Performance evaluation of digital pulse position modulation for wavelength division multiplexing FSO systems impaired by interchannel crosstalk

Wavelength division multiplexing (WDM) has been proposed for fibre, intersatellite, free space and indoor optical communication systems. Digital pulse position modulation (DPPM) is a more power efficient modulation format than on-off keying (OOK) and a strong contender for the modulation of free-space systems. Although DPPM obtains this advantage in exchange for a bandwidth expansion, WDM systems using it are still potentially attractive, particularly for moderate coding levels. However, WDM systems are susceptible to interchannel crosstalk and modelling this in a WDM DPPM system is necessary. Models of varying complexity, based on simplifying assumptions, are presented and evaluated for the case of a single crosstalk wavelength. For a single crosstalk, results can be straightforwardly obtained by artificially imposing the computationally convenient constraint that frames (and thus slots also) align. Multiple crosstalk effects are additionally investigated, for the most practically relevant cases of modest coding level, and using both simulation and analytical methods. In general, DPPM maintains its sensitivity advantage over OOK even in the presence of crosstalk while predicting lower power penalty at low coding level in WDM systems

Expression of phospho-ERK1/2 and PI3-K in benign and malignant gallbladder lesions and its clinical and pathological correlations

Abstract Background An increasing number of studies have shown that ERK and PI3-K/AKT signaling pathways are involved in various human cancers including hepatocellular carcinoma and cholangiocarcinoma. However, few studies have examined gallbladder cancer specimens, and little is known about the clinical and pathological significance of ERK1/2 and PI3-K/AKT signaling changes in gallbladder adenocarcinoma. In this study, we examined phospho-ERK1/2 (p-ERK1/2) and PI3K expression and analyzed its clinicopathological impact in gallbladder adenocarcinoma. Methods Immunohistochemistry was used to detect and compare the frequency of p-ERK1/2 and PI3-K expression in gallbladder adenocarcinoma, peri-tumor tissues, adenomatous polyps, and chronic cholecystitis specimens. Results The positive staining for p-EKR1/2 and PI3-K were 63/108 (58.3%) and 55/108 (50.9%) in gallbladder adenocarcinoma; 14/46 (30.4%) and 5/46 (10.1%) in peri-tumor tissues; 3/15 (20%) and 3/15 (20%) in adenomatous polyps; and 4/35 (11.4%) and 3/35 (8.6%) in chronic cholecystitis. The positive rate of p-ERK1/2 or PI3-K in gallbladder adenocarcinoma was significantly higher than that in peri-tumor tissue (both, P P P P P P P P = 0.062) was associated with decreased overall survival. Multivariate Cox regression analysis showed that increased p-ERK1/2 expression was an independent prognostic predictor in gallbladder carcinoma (P = 0.028). Conclusion Increased expression of p-ERK1/2 and PI3K might contribute to gallbladder carcinogenesis. p-ERK1/2 over-expression is correlated with decreased survival and therefore may serve as an important biological marker in development of gallbladder adenocarcinoma.</p

Oroxylin A promotes PTEN-mediated negative regulation of MDM2 transcription via SIRT3-mediated deacetylation to stabilize p53 and inhibit glycolysis in wt-p53 cancer cells

Introduction p53 plays important roles in regulating the metabolic reprogramming of cancer, such as aerobic glycolysis. Oroxylin A is a natural active flavonoid with strong anticancer effects both in vitro and in vivo. Methods wt-p53 (MCF-7 and HCT116 cells) cancer cells and p53-null H1299 cancer cells were used. The glucose uptake and lactate production were analyzed using Lactic Acid production Detection kit and the Amplex Red Glucose Assay Kit. Then, the protein levels and RNA levels of p53, mouse double minute 2 (MDM2), and p53-targeted glycolytic enzymes were quantified using Western blotting and quantitative polymerase chain reaction (PCR), respectively. Immunoprecipitation were performed to assess the binding between p53, MDM2, and sirtuin-3 (SIRT3), and the deacetylation of phosphatase and tensin homolog (PTEN). Reporter assays were performed to assess the transcriptional activity of PTEN. In vivo, effects of oroxylin A was investigated in nude mice xenograft tumor-inoculated MCF-7 or HCT116 cells. Results Here, we analyzed the underlying mechanisms that oroxylin A regulated p53 level and glycolytic metabolism in wt-p53 cancer cells, and found that oroxylin A inhibited glycolysis through upregulating p53 level. Oroxylin A did not directly affect the transcription of wt-p53, but suppressed the MDM2-mediated degradation of p53 via downregulating MDM2 transcription in wt-p53 cancer cells. In further studies, we found that oroxylin A induced a reduction in MDM2 transcription by promoting the lipid phosphatase activity of phosphatase and tensin homolog, which was upregulated via sirtuin3-mediated deacetylation. In vivo, oroxylin A inhibited the tumor growth of nude mice-inoculated MCF-7 or HCT116 cells. The expression of MDM2 protein in tumor tissue was downregulated by oroxylin A as well. Conclusions These results provide a p53-independent mechanism of MDM2 transcription and reveal the potential of oroxylin A on glycolytic regulation in both wt-p53 and mut-p53 cancer cells. The studies have important implications for the investigation on anticancer effects of oroxylin A, and provide the academic basis for the clinical trial of oroxylin A in cancer patients

