Building Footprint Extraction in Dense Areas using Super Resolution and Frame Field Learning

Despite notable results on standard aerial datasets, current state-of-the-arts fail to produce accurate building footprints in dense areas due to challenging properties posed by these areas and limited data availability. In this paper, we propose a framework to address such issues in polygonal building extraction. First, super resolution is employed to enhance the spatial resolution of aerial image, allowing for finer details to be captured. This enhanced imagery serves as input to a multitask learning module, which consists of a segmentation head and a frame field learning head to effectively handle the irregular building structures. Our model is supervised by adaptive loss weighting, enabling extraction of sharp edges and fine-grained polygons which is difficult due to overlapping buildings and low data quality. Extensive experiments on a slum area in India that mimics a dense area demonstrate that our proposed approach significantly outperforms the current state-of-the-art methods by a large margin.Comment: Accepted at The 12th International Conference on Awareness Science and Technolog

Efficient inference of large prokaryotic pangenomes with PanTA

Pangenome inference is an indispensable step in bacterial genomics, yet its scalability poses a challenge due to the rapid growth of genomic collections. This paper presents PanTA, a software package designed for constructing pangenomes of large bacterial datasets, showing unprecedented efficiency levels multiple times higher than existing tools. PanTA introduces a novel mechanism to construct the pangenome progressively without rebuilding the accumulated collection from scratch. The progressive mode is shown to consume orders of magnitude less computational resources than existing solutions in managing growing datasets. The software is open source and is publicly available at https://github.com/amromics/panta and at 10.6084/m9.figshare.23724705

AMRViz enables seamless genomics analysis and visualization of antimicrobial resistance

We have developed AMRViz, a toolkit for analyzing, visualizing, and managing bacterial genomics samples. The toolkit is bundled with the current best practice analysis pipeline allowing researchers to perform comprehensive analysis of a collection of samples directly from raw sequencing data with a single command line. The analysis results in a report showing the genome structure, genome annotations, antibiotic resistance and virulence profile for each sample. The pan-genome of all samples of the collection is analyzed to identify core- and accessory-genes. Phylogenies of the whole genome as well as all gene clusters are also generated. The toolkit provides a web-based visualization dashboard allowing researchers to interactively examine various aspects of the analysis results. Availability: AMRViz is implemented in Python and NodeJS, and is publicly available under open source MIT license at https://github.com/amromics/amrviz

AMRomics: a scalable workflow to analyze large microbial genome collections

Whole genome analysis for microbial genomics is critical to studying and monitoring antimicrobial resistance strains. The exponential growth of microbial sequencing data necessitates a fast and scalable computational pipeline to generate the desired outputs in a timely and cost-effective manner. Recent methods have been implemented to integrate individual genomes into large collections of specific bacterial populations and are widely employed for systematic genomic surveillance. However, they do not scale well when the population expands and turnaround time remains the main issue for this type of analysis. Here, we introduce AMRomics, an optimized microbial genomics pipeline that can work efficiently with big datasets. We use different bacterial data collections to compare AMRomics against competitive tools and show that our pipeline can generate similar results of interest but with better performance. The software is open source and is publicly available at https://github.com/amromics/amromics under an MIT license

Acidifiers as Alternatives for Antibiotics Reduction and Gut Health Improvement for Poultry and Swine

Using antibiotics of low doses as feed additives could support to improve poultry and swine performances. However, these applications have caused resistance of bacteria and antibiotic residues in foods of animal origins. Therefore, efforts were focused on solutions to replace antibiotics as growth promoters (AGPs). There are many alternatives for AGPs, in which organic acids are one of the important alternatives. The aim of this chapter is to review publications on these acids and their other forms namely as acidifiers using as feed additives including their names and forms, mode of actions, spectrum against bacteria, combinations among them, and latest updates on their effects on swine and poultry production. The scientific findings show that acidifiers can inhibit pathogenic bacteria growth, improve nutrient digestibility, enhance immunity and overall gut health, consequently increase performances of poultry and swine. Several acids and their salts in both liquid and solid forms have been studied and applied as poultry and swine feed additives; however, the efficacy levels and the mode of actions are dependent on the single acidifiers, their salts, and combinations among them. The uses of acidifiers in their salts and derivative forms and mixtures of different acidifiers seem to be more favorable

Disinfection performance of an ultraviolet lamp: a CFD investigation

Ultraviolet (UV)-based devices have shown their effectiveness on various germicidal purposes. To serve their design optimisation, the disinfection effectiveness of a vertically cylindrical UV lamp, whose wattage ranges from P = 30 − 100 W, is numerically investigated in this work. The UV radiation is solved by the Finite Volume Method together with the Discrete Ordinates model. Various results for the UV intensity and its bactericidal effects against several popular virus types, i.e., Corona-SARS, Herpes (type 2), and HIV, are reported and analysed in detail. Results show that the UV irradiance is greatly dependent on the lamp power. Additionally, it is indicated that the higher the lamp wattage employed, the larger the bactericidal rate is observed, resulting in the greater effectiveness of the UV disinfection process. Nevertheless, the wattage of P ≤ 100W is determined to be insufficient for an effective disinfection performance in a whole room; higher values of power must hence be considered in case intensive sterilization is required. Furthermore, the germicidal effect gets reduced with the viruses less sensitive to UV rays, e.g, the bactericidal rate against the HIV virus is only ∼8.98% at the surrounding walls

Applying DDPG Algorithm to Swing-Up and Balance Control for a Double Inverted Pendulum on a Cart

In this study, we apply the Deep Deterministic Policy Gradient (DDPG) algorithm in reinforcement learning to control a double inverted pendulum on a cart (DIPC)- a high order single input-multi output (SIMO) system . The simulation results demonstrate DDPG’s stability and effectiveness in achieving swing-up and balance, showing its potential for tackling challenging control tasks in robotics

A NOVEL DATASET FOR VIETNAMESE NEW YEAR FOOD CLASSIFICATION

Food classification has always piqued the interest of both domestic and international researchers, but numerous challenges remain. We present the dataset UIT-TASTET21, which contains over 77,000 color images of 18 traditional Vietnamese Lunar New Year dishes. We have experimented with classification using feature vectors from network architectures such as VGG16, Inception-v3, ResNet-50, Xception, and MobileNet-v2 to train support vector machines (SVMs), meeting the dataset’s challenges and laying the groundwork for the development of many optimal methods in the future that promise scientific breakthroughs in the service and commercial industries. At the same time, the authors desire to share a piece of Vietnamese cuisine’s distinctiveness with worldwide friends