Rethinking Temporal Fusion for Video-based Person Re-identification on Semantic and Time Aspect

Recently, the research interest of person re-identification (ReID) has gradually turned to video-based methods, which acquire a person representation by aggregating frame features of an entire video. However, existing video-based ReID methods do not consider the semantic difference brought by the outputs of different network stages, which potentially compromises the information richness of the person features. Furthermore, traditional methods ignore important relationship among frames, which causes information redundancy in fusion along the time axis. To address these issues, we propose a novel general temporal fusion framework to aggregate frame features on both semantic aspect and time aspect. As for the semantic aspect, a multi-stage fusion network is explored to fuse richer frame features at multiple semantic levels, which can effectively reduce the information loss caused by the traditional single-stage fusion. While, for the time axis, the existing intra-frame attention method is improved by adding a novel inter-frame attention module, which effectively reduces the information redundancy in temporal fusion by taking the relationship among frames into consideration. The experimental results show that our approach can effectively improve the video-based re-identification accuracy, achieving the state-of-the-art performance

Neural network observer based LPV fault tolerant control of a flying-wing aircraft

For the problem of fault tolerant trajectory tracking control for a large Flying-Wing (FW) aircraft with Linear Parameter-Varying (LPV) model, a gain scheduled H ∞ controller is designed by dynamic output feedback. Robust synthesis of this gain scheduled H ∞ control is carried out by an affine Parameter Dependent Lyapunov Function (PDLF). The problem of trajectory tracking control for the LPV plant is transformed into solving an infinite number of linear matrix inequalities by the PDLF design, and the linear matrix inequalities are solved by convex optimization techniques. To overcome model uncertainties due to linearization and external disturbances, a radial basis function neural network disturbance observer is proposed, and to estimate actuator faults, an LPV fault estimator is designed. Furthermore, a composite controller is proposed to realize fault tolerant trajectory tracking control, which combines the LPV control with the fault estimator and disturbance observer, as well as an active-set based control allocation to avoiding actuator saturation. The approach is tested by simulation of two scenarios that show responses of the altitude, speed and heading angle to (i) unknown disturbances and (ii) actuator faults. The results show that the proposed neural network observer based LPV control has better performances for both disturbance rejecting and fault-tolerant trajectory tracking

The relation between amyotrophic lateral sclerosis and inorganic selenium in drinking water: a population-based case-control study

<p>Abstract</p> <p>Background</p> <p>A community in northern Italy was previously reported to have an excess incidence of amyotrophic lateral sclerosis among residents exposed to high levels of inorganic selenium in their drinking water.</p> <p>Methods</p> <p>To assess the extent to which such association persisted in the decade following its initial observation, we conducted a population-based case-control study encompassing forty-one newly-diagnosed cases of amyotrophic lateral sclerosis and eighty-two age- and sex-matched controls. We measured long-term intake of inorganic selenium along with other potentially neurotoxic trace elements.</p> <p>Results</p> <p>We found that consumption of drinking water containing ≥ 1 μg/l of inorganic selenium was associated with a relative risk for amyotrophic lateral sclerosis of 5.4 (95% confidence interval 1.1-26) after adjustment for confounding factors. Greater amounts of cumulative inorganic selenium intake were associated with progressively increasing effects, with a relative risk of 2.1 (95% confidence interval 0.5-9.1) for intermediate levels of cumulative intake and 6.4 (95% confidence interval 1.3-31) for high intake.</p> <p>Conclusion</p> <p>Based on these results, coupled with other epidemiologic data and with findings from animal studies that show specific toxicity of the trace element on motor neurons, we hypothesize that dietary intake of inorganic selenium through drinking water increases the risk for amyotrophic lateral sclerosis.</p

Single cell atlas for 11 non-model mammals, reptiles and birds.

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs

Deep-Learning-Based Remaining Useful Life Prediction Based on a Multi-Scale Dilated Convolution Network

Remaining useful life (RUL) prediction of key components is an important influencing factor in making accurate maintenance decisions for mechanical systems. With the rapid development of deep learning (DL) techniques, the research on RUL prediction based on the data-driven model is increasingly widespread. Compared with the conventional convolution neural networks (CNNs), the multi-scale CNNs can extract different-scale feature information, which exhibits a better performance in the RUL prediction. However, the existing multi-scale CNNs employ multiple convolution kernels with different sizes to construct the network framework. There are two main shortcomings of this approach: (1) the convolution operation based on multiple size convolution kernels requires enormous computation and has a low operational efficiency, which severely restricts its application in practical engineering. (2) The convolutional layer with a large size convolution kernel needs a mass of weight parameters, leading to a dramatic increase in the network training time and making it prone to overfitting in the case of small datasets. To address the above issues, a multi-scale dilated convolution network (MsDCN) is proposed for RUL prediction in this article. The MsDCN adopts a new multi-scale dilation convolution fusion unit (MsDCFU), in which the multi-scale network framework is composed of convolution operations with different dilated factors. This effectively expands the range of receptive field (RF) for the convolution kernel without an additional computational burden. Moreover, the MsDCFU employs the depthwise separable convolution (DSC) to further improve the operational efficiency of the prognostics model. Finally, the proposed method was validated with the accelerated degradation test data of rolling element bearings (REBs). The experimental results demonstrate that the proposed MSDCN has a higher RUL prediction accuracy compared to some typical CNNs and better operational efficiency than the existing multi-scale CNNs based on different convolution kernel sizes

Open pit limit optimization considering the pumped storage benefit after mine closure: a case study

Abstract Repurposing a closed mine as lower reservoir is a cost-effective way for the construction of pumped storage hydropower (PSH) plant. This method can eliminate the expenses of mine reclamation, reservoir construction, and land acquisition, resulting in significant cost savings and benefits for the PSH project, known as the PSH benefit. The construction of PSH plants within a closed mine is divided into surface mode and semi-underground mode in this paper. Through a general comparison of two in-situ cases, the finding highlight that the surface mode can achieve a larger potential installed capacity and lower construction cost. Furthermore, the PSH benefit is quantified and internalized as an economic parameter in the ultimate pit limit (UPL) optimization by allocating it into unit ore. Taken an undisclosed open-pit iron mine as example, the UPL is optimized by considering the PSH benefit. The internalized PSH benefit is calculated to be 6.59 CN¥/t when the installed capacity is 2000 MW, and ore amount within the optimized UPL is increased by 1.4%. The results indicated that the PSH benefit does influence the shape and size of UPL, but not significantly. Besides, converting several bottoms in a single open-pit into lower and upper reservoirs presents more challenges for UPL optimization, which further explorations is needed