A graphical method of presenting property rights, building types, and residential behaviors: A case study of Xiaoxihu historic area, Nanjing

Il saggio descrive le possibili implementazioni di mappe tipologiche attraverso informazioni legate ai diritti di proprietà. Nel caso studio di Xiaoxihu a Nanjing, queste implementazioni hanno avuto una ricaduta diretta sulla gestione di processi di rigenerazione urbana

Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method

Graph Representation Learning (GRL) is an influential methodology, enabling a more profound understanding of graph-structured data and aiding graph clustering, a critical task across various domains. The recent incursion of attention mechanisms, originally an artifact of Natural Language Processing (NLP), into the realm of graph learning has spearheaded a notable shift in research trends. Consequently, Graph Attention Networks (GATs) and Graph Attention Auto-Encoders have emerged as preferred tools for graph clustering tasks. Yet, these methods primarily employ a local attention mechanism, thereby curbing their capacity to apprehend the intricate global dependencies between nodes within graphs. Addressing these impediments, this study introduces an innovative method known as the Graph Transformer Auto-Encoder for Graph Clustering (GTAGC). By melding the Graph Auto-Encoder with the Graph Transformer, GTAGC is adept at capturing global dependencies between nodes. This integration amplifies the graph representation and surmounts the constraints posed by the local attention mechanism. The architecture of GTAGC encompasses graph embedding, integration of the Graph Transformer within the autoencoder structure, and a clustering component. It strategically alternates between graph embedding and clustering, thereby tailoring the Graph Transformer for clustering tasks, whilst preserving the graph's global structural information. Through extensive experimentation on diverse benchmark datasets, GTAGC has exhibited superior performance against existing state-of-the-art graph clustering methodologies

A Tungsten Deep Potential with High Accuracy and Generalization Ability based on a Newly Designed Three-body Embedding Formalism

Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics (MD)simulations reveal the atomisttic scale mechanisms, so they are crucial for the understanding ofthe macroscopic property deterioration of tungsten under harsh and complex service environment.The interatomic potential used in the MD simulations is required to accurately describe a widespectrum of relevant defect properties, which is by far challenging to the existing interatomicpotentials. In this paper, we propose a new three-body embedding descriptor and hybridize it intothe Deep-Potential (DP) framework, an end-to-end deep learning interatomic potential model.Trained with the dataset generated by a concurrent learning method, the potential model fortungsten, named by DP-HYB, is able to accurately predict a wide range of properties includingelastic constants, the formation energies of free surfaces, grain boundaries, point defects and defectclusters, stacking fault energies, the core structure of screw dislocation, the energy barrier and thetransition path of the screw dislocation migration. Since most of the properties are not explicitlyincluded in the training dataset, the strong generalizability of the DP-HYB model indicates thatit is a good candidate for the atomistic simulations of tungsten property deterioration, especiallythose involving the mechanical property changing under the harsh service environment

Integrating spatial continuous wavelet transform and normalized difference vegetation index to map the agro-pastoral transitional zone in Northern China

The agro-pastoral transitional zone (APTZ) in Northern China is one of the most important ecological barriers of the world. The commonly-used method to identify the spatial distribution of ATPZ is to apply a threshold rule on climatic or land use indicators. This approach is highly subjective, and the quantity standards vary among the studies. In this study, we adopted the spatial continuous wavelet transform (SCWT) technique to detect the spatial fluctuation in normalized difference vegetation index (NDVI) sequences, and as such identify the APTZ. To carry out this analysis, the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI 1-month data (MODND1M) covering the period 2006–2015 were used. Based on the spatial variation in NDVI, we identified two sub-regions within the APTZ. The temporal change of APTZ showed that although vegetation spatial pattern changed annually, certain areas appeared to be stable, while others showed higher sensitivity to environmental variance. Through correlation analysis between the dynamics of APTZ and precipitation, we found that the mean center of the APTZ moved toward the southeast during dry years and toward the northwest during humid years. By comparing the APTZ spatial pattern obtained in the present study with the outcome following the traditional approach based on mean annual precipitation data, it can be concluded that our study provides a reliable basis to advance the methodological framework to identify accurately transitional zones. The identification framework is of high importance to support decision-making in land use management in Northern China as well as other similar regions around the world

Isoflavone Content of Soybean Cultivars from Maturity Group 0 to VI Grown in Northern and Southern China

Soybean isoflavone content has long been considered to be a desirable trait to target in selection programs for their contribution to human health and plant defense systems. The objective of this study was to determine isoflavone concentrations of various soybean cultivars from maturity groups 0 to VI grown in various environments and to analyze their relationship to other important seed characters. Forty soybean cultivars were grown in replicated trials at Wuhan and Beijing of China in 2009/2010 and their individual and total isoflavone concentrations were determined by HPLC. Their yield and quality traits were also concurrently analyzed. The isoflavone components had abundant genetic variation in soybean seed, with a range of coefficient variation from 45.01% to 69.61%. Moreover, individual and total isoflavone concentrations were significantly affected by cultivar, maturity group, site and year. Total isoflavone concentration ranged from 551.15 to 7584.07 μg g(−1), and averaged 2972.64 μg g(−1) across environments and cultivars. There was a similar trend regarding the isoflavone contents, in which a lower isoflavone concentration was generally presented in early rather than late maturing soybean cultivars. In spite of significant cultivar × year × site interactions, cultivars with consistently high or low isoflavone concentrations across environments were identified, indicating that a genetic factor plays the most important role for isoflavone accumulation. The total isoflavone concentration had significant positive correlations with plant height, effective branches, pods per plant, seeds per plant, linoleic acid and linolenic acid, while significant negative correlations with oleic acid and oil content, indicating that isoflavone concentration can be predicted as being associated with other desirable seed characteristics

xFraud: Explainable Fraud Transaction Detection

At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss. In this work, we propose xFraud, an explainable fraud transaction prediction framework which is mainly composed of a detector and an explainer. The xFraud detector can effectively and efficiently predict the legitimacy of incoming transactions. Specifically, it utilizes a heterogeneous graph neural network to learn expressive representations from the informative heterogeneously typed entities in the transaction logs. The explainer in xFraud can generate meaningful and human-understandable explanations from graphs to facilitate further processes in the business unit. In our experiments with xFraud on real transaction networks with