183 research outputs found

    A Review: Peanut Fatty Acids Determination Using Hyper Spectroscopy Imaging and Its Significance on Food Quality and Safety

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    This paper is a review of determination of peanut fatty acids by using Hyper Spectral Imaging (HSI) methods as a non-destructive food quality and safety monitoring. The key spectral areas are the visual and near-infrared wavelengths. Few have been published on determination of peanut fatty acids by using HSI as an efficient and effective method for evaluating the quality and safety of oil. Providentially, the use of HSI has been observed to have positive effects on determination of food quality and safety (Smith B. 2012). It has gained a wide recognition as a non-destructive, fast, quality and safety analysis, and assessment method for a wide range of food products.  Literature shows that, HSI is not commonly and widely used therefore this paper aspires to emphasize the use of HSI on improving the quality and safety of peanut oil and its products based on the determination of peanut fatty acids. The authors predicted that even in its current imperfect on the affordability, maintenance and complexity on finding solutions or model approaches to their food quality problems from optics, imaging, and spectroscopy, yet HSI is the best method than other current existing methods, and can give an idea of how to better meet market and consumer needs on high food quality and safety for their better healthy. Key words: Hyper spectral imaging, Peanut (Arachis hypogaea), oil, Oleic and linoleic fatty acid, Food quality, food safety

    DeepDPM: Dynamic Population Mapping via Deep Neural Network

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    Dynamic high resolution data on human population distribution is of great importance for a wide spectrum of activities and real-life applications, but is too difficult and expensive to obtain directly. Therefore, generating fine-scaled population distributions from coarse population data is of great significance. However, there are three major challenges: 1) the complexity in spatial relations between high and low resolution population; 2) the dependence of population distributions on other external information; 3) the difficulty in retrieving temporal distribution patterns. In this paper, we first propose the idea to generate dynamic population distributions in full-time series, then we design dynamic population mapping via deep neural network(DeepDPM), a model that describes both spatial and temporal patterns using coarse data and point of interest information. In DeepDPM, we utilize super-resolution convolutional neural network(SRCNN) based model to directly map coarse data into higher resolution data, and a time-embedded long short-term memory model to effectively capture the periodicity nature to smooth the finer-scaled results from the previous static SRCNN model. We perform extensive experiments on a real-life mobile dataset collected from Shanghai. Our results demonstrate that DeepDPM outperforms previous state-of-the-art methods and a suite of frequent data-mining approaches. Moreover, DeepDPM breaks through the limitation from previous works in time dimension so that dynamic predictions in all-day time slots can be obtained.Comment: AAAI201

    Japanese Legal Scholars and Political Reformation During the Late Qing Dynasty

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    In this essay, I have examined the Sino-Japanese relations during the ten years immediately preceding the Qinhai Revolution from three closely related perspectives. The first is the frequency with which the elite of the two countries travelled to each country. The second is the translation of editorials written by the Japanese elite on the Qing reformations that were published in each of the Chinese newspapers. The third is that many of the exchange students in Japan returned to China where they played important roles in the social reformation occurring at the end of the Qing Dynasty. I have also closely examined the influence of the Japanese legal scholars on the Qing political reformations as they were extremely important figures in the cultural exchange between the two countries and furthered the transformation of modern Chinese thought and systems

    Model and Data Agreement for Learning with Noisy Labels

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    Learning with noisy labels is a vital topic for practical deep learning as models should be robust to noisy open-world datasets in the wild. The state-of-the-art noisy label learning approach JoCoR fails when faced with a large ratio of noisy labels. Moreover, selecting small-loss samples can also cause error accumulation as once the noisy samples are mistakenly selected as small-loss samples, they are more likely to be selected again. In this paper, we try to deal with error accumulation in noisy label learning from both model and data perspectives. We introduce mean point ensemble to utilize a more robust loss function and more information from unselected samples to reduce error accumulation from the model perspective. Furthermore, as the flip images have the same semantic meaning as the original images, we select small-loss samples according to the loss values of flip images instead of the original ones to reduce error accumulation from the data perspective. Extensive experiments on CIFAR-10, CIFAR-100, and large-scale Clothing1M show that our method outperforms state-of-the-art noisy label learning methods with different levels of label noise. Our method can also be seamlessly combined with other noisy label learning methods to further improve their performance and generalize well to other tasks. The code is available in https://github.com/zyh-uaiaaaa/MDA-noisy-label-learning.Comment: Accepted by AAAI2023 Worksho

    BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection

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    Graph anomaly detection (GAD) has gained increasing attention in recent years due to its critical application in a wide range of domains, such as social networks, financial risk management, and traffic analysis. Existing GAD methods can be categorized into node and edge anomaly detection models based on the type of graph objects being detected. However, these methods typically treat node and edge anomalies as separate tasks, overlooking their associations and frequent co-occurrences in real-world graphs. As a result, they fail to leverage the complementary information provided by node and edge anomalies for mutual detection. Additionally, state-of-the-art GAD methods, such as CoLA and SL-GAD, heavily rely on negative pair sampling in contrastive learning, which incurs high computational costs, hindering their scalability to large graphs. To address these limitations, we propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE). We extract a subgraph (graph view) centered on each target node as node context and transform it into a dual hypergraph (hypergraph view) as edge context. These views are encoded using graph and hypergraph neural networks to capture the representations of nodes, edges, and their associated contexts. By swapping the context embeddings between nodes and edges and measuring the agreement in the embedding space, we enable the mutual detection of node and edge anomalies. Furthermore, we adopt a bootstrapped training strategy that eliminates the need for negative sampling, enabling BOURNE to handle large graphs efficiently. Extensive experiments conducted on six benchmark datasets demonstrate the superior effectiveness and efficiency of BOURNE in detecting both node and edge anomalies

