Cascaded information enhancement and cross-modal attention feature fusion for multispectral pedestrian detection

Multispectral pedestrian detection is a technology designed to detect and locate pedestrians in Color and Thermal images, which has been widely used in automatic driving, video surveillance, etc. So far most available multispectral pedestrian detection algorithms only achieved limited success in pedestrian detection because of the lacking take into account the confusion of pedestrian information and background noise in Color and Thermal images. Here we propose a multispectral pedestrian detection algorithm, which mainly consists of a cascaded information enhancement module and a cross-modal attention feature fusion module. On the one hand, the cascaded information enhancement module adopts the channel and spatial attention mechanism to perform attention weighting on the features fused by the cascaded feature fusion block. Moreover, it multiplies the single-modal features with the attention weight element by element to enhance the pedestrian features in the single-modal and thus suppress the interference from the background. On the other hand, the cross-modal attention feature fusion module mines the features of both Color and Thermal modalities to complement each other, then the global features are constructed by adding the cross-modal complemented features element by element, which are attentionally weighted to achieve the effective fusion of the two modal features. Finally, the fused features are input into the detection head to detect and locate pedestrians. Extensive experiments have been performed on two improved versions of annotations (sanitized annotations and paired annotations) of the public dataset KAIST. The experimental results show that our method demonstrates a lower pedestrian miss rate and more accurate pedestrian detection boxes compared to the comparison method. Additionally, the ablation experiment also proved the effectiveness of each module designed in this paper

TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems

There has been an explosion of interest in designing various Knowledge Graph Neural Networks (KGNNs), which achieve state-of-the-art performance and provide great explainability for recommendation. The promising performance is mainly resulting from their capability of capturing high-order proximity messages over the knowledge graphs. However, training KGNNs at scale is challenging due to the high memory usage. In the forward pass, the automatic differentiation engines (\textsl{e.g.}, TensorFlow/PyTorch) generally need to cache all intermediate activation maps in order to compute gradients in the backward pass, which leads to a large GPU memory footprint. Existing work solves this problem by utilizing multi-GPU distributed frameworks. Nonetheless, this poses a practical challenge when seeking to deploy KGNNs in memory-constrained environments, especially for industry-scale graphs. Here we present TinyKG, a memory-efficient GPU-based training framework for KGNNs for the tasks of recommendation. Specifically, TinyKG uses exact activations in the forward pass while storing a quantized version of activations in the GPU buffers. During the backward pass, these low-precision activations are dequantized back to full-precision tensors, in order to compute gradients. To reduce the quantization errors, TinyKG applies a simple yet effective quantization algorithm to compress the activations, which ensures unbiasedness with low variance. As such, the training memory footprint of KGNNs is largely reduced with negligible accuracy loss. To evaluate the performance of our TinyKG, we conduct comprehensive experiments on real-world datasets. We found that our TinyKG with INT2 quantization aggressively reduces the memory footprint of activation maps with $7 \times$, only with $2\%$ loss in accuracy, allowing us to deploy KGNNs on memory-constrained devices

Hessian-aware Quantized Node Embeddings for Recommendation

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in recommender systems. Nevertheless, the process of searching and ranking from a large item corpus usually requires high latency, which limits the widespread deployment of GNNs in industry-scale applications. To address this issue, many methods compress user/item representations into the binary embedding space to reduce space requirements and accelerate inference. Also, they use the Straight-through Estimator (STE) to prevent vanishing gradients during back-propagation. However, the STE often causes the gradient mismatch problem, leading to sub-optimal results. In this work, we present the Hessian-aware Quantized GNN (HQ-GNN) as an effective solution for discrete representations of users/items that enable fast retrieval. HQ-GNN is composed of two components: a GNN encoder for learning continuous node embeddings and a quantized module for compressing full-precision embeddings into low-bit ones. Consequently, HQ-GNN benefits from both lower memory requirements and faster inference speeds compared to vanilla GNNs. To address the gradient mismatch problem in STE, we further consider the quantized errors and its second-order derivatives for better stability. The experimental results on several large-scale datasets show that HQ-GNN achieves a good balance between latency and performance

Characteristics and formation mechanism of intestinal bacteria particles emitted from aerated wastewater treatment tanks

