Accelerated degradation modeling considering long-range dependence and unit-to-unit variability

Accelerated degradation testing (ADT) is an effective way to evaluate the reliability and lifetime of highly reliable products. Existing studies have shown that the degradation processes of some products are non-Markovian with long-range dependence due to the interaction with environments. Besides, the degradation processes of products from the same population generally vary from each other due to various uncertainties. These two aspects bring great difficulty for ADT modeling. In this paper, we propose an improved ADT model considering both long-range dependence and unit-to-unit variability. To be specific, fractional Brownian motion (FBM) is utilized to capture the long-range dependence in the degradation process. The unit-to-unit variability among multiple products is captured by a random variable in the degradation rate function. To ensure the accuracy of the parameter estimations, a novel statistical inference method based on expectation maximization (EM) algorithm is proposed, in which the maximization of the overall likelihood function is achieved. The effectiveness of the proposed method is fully verified by a simulation case and a microwave case. The results show that the proposed model is more suitable for ADT modeling and analysis than existing ADT models

cis-Diaqua­bis(2,2′,2′′-tripyridylamine)zinc(II) bis­(perchlorate)

In the title compound, [Zn(2,2′,2′′-tpa)2(H2O)2](ClO4)2 (2,2′,2′′-tpa is 2,2′,2′′-tripyridylamine, C15H12N4), the Zn center lies on a twofold axis and is coordinated octa­hedrally by two water mol­ecules and two bidentate 2,2′,2′′-tpa ligands. The perchlorate anions are linked to the coordinated water mol­ecules in the complex cations via O—H⋯O hydrogen bonds

Political ‘advice’ in Chinese public discourse (s)

Abstract The practice of ‘political advice’ covers events such as media appearances, in the course of which the representatives of a country deliver symbolic ‘advice’ to another country through a monologous announcement. As such, political ‘advice’ is a ritual practice (Kádár 2017): on the surface level it represents communication with another country and its style is formed according to this symbolic surface function; however, its implicit function is to form alignment between the political authorities who deliver the advice and the citizens of their country. Studying political advice provides a twofold contribution to politeness theory. First, on the empirical level, this discursive ritual practice has not received sufficient academic attention, and so modelling it through the lens of interactional ritual theory fills an empirical knowledge gap in the field of pragmatics and broader sense language and society. Second, by modelling the complex relationship between politeness and political advice, the paper delivers a contribution to the theory of language use, since it demonstrates that in certain ritual practices such as political advice, and arguably a variety of similar ritual practices in the political arena. It is challenging to capture ‘politeness’ in the conventional sense as an other-oriented (and interpersonal) form of pragmatic behaviour, in spite of the fact that on the surface level such forms of communication are veiled as abundantly polite and as other-oriented. We argue that one needs to deploy interactional ritual theory to model the pragmatic operation of this phenomenon. The data studied is drawn from the website of the Chinese Ministry of Foreign Affairs

Bis(tri-2-pyridyl­amine)­nickel(II) bis­(perchlorate)

In the title compound, [Ni(C15H12N4)2](ClO4)2, the NiII atom lies on an inversion center and is octa­hedrally coordinated by the N atoms of two tridentate tri-2-pyridyl­amine ligands. The two perchlorate anions are disordered over two sites with a refined occupancy ratio of 0.528 (19):0.472 (19)

Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners

Representation learning has been evolving from traditional supervised training to Contrastive Learning (CL) and Masked Image Modeling (MIM). Previous works have demonstrated their pros and cons in specific scenarios, i.e., CL and supervised pre-training excel at capturing longer-range global patterns and enabling better feature discrimination, while MIM can introduce more local and diverse attention across all transformer layers. In this paper, we explore how to obtain a model that combines their strengths. We start by examining previous feature distillation and mask feature reconstruction methods and identify their limitations. We find that their increasing diversity mainly derives from the asymmetric designs, but these designs may in turn compromise the discrimination ability. In order to better obtain both discrimination and diversity, we propose a simple but effective Hybrid Distillation strategy, which utilizes both the supervised/CL teacher and the MIM teacher to jointly guide the student model. Hybrid Distill imitates the token relations of the MIM teacher to alleviate attention collapse, as well as distills the feature maps of the supervised/CL teacher to enable discrimination. Furthermore, a progressive redundant token masking strategy is also utilized to reduce the distilling costs and avoid falling into local optima. Experiment results prove that Hybrid Distill can achieve superior performance on different benchmarks

Prospects for shale gas production in China: Implications for water demand

AbstractDevelopment of shale gas resources is expected to play an important role in China's projected transition to a low-carbon energy future. The question arises whether the availability of water could limit this development. The paper considers a range of scenarios to define the demand for water needed to accommodate China's projected shale gas production through 2020. Based on data from the gas field at Fuling, the first large-scale shale gas field in China, it is concluded that the water intensity for shale gas development in China (water demand per unit lateral length) is likely to exceed that in the US by about 50%. Fuling field would require a total of 39.9–132.9Mm3 of water to achieve full development of its shale gas, with well spacing assumed to vary between 300 and 1000m. To achieve the 2020 production goal set by Sinopec, the key Chinese developer, water consumption is projected to peak at 7.22Mm3 in 2018. Maximum water consumption would account for 1% and 3%, respectively, of the available water resource and annual water use in the Fuling district. To achieve China's nationwide shale gas production goal set for 2020, water consumption is projected to peak at 15.03Mm3 in 2019 in a high-use scenario. It is concluded that supplies of water are adequate to meet demand in Fuling and most projected shale plays in China, with the exception of localized regions in the Tarim and Jungger Basins