    Gradient Attention Balance Network: Mitigating Face Recognition Racial Bias via Gradient Attention

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    Although face recognition has made impressive progress in recent years, we ignore the racial bias of the recognition system when we pursue a high level of accuracy. Previous work found that for different races, face recognition networks focus on different facial regions, and the sensitive regions of darker-skinned people are much smaller. Based on this discovery, we propose a new de-bias method based on gradient attention, called Gradient Attention Balance Network (GABN). Specifically, we use the gradient attention map (GAM) of the face recognition network to track the sensitive facial regions and make the GAMs of different races tend to be consistent through adversarial learning. This method mitigates the bias by making the network focus on similar facial regions. In addition, we also use masks to erase the Top-N sensitive facial regions, forcing the network to allocate its attention to a larger facial region. This method expands the sensitive region of darker-skinned people and further reduces the gap between GAM of darker-skinned people and GAM of Caucasians. Extensive experiments show that GABN successfully mitigates racial bias in face recognition and learns more balanced performance for people of different races.Comment: Accepted by CVPR 2023 worksho

    Glycolipid Metabolism Disorder in the Liver of Obese Mice Is Improved by TUDCA via the Restoration of Defective Hepatic Autophagy

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    Objective. Tauroursodeoxycholic acid (TUDCA) has been considered an important regulator of energy metabolism in obesity. However, the mechanism underlying how TUDCA is involved in insulin resistance is not fully understood. We tested the effects of TUDCA on autophagic dysfunction in obese mice. Material and Methods. 500 mg/kg of TUDCA was injected into obese mice, and metabolic parameters, autophagy markers, and insulin signaling molecular were assessed by Western blotting and real-time PCR. Results. The TUDCA injections in the obese mice resulted in a reduced body weight gain, lower blood glucose, and improved insulin sensitivity compared with obese mice that were injected with vehicle. Meanwhile, TUDCA treatment not only reversed autophagic dysfunction and endoplasmic reticulum stress, but also improved the impaired insulin signaling in the liver of obese mice. Additionally, the same results obtained with TUDCA were evident in obese mice treated with the adenoviral Atg7. Conclusions. We found that TUDCA reversed abnormal autophagy, reduced ER stress, and restored insulin sensitivity in the liver of obese mice and that glycolipid metabolism disorder was also improved via the restoration of defective hepatic autophagy

    Nitrate and Nitrite Promote Formation of Tobacco-Specific Nitrosamines via Nitrogen Oxides Intermediates during Postcured Storage under Warm Temperature

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    Tobacco-specific nitrosamines (TSNAs) are carcinogenic and are present in cured tobacco leaves. This study was designed to elucidate the mechanisms of TSNAs formation under warm temperature storage conditions. Results showed that nitrogen oxides (NOx) were produced from nitrate and nitrite in a short period of time under 45 ∘ C and then reacted with alkaloids to form TSNAs. Nitrite was more effective than nitrate in promoting TSNAs formation during 45 ∘ C storage which may be due to the fact that nitrite can produce a large amount of NOx in comparison with nitrate. Presence of activated carbon effectively inhibited the TSNAs formation because of the adsorption of NOx on the activated carbon. The results indicated that TSNAs are derived from a gas/solid phase nitrosation reaction between NOx and alkaloids. Nitrate and nitrite are major contributors to the formation of TSNAs during warm temperature storage of tobacco

    Spatial distribution and ecological risks of polychlorinated biphenyls in a river basin affected by traditional and emerging electronic waste recycling in South China

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    With development of e-waste related legislation in China, formal recycling activities are designated in some areas while informal ones are illegally transferred to emerging areas to avoid supervision. However, the resulting environmental impact and ecological risks are not clear. Here, we investigated the discharge of polychlorinated biphenyls (PCBs) to soil and aquatic environments by e-waste recycling activities in the Lian River Basin, China. The study area included a designated industrial park in the traditional e-waste recycling area (Guiyu, known as the world's largest e-waste center), several emerging informal recycling zones, and their surrounding areas and coastal area. A total of 27 PCBs were analyzed, and the highest concentration was found in an emerging site for soil (354 ng g−1) and in a traditional site for sediment (1350 ng g‐−1) respectively. The pollution levels were significantly higher in both the traditional and emerging recycling areas than in their respective upstream countryside areas (p = 0.0356 and 0.0179, respectively). Source analysis revealed that the traditional and emerging areas had similar PCB sources mainly associated with three PCB technical mixtures manufactured in Japan (KC600) and the USA (Aroclor 1260 and Aroclor 1262). The PCB pollution in their downstream areas including the coastal area was evidently affected by the formal and informal recycling activities through river runoff. The ecological risk assessments showed that PCBs in soils and sediments in the Lian River Basin could cause adverse ecotoxicological consequences to humans and aquatic organisms
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