FairGen: Towards Fair Graph Generation

There have been tremendous efforts over the past decades dedicated to the generation of realistic graphs in a variety of domains, ranging from social networks to computer networks, from gene regulatory networks to online transaction networks. Despite the remarkable success, the vast majority of these works are unsupervised in nature and are typically trained to minimize the expected graph reconstruction loss, which would result in the representation disparity issue in the generated graphs, i.e., the protected groups (often minorities) contribute less to the objective and thus suffer from systematically higher errors. In this paper, we aim to tailor graph generation to downstream mining tasks by leveraging label information and user-preferred parity constraint. In particular, we start from the investigation of representation disparity in the context of graph generative models. To mitigate the disparity, we propose a fairness-aware graph generative model named FairGen. Our model jointly trains a label-informed graph generation module and a fair representation learning module by progressively learning the behaviors of the protected and unprotected groups, from the `easy' concepts to the `hard' ones. In addition, we propose a generic context sampling strategy for graph generative models, which is proven to be capable of fairly capturing the contextual information of each group with a high probability. Experimental results on seven real-world data sets, including web-based graphs, demonstrate that FairGen (1) obtains performance on par with state-of-the-art graph generative models across six network properties, (2) mitigates the representation disparity issues in the generated graphs, and (3) substantially boosts the model performance by up to 17% in downstream tasks via data augmentation

Ischemic Duration and Frequency Determines AKI-to-CKD Progression Monitored by Dynamic Changes of Tubular Biomarkers in IRI Mice

Ischemia reperfusion injury (IRI) is one of the most common causes of acute kidney injury (AKI). However, the pathogenesis and biomarkers predicting the progression of IRI-induced AKI to chronic kidney disease (CKD) remain unclear. A side-by-side comparison between different IRI animal models with variable ischemic duration and episodes was performed. The dynamic changes of KIM-1 and NGAL continuously from AKI to CKD phases were studied as well. Short-term duration of ischemia induced mild renal tubule-interstitial injury which was completely reversed at acute phase of kidney injury, while long-term duration of ischemia caused severe tubular damage, cell apoptosis and inflammatory infiltration at early disease stage, leading to permanent chronic kidney fibrosis at the late stage. Repeated attacks of moderate IRI accelerated the progression of AKI to CKD. Different from serum and urine levels of KIM-1 that increased at acute phase of IRI then declined gradually in chronic phase, NGAL increased continuously during AKI-to-CKD transition. Severity and frequency of ischemia injury determines the progression and outcome of ischemia-induced AKI. Inflammation, apoptosis and fibrogenesis likely participate in the progression of AKI to CKD. Both KIM-1 and NGAL enable noninvasive and early detection of AKI, but NGAL is associated better with the process of AKI-to-CKD progression

Improve the Mechanical Properties of Mg–3Al–1Zn Alloy via Simultaneous Annealing and Loading

A strengthening phenomenon has been demonstrated in the Mg–3Al–1Zn (AZ31) alloy after simultaneous annealing and loading at 180 °C. The microstructural analysis reveals that a few second-phase particles appear in the material after direct annealing at 180 °C for 20 h. However, a higher quantity of the second-phase particles (Al12Mg17) is observed in the alloy after simultaneous annealing and loading. Further, a high stress is observed to be more beneficial for the precipitation of the particles. It is speculated that the stress applied during annealing can provide a better condition for the nucleation of the precipitates. The enhanced extent of precipitates may play a significant role in preventing the dislocation gliding, thus, leading to a strengthening effect. The observed phenomenon may also provide a novel strategy for strengthening the magnesium alloys

Strengthening of Mg-6Al-1Zn Alloy via Simultaneous Loading and Aging

An obvious strengthening phenomenon has been observed in the Mg-6Al-1Zn (AZ61) alloy after simultaneous loading and aging at 170 °C. Being different to aging after pre-strain, the simultaneous loading and aging can obviously increase the yield stress of the alloy. Microstructural analysis shows that a larger quantity of the Al12Mg17 can be obtained by simultaneous loading and aging in a relatively short aging time, compared with aging after pre-strain. It is speculated that the loading during aging is more beneficial for nucleation of the precipitates. In the same aging time, it is found that the sample subjected to simultaneous loading and aging shows a higher yield stress than the sample aged after pre-strain. To extend aging time, a large quantity of Al12Mg17 can be obtained in the pre-strained sample. However, it is demonstrated that the yield stress of the sample subjected to aging after pre-strain is lower than that of the sample subjected to simultaneous loading and aging, despite these two samples containing the same quantity of precipitates. It is speculated that the occurrence of the precipitates plays a role in preventing dislocation gliding and twin expanding, thus leading to a strengthening effect. Additionally, atoms segregated in twin boundaries may partly strengthen the material. It is found that a large quantity of precipitates can be obtained in a relatively short aging time by using the simultaneous loading and aging, reducing the softening effect caused by aging. The observed phenomenon may provide a new strategy for strengthening magnesium alloys

