%, and 80.6%, respectively, and the kappa coefficients were 0.61, 0.672, and 0.707, respectively. SVM performed the best. (2) Vegetation types and vegetation seasonal phases are closely related to rocky desertification. After combining them, the model accuracy and kappa coefficient improved to 91.1% and 0.861. (3) The spatial distribution characteristics of rocky desertification in Guizhou are obvious, showing a pattern of being heavy in the west, light in the east, heavy in the south, and light in the north. Rocky desertification has continuously increased from 2001 to 2019. In conclusion, combining the vertical spatial structure of vegetation and the differences in seasonal phase is an effective method to improve the modeling accuracy of rocky desertification, and the SVM model has the highest rocky desertification classification accuracy. The research results provide data support for exploring the spatiotemporal evolution pattern of rocky desertification in Guizhou.

scholarly journals The Role of Spatio-Temporal Information to Govern the COVID-19 Pandemic: A European Perspective

2021 ◽  
Vol 10 (3) ◽  
pp. 166
Author(s):  
Hartmut Müller ◽  
Marije Louwsma
Keyword(s):  
European Union ◽  
Spatial Distribution ◽  
Spatial Information ◽  
National Level ◽  
Temporal Information ◽  
The European Union ◽  
Member States ◽  
Heavy Burden ◽  
Spatio Temporal

The Covid-19 pandemic put a heavy burden on member states in the European Union. To govern the pandemic, having access to reliable geo-information is key for monitoring the spatial distribution of the outbreak over time. This study aims to analyze the role of spatio-temporal information in governing the pandemic in the European Union and its member states. The European Nomenclature of Territorial Units for Statistics (NUTS) system and selected national dashboards from member states were assessed to analyze which spatio-temporal information was used, how the information was visualized and whether this changed over the course of the pandemic. Initially, member states focused on their own jurisdiction by creating national dashboards to monitor the pandemic. Information between member states was not aligned. Producing reliable data and timeliness reporting was problematic, just like selecting indictors to monitor the spatial distribution and intensity of the outbreak. Over the course of the pandemic, with more knowledge about the virus and its characteristics, interventions of member states to govern the outbreak were better aligned at the European level. However, further integration and alignment of public health data, statistical data and spatio-temporal data could provide even better information for governments and actors involved in managing the outbreak, both at national and supra-national level. The Infrastructure for Spatial Information in Europe (INSPIRE) initiative and the NUTS system provide a framework to guide future integration and extension of existing systems.

MLS: An MAE-aware LiDAR sampling framework for On-road Environments using Spatio-Temporal Information

2021 ◽  
pp. 1-1
Author(s):  
Quan-Dung Pham ◽  
Xuan Truong Nguyen ◽  
Khac-Thai Nguyen ◽  
Hyun Kim ◽  
Hyuk-Jae Lee
Keyword(s):  
Temporal Information ◽  
Spatio Temporal

Efficient Scalable Video Coding Method Using Discrete Bandelet Transform

2020 ◽  
Vol 34 (14) ◽  
pp. 2055023
Author(s):  
Yogananda Patnaik ◽  
Dipti Patra
Keyword(s):  
Video Coding ◽  
Scalable Video Coding ◽  
Research Area ◽  
Set Partitioning ◽  
Scalable Video ◽  
Vector Decomposition ◽  
Coding Method ◽  
Benchmark Datasets ◽  
Spatio Temporal ◽  
Ordered Data

Video coding is an imperative part of the modern day communication system. Furthermore, it has vital roles in the fields of video streaming, multimedia, video conferencing and much more. Scalable Video Coding (SVC) is an emerging research area, due to its extensive application in most of the multimedia devices as well as public demand. The proposed coding technique is capable of eliminating the Spatio-temporal regularity of a video sequence. In Discrete Bandelet Transform (DBT), the directions are modeled by a three-directional vector field, known as structural flow. Regularity is decided by this flow where the data entropy is low. The wavelet vector decomposition of geometrically ordered data results in a lesser extent of significant coefficients. The directions of geometrical regularity are interpreted with a two-dimensional vector, and the approximation of these directions is found with spline functions. This paper deals with a novel SVC technique by exploiting the DBT. The bandelet coefficients are further encoded by utilizing Set Partitioning in Hierarchical Trees (SPIHT) encoder, followed by global thresholding mechanism. The proposed method is verified with several benchmark datasets using the performance measures which gives enhanced performance. Thus, the experimental results bring out the superiority of the proposed technique over the state-of-arts.

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