Using Grouped Linear Prediction and Accelerated Reinforcement Learning for Online Content Caching

Proactive caching is an effective way to alleviate peak-hour traffic congestion by prefetching popular contents at the wireless network edge. To maximize the caching efficiency requires the knowledge of content popularity profile, which however is often unavailable in advance. In this paper, we first propose a new linear prediction model, named grouped linear model (GLM) to estimate the future content requests based on historical data. Unlike many existing works that assumed the static content popularity profile, our model can adapt to the temporal variation of the content popularity in practical systems due to the arrival of new contents and dynamics of user preference. Based on the predicted content requests, we then propose a reinforcement learning approach with model-free acceleration (RLMA) for online cache replacement by taking into account both the cache hits and replacement cost. This approach accelerates the learning process in non-stationary environment by generating imaginary samples for Q-value updates. Numerical results based on real-world traces show that the proposed prediction and learning based online caching policy outperform all considered existing schemes.Comment: 6 pages, 4 figures, ICC 2018 worksho

Split Learning in 6G Edge Networks

With the proliferation of distributed edge computing resources, the 6G mobile network will evolve into a network for connected intelligence. Along this line, the proposal to incorporate federated learning into the mobile edge has gained considerable interest in recent years. However, the deployment of federated learning faces substantial challenges as massive resource-limited IoT devices can hardly support on-device model training. This leads to the emergence of split learning (SL) which enables servers to handle the major training workload while still enhancing data privacy. In this article, we offer a brief overview of key advancements in SL and articulate its seamless integration with wireless edge networks. We begin by illustrating the tailored 6G architecture to support edge SL. Then, we examine the critical design issues for edge SL, including innovative resource-efficient learning frameworks and resource management strategies under a single edge server. Additionally, we expand the scope to multi-edge scenarios, exploring multi-edge collaboration and mobility management from a networking perspective. Finally, we discuss open problems for edge SL, including convergence analysis, asynchronous SL and U-shaped SL.Comment: 7 pages, 6 figure

Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities

Large language models (LLMs), which have shown remarkable capabilities, are revolutionizing AI development and potentially shaping our future. However, given their multimodality, the status quo cloud-based deployment faces some critical challenges: 1) long response time; 2) high bandwidth costs; and 3) the violation of data privacy. 6G mobile edge computing (MEC) systems may resolve these pressing issues. In this article, we explore the potential of deploying LLMs at the 6G edge. We start by introducing killer applications powered by multimodal LLMs, including robotics and healthcare, to highlight the need for deploying LLMs in the vicinity of end users. Then, we identify the critical challenges for LLM deployment at the edge and envision the 6G MEC architecture for LLMs. Furthermore, we delve into two design aspects, i.e., edge training and edge inference for LLMs. In both aspects, considering the inherent resource limitations at the edge, we discuss various cutting-edge techniques, including split learning/inference, parameter-efficient fine-tuning, quantization, and parameter-sharing inference, to facilitate the efficient deployment of LLMs. This article serves as a position paper for thoroughly identifying the motivation, challenges, and pathway for empowering LLMs at the 6G edge.Comment: 7 pages, 5 figure

Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks

The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To overcome this challenge, in this paper, we advocate the integration of edge computing paradigm and parallel split learning (PSL), allowing multiple client devices to offload substantial training workloads to an edge server via layer-wise model split. By observing that existing PSL schemes incur excessive training latency and large volume of data transmissions, we propose an innovative PSL framework, namely, efficient parallel split learning (EPSL), to accelerate model training. To be specific, EPSL parallelizes client-side model training and reduces the dimension of local gradients for back propagation (BP) via last-layer gradient aggregation, leading to a significant reduction in server-side training and communication latency. Moreover, by considering the heterogeneous channel conditions and computing capabilities at client devices, we jointly optimize subchannel allocation, power control, and cut layer selection to minimize the per-round latency. Simulation results show that the proposed EPSL framework significantly decreases the training latency needed to achieve a target accuracy compared with the state-of-the-art benchmarks, and the tailored resource management and layer split strategy can considerably reduce latency than the counterpart without optimization.Comment: 15 pages, 13 figure