ActionPrompt: Action-Guided 3D Human Pose Estimation With Text and Pose Prompting

Recent 2D-to-3D human pose estimation (HPE) utilizes temporal consistency across sequences to alleviate the depth ambiguity problem but ignore the action related prior knowledge hidden in the pose sequence. In this paper, we propose a plug-and-play module named Action Prompt Module (APM) that effectively mines different kinds of action clues for 3D HPE. The highlight is that, the mining scheme of APM can be widely adapted to different frameworks and bring consistent benefits. Specifically, we first present a novel Action-related Text Prompt module (ATP) that directly embeds action labels and transfers the rich language information in the label to the pose sequence. Besides, we further introduce Action-specific Pose Prompt module (APP) to mine the position-aware pose pattern of each action, and exploit the correlation between the mined patterns and input pose sequence for further pose refinement. Experiments show that APM can improve the performance of most video-based 2D-to-3D HPE frameworks by a large margin.Comment: 6 pages, 4 figures, 2023ICM

Significance of fatty liver index to detect prevalent ischemic heart disease: evidence from national health and nutrition examination survey 1999–2016

BackgroundNon-alcoholic fatty liver disease (NAFLD) contributes to the development of ischemic heart disease via multiple mechanisms. Fatty liver index (FLI) has been proposed as an accurate, convenient, and economic surrogate of the severity of NAFLD. Our present study aims to assess the association between FLI and the prevalent IHD and to evaluate the potential value of FLI to refine the detection of prevalent IHD in the general population.MethodsOur work recruited 32,938 subjects from the National Health and Nutrition Examination Survey 1999–2016. IHD was diagnosed according to the subjects’ self-report. FLI was determined based on triglycerides, BMI, γ-glutamyltransferase, and waist circumference.Results2,370 (7.20%) subjects were diagnosed with IHD. After adjustment of age, sex, race, current smoking, current drinking, PIR, BMI, WC, TC, TG, GGT, Scr, FPG, SBP, anti-hypertensive therapy, anti-diabetic therapy, and lipid-lowering therapy, one standard deviation increase of FLI resulted in a 27.0% increment of the risk of prevalent IHD. In the quartile analysis, we observed a 1.684 times risk of prevalent IHD when comparing the fourth quartile with the first quartile, and there was a trend towards higher risk across the quartiles. The smooth curve fitting displayed a linear relationship between FLI and the presence of IHD without any threshold or saturation effect. Subgroup analysis revealed a robust association in conventional cardiovascular subpopulations, and the association could be more prominent in female subjects and diabetes patients. ROC analysis demonstrated an incremental value of FLI for detecting prevalent IHD after introducing it to conventional cardiovascular risk factors (AUC: 0.823 vs. 0.859, P for comparison <0.001). Also, results from reclassification analysis implicated that more IHD patients could be correctly identified by introducing FLI into conventional cardiovascular risk factors (continuous net reclassification index: 0.633, P < 0.001; integrated discrimination index: 0.034, P < 0.001).ConclusionThe current analysis revealed a positive and linear relationship between FLI and the prevalent IHD. Furthermore, our findings suggest the incremental value of FLI to refine the detection of prevalent IHD in the general population

AiluRus: A Scalable ViT Framework for Dense Prediction

Vision transformers (ViTs) have emerged as a prevalent architecture for vision tasks owing to their impressive performance. However, when it comes to handling long token sequences, especially in dense prediction tasks that require high-resolution input, the complexity of ViTs increases significantly. Notably, dense prediction tasks, such as semantic segmentation or object detection, emphasize more on the contours or shapes of objects, while the texture inside objects is less informative. Motivated by this observation, we propose to apply adaptive resolution for different regions in the image according to their importance. Specifically, at the intermediate layer of the ViT, we utilize a spatial-aware density-based clustering algorithm to select representative tokens from the token sequence. Once the representative tokens are determined, we proceed to merge other tokens into their closest representative token. Consequently, semantic similar tokens are merged together to form low-resolution regions, while semantic irrelevant tokens are preserved independently as high-resolution regions. This strategy effectively reduces the number of tokens, allowing subsequent layers to handle a reduced token sequence and achieve acceleration. We evaluate our proposed method on three different datasets and observe promising performance. For example, the "Segmenter ViT-L" model can be accelerated by 48% FPS without fine-tuning, while maintaining the performance. Additionally, our method can be applied to accelerate fine-tuning as well. Experimental results demonstrate that we can save 52% training time while accelerating 2.46 times FPS with only a 0.09% performance drop. The code is available at https://github.com/caddyless/ailurus/tree/main.Comment: Accepted by NeurIPS 202