International Journal of Applied Science

On Farm Performance Evaluation of Exotic Chickens in Central Tigray, Northern Ethiopia

Berhe Teklay — Wed, 07 Aug 2024 00:00:00 +0800

The study was carried out on two purposively selected districts of central zone of Tigray, viz., Mereb Leke and Tahtay Michew. The aim of the study was to evaluate the on-farm performance of three exotic chicken strains under the farmer condition. A total of 96 households (48 household per district) were participated and then the selected strains (Sasso Rhode Island Red, Kuroiler and Koekoek) were distributed. GLM procedure was used for the on-farm data analysis. Data collected during the entire study were, growth data, fertility and hatchability, egg production, weight and age at point of lay. Better overall average daily body weight gains were achieved from 12-16 and 16-20 weeks by kuroiler chicken strains with value of 10.29, 13.68 gram respectively. Egg at first lay of SRIR and Kuroiler strains were 23.69 and 25.25 weeks respectively. The overall average egg production in the study area was 63.36% which was scored by SRIR strain. According to this study, based on their fast-growing kuroiler strain and based on their egg production potential SRIR strain were recommended to the study area and like agro ecologies.

Ergodic Foundations of Langevin-Based MCMC

Ruoming Geng — Thu, 05 Sep 2024 00:00:00 +0800

In this work, we provide a comprehensive theoretical analysis of Langevin diffusion and its applications to Markov Chain Monte Carlo (MCMC) methods. We establish the ergodicity of continuous-time Langevin diffusion processes, proving their convergence to target distributions under suitable regularity conditions. The analysis is then extended to discrete-time settings, examining the properties of the Unadjusted Langevin Algorithm (ULA) and the Metropolis-Adjusted Langevin Algorithm (MALA). Employing tools from stochastic processes, ergodic theory, and Markov chain theory, we establish strong convergence results using Foster-Lyapunov drift conditions, coupling arguments, and geometric ergodicity. The paper explores connections between Langevin diffusion and optimal transport theory, highlighting recent developments in adaptive methods, transport map accelerated MCMC, and applications to high-dimensional Bayesian inference. Our theoretical results provide insights into algorithm design, parameter tuning, and convergence diagnostics for Langevin-based MCMC methods, bridging the gap between theory and practice in the development of efficient sampling algorithms for complex probability distributions.

3D Object Detection via Residual SqueezeDet

Xuanhao Zhou — Wed, 18 Sep 2024 00:00:00 +0800

Three-dimensional object detection is a critical task in computer vision with applications in autonomous driving, robotics, and augmented reality. This paper introduces Residual SqueezeDet, a novel network architecture that enhances the performance of 3D object detection on the KITTI dataset. Building upon the efficient SqueezeDet framework, we propose the Residual Fire module, which incorporates skip connections inspired by ResNet architectures into the original Fire module. This innovation improves gradient flow, enhances feature propagation, and allows for more effective training of deeper networks. Our method leverages point cloud and image-based features, employing a Residual SqueezeDet to effectively capture local and global context. Extensive experiments on the KITTI dataset demonstrate that Residual SqueezeDet significantly outperforms the original SqueezeDet, with particularly notable improvements in challenging scenarios. The proposed model maintains computational efficiency while achieving state-of-the-art performance, making it well-suited for real-time applications in autonomous driving. Our work contributes to the field by providing a more accurate and robust solution for 3D object detection, paving the way for improved perception systems in dynamic environments.

Theoretical Analysis of Adam Optimizer in the Presence of Gradient Skewness

Luyi Yang — Fri, 04 Oct 2024 00:00:00 +0800

The Adam optimizer has become a cornerstone in deep learning, widely adopted for its adaptive learning rates and momentumbased updates. However, its behavior under non-standard conditions, particularly skewed gradient distributions, remains underexplored. This paper presents a novel theoretical analysis of the Adam optimizer in the presence of skewed gradients, a scenario frequently encountered in real-world applications due to imbalanced datasets or inherent problem characteristics. We extend the standard convergence analysis of Adam to explicitly account for gradient skewness, deriving new bounds that characterize the optimizer’s performance under these conditions. Our main contributions include: (1) a formal proof of Adam’s convergence under skewed gradient distributions, (2) quantitative error bounds that capture the impact of skewness on optimization outcomes, and (3) insights into how skewness affects Adam’s adaptive learning rate mechanism. We demonstrate that gradient skewness can lead to biased parameter updates and potentially slower convergence compared to scenarios with symmetric distributions. Additionally, we provide practical recommendations for mitigating these effects, including adaptive gradient clipping and distribution-aware hyperparameter tuning. Our findings bridge a critical gap between Adam’s empirical success and its theoretical underpinnings, offering valuable insights for practitioners dealing with non-standard optimization landscapes in deep learning.