The field of human motion prediction has gained prominence, finding applications in various domains such as intelligent surveillance and human–robot interaction. However, predicting full-body human motion poses challenges in capturing joint interactions, handling diverse movement patterns, managing occlusions, and ensuring real-time performance. To address these challenges, the proposed model adopts a skeleton-parted strategy to dissect the skeleton structure, enhancing coordination and fusion between body parts. This novel method combines transformer-enabled graph convolutional networks for predicting human motion in 3-D skeleton data. It integrates a temporal transformer (T-Transformer) for comprehensive temporal feature extraction and a spatial graph convolutional network (S-GCN) for capturing spatial characteristics of human motion. The model's performance is evaluated on two comprehensive human motion datasets, Human3.6M and CMU motion capture (CMU Mocap), containing numerous videos encompassing short and long human motion sequences. Results indicate that the proposed model outperforms state-of-the-art methods on both datasets, significantly improving the average mean per joint positional error (avg-MPJPE) by 3.50% and 11.45% for short-term and long-term motion prediction, respectively. Similarly, on the CMU Mocap dataset, it achieves avg-MPJPE improvements of 2.69% and 1.05% for short-term and long-term motion prediction, respectively, demonstrating its superior accuracy in predicting human motion over extended periods. The study also investigates the impact of different numbers of T-Transformers and S-GCNs and explores the specific roles and contributions of the T-Transformer, S-GCN, and cross-part components.

Abstract Recognizing entities and their corresponding roles are important in human activity recognition. In light of recent advancements, the primary emphasis is recognizing the abstract activities involving person‐person interaction. The contribution of this work is proposing an architecture, which utilizes the knowledge of the human body parts coordinates in role detection of each individual. The network preprocesses the coordinates to build intra‐body and inter‐body features. The extracted features build the relationship between the interacting bodies and learn the temporal relation corresponding to each role using the human memory‐inspired hierarchical temporal memory. The model is tested on vague samples of mutual actions in the experimental work. The model is found robust in action and role recognition tasks and performed well per expectations.

SUMMARY: We launch a webserver for RNA structure prediction and design corresponding to tools developed using our RNA-As-Graphs (RAG) approach. RAG uses coarse-grained tree graphs to represent RNA secondary structure, allowing the application of graph theory to analyze and advance RNA structure discovery. Our webserver consists of three modules: (a) RAG Sampler: samples tree graph topologies from an RNA secondary structure to predict corresponding tertiary topologies, (b) RAG Builder: builds three-dimensional atomic models from candidate graphs generated by RAG Sampler, and (c) RAG Designer: designs sequences that fold onto novel RNA motifs (described by tree graph topologies). Results analyses are performed for further assessment/selection. The Results page provides links to download results and indicates possible errors encountered. RAG-Web offers a user-friendly interface to utilize our RAG software suite to predict and design RNA structures and sequences. AVAILABILITY AND IMPLEMENTATION: The webserver is freely available online at: http://www.biomath.nyu.edu/ragtop/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Protein design algorithms enumerate a combinatorial number of candidate structures to compute the Global Minimum Energy Conformation (GMEC). To efficiently find the GMEC, protein design algorithms must methodically reduce the conformational search space. By applying distance and energy cutoffs, the protein system to be designed can thus be represented using a sparse residue interaction graph, where the number of interacting residue pairs is less than all pairs of mutable residues, and the corresponding GMEC is called the sparse GMEC. However, ignoring some pairwise residue interactions can lead to a change in the energy, conformation, or sequence of the sparse GMEC vs. the original or the full GMEC. Despite the widespread use of sparse residue interaction graphs in protein design, the above mentioned effects of their use have not been previously analyzed. To analyze the costs and benefits of designing with sparse residue interaction graphs, we computed the GMECs for 136 different protein design problems both with and without distance and energy cutoffs, and compared their energies, conformations, and sequences. Our analysis shows that the differences between the GMECs depend critically on whether or not the design includes core, boundary, or surface residues. Moreover, neglecting long-range interactions can alter local interactions and introduce large sequence differences, both of which can result in significant structural and functional changes. Designs on proteins with experimentally measured thermostability show it is beneficial to compute both the full and the sparse GMEC accurately and efficiently. To this end, we show that a provable, ensemble-based algorithm can efficiently compute both GMECs by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine sparse residue interaction graphs with provable, ensemble-based algorithms to reap the benefits of sparse residue interaction graphs while avoiding their potential inaccuracies.

In this paper, we have proposed a parallel approach to the morphological feature extraction process and demonstrated a good computational speedup. Remote sensing images have a typical property of incrementing constantly and each image being very large. Since the images are acquired constantly and hence added into the database regularly in good numbers, hence there is a need to make the feature extraction work more efficient. Moreover morphological features are good texture descriptors and are extremely compute-intensive as well. It is hence attempted to utilize the power of multi-core architecture and expedite the process of feature extraction. These feature descriptors are tested on UC Merced Land Use Land Cover Data set. Experimentation shows that with the use of parallel programming and architecture speed up of as good as 20X is obtained for CCH and RIT feature sets.

We analyze the performance of the decorrelating multiuser detector for a code-division multiple-access system in a correlated Rayleigh fading environment in terms of the bit error probability (BEP) of an user. The fading gains are independent among the users but correlated among the receive diversity branches. The branches are combined using maximal ratio combining, which requires channel estimation. The channel is estimated by means of pilot symbols, and both maximum likelihood criterion and maximum aposteriori probability criterion are considered. A closed-form expression for the BEP is obtained using a characteristic function approach. Numerical results for the case of exponentially correlated branches and equicorrelated signature waveforms show that (1) the signature cross-correlation has a stronger effect on the performance than the branch correlation, (2) the BEP steadily decreases with increase in the number of branches, and increases with increase in the number of users, rapidly tending to reach saturation as the number of users becomes large.

In this letter, we analyze the performance of the minimum mean-square error (MMSE) detector for a code-division multiple-access system employing binary phase-shift keying. The performance is measured in terms of the probability distribution and the average number of correctly decoded users. We consider the case of equipower users having equicorrelated signature waveforms. The distribution of the number of correctly decoded users is expressed as an integral over sum of products of Gaussian Q-functions. From it, a closed-form expression for the average number of correctly decoded users is derived.