Anchor Point Approach For Initial Population Of Bat Algorithm For Protein Multiple Sequence Alignment

Penjajaran pelbagai turutan atau Multiple sequence alignment (MSA) adalah satu langkah asas kepada banyak aplikasi bio-informatik seperti pembinaan pokok filogenetik, ramalan struktur sekunder dan pengenalpastian motif domain dan yang dipulihara. Kebolehpercayaan dan ketepatan aplikasi-aplikasi ini bergantung kepada kualiti MSA. Multiple sequence alignment (MSA) is a fundamental step for many bioinformatics applications such as phylogenetic tree construction, prediction of the secondary structure and identification of domains and conserved motifs. The reliability and accuracy of these applications depend on the quality of MSA

Algorithmic And Computational Approaches For Improving The Efficiency Of Mobile Genomic Element Discovery, A Bioinformatics Framework

Through this research, we are showcasing the application of computational approaches to the discoveries in the life sciences spectrum. Our current research not only focused on mobile genetic elements but also developed the computational methods that enabled these findings. We combined the biology sciences and computer science in our research, which is essentially multidisciplinary. To that end, this research intricately probed the role and implications of mobile genetic elements, emphasizing transposable elements. These dynamic components wielded substantial influence over genomic architecture\u27s structure, function, and evolutionary adaptations. An integral component of our study is the innovative computational tool, Target/IGE Retriever (TIGER), employed to detect and map these mobile genetic elements. Given the pronounced impact of these elements on gene regulation and their involvement in various genetic diseases, their precise detection and mapping within a genome were crucial for understanding intricate genetic dynamics and disease etiology. Addressing computational challenges, the study introduces three new algorithms to enhance TIGER\u27s performance, tested using E. coli genomes. This testing aimed to determine the impact of database size reduction on result accuracy and performance. Findings indicate that while prophage yields are less affected by database size, non-phage islands show sensitivity, suggesting performance improvements with smaller databases. Furthermore, the research conducts a comparative analysis of TIGER and BLAST outputs, focusing on validating transposons identified in E. coli genomes. This involves cross-referencing with established databases and employing statistical methods for match categorization, enhancing the authenticity of transposon location identification.. Within the purview of this rigorous analytical process, particular attention is accorded to evaluating sequence alignment results and the quality of BLAST hits, focusing specifically on identifying direct repeats within insertion sequences. The study underscores TIGER\u27s efficacy in transposon discovery and yields critical insights into its performance relative to BLAST. This research illuminates potential avenues for enhancing computational tools in bioinformatics, all within the larger framework of contributing significantly to genomics and bioinformatics research\u27s ongoing advancements. Our work deepens our understanding of the role and influence of mobile genetic elements on genomic architecture. Index Term: Computational biology, bioinformatics, mobile genetic elements, transposon, validation, database

A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation

Body language (BL) refers to the non-verbal communication expressed through physical movements, gestures, facial expressions, and postures. It is a form of communication that conveys information, emotions, attitudes, and intentions without the use of spoken or written words. It plays a crucial role in interpersonal interactions and can complement or even override verbal communication. Deep multi-modal learning techniques have shown promise in understanding and analyzing these diverse aspects of BL. The survey emphasizes their applications to BL generation and recognition. Several common BLs are considered i.e., Sign Language (SL), Cued Speech (CS), Co-speech (CoS), and Talking Head (TH), and we have conducted an analysis and established the connections among these four BL for the first time. Their generation and recognition often involve multi-modal approaches. Benchmark datasets for BL research are well collected and organized, along with the evaluation of SOTA methods on these datasets. The survey highlights challenges such as limited labeled data, multi-modal learning, and the need for domain adaptation to generalize models to unseen speakers or languages. Future research directions are presented, including exploring self-supervised learning techniques, integrating contextual information from other modalities, and exploiting large-scale pre-trained multi-modal models. In summary, this survey paper provides a comprehensive understanding of deep multi-modal learning for various BL generations and recognitions for the first time. By analyzing advancements, challenges, and future directions, it serves as a valuable resource for researchers and practitioners in advancing this field. n addition, we maintain a continuously updated paper list for deep multi-modal learning for BL recognition and generation: https://github.com/wentaoL86/awesome-body-language

Innovation Policy Roadmapping for the Future Finnish Smart City Digital Twins : Towards Finland National Digital Twin Programme

Smart City Digital Twins (SCDTs) emerge as a transforming concept with the ability to redefine the future of cities in the fast-paced evolving landscape of urban development. This qualitative futures research explores thoroughly into the complex interaction of socio-technical dynamics in the Finnish setting, investigating the several ways SCDTs might revolutionise urban spaces and create resilience. By utilizing Innovation Policy Roadmapping (IPRM) method for the first time on SCDTs, it reveals the diverse capacities of SCDTs across domains such as urban planning, scenario developing, What-IF analysis, and public involvement through a rigorous examination of academic literature and multi-level analysis of expert interviews. The research emphasises the critical role of policymakers and sectoral actors in building an environment that allows Finnish SCDTs to survive in the face of technological improvements. Furthermore, it emphasises the convergence of SCDTs and Futures Studies approaches, giving a visionary path to adaptable and forward-thinking urban futures. The contributions of this study extend beyond the scope of Finnish SCDTs, giving inspiration for sustainable smart city transformations, potential foundational insights towards Finland National Digital Twin Programme and paving the way for the incorporation of futures studies methodologies and digital twins to mitigate uncertainties and create resilient urban futures. Longitudinal impact assessments, real-time citizen-centric foresight applications via SCDT, and the investigation of SCDTs' role in disaster mitigation and social well-being are among the identified future research directions, providing a comprehensive roadmap for leveraging SCDTs as transformative tools for building sustainable urban futures

An Enhanced Flower Pollination Algorithm For Multiple Sequence Alignment

Multiple sequence alignment (MSA) is an alignment of three or more sequences. Studies show that MSA exhibits a challenge, that is, how to find the MSA that maximises the Quality (Q) score and Total Column (TC) score. This problem is an NP-complete problem. Hence, numerous researchers have devoted their efforts to tackle the MSA problem, yet, there is still a shortcoming in the accuracy

Create and Find Flatness: Building Flat Training Spaces in Advance for Continual Learning

Catastrophic forgetting remains a critical challenge in the field of continual learning, where neural networks struggle to retain prior knowledge while assimilating new information. Most existing studies emphasize mitigating this issue only when encountering new tasks, overlooking the significance of the pre-task phase. Therefore, we shift the attention to the current task learning stage, presenting a novel framework, C&F (Create and Find Flatness), which builds a flat training space for each task in advance. Specifically, during the learning of the current task, our framework adaptively creates a flat region around the minimum in the loss landscape. Subsequently, it finds the parameters' importance to the current task based on their flatness degrees. When adapting the model to a new task, constraints are applied according to the flatness and a flat space is simultaneously prepared for the impending task. We theoretically demonstrate the consistency between the created and found flatness. In this manner, our framework not only accommodates ample parameter space for learning new tasks but also preserves the preceding knowledge of earlier tasks. Experimental results exhibit C&F's state-of-the-art performance as a standalone continual learning approach and its efficacy as a framework incorporating other methods. Our work is available at https://github.com/Eric8932/Create-and-Find-Flatness.Comment: 10pages, ECAI2023 conferenc