Transcriptome sequencing reveals the effects of circRNA on testicular development and spermatogenesis in Qianbei Ma goats

Circular RNAs (circRNAs) play an important role in regulating the mammalian reproductive system, especially testicular development and spermatogenesis. However, their functions in testicular development and spermatogenesis in the Qianbei Ma goat, the Guizhou endemic breed are still unclear. In this study, tissue sectioning and circRNAs transcriptome analysis were conducted to compare the changes of morphology and circular RNAs gene expression profile at four different developmental stages (0Y, 0-month-old; 6Y, 6-month-old; 12Y, 12-month-old; 18Y, 18-month-old). The results showed that the circumferences and area of the seminiferous tubule gradually increased with age, and the lumen of the seminiferous tubule in the testis differentiated significantly. 12,784 circRNAs were detected from testicular tissues at four different developmental stages by RNA sequencing, and 8,140 DEcircRNAs (differentially expressed circRNAs) were found in 0Y vs. 6Y, 6Y vs. 12Y, 12Y vs. 18Y and 0Y vs. 18Y, 0Y vs. 12Y, 6Y vs. 18Y Functional enrichment analysis of the source genes showed that they were mainly enriched in testicular development and spermatogenesis. In addition, the miRNAs and mRNAs associated with DECircRNAs in 6 control groups were predicted by bioinformatics, and 81 highly expressed DECircRNAs and their associated miRNAs and mRNAs were selected to construct the ceRNA network. Through functional enrichment analysis of the target genes of circRNAs in the network, some candidate circRNAs related to testicular development and spermatogenesis were obtained. Such as circRNA_07172, circRNA_04859, circRNA_07832, circRNA_00032 and circRNA_07510. These results will help to reveal the mechanism of circRNAs in testicular development and spermatogenesis, and also provide some guidance for goat reproduction

Automatic three-dimensional segmentation of endoscopic airway OCT images.

Automatic delineation and segmentation of airway structures from endoscopic optical coherence tomography (OCT) images improve image analysis efficiency and thus has been of particular interest. Conventional two-dimensional automatic segmentation methods, such as the dynamic programming approach, ensures the edge-continuity in the xz-direction (intra-B-scan), but fails to preserve the surface-continuity when concerning the y-direction (inter-B-scan). To solve this, we present a novel automatic three-dimensional (3D) airway segmentation strategy. Our segmentation scheme includes an artifact-oriented pre-processing pipeline and a modified 3D optimal graph search algorithm incorporating adaptive tissue-curvature adjustment. The proposed algorithm is tested on endoscopic airway OCT image data sets acquired by different swept-source OCT platforms, and on different animal and human models. With our method, the results show continuous surface segmentation performance, which is both robust and accurate

Endothelial Adhesion of Targeted Microbubbles in Both Small and Great Vessels Using Ultrasound Radiation Force

The effectiveness of microbubble-mediated ultrasound molecular imaging and drug delivery has been significantly affected by the axial laminar flow of vessels which prevents ultrasound contrast agents (UCAs) from targeting vascular endothelium. Studies show that acoustic manipulation could increase targeted UCA adhesion in microcirculation and some small vessels. In this study we demonstrate that ultrasound radiation force (USRF) can also significantly enhance the targeted adhesion of microbubbles in both small and great vessels. Our results indicate that the UCA adhesion targeted to ICAM-1 expressed on mouse cremaster microvascular endothelial cells increase about 9-fold when USRF is applied at 1 MHz and 73.9 kPa. The adhesion of anti-CD34 microbubbles to the endothelia of rat abdominal aorta was visually analyzed using scanning electron microscopy for the first time and thousands of microbubbles were found attached to the aortic endothelia after USRF application at the same acoustic parameters. Our data illustrate that targeted adhesion of anti-CD34 microbubbles is possible in normal abdominal aorta and we demonstrate the potential of using USRF in molecular imaging of a vascular target

Automated detection of otosclerosis with interpretable deep learning using temporal bone computed tomography images