Aeration tanks in municipal wastewater treatment plants (WWTPs) are regarded as sources of bioaerosols, often containing particles and microbes. In this study, intestinal bacteria were investigated from biochemical reaction tanks (BRTs) of six municipal WWTPs. It was observed that 86 CFU/m3 of intestinal bacteria (in average) occurred in the BRTs installed surface aerator, which was higher than those adopted submerged aeration (67 CFU/m3 in average). 62.72% of fine particles were observed in the BRTs supplied oxygen by submerged aerator, while 75.73% of coarse particles emitted during surface aeration. Pseudomonas sp., Serratia sp. and Acinetobacter sp. were identified as pathogenic bacteria presented in the intestinal bacteria population and most of them existed initially in water or sludge, particularly in water surface. The emission level and particle size distribution were significantly correlated with aeration mode adopted by the WWTPs. The bioaerosols particles emitted from surface aeration process was higher than that from submerged aeration process. Meanwhile, the BRTs with submerged aerators released more fine particles, which can get into the alveoli and represented the potential challenge to human health. Canonical correspondence analysis results exhibited that population of intestinal bacteria had a positive correlation with aeration rate and water quality. As the intestinal bacteria in the bioaerosols emitted from the WWTPs may pose a potential risk to onsite operators, aeration tanks in WWTPs should be paid more attention as a source of intestinal bacterial emissions

Psychological states could affect postsurgical pain after hemorrhoidectomy: A prospective cohort study

BackgroundOpen hemorrhoidectomy is one of the standard procedures for grade IV hemorrhoids. Postsurgical pain is a common problem for patients. We aim to prospectively evaluate potential factors affecting postoperative pain among hemorrhoidectomy patients.MethodsAn observational study was conducted on 360 patients who had undergone Milligan-Morgan open hemorrhoidectomy. Details of the surgery and baseline information were recorded. Preoperative anxiety and depression were analyzed via the self-rating anxiety scale 20 (SAS-20) and self-rating depression scales 20 (SDS-20), respectively. Postoperative pain score was performed daily after surgery until the patient was discharged. The numerical pain score was evaluated by the visual analogue scale (VAS). The association between preoperative psychological states (anxiety or depression) and postoperative pain was analyzed using a generalized additive mixed model.ResultsA total of 340 patients eventually provided complete data and were included in our study. The average age was 43.3 ± 14.4 years, and 62.1% of patients were women. In total, 14.9% of patients had presurgical anxiety and 47.1% had presurgical depression. Postsurgical pain reached a peak point 1–2 days after surgery and went down to a very low level around 4–5 days after surgery. More excision of hemorrhoids could lead to more pain experience after surgery. Presurgical depression was associated with postsurgical pain. Patients who had presurgical depression had higher pain scores after surgery (2.3 ± 1.9 vs. 3.3 ± 1.9, p = 0.025).ConclusionPreoperative depression and the amount of excisional hemorrhoids are positively related to postsurgical pain

DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research

The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-based search engines often fall short in assisting users who may not be familiar with specific terminologies. To address this, we present a knowledge graph-based paper search engine for biomedical research to enhance the user experience in discovering relevant queries and articles. The system, dubbed DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS) tagging to extract terminologies and relationships from article abstracts to create a KG. To reduce information overload, DiscoverPath presents users with a focused subgraph containing the queried entity and its neighboring nodes and incorporates a query recommendation system, enabling users to iteratively refine their queries. The system is equipped with an accessible Graphical User Interface that provides an intuitive visualization of the KG, query recommendations, and detailed article information, enabling efficient article retrieval, thus fostering interdisciplinary knowledge exploration. DiscoverPath is open-sourced at https://github.com/ynchuang/DiscoverPath

Soil functions and ecosystem services research in the Chinese karst Critical Zone

Covering extensive parts of China, karst is a critically important landscape that has experienced rapid and intensive land use change and associated ecosystem degradation within only the last 50 years. In the natural state, key ecosystem services delivered by these landscapes include regulation of the hydrological cycle, nutrient cycling and supply, carbon storage in soils and biomass, biodiversity and food production. Intensification of agriculture since the late-20th century has led to a rapid deterioration in Critical Zone (CZ) state, evidenced by reduced crop production and rapid loss of soil. In many areas, an ecological ‘tipping point’ appears to have been passed as basement rock is exposed and ‘rocky desertification’ dominates. This paper reviews contemporary research of soil processes and ecosystems service delivery in Chinese karst ecosystems, with an emphasis on soil degradation and the potential for ecosystem recovery through sustainable management. It is clear that currently there is limited understanding of the geological, hydrological and ecological processes that control soil functions in these landscapes, which is critical for developing management strategies to optimise ecosystem service delivery. This knowledge gap presents a classic CZ scientific challenge because an integrated multi-disciplinary approach is essential to quantify the responses of soils in the Chinese karst CZ to extreme anthropogenic perturbation, to develop a mechanistic understanding of their resilience to environmental stressors, and thereby to inform strategies to recover and maintain sustainable soil function. © 2019 Elsevier B.V