Deep Nuisance Disentanglement for Robust Object Detection from Unmanned Aerial Vehicles

Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is becoming dramatically useful. Despite the great success of the generic object detection methods trained on ground-to-ground images, a huge performance drop is observed when these methods are directly applied to images captured by UAVs. The unsatisfactory performance is owing to many UAV-specific nuisances, such as varying flying altitudes, adverse weather conditions, dynamically changing viewing angles, etc., constituting a large number of fine-grained domains across which the detection model has to stay robust. Fortunately, UAVs record meta-data corresponding to the same varying attributes, which can either be freely available along with the UAV images, or easily obtained. We propose to utilize the free meta-data in conjunction with the associated UAV images to learn domain-robust features via an adversarial training framework. This model is dubbed Nuisance Disentangled Feature Transforms (NDFT), for the specific challenging problem of object detection in UAV images. It achieves a substantial gain in robustness to these nuisances. This work demonstrates the effectiveness of our proposed algorithm by showing both quantitative improvements on two existing UAV-based object detection benchmarks, as well as qualitative improvements on self-collected UAV imagery. Reprinted with permission from the Abstract section of Deep Nuisance Disentanglement for Robust Object Detection from Unmanned Aerial Vehicles by Zhenyu Wu† , Karthik Suresh† , Priya Narayanan, Hongyu Xu, Heesung Kwon, Zhangyang Wang, 2019, International Conference on Computer Vision (ICCV 2019) Proceedings (Under Review). † indicates equal contributio

Deep Nuisance Disentanglement for Robust Object Detection from Unmanned Aerial Vehicles

Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is becoming dramatically useful. Despite the great success of the generic object detection methods trained on ground-to-ground images, a huge performance drop is observed when these methods are directly applied to images captured by UAVs. The unsatisfactory performance is owing to many UAV-specific nuisances, such as varying flying altitudes, adverse weather conditions, dynamically changing viewing angles, etc., constituting a large number of fine-grained domains across which the detection model has to stay robust. Fortunately, UAVs record meta-data corresponding to the same varying attributes, which can either be freely available along with the UAV images, or easily obtained. We propose to utilize the free meta-data in conjunction with the associated UAV images to learn domain-robust features via an adversarial training framework. This model is dubbed Nuisance Disentangled Feature Transforms (NDFT), for the specific challenging problem of object detection in UAV images. It achieves a substantial gain in robustness to these nuisances. This work demonstrates the effectiveness of our proposed algorithm by showing both quantitative improvements on two existing UAV-based object detection benchmarks, as well as qualitative improvements on self-collected UAV imagery. Reprinted with permission from the Abstract section of Deep Nuisance Disentanglement for Robust Object Detection from Unmanned Aerial Vehicles by Zhenyu Wu† , Karthik Suresh† , Priya Narayanan, Hongyu Xu, Heesung Kwon, Zhangyang Wang, 2019, International Conference on Computer Vision (ICCV 2019) Proceedings (Under Review). † indicates equal contributio

Power Loss Minimization in a Radial Distribution Network by Optimal Sizing and Placement of Energy Storage Units

It is possible to reduce distribution losses by strategically placing and sizing DG and BESS sources. Assuring low loss requires strategically placing the aforementioned devices; otherwise, the system may experience either under- or overvoltage. It is preferable to choose bus stations with less risk for loss. The proposed approach tries to pinpoint the optimal BESS size and placement to cut down on investment and operating expenses while still achieving the desired level of energy reduction. The development of optimisation algorithms for finding and scaling BESS units is the fundamental focus of this study. Two such strategies are being explored here: the Genetic Algorithm (GA) and the Ant Colony Optimization Algorithm (ACOA). The goal function, like the original issue, seeks to minimise system-wide power losses while adhering to specified levels of equality and inequality. This article explores the appropriate capacity and placement of the DGs in a 33-bus radial distribution grid to reduce power dissipations. Matlab code is used to perform a simulation, and the results are put to use gauging the method's sturdiness

NURBS-Enhanced finite element hybridizable discontinuous Galerkin method with degree adaptivity for steady Stokes flow

The aim of this project is to develop a high-order accurate space discretisation method for the solution of Stokes problems. The approach will consider a p-adaptive HDG method and NURBS-enhanced finite element method to account for the exact geometric description given by a CAD model. The proposed approach will be tested by using problems with analytical solution and compared to a p-adaptive HDG methodology with isoparametric elements

NURBS-Enhanced finite element hybridizable discontinuous Galerkin method with degree adaptivity for steady Stokes flow

The aim of this project is to develop a high-order accurate space discretisation method for the solution of Stokes problems. The approach will consider a p-adaptive HDG method and NURBS-enhanced finite element method to account for the exact geometric description given by a CAD model. The proposed approach will be tested by using problems with analytical solution and compared to a p-adaptive HDG methodology with isoparametric elements

NURBS-Enhanced finite element hybridizable discontinuous Galerkin method with degree adaptivity for steady Stokes flow

The aim of this project is to develop a high-order accurate space discretisation method for the solution of Stokes problems. The approach will consider a p-adaptive HDG method and NURBS-enhanced finite element method to account for the exact geometric description given by a CAD model. The proposed approach will be tested by using problems with analytical solution and compared to a p-adaptive HDG methodology with isoparametric elements

Symbiotic Human Gut Bacteria with Variable Metabolic Priorities for Host Mucosal Glycans.

UnlabelledMany symbiotic gut bacteria possess the ability to degrade multiple polysaccharides, thereby providing nutritional advantages to their hosts. Like microorganisms adapted to other complex nutrient environments, gut symbionts give different metabolic priorities to substrates present in mixtures. We investigated the responses of Bacteroides thetaiotaomicron, a common human intestinal bacterium that metabolizes more than a dozen different polysaccharides, including the O-linked glycans that are abundant in secreted mucin. Experiments in which mucin glycans were presented simultaneously with other carbohydrates show that degradation of these host carbohydrates is consistently repressed in the presence of alternative substrates, even by B. thetaiotaomicron previously acclimated to growth in pure mucin glycans. Experiments with media containing systematically varied carbohydrate cues and genetic mutants reveal that transcriptional repression of genes involved in mucin glycan metabolism is imposed by simple sugars and, in one example that was tested, is mediated through a small intergenic region in a transcript-autonomous fashion. Repression of mucin glycan-responsive gene clusters in two other human gut bacteria, Bacteroides massiliensis and Bacteroides fragilis, exhibited variable and sometimes reciprocal responses compared to those of B. thetaiotaomicron, revealing that these symbionts vary in their preference for mucin glycans and that these differences occur at the level of controlling individual gene clusters. Our results reveal that sensing and metabolic triaging of glycans are complex processes that vary among species, underscoring the idea that these phenomena are likely to be hidden drivers of microbiota community dynamics and may dictate which microorganisms preferentially commit to various niches in a constantly changing nutritional environment.ImportanceHuman intestinal microorganisms impact many aspects of health and disease, including digestion and the propensity to develop disorders such as inflammation and colon cancer. Complex carbohydrates are a major component of the intestinal habitat, and numerous species have evolved and refined strategies to compete for these coveted nutrients. Our findings reveal that individual bacteria exhibit different preferences for carbohydrates emanating from host diet and mucosal secretions and that some of these prioritization strategies are opposite to one another. Thus, we reveal new aspects of how individual bacteria, some with otherwise similar metabolic potential, partition to "preferred niches" in the complex gut ecosystem, which has important and immediate implications for understanding and predicting the behavioral dynamics of this community