Transferring Cross-domain Knowledge for Video Sign Language Recognition

Word-level sign language recognition (WSLR) is a fundamental task in sign language interpretation. It requires models to recognize isolated sign words from videos. However, annotating WSLR data needs expert knowledge, thus limiting WSLR dataset acquisition. On the contrary, there are abundant subtitled sign news videos on the internet. Since these videos have no word-level annotation and exhibit a large domain gap from isolated signs, they cannot be directly used for training WSLR models. We observe that despite the existence of a large domain gap, isolated and news signs share the same visual concepts, such as hand gestures and body movements. Motivated by this observation, we propose a novel method that learns domain-invariant visual concepts and fertilizes WSLR models by transferring knowledge of subtitled news sign to them. To this end, we extract news signs using a base WSLR model, and then design a classifier jointly trained on news and isolated signs to coarsely align these two domain features. In order to learn domain-invariant features within each class and suppress domain-specific features, our method further resorts to an external memory to store the class centroids of the aligned news signs. We then design a temporal attention based on the learnt descriptor to improve recognition performance. Experimental results on standard WSLR datasets show that our method outperforms previous state-of-the-art methods significantly. We also demonstrate the effectiveness of our method on automatically localizing signs from sign news, achieving 28.1 for [email protected]: CVPR2020 (oral) preprin

Beneficial Effects of Berberine on Oxidized LDL-induced Cytotoxicity in Human Retinal Müller Cells

PURPOSE: Limited mechanistic understanding of diabetic retinopathy (DR) has hindered therapeutic advances. Berberine, an isoquinolone alkaloid, has shown favorable effects on glucose and lipid metabolism in animal and human studies, but effects on DR are unknown. We previously demonstrated intraretinal extravasation and modification of LDL in human diabetes, and toxicity of modified LDL to human retinal Müller cells. We now explore pathogenic effects of modified LDL on Müller cells, and the efficacy of berberine in mitigating this cytotoxicity. METHODS: Confluent human Müller cells were exposed to in vitro–modified ‘highly oxidized, glycated (HOG-) LDL versus native-LDL (N-LDL; 200 mg protein/L) for 6 or 24 hours, with/without pretreatment with berberine (5 μM, 1 hour) and/or the adenosine monophosphate (AMP)-activated protein kinase (AMPK) inhibitor, Compound C (5 μM, 1 hour). Using techniques including Western blots, reactive oxygen species (ROS) detection assay, and quantitative real-time PCR, the following outcomes were assessed: cell viability (CCK-8 assay), autophagy (LC3, Beclin-1, ATG-5), apoptosis (cleaved caspase 3, cleaved poly-ADP ribose polymerase), oxidative stress (ROS, nuclear factor erythroid 2-related factor 2, glutathione peroxidase 1, NADPH oxidase 4), angiogenesis (VEGF, pigment epithelium-derived factor), inflammation (inducible nitric oxide synthase, intercellular adhesion molecule 1, IL-6, IL-8, TNF-α), and glial cell activation (glial fibrillary acidic protein). RESULTS: Native-LDL had no effect on cultured human Müller cells, but HOG-LDL exhibited marked toxicity, significantly decreasing viability and inducing autophagy, apoptosis, oxidative stress, expression of angiogenic factors, inflammation, and glial cell activation. Berberine attenuated all the effects of HOG-LDL (all P < 0.05), and its effects were mitigated by AMPK inhibition (P < 0.05). CONCLUSIONS: Berberine inhibits modified LDL-induced Müller cell injury by activating the AMPK pathway, and merits further study as an agent for preventing and/or treating DR

Characterising User Transfer Amid Industrial Resource Variation: A Bayesian Nonparametric Approach

In a multitude of industrial fields, a key objective entails optimising resource management whilst satisfying user requirements. Resource management by industrial practitioners can result in a passive transfer of user loads across resource providers, a phenomenon whose accurate characterisation is both challenging and crucial. This research reveals the existence of user clusters, which capture macro-level user transfer patterns amid resource variation. We then propose CLUSTER, an interpretable hierarchical Bayesian nonparametric model capable of automating cluster identification, and thereby predicting user transfer in response to resource variation. Furthermore, CLUSTER facilitates uncertainty quantification for further reliable decision-making. Our method enables privacy protection by functioning independently of personally identifiable information. Experiments with simulated and real-world data from the communications industry reveal a pronounced alignment between prediction results and empirical observations across a spectrum of resource management scenarios. This research establishes a solid groundwork for advancing resource management strategy development