EdgeVisionBench: A Benchmark of Evolving Input Domains for Vision Applications at Edge

Vision applications powered by deep neural networks (DNNs) are widely deployed on edge devices and solve the learning tasks of incoming data streams whose class label and input feature continuously evolve, known as domain shift. Despite its prominent presence in real-world edge scenarios, existing benchmarks used by domain adaptation methods overlook evolving domains and under represent their shifts in label and feature distributions. To address this gap, we present EdgeVisionBench, a benchmark seeking to generate evolving domains of various types and reflect their realistic label and feature shifts encountered by edge-based vision applications. To facilitate evaluating domain adaptation methods on edge devices, we provide an open-source package that automates workload generation, contains popular DNN models and compression techniques, and standardizes evaluations with interactive interfaces. Code and datasets are available at https://github.com/LINC-BIT/EdgeVisionBench. Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Data-Intensive System

FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge

Deep Neural Networks (DNNs) have been ubiquitously adopted in internet of things and are becoming an integral of our daily life. When tackling the evolving learning tasks in real world, such as classifying different types of objects, DNNs face the challenge to continually retrain themselves according to the tasks on different edge devices. Federated continual learning is a promising technique that offers partial solutions but yet to overcome the following difficulties: the significant accuracy loss due to the limited on-device processing, the negative knowledge transfer caused by the limited communication of non-IID data, and the limited scalability on the tasks and edge devices. In this paper, we propose FedKNOW, an accurate and scalable federated continual learning framework, via a novel concept of signature task knowledge. FedKNOW is a client side solution that continuously extracts and integrates the knowledge of signature tasks which are highly influenced by the current task. Each client of FedKNOW is composed of a knowledge extractor, a gradient restorer and, most importantly, a gradient integrator. Upon training for a new task, the gradient integrator ensures the prevention of catastrophic forgetting and mitigation of negative knowledge transfer by effectively combining signature tasks identified from the past local tasks and other clients' current tasks through the global model. We implement FedKNOW in PyTorch and extensively evaluate it against state-of-the-art techniques using popular federated continual learning benchmarks. Extensive evaluation results on heterogeneous edge devices show that FedKNOW improves model accuracy by 63.24% without increasing model training time, reduces communication cost by 34.28%, and achieves more improvements under difficult scenarios such as large numbers of tasks or clients, and training different complex networks. Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Data-Intensive System

Lightweight and Accurate DNN-Based Anomaly Detection at Edge

Deep neural networks (DNNs) have been showing significant success in various anomaly detection applications such as smart surveillance and industrial quality control. It is increasingly important to detect anomalies directly on edge devices, because of high responsiveness requirements and tight latency constraints. The accuracy of DNN-based solutions rely on large model capacity and thus long training and inference time, making them inapplicable on resource strenuous edge devices. It is hence imperative to scale DNN model sizes in correspondence to the run-time system requirements, i.e., meeting deadlines with minimal accuracy losses, which are highly dependent on the platforms and real-time system status. Existing scaling techniques either take long training time to pre-generate scaling options or disturb the unsteady training process of anomaly detection DNNs, lacking the adaptability to heterogeneous edge systems and incurring low inference accuracies. In this article, we present LightDNN to scale DNN models for anomaly detection applications at edge, featuring high detection accuracies with lightweight training and inference time. To this end, LightDNN quickly extracts and compresses blocks in a DNN, and provides large scaling space (e.g., 1 million options) by dynamically combining these compressed blocks online. At run-time, LightDNN predicts the DNN's inference latency according to the monitored system status, and optimizes the combination of blocks to maximize its accuracy under deadline constraints. We implement and extensively evaluate LightDNN on both CPU and GPU edge platforms using 8 popular anomaly detection workloads. Comparative experiments with state-of-the-art methods show that our approach provides 145.8 to 0.56 trillion times more scaling options without increasing training and inference overheads, thus achieving as much as 15.05% increase in accuracy under the same deadlines. Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Distributed System