Objective: This study aimed to develop an automated detection schema for otosclerosis with interpretable deep learning using temporal bone computed tomography images. Methods: With approval from the institutional review board, we retrospectively analyzed high-resolution computed tomography scans of the temporal bone of 182 participants with otosclerosis (67 male subjects and 115 female subjects; average age, 36.42 years) and 157 participants without otosclerosis (52 male subjects and 102 female subjects; average age, 30.61 years) using deep learning. Transfer learning with the pretrained VGG19, Mask RCNN, and EfficientNet models was used. In addition, 3 clinical experts compared the system's performance by reading the same computed tomography images for a subset of 35 unseen subjects. An area under the receiver operating characteristic curve and a saliency map were used to further evaluate the diagnostic performance. Results: In prospective unseen test data, the diagnostic performance of the automatically interpretable otosclerosis detection system at the optimal threshold was 0.97 and 0.98 for sensitivity and specificity, respectively. In comparison with the clinical acumen of otolaryngologists at P < 0.05, the proposed system was not significantly different. Moreover, the area under the receiver operating characteristic curve for the proposed system was 0.99, indicating satisfactory diagnostic accuracy. Conclusion: Our research develops and evaluates a deep learning system that detects otosclerosis at a level comparable with clinical otolaryngologists. Our system is an effective schema for the differential diagnosis of otosclerosis in computed tomography examinations

Ultrasound Mediated Microbubbles Destruction Augmented Sonolysis: An In Vitro and In Vivo Study

Objective. This study was aimed at exploring ultrasound mediated microbubbles destruction (UMMD) assisted sonolysis in both the in vitro and in vivo clots. Methods. Therapeutic ultrasound (TUS) and lipid microbubbles (MBs) were used in whole blood clots and divided into the control, TUS group, and TUS + MB group. Thrombolytic rates and microscopy were performed. Color Doppler flow imaging (CDFI) and angiography were performed to evaluate the recanalization rates and flow scores in femoral arterial thrombus (FAT) in rabbits. FAT were dyed with H&E. Results. The average thrombolytic ratios of TUS + MB group were significantly higher than those of TUS group and the control group (both P<0.05). Clots had different pathological changes. Recanalization rates and flow scores in TUS + MB group were significantly higher than the control and TUS group. Flow scores and recanalization ratios were grade 0 in 0% of the control group, grade I in 25% of TUS group, and grade II or higher in 87.5% of TUS + MB group after 30 min sonolysis. Conclusions. Both the in vitro and in vivo sonolysis can be significantly augmented by the introduction of MBs without thrombolytic agents, which might be induced by the enhanced cavitation via UMMD

A new machine learning approach in detecting the oil palm plantations using remote sensing data

The rapid expansion of oil palm is a major driver of deforestation and other associated damage to the climate and ecosystem in tropical regions, especially Southeast Asia. It is therefore necessary to precisely detect and monitor oil palm plantations to safeguard the ecosystem services and biodiversity of tropical forests. Compared with optical data, which are vulnerable to cloud cover, the Sentinel-1 dual-polarization C-band synthetic aperture radar (SAR) acquires global observations under all weather conditions and times of day and shows good performance for oil palm detection in the humid tropics. However, because accurately distinguishing mature and young oil palm trees by using optical and SAR data is difficult and considering the strong dependence on the input parameter values when detecting oil palm plantations by employing existing classification algorithms, we propose an innovative method to improve the accuracy of classifying the oil palm type (mature or young) and detecting the oil palm planting area in Sumatra by fusing Landsat-8 and Sentinel-1 images. We extract multitemporal spectral characteristics, SAR backscattering values, vegetation indices, and texture features to establish different feature combinations. Then, we use the random forest algorithm based on improved grid search optimization (IGSO-RF) and select optimal feature subsets to establish a classification model and detect oil palm plantations. Based on the IGSO-RF classifier and optimal features, our method improved the oil palm detection accuracy and obtained the best model performance (OA = 96.08% and kappa = 0.9462). Moreover, the contributions of different features to oil palm detection are different; nevertheless, the optimal feature subset performed the best and demonstrated good potential for the detection of oil palm plantations