Belief rule-based system for portfolio optimisation with nonlinear cash-flows and constraints

AbstractA belief rule-based (BRB) system is a generic nonlinear modelling and inference scheme. It is based on the concept of belief structures and evidential reasoning (ER), and has been shown to be capable of capturing complicated nonlinear causal relationships between antecedent attributes and consequents. The aim of this paper is to develop a BRB system that complements the RiskMetrics WealthBench system for portfolio optimisation with nonlinear cash-flows and constraints. Two optimisation methods are presented to locate efficient portfolios under different constraints specified by the investors. Numerical studies demonstrate the effectiveness and efficiency of the proposed methodology

Ultra-short-term load prediction of integrated energy system based on load similar fluctuation set classification

Due to the strong coupling characteristics and daily correlation characteristics of multiple load sequences, the prediction method based on time series extrapolation and combined with multiple load meteorological data has limited accuracy improvement, which is tested by the fluctuation of load sequences and the accuracy of Numerical Weather Prediction (NWP). This paper proposes a multiple load prediction method considering the coupling characteristics of multiple loads and the division of load similar fluctuation sets. Firstly, the coupling characteristics of multivariate loads are studied to explore the interaction relationship between multivariate loads and find out the priority of multivariate load prediction. Secondly, the similar fluctuating sets of loads are divided considering the similarity and fluctuation of load sequences. Thirdly, the load scenarios are divided by k-means clustering for the inter-set sequences of similar fluctuating sets, and the Bi-directional Long Short-Term Memory (BI-LSTM) models are trained separately for the sub-set of scenarios and prioritized by prediction. Finally, the effectiveness of the proposed method was verified by combining the multivariate load data provided by the Campus Metabolism system of Arizona State University

Survival or death: a dual role of autophagy in stress-induced pericyte loss in diabetic retinopathy

AIMS/HYPOTHESIS: Intra-retinal extravasation and modification of LDL have been implicated in diabetic retinopathy: autophagy may mediate these effects. METHODS: Immunohistochemistry was used to detect autophagy marker LC3B in human and murine diabetic and non-diabetic retinas. Cultured human retinal capillary pericytes (HRCPs) were treated with in vitro-modified heavily-oxidised glycated LDL (HOG-LDL) vs native LDL (N-LDL) with or without autophagy modulators: green fluorescent protein–LC3 transfection; small interfering RNAs against Beclin-1, c-Jun NH(2)-terminal kinase (JNK) and C/EBP-homologous protein (CHOP); autophagy inhibitor 3-MA (5 mmol/l) and/or caspase inhibitor Z-VAD-fmk (100 μmol/l). Autophagy, cell viability, oxidative stress, endoplasmic reticulum stress, JNK activation, apoptosis and CHOP expression were assessed by western blots, CCK-8 assay and TUNEL assay. Finally, HOG-LDL vs N-LDL were injected intravitreally to STZ-induced diabetic vs control rats (yielding 50 and 200 mg protein/l intravitreal concentration) and, after 7 days, retinas were analysed for ER stress, autophagy and apoptosis. RESULTS: Intra-retinal autophagy (LC3B staining) was increased in diabetic vs non-diabetic humans and mice. In HRCPs, 50 mg/l HOG-LDL elicited autophagy without altering cell viability, and inhibition of autophagy decreased survival. At 100–200 mg/l, HOG-LDL caused significant cell death, and inhibition of either autophagy or apoptosis improved survival. Further, 25–200 mg/l HOG-LDL dose-dependently induced oxidative and ER stress. JNK activation was implicated in autophagy but not in apoptosis. In diabetic rat retina, 50 mg/l intravitreal HOG-LDL elicited autophagy and ER stress but not apoptosis; 200 mg/l elicited greater ER stress and apoptosis. CONCLUSIONS: Autophagy has a dual role in diabetic retinopathy: under mild stress (50 mg/l HOG-LDL) it is protective; under more severe stress (200 mg/l HOG-LDL) it promotes cell death. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00125-016-4058-5) contains peer-reviewed but unedited supplementary material, which is available to authorised users