A fault location strategy based on information fusion and CODAS algorithm under epistemic uncertainty

Application of new technology in modern systems not only substantially improves the performance, but also presents a severe challenge to fault location of these systems. This paper presents a new fault location strategy for maintenance personnel to recover them based on information fusion and improved CODAS algorithm. Firstly, a fault tree is adopted to develop the failure model of a complex system, and failure probability of components is determined by expert evaluations to handle the uncertainty problem. Moreover, a fault tree is converted into an evidence network to obtain importance degrees, which are used to construct a diagnostic decision table together with the risk priority number. Additionally, these results are updated to optimize the maintenance process using sensor information. A novel dynamic location strategy is designed based on interval CODAS algorithm and optimal fault location strategy can be obtained. Finally, a real system is analyzed to demonstrate the feasibility of the proposed maintenance strategy

The occurrence and identification of Setaria italica (L.) P. Beauv. (foxtail millet) grains from the Chengtoushan site (ca. 5800 cal B.P.) in central China, with reference to the domestication centre in Asia

Archaeobotanical remains of Setaria grains and chaff were found at the Chengtoushan site in south-central China (ca. 5800 cal b.p.). Grain shape was determined, using length to breadth ratios, and morphological variation in the upper lemma of modern domesticated and wild Setaria species were examined using scanning electron microscopy as a basis for identifying archaeobotanical remains. Grains of S. viridis, S. yunnanensis, and S.×pycnocoma are slender, whereas S. italica, S. italica var. germinica, S. lutescenes, S. faberi, S. glauca, S. pallidefusca and S. intermedia are round in shape. The papillae distributed on the upper lemma of S. italica are small (8-15 μm) with a non-ridged base, while other Setaria species have large papillae (15-20 μm) with a widely ridged base. The remains of the Setaria from the Neolithic layers at Chengtoushan included S. italica, based on these identification characters. These new finds of foxtail millet are the earliest discoveries from the Yangtze River basin of southern China and are also the earliest evidence for co-cultivation of foxtail millet with rice. The implications of these findings for understanding foxtail millet domestication centres are discussed

Multi-Scale Semantic Segmentation and Spatial Relationship Recognition of Remote Sensing Images Based on an Attention Model

A comprehensive interpretation of remote sensing images involves not only remote sensing object recognition but also the recognition of spatial relations between objects. Especially in the case of different objects with the same spectrum, the spatial relationship can help interpret remote sensing objects more accurately. Compared with traditional remote sensing object recognition methods, deep learning has the advantages of high accuracy and strong generalizability regarding scene classification and semantic segmentation. However, it is difficult to simultaneously recognize remote sensing objects and their spatial relationship from end-to-end only relying on present deep learning networks. To address this problem, we propose a multi-scale remote sensing image interpretation network, called the MSRIN. The architecture of the MSRIN is a parallel deep neural network based on a fully convolutional network (FCN), a U-Net, and a long short-term memory network (LSTM). The MSRIN recognizes remote sensing objects and their spatial relationship through three processes. First, the MSRIN defines a multi-scale remote sensing image caption strategy and simultaneously segments the same image using the FCN and U-Net on different spatial scales so that a two-scale hierarchy is formed. The output of the FCN and U-Net are masked to obtain the location and boundaries of remote sensing objects. Second, using an attention-based LSTM, the remote sensing image captions include the remote sensing objects (nouns) and their spatial relationships described with natural language. Finally, we designed a remote sensing object recognition and correction mechanism to build the relationship between nouns in captions and object mask graphs using an attention weight matrix to transfer the spatial relationship from captions to objects mask graphs. In other words, the MSRIN simultaneously realizes the semantic segmentation of the remote sensing objects and their spatial relationship identification end-to-end. Experimental results demonstrated that the matching rate between samples and the mask graph increased by 67.37 percentage points, and the matching rate between nouns and the mask graph increased by 41.78 percentage points compared to before correction. The proposed MSRIN has achieved remarkable results