Browse Articles

Molecular profiling and bioinformatics validation of CEACAM6, HOXA-AS3 and miR29a in colorectal cancer

Increased cancer risk in patients with IgG4-related disease in a nationwide South Korean cohort, 2012–2021

SMFR-Net: simple multi-domain flare removal network

Positron emission tomography reveals increased myocardial glucose uptake in a subset of Friedreich ataxia patients

Study on roof partition fracture based on dominant primary fracture

MobileDANet integrating transfer learning and dynamic attention for classifying multi target histopathology images with explainable AI

Zoonotic brugian filariasis past and present trends in malaysia: A systematic review and proportionate meta-analysis

Beliefs about god buffer against the health risks of loneliness and social isolation in five major religions and 22 countries

Object state optimization algorithm based on Bayesian random sampling for visual object tracking

Abstract From the perspective of object state modeling, visual object tracking can be regarded as a unified process that combines object state estimation and object localization. In this framework, state estimation refers to predicting the complete state vector of the object–such as its position, scale, and motion dynamics–while localization specifically denotes identifying the object’s spatial position within the image, typically in the form of bounding box coordinates. Traditional optimization-based methods for state estimation often suffer from getting trapped in local optima, primarily due to the non-convexity of the objective function and the algorithm’s sensitivity to initialization. To address these issues, this research proposes an object state optimization algorithm based on Bayesian random sampling for visual object tracking. Firstly, a dense sampling method is introduced to mitigate the problem of local optima. Secondly, a hybrid model that merges Bayesian random sampling and gradient ascent is proposed to refine the bounding box, successfully alleviating convergence instability. Finally, our experimental results show that the proposed algorithm significantly improves tracking performance on multiple datasets, validating its efficiency and applicability in object state estimation tasks.

Diplexer based microwave sensor for noninvasive detection of sucrose and sorbitol in pharmaceutical syrups

Experimental and machine learning-based analysis of red mud influence on recycled aggregate concrete properties

An enhanced neural network model for predicting the remaining useful life of proton exchange membrane fuel cells

Environmental lag effects and clinical characteristics of pediatric human parainfluenza virus type 3 infections

Dromion solutions for system of ion sound and Langmuir waves using truncated Painlevé approach

Reducing T-count and T-depth in approximate quantum Fourier transform circuits

Cerebral oxygenation responses to obstructive sleep apnea in cognitively normal older adults: a study using simultaneous polysomnography and functional near-infrared spectroscopy

The impact of PANoptosis-related genes on immune profiles and subtype classification in ischemic stroke

Genetic ancestry influences body shape and obesity risk in Latin American populations

BirdNeRF: fast neural reconstruction of large-scale scenes from aerial imagery

Abstract In this study, we introduce BirdNeRF, an adaptation of Neural Radiance Fields (NeRF) specifically designed for reconstructing large-scale scenes using aerial imagery. Unlike previous research which focused on small-scale and object-centric NeRF reconstruction, our approach addresses multiple challenges, including (1) Addressing the issue of slow training and rendering associated with large models. (2) Meeting the computational demands necessitated by modeling a substantial number of images, requiring extensive resources such as high-performance GPUs. (3) Overcoming significant artifacts and low visual fidelity commonly observed in large-scale reconstruction tasks due to limited model capacity. Specifically, we present a novel bird-view pose-based spatial decomposition algorithm. This algorithm decomposes a large aerial image set into multiple small sets with appropriately sized overlaps, allowing us to train individual NeRFs of sub-scene. This decomposition approach enables rendering to scale seamlessly to arbitrarily large environments. Moreover, it allows for per-block updates of the environment, enhancing the flexibility and adaptability of the reconstruction process. Additionally, we propose a projection-guided novel view re-rendering strategy, which aids in effectively utilizing the independently trained sub-scenes to generate superior rendering results. We evaluate our approach on existing datasets as well as against our own drone footage, achieving a reconstruction speed improvement of 10x over classical photogrammetry software and 50x over the state-of-the-art large-scale NeRF solution, all on a single GPU with comparable or superior rendering quality.

Productive engagement patterns and their association with life satisfaction among elderly sandwich generation