Coverage and connectivity management in wireless sensor networks

Ph.DDOCTOR OF PHILOSOPH

Q-YOLO: Efficient Inference for Real-time Object Detection

Real-time object detection plays a vital role in various computer vision applications. However, deploying real-time object detectors on resource-constrained platforms poses challenges due to high computational and memory requirements. This paper describes a low-bit quantization method to build a highly efficient one-stage detector, dubbed as Q-YOLO, which can effectively address the performance degradation problem caused by activation distribution imbalance in traditional quantized YOLO models. Q-YOLO introduces a fully end-to-end Post-Training Quantization (PTQ) pipeline with a well-designed Unilateral Histogram-based (UH) activation quantization scheme, which determines the maximum truncation values through histogram analysis by minimizing the Mean Squared Error (MSE) quantization errors. Extensive experiments on the COCO dataset demonstrate the effectiveness of Q-YOLO, outperforming other PTQ methods while achieving a more favorable balance between accuracy and computational cost. This research contributes to advancing the efficient deployment of object detection models on resource-limited edge devices, enabling real-time detection with reduced computational and memory overhead

AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations

Multi-task learning (MTL) aims at enhancing the performance and efficiency of machine learning models by training them on multiple tasks simultaneously. However, MTL research faces two challenges: 1) modeling the relationships between tasks to effectively share knowledge between them, and 2) jointly learning task-specific and shared knowledge. In this paper, we present a novel model Adaptive Task-to-Task Fusion Network (AdaTT) to address both challenges. AdaTT is a deep fusion network built with task specific and optional shared fusion units at multiple levels. By leveraging a residual mechanism and gating mechanism for task-to-task fusion, these units adaptively learn shared knowledge and task specific knowledge. To evaluate the performance of AdaTT, we conduct experiments on a public benchmark and an industrial recommendation dataset using various task groups. Results demonstrate AdaTT can significantly outperform existing state-of-the-art baselines

Demand Response Method Considering Multiple Types of Flexible Loads in Industrial Parks

With the rapid development of the energy internet, the proportion of flexible loads in smart grid is getting much higher than before. It is highly important to model flexible loads based on demand response. Therefore, a new demand response method considering multiple flexible loads is proposed in this paper to character the integrated demand response (IDR) resources. Firstly, a physical process analytical deduction (PPAD) model is proposed to improve the classification of flexible loads in industrial parks. Scenario generation, data point augmentation, and smooth curves under various operating conditions are considered to enhance the applicability of the model. Secondly, in view of the strong volatility and poor modeling effect of Wasserstein-generative adversarial networks (WGAN), an improved WGAN-gradient penalty (IWGAN-GP) model is developed to get a faster convergence speed than traditional WGAN and generate a higher quality samples. Finally, the PPAD and IWGAN-GP models are jointly implemented to reveal the degree of correlation between flexible loads. Meanwhile, an intelligent offline database is built to deal with the impact of nonlinear factors in different response scenarios. Numerical examples have been performed with the results proving that the proposed method is significantly better than the existing technologies in reducing load modeling deviation and improving the responsiveness of park loads.Comment: Submitted to Expert Systems with Application

Deep Intraspecific Divergence in the Endemic Herb Lancea tibetica (Mazaceae) Distributed Over the Qinghai-Tibetan Plateau

Qinghai-Tibetan Plateau (QTP) is an important biodiversity hub, which is very sensitive to climate change. Here in this study, we investigated genetic diversity and past population dynamics of Lancea tibetica (Mazaceae), an endemic herb to QTP and adjacent highlands. We sequenced chloroplast and nuclear ribosomal DNA fragments for 429 individuals, collected from 29 localities, covering their major distribution range at the QTP. A total of 19 chloroplast haplotypes and 13 nuclear genotypes in two well-differentiated lineages, corresponding to populations into two groups isolated by Tanggula and Bayangela Mountains. Meanwhile, significant phylogeographical structure was detected among sampling range of L. tibetica, and 61.50% of genetic variations was partitioned between groups. Gene flow across the whole region appears to be restricted by high mountains, suggesting a significant role of geography in the genetic differences between the two groups. Divergence time between the two lineages dated to 8.63 million years ago, which corresponded to the uplifting of QTP during the late Miocene and Pliocene. Ecological differences were found between both the lineages represent species-specific characteristics, sufficient to keep the lineages separated to a high degree. The simulated distribution from the last interglacial period to the current period showed that the distribution of L. tibetica experienced shrinkage and expansion. Climate changes during the Pleistocene glacial-interglacial cycles had a dramatic effect on L. tibetica distribution ranges. Multiple refugia of L. tibetica might have remained during the species history, to south of the Tanggula and north of Bayangela Mountains, both appeared as topological barrier and contributed to restricting gene flow between the two lineages. Together, geographic isolation and climatic factors have played a fundamental role in promoting diversification and evolution of L. tibetica

Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans

Gastric cancer is the third leading cause of cancer-related mortality worldwide, but no guideline-recommended screening test exists. Existing methods can be invasive, expensive, and lack sensitivity to identify early-stage gastric cancer. In this study, we explore the feasibility of using a deep learning approach on non-contrast CT scans for gastric cancer detection. We propose a novel cluster-induced Mask Transformer that jointly segments the tumor and classifies abnormality in a multi-task manner. Our model incorporates learnable clusters that encode the texture and shape prototypes of gastric cancer, utilizing self- and cross-attention to interact with convolutional features. In our experiments, the proposed method achieves a sensitivity of 85.0% and specificity of 92.6% for detecting gastric tumors on a hold-out test set consisting of 100 patients with cancer and 148 normal. In comparison, two radiologists have an average sensitivity of 73.5% and specificity of 84.3%. We also obtain a specificity of 97.7% on an external test set with 903 normal cases. Our approach performs comparably to established state-of-the-art gastric cancer screening tools like blood testing and endoscopy, while also being more sensitive in detecting early-stage cancer. This demonstrates the potential of our approach as a novel, non-invasive, low-cost, and accurate method for opportunistic gastric cancer screening.Comment: MICCAI 202

Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in which the tumor-vascular involvement greatly affects the resectability and, thus, overall survival of patients. However, current prognostic prediction methods fail to explicitly and accurately investigate relationships between the tumor and nearby important vessels. This paper proposes a novel learnable neural distance that describes the precise relationship between the tumor and vessels in CT images of different patients, adopting it as a major feature for prognosis prediction. Besides, different from existing models that used CNNs or LSTMs to exploit tumor enhancement patterns on dynamic contrast-enhanced CT imaging, we improved the extraction of dynamic tumor-related texture features in multi-phase contrast-enhanced CT by fusing local and global features using CNN and transformer modules, further enhancing the features extracted across multi-phase CT images. We extensively evaluated and compared the proposed method with existing methods in the multi-center (n=4) dataset with 1,070 patients with PDAC, and statistical analysis confirmed its clinical effectiveness in the external test set consisting of three centers. The developed risk marker was the strongest predictor of overall survival among preoperative factors and it has the potential to be combined with established clinical factors to select patients at higher risk who might benefit from neoadjuvant therapy.Comment: MICCAI 202

Janus aramid nanofiber aerogel incorporating plasmonic nanoparticles for high-efficiency interfacial solar steam generation

Interfacial solar steam generation (ISSG) is a novel and potential solution to global freshwater crisis. Here, based on a facile sol-gel fabrication process, we demonstrate a highly scalable Janus aramid nanofiber aerogel (JANA) as a high-efficiency ISSG device. JANA performs near-perfect broadband optical absorption, rapid photothermal conversion and effective water transportation. Owning to these features, efficient desalination of salty water and purification of municipal sewage are successfully demonstrated using JANA. In addition, benefiting from the mechanical property and chemical stability of constituent aramid nanofibers, JANA not only possesses outstanding flexibility and fire-resistance properties, but its solar steaming efficiency is also free from the influences of elastic deformations and fire treatments. We envision JANA provides a promising platform for mass-production of high-efficiency ISSG devices with supplementary capabilities of convenient transportation and long-term storage, which could further promote the realistic applications of ISSG technology