Subject:
All Disciplines Arts & Humanities Biology & Life Sciences Business & Economics Chemistry & Materials Science Computer Science & Mathematics Engineering Environmental & Earth Sciences Medicine & Pharmacology Physical Sciences Public Health & Healthcare Social Sciences
Sort:
Hottest Latest Alphabetical (A-Z) Alphabetical (Z-A)
Type:
All Topic Review Biography
Topic Review
Spatio-Temporal Hybrid Neural Network
The prediction of crowd flow in key urban areas is an important basis for city informatization development and management. Timely understanding of crowd flow trends can provide cities with data support in epidemic prevention, public security management, and other aspects. The model uses the Node2Vec graph embedding algorithm combined with LSTM (NDV-LSTM) to predict crowd flow.
  • 238
  • 26 May 2023
Topic Review
Spatially Bounded Airspace Axiom
Axiom is a fundamental truth in mathematics and philosophy. A claim that we do not have to prove, but we accept it as a true statement. The term axiom, in addition to theorems and postulates, can already be found in Euclid’s Elements (BC. 300). We can find the basic truths in all walks of life, including air transport. Air transport takes place in the airspace. Our entry describes an axiom of an airspace based on the latest airspace concept.
  • 755
  • 17 Jun 2022
Topic Review
Spatiality Sensitive Learning for Cancer Metastasis Detection
Metastasis detection in lymph nodes via microscopic examination of histopathological images is one of the most crucial diagnostic procedures for breast cancer staging. The manual analysis is extremely labor-intensive and time-consuming because of complexities and diversities of histopathology images. Deep learning has been utilized in automatic cancer metastasis detection in recent years. Due to the huge size of whole-slide images, most existing approaches split each image into smaller patches and simply treat these patches independently, which ignores the spatial correlations among them.
  • 337
  • 08 Sep 2022
Topic Review
Spatial-Frequency Domain Imaging
Measurement of optical properties is critical for understanding light-tissue interaction, properly interpreting measurement data, and gaining better knowledge of tissue physicochemical properties. However, conventional optical measuring techniques are limited in point measurement, which partly hinders the applications on characterizing spatial distribution and inhomogeneity of optical properties of biological tissues. Spatial-frequency domain imaging (SFDI), as an emerging non-contact, depth-varying and wide-field optical imaging technique, is capable of measuring the optical properties in a wide field-of-view on a pixel-by-pixel basis. 
  • 1.1K
  • 01 Jun 2021
Topic Review
Spatial Keyword Group Query
Location-based services have been commonly used in daily life. Spatial keyword query, as one of the important technologies in location-based services, has been widely used in many fields, such as intelligent navigation systems and spatial positioning systems. Different types of spatial keyword query problems have been studied in depth by scholars.
  • 241
  • 24 Nov 2023
Topic Review Peer Reviewed
Spatial Hurst–Kolmogorov Clustering
The stochastic analysis in the scale domain (instead of the traditional lag or frequency domains) is introduced as a robust means to identify, model and simulate the Hurst–Kolmogorov (HK) dynamics, ranging from small (fractal) to large scales exhibiting the clustering behavior (else known as the Hurst phenomenon or long-range dependence). The HK clustering is an attribute of a multidimensional (1D, 2D, etc.) spatio-temporal stationary stochastic process with an arbitrary marginal distribution function, and a fractal behavior on small spatio-temporal scales of the dependence structure and a power-type on large scales, yielding a high probability of low- or high-magnitude events to group together in space and time. This behavior is preferably analyzed through the second-order statistics, and in the scale domain, by the stochastic metric of the climacogram, i.e., the variance of the averaged spatio-temporal process vs. spatio-temporal scale.
  • 1.2K
  • 14 Apr 2022
Topic Review
Spatial and Temporal Human Action Recognition Analysis
Human action recognition in computer vision is the task that identifies how a person or a group acts on a video sequence. Early methods that rely on representation-based solutions, like the histogram of oriented gradients (HOG), local binary patterns (LBP), and motion analysis, have been used to address this problem over the years. Later works are based on machine and deep-learning techniques, such as support vector machines (SVM), two- or three-dimensional convolutional neural networks (2D-CNNs, 3D-CNNs), recurrent neural networks (RNNs), and vision transformers (ViT), aiming to enhance the performance and reduce bias.
  • 127
  • 22 Dec 2023
Topic Review
Spatial and Temporal Hierarchy for Autonomous Navigation
Robust evidence suggests that humans explore their environment using a combination of topological landmarks and coarse-grained path integration. This approach relies on identifiable environmental features (topological landmarks) in tandem with estimations of distance and direction (coarse-grained path integration) to construct cognitive maps of the surroundings. This cognitive map is believed to exhibit a hierarchical structure, allowing efficient planning when solving complex navigation tasks.
  • 264
  • 29 Jan 2024
Topic Review
Spark Streaming Backpressure for Data-Intensive Pipelines
A significant rise in the adoption of streaming applications has changed the decision-making processes in the last decade. This movement has led to the emergence of several Big Data technologies for in-memory processing, such as the systems Apache Storm, Spark, Heron, Samza, Flink, and others. Spark Streaming, a widespread open-source implementation, processes data-intensive applications that often require large amounts of memory. However, Spark Unified Memory Manager cannot properly manage sudden or intensive data surges and their related in-memory caching needs, resulting in performance and throughput degradation, high latency, a large number of garbage collection operations, out-of-memory issues,  and data loss. This work presents a comprehensive performance evaluation of Spark Streaming backpressure to investigate the hypothesis that it could support data-intensive pipelines under specific pressure requirements.  The results reveal that backpressure is suitable only for small and medium pipelines for stateless and stateful applications. Furthermore, it points out the Spark Streaming limitations that lead to in-memory-based issues for data-intensive pipelines and stateful applications. In addition, the work indicates potential solutions.
  • 476
  • 19 Jul 2022
Topic Review
SPAdes
SPAdes (St. Petersburg genome assembler) is a genome assembly algorithm which was designed for single cell and multi-cells bacterial data sets. Therefore, it might not be suitable for large genomes projects. SPAdes works with Ion Torrent, PacBio, Oxford Nanopore, and Illumina paired-end, mate-pairs and single reads. SPAdes has been integrated into Galaxy pipelines by Guy Lionel and Philip Mabon.
  • 602
  • 04 Nov 2022
  • Page
  • of
  • 371
Featured Entry Collections
>>

Featured Books

>>
Encyclopedia of Social Sciences
Encyclopedia of COVID-19
Encyclopedia of Fungi
Encyclopedia of Digital Society, Industry 5.0 and Smart City
Video Production Service
Feedback