arXiv subjects

Thanh T. Nguyen

Publications and source records attributed to Thanh T. Nguyen.

7 recordsLinked to original sources

arXiv2024

In-plane Density Gradation of Shoe Midsoles for Optimized Cushioning Performance

Midsoles are important components in footwear as they provide shock absorption and stability, thereby improving comfort and effectively preventing certain foot and ankle injuries. A rationally tailored midsole can potentially mitigate plantar pressure, improving performance and comfort levels. Despite the importance of midsole design, the potential of using in-plane density gradation in midsole has been rarely explored in earlier studies. The present work investigates the effectiveness of in-plane density gradation in shoe midsoles using a new class of polyurea foams as the material candidate. Their excellent cushioning properties justify the use of polyurea foams. Different polyurea foam densities, ranging from 95 to 350 kg/m3 are examined and tested to construct density-dependent correlative mathematical relations required for the optimization process. An optimization framework is then created to allocate foam densities at certain plantar zones based on the required cushioning performance constrained by the local pressures. The interior-point algorithm was used to solve the constrained optimization problem. The optimization algorithm introduces a novel approach, utilizing the maximum specific energy absorption as the objective function. The optimization process identifies specific foam densities at various plantar regions for maximum biomechanical energy dissipation without incurring additional weight penalties. Our results suggest midsole design can benefit from horizontal (in-plane) density gradation, leading to potential weight reduction and localized cushioning improvements. With local plantar peak pressure data analysis, the optimization results indicate low-density polyurea foams (140 kg/m3) for central and lateral phalanges, whereas stiffer foams (185-230 kg/m3) are identified as suitable candidates for metatarsal and arch regions in an in-plane density graded midsole design.

cond-mat.mtrl-sci↗

arXiv2022

Groups Influence with Minimum Cost in Social Networks

This paper studies a Group Influence with Minimum cost which aims to find a seed set with smallest cost that can influence all target groups, where each user is associated with a cost and a group is influenced if the total score of the influenced users belonging to the group is at least a certain threshold. As the group-influence function is neither submodular nor supermodular, theoretical bounds on the quality of solutions returned by the well-known greedy approach may not be guaranteed. To address this challenge, we propose a bi-criteria polynomial-time approximation algorithm with high certainty. At the heart of the algorithm is a novel group reachable reverse sample concept, which helps speed up the estimation of the group influence function. Finally, extensive experiments conducted on real social networks show that our proposed algorithm outperform the state-of-the-art algorithms in terms of the objective value and the running time.

cs.GT↗

arXiv2020

Learning to Attend Relevant Regions in Videos from Eye Fixations

Attentively important regions in video frames account for a majority part of the semantics in each frame. This information is helpful in many applications not only for entertainment (such as auto generating commentary and tourist guide) but also for robotic control which holds a larascope supported for laparoscopic surgery. However, it is not always straightforward to define and locate such semantic regions in videos. In this work, we attempt to address the problem of attending relevant regions in videos by leveraging the eye fixations labels with a RNN-based visual attention model. Our experimental results suggest that this approach holds a good potential to learn to attend semantic regions in videos while its performance also heavily relies on the quality of eye fixations labels.

cs.CV↗

arXiv2020

Interpretation of smartphone-captured radiographs utilizing a deep learning-based approach

Recently, computer-aided diagnostic systems (CADs) that could automatically interpret medical images effectively have been the emerging subject of recent academic attention. For radiographs, several deep learning-based systems or models have been developed to study the multi-label diseases recognition tasks. However, none of them have been trained to work on smartphone-captured chest radiographs. In this study, we proposed a system that comprises a sequence of deep learning-based neural networks trained on the newly released CheXphoto dataset to tackle this issue. The proposed approach achieved promising results of 0.684 in AUC and 0.699 in average F1 score. To the best of our knowledge, this is the first published study that showed to be capable of processing smartphone-captured radiographs.

cs.CV↗

arXiv2020

A novel approach to remove foreign objects from chest X-ray images

We initially proposed a deep learning approach for foreign objects inpainting in smartphone-camera captured chest radiographs utilizing the cheXphoto dataset. Foreign objects which can significantly affect the quality of a computer-aided diagnostic prediction are captured under various settings. In this paper, we used multi-method to tackle both removal and inpainting chest radiographs. Firstly, an object detection model is trained to separate the foreign objects from the given image. Subsequently, the binary mask of each object is extracted utilizing a segmentation model. Each pair of the binary mask and the extracted object are then used for inpainting purposes. Finally, the in-painted regions are now merged back to the original image, resulting in a clean and non-foreign-object-existing output. To conclude, we achieved state-of-the-art accuracy. The experimental results showed a new approach to the possible applications of this method for chest X-ray images detection.

cs.CV↗

arXiv2019

Layer-wise Learning of Stochastic Neural Networks with Information Bottleneck

Information Bottleneck (IB) is a generalization of rate-distortion theory that naturally incorporates compression and relevance trade-offs for learning. Though the original IB has been extensively studied, there has not been much understanding of multiple bottlenecks which better fit in the context of neural networks. In this work, we propose Information Multi-Bottlenecks (IMBs) as an extension of IB to multiple bottlenecks which has a direct application to training neural networks by considering layers as multiple bottlenecks and weights as parameterized encoders and decoders. We show that the multiple optimality of IMB is not simultaneously achievable for stochastic encoders. We thus propose a simple compromised scheme of IMB which in turn generalizes maximum likelihood estimate (MLE) principle in the context of stochastic neural networks. We demonstrate the effectiveness of IMB on classification tasks and adversarial robustness in MNIST and CIFAR10.

cs.LG↗

arXiv2019

Solving a 1-D inverse medium scattering problem using a new multi-frequency globally strictly convex objective functional

We propose a new approach to constructing globally strictly convex objective functional in a 1-D inverse medium scattering problem using multi-frequency backscattering data. The global convexity of the proposed objective functional is proved using a Carleman estimate. Due to its convexity, no good first guess is required in minimizing this objective functional. We also prove the global convergence of the gradient projection algorithm and derive an error estimate for the reconstructed coefficient. Numerical results show reasonable reconstruction accuracy for simulated data.

math.NA↗