Accelerating Split Federated Learning over Wireless Communication Networks

The development of artificial intelligence (AI) provides opportunities for the promotion of deep neural network (DNN)-based applications. However, the large amount of parameters and computational complexity of DNN makes it difficult to deploy it on edge devices which are resource-constrained. An efficient method to address this challenge is model partition/splitting, in which DNN is divided into two parts which are deployed on device and server respectively for co-training or co-inference. In this paper, we consider a split federated learning (SFL) framework that combines the parallel model training mechanism of federated learning (FL) and the model splitting structure of split learning (SL). We consider a practical scenario of heterogeneous devices with individual split points of DNN. We formulate a joint problem of split point selection and bandwidth allocation to minimize the system latency. By using alternating optimization, we decompose the problem into two sub-problems and solve them optimally. Experiment results demonstrate the superiority of our work in latency reduction and accuracy improvement

MSP-RON Signaling Is Activated in the Transition From Pancreatic Intraepithelial Neoplasia (PanIN) to Pancreatic Ductal Adenocarcinoma (PDAC)

Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest epithelial malignancies and remains difficult to treat. Pancreatic intraepithelial neoplasias (PanINs) represent the majority of the pre-cancer lesions in the pancreas. The PDAC microenvironment consists of activated pancreatic stellate cells (PSCs) and immune cells, which are thought to contribute to neoplastic transformation. However, the signaling events involved in driving the transition from the neoplastic precursor to the more advanced and aggressive forms in the pancreas are not well understood. Recepteur d’Origine Nantais (RON) is a c-MET family receptor tyrosine kinase that is implicated in playing a role in cell proliferation, migration and other aspects of tumorigenesis. Macrophage stimulating protein (MSP) is the ligand for RON and becomes activated upon proteolytic cleavage by matriptase (also known as ST14), a type II transmembrane serine protease. In the current study, by immunohistochemistry (IHC) analysis of human pancreatic tissues, we found that the expression levels MSP and matriptase are drastically increased during the transition from the preneoplastic PanIN stages to the more advanced and aggressive PDAC. Moreover, RON is highly expressed in both PDAC and in cancer-associated stellate cells. In contrast, MSP, RON, and matriptase are expressed at low levels, if any, in normal pancreas. Our study underscores an emerging role of MSP-RON autocrine and paracrine signaling events in driving malignant progression in the pancreas

K-LITE: Learning Transferable Visual Models with External Knowledge

Recent state-of-the-art computer vision systems are trained from natural language supervision, ranging from simple object category names to descriptive captions. This free form of supervision ensures high generality and usability of the learned visual models, based on extensive heuristics on data collection to cover as many visual concepts as possible. Alternatively, learning with external knowledge about images is a promising way which leverages a much more structured source of supervision. In this paper, we propose K-LITE (Knowledge-augmented Language-Image Training and Evaluation), a simple strategy to leverage external knowledge to build transferable visual systems: In training, it enriches entities in natural language with WordNet and Wiktionary knowledge, leading to an efficient and scalable approach to learning image representations that can understand both visual concepts and their knowledge; In evaluation, the natural language is also augmented with external knowledge and then used to reference learned visual concepts (or describe new ones) to enable zero-shot and few-shot transfer of the pre-trained models. We study the performance of K-LITE on two important computer vision problems, image classification and object detection, benchmarking on 20 and 13 different existing datasets, respectively. The proposed knowledge-augmented models show significant improvement in transfer learning performance over existing methods.Comment: Preprint. The first three authors contribute equall

Improvements and Applications of AMS Radiocarbon Measurement at Peking University

AMS radiocarbon measurements were started at Peking University in 1992 with a modified HICONEX 834 ion source. Some archaeological samples were measured at a sensitivity of 10(-14) with ca. 1.7% precision for modern samples. We have made many improvements in our first two years of operation: a high-intensity Cs sputtering ion source was installed; the graphite sample preparation technique was investigated; and the system stability has been improved. The blank sample background is currently ca. 0.006 MC and a precision within 1% can be reached for modern samples. Geological, archaeological, environmental and biomedical samples can be measured routinely. We present some typical applications.Geochemistry & GeophysicsSCI(E)

Data Release of the AST3-2 Automatic Survey from Dome A, Antarctica

AST3-2 is the second of the three Antarctic Survey Telescopes, aimed at wide-field time-domain optical astronomy. It is located at Dome A, Antarctica, which is by many measures the best optical astronomy site on the Earth's surface. Here we present the data from the AST3-2 automatic survey in 2016 and the photometry results. The median 5$\sigma$ limiting magnitude in $i$-band is 17.8 mag and the light curve precision is 4 mmag for bright stars. The data release includes photometry for over 7~million stars, from which over 3,500 variable stars were detected, with 70 of them newly discovered. We classify these new variables into different types by combining their light curve features with stellar properties from surveys such as StarHorse.Comment: 16 pages, 20 figures, accepted for publication in MNRA

Physalis pubescens L. branch and leaf extracts inhibit lymphoma proliferation by inducing apoptosis and cell cycle arrest

Physalis pubescens L. is an annual or perennial plant in the family Solanaceae It is used in traditional medicine for treating sore throats, coughs, urinary discomfort, and astringent pain, and externally for pemphigus and eczema in northern China. The proliferation inhibitory activity and mechanisms of the ethyl acetate extract (PHY-EA) from the leaves of Physalis pubescens were investigated. High performance liquid chromatography was used to identify the chemical composition of PHY-EA; sulforhodamine B was used to detect the proliferation inhibitory effect of PHY-EA on MCF-7, CA-46, Hela, HepG2, B16, and other tumor cells; flow cytometry was used to detect the effect of PHY-EA on the lymphoma cell cycle and apoptosis; Western blot was used to detect the expression of the cycle- and apoptosis-related proteins. The expression of Ki-67 and cleaved caspase 3 was detected by immunohistochemistry. The results showed that PHY-EA contained physalin B, physalin O, and physalin L. PHY-EA blocked the cell cycle of G2/M→G0/G1 in lymphoma cells and induced apoptosis in tumor cells. Mouse transplantation tumor experiments showed that PHY-EA had a significant inhibitory effect on mouse transplantation tumors, and the tumor volume and weight were significantly reduced. In conclusion, PHY-EA has a good antiproliferative effect on Burkkit lymphoma, indicating its potential medicinal value

Enhanced effects of cigarette smoke extract on inflammatory cytokine expression in IL-1β-activated human mast cells were inhibited by Baicalein via regulation of the NF-κB pathway

Background: Human mast cells are capable of a wide variety of inflammatory responses and play a vital role in the pathogenesis of inflammatory diseases such as allergy, asthma, and atherosclerosis. We have reported that cigarette smoke extract (CSE) significantly increased IL-6 and IL-8 production in IL-1β-activated human mast cell line (HMC-1). Baicalein (BAI) has anti-inflammatory properties and inhibits IL-1β- and TNF-α-induced inflammatory cytokine production from HMC-1. The goal of the present study was to examine the effect of BAI on IL-6 and IL-8 production from CSE-treated and IL-1β-activated HMC-1.Methods: Main-stream (Ms) and Side-stream (Ss) cigarette smoke were collected onto fiber filters and extracted in RPMI-1640 medium. Two ml of HMC-1 at 1 × 10 6 cells/mL were cultured with CSE in the presence or absence of IL-1β (10 ng/mL) for 24 hrs. A group of HMC-1 cells stimulated with both IL-1β (10 ng/ml) and CSE was also treated with BAI. The expression of IL-6 and IL-8 was assessed by ELISA and RT-PCR. NF-κB activation was measured by electrophoretic mobility shift assay (EMSA) and IκBα degradation by Western blot.Results: Both Ms and Ss CSE significantly increased IL-6 and IL-8 production (p \u3c 0.001) in IL-1β-activated HMC-1. CSE increased NF-κB activation and decreased cytoplasmic IκBα proteins in IL-1β-activated HMC-1. BAI (1.8 to 30 μM) significantly inhibited production of IL-6 and IL-8 in a dose-dependent manner in IL-1β-activated HMC-1 with the optimal inhibition concentration at 30 μM, which also significantly inhibited the enhancing effect of CSE on IL-6 and IL-8 production in IL-1β-activated HMC-1. BAI inhibited NF-κB activation and increased cytoplasmic IκBα proteins in CSE-treated and IL-1β-activated HMC-1.Conclusions: Our results showed that CSE significantly increased inflammatory cytokines IL-6 and IL-8 production in IL-1β-activated HMC-1. It may partially explain why cigarette smoke contributes to lung and cardiovascular diseases. BAI inhibited the production of inflammatory cytokines through inhibition of NF-κB activation and IκBα phosphorylation and degradation. This inhibitory effect of BAI on the expression of inflammatory cytokines induced by CSE suggests its usefulness in the development of novel anti-inflammatory therapies