Investigating data mining techniques for extracting information from Alzheimer\u27s disease data

Data mining techniques have been used widely in many areas such as business, science, engineering and more recently in clinical medicine. These techniques allow an enormous amount of high dimensional data to be analysed for extraction of interesting information as well as the construction of models for prediction. One of the foci in health related research is Alzheimer\u27s disease which is currently a non-curable disease where diagnosis can only be confirmed after death via an autopsy. Using multi-dimensional data and the applications of data mining techniques, researchers hope to find biomarkers that will diagnose Alzheimer\u27s disease as early as possible. The primary purpose of this research project is to investigate the application of data mining techniques for finding interesting biomarkers from a set of Alzheimer\u27s disease related data. The findings from this project will help to analyse the data more effectively and contribute to methods of providing earlier diagnosis of the disease

A Survey of Vision Transformers in Autonomous Driving: Current Trends and Future Directions

This survey explores the adaptation of visual transformer models in Autonomous Driving, a transition inspired by their success in Natural Language Processing. Surpassing traditional Recurrent Neural Networks in tasks like sequential image processing and outperforming Convolutional Neural Networks in global context capture, as evidenced in complex scene recognition, Transformers are gaining traction in computer vision. These capabilities are crucial in Autonomous Driving for real-time, dynamic visual scene processing. Our survey provides a comprehensive overview of Vision Transformer applications in Autonomous Driving, focusing on foundational concepts such as self-attention, multi-head attention, and encoder-decoder architecture. We cover applications in object detection, segmentation, pedestrian detection, lane detection, and more, comparing their architectural merits and limitations. The survey concludes with future research directions, highlighting the growing role of Vision Transformers in Autonomous Driving.Comment: 9 pages, 3 figure

Sensor Fusion by Spatial Encoding for Autonomous Driving

Sensor fusion is critical to perception systems for task domains such as autonomous driving and robotics. Recently, the Transformer integrated with CNN has demonstrated high performance in sensor fusion for various perception tasks. In this work, we introduce a method for fusing data from camera and LiDAR. By employing Transformer modules at multiple resolutions, proposed method effectively combines local and global contextual relationships. The performance of the proposed method is validated by extensive experiments with two adversarial benchmarks with lengthy routes and high-density traffics. The proposed method outperforms previous approaches with the most challenging benchmarks, achieving significantly higher driving and infraction scores. Compared with TransFuser, it achieves 8% and 19% improvement in driving scores for the Longest6 and Town05 Long benchmarks, respectively.Comment: This paper has been accepted for Lecture presentation at the 2023 IEEE SENSORS conferenc

Les outils et moyens disponibles permettant aux diététicien-ne-s de maintenir leurs connaissances à jour et d'avoir une pratique fondée sur les preuves: travail de Bachelor

Introduction : Dans le milieu de la santé, l'Evidence-Based Practice, ou la pratique fondée sur les preuves occupe une place centrale dans les prises de décisions cliniques. C'est également le cas pour les diététicien-ne-s qui, au travers de l'Evidence-Based Dietetics Practice, s'emploient à utiliser les données issues de la recherche afin de fournir des soins de qualité à leurs patients. En Suisse romande, les diététicien-ne-s se doivent d'avoir accès à ces informations, mais comment le font-ils/elles ? Selon nos recherches, il n'existe actuellement pas de document mettant en avant des outils et moyens afin d'aider les diététicien-ne-s à tenir leurs connaissances à jour. L'objectif de notre travail est de proposer un catalogue référençant différents outils et moyens permettant aux diététicien-ne-s de Suisse romande d’avoir accès aux données probantes issues de la recherche. Méthodologie : Notre travail a été effectué sous la forme d'un scan environnemental comportant 4 volets : un scan dans les références des modules de nutrition clinique du Bachelor en nutrition et diététique ; dans les bases de données Medline via Pubmed et Google Scholar ; sur Google ; et au travers de deux interviews réalisées auprès de diététicien-ne-s travaillant en Suisse romande. Résultats : Différents outils et moyens ont été relevés, avec notamment des guidelines/recommandations, des boîtes à outils, des newsletters, des alertes, des journaux et des ouvrages. Ceux-ci ont été répertoriés dans un catalogue sous la forme d'un tableau Excel. De plus, il est ressorti lors de nos interviews et discussions que la manière de rechercher de l’information se faisait par pathologie, c’est pourquoi nous avons décidé de les classer selon cette catégorie. Des bases de données spécifiques à la nutrition et diététique ont également été relevées lors de ces scans. Il s'agit de Practice-Based Evidence in Nutrition des Dietitians of Canada avec la collaboration de la British Dietetic Association et de la Dietitians Association of Australia ainsi que de l'Evidence Analysis Library de l'Academy of Nutrition and Dietetics. Conclusion : Notre travail a permis la conception d’un catalogue dans lequel sont classés et décrits des outils et moyens pouvant être susceptibles d’aider les diététiciens dans leur pratique clinique. Néanmoins, certains aspects devront encore être retravaillés pour faciliter l’accès aux données probantes. Finalement, la perspective d’offrir la possibilité à tous les utilisateurs d’étoffer ce catalogue, permettra d’améliorer et d’enrichir celui-ci, avec peut-être comme objectif de devenir un espace d’échange et de partage pour les diététicien-ne-s

A Lightweight Domain Adaptive Absolute Pose Regressor Using Barlow Twins Objective

Identifying the camera pose for a given image is a challenging problem with applications in robotics, autonomous vehicles, and augmented/virtual reality. Lately, learning-based methods have shown to be effective for absolute camera pose estimation. However, these methods are not accurate when generalizing to different domains. In this paper, a domain adaptive training framework for absolute pose regression is introduced. In the proposed framework, the scene image is augmented for different domains by using generative methods to train parallel branches using Barlow Twins objective. The parallel branches leverage a lightweight CNN-based absolute pose regressor architecture. Further, the efficacy of incorporating spatial and channel-wise attention in the regression head for rotation prediction is investigated. Our method is evaluated with two datasets, Cambridge landmarks and 7Scenes. The results demonstrate that, even with using roughly 24 times fewer FLOPs, 12 times fewer activations, and 5 times fewer parameters than MS-Transformer, our approach outperforms all the CNN-based architectures and achieves performance comparable to transformer-based architectures. Our method ranks 2nd and 4th with the Cambridge Landmarks and 7Scenes datasets, respectively. In addition, for augmented domains not encountered during training, our approach significantly outperforms the MS-transformer. Furthermore, it is shown that our domain adaptive framework achieves better performance than the single branch model trained with the identical CNN backbone with all instances of the unseen distribution.Comment: [draft-v1] 18 pages, 8 figures, and 10 table

A solution for synchronous incremental maintenance of materialized views based on SQL recursive query

Materialized views are excessively stored query execution results in the database. They can be used to partially or completely answer queries which will be further appeared instead of re-executing query from the scratch. There is a large number of published works that address the maintenance, especially incremental update, of materialized views and query rewriting for using those ones. Some of them support materialized views based on recursive query in datalog language. Although most of datalog queries can be transferred into SQL queries and vise versa but it is not the case for recursive queries. Recursive queries in the data log try to find all possible transitive closures. Recursive queries in SQL (Common Table Expression – CTE) return direct links but not transitive closures. In this paper, we propose efficient methods for incremental update of materialized views based on CTE; and then propose an algorithm for generating source codes in C language for any input SQL recursive queries. The synthesized source codes implement our proposed incremental update algorithms according to inserted/deleted/updated record set in the base tables. This paper focuses mainly on the recursive queries whose execution results are directed tree-structured data. The two cases of tree node are considered. In the first case, a child node has only one parent node and in the second case, a child node can have many parent nodes. Those two cases represent the two types of relationships between entities in real world, that are one–to–many and many–to–many, respectively. For the one–to–many relationships, the relationship data is accompanied with the records describing the child using some fields. Those fields are set as null in deleting a concrete relationship. For the many–to–many relationships, it is stored in a separate table and the concrete relationships are removed by deleting describing records from that table. Considering of enforcing referential integrity may help to reduce the searching space and therefore, help to improve the performance. However, the set of tree nodes or tree edges can be manipulated. All those combinations lead to different algorithms. The experimental results are provided and discussed to confirm the effectiveness of our proposed method

A solution for synchronous incremental maintenance of materialized views based on SQL recursive query

Materialized views are excessively stored query execution results in the database. They can be used to partially or completely answer queries which will be further appeared instead of re-executing query from the scratch. There is a large number of published works that address the maintenance, especially incremental update, of materialized views and query rewriting for using those ones. Some of them support materialized views based on recursive query in datalog language. Although most of datalog queries can be transferred into SQL queries and vise versa but it is not the case for recursive queries. Recursive queries in the data log try to find all possible transitive closures. Recursive queries in SQL (Common Table Expression – CTE) return direct links but not transitive closures. In this paper, we propose efficient methods for incremental update of materialized views based on CTE; and then propose an algorithm for generating source codes in C language for any input SQL recursive queries. The synthesized source codes implement our proposed incremental update algorithms according to inserted/deleted/updated record set in the base tables. This paper focuses mainly on the recursive queries whose execution results are directed tree-structured data. The two cases of tree node are considered. In the first case, a child node has only one parent node and in the second case, a child node can have many parent nodes. Those two cases represent the two types of relationships between entities in real world, that are one–to–many and many–to–many, respectively. For the one–to–many relationships, the relationship data is accompanied with the records describing the child using some fields. Those fields are set as null in deleting a concrete relationship. For the many–to–many relationships, it is stored in a separate table and the concrete relationships are removed by deleting describing records from that table. Considering of enforcing referential integrity may help to reduce the searching space and therefore, help to improve the performance. However, the set of tree nodes or tree edges can be manipulated. All those combinations lead to different algorithms. The experimental results are provided and discussed to confirm the effectiveness of our proposed method

Study on total lipid content, lipid class composition of some fire and soft corals collected in Nha Trang, Vietnam

For the first time, the total lipid content and lipid class composition of  the Vietnamese soft corals (Sinularia brassica, Sinularia flexibilis) and fire corals (Millepora dichotoma, Millepora platyphylla) were investigated. The results indicated that the total lipid content of the investigated species was significantly different. Compositions of the lipid classes were analyzed using TLC and image analysis program Sorbfil TLC Videodensitometer DV and the results showed that phospholipids (PL, 10.91–16.02%), monoalkyldiacylglycerols (MADAG, 20.69-39.92%) and hydrocarbon wax (HW, 29.83-37.17%) were the main lipid classes of the total lipid in soft coral species. Meanwhile, PL (24.11-33.23%), TG (14.27–34.92%), ST (10.10–14.50%) and HW (12.08–19.95%) were predominant in fire coral species. ST, TG and FFA contents in soft and fire corals were at low level. DG was only present in the Sinularia flexibilis but not in other studied corals

Application of Genetic Algorithm in Optimizing LQR Control for Ball and Beam

In this paper, we apply genetic algorithm (GA) to optimize LQR controller – a linear control algorithm which stability is guaranteed by mathematics. This searching algorithm proves its ability in finding better control parameters through generations. Our model is ball and beam (B&B) – a classical single input – multi output (SIMO) system. This system is balanced around equilibrium point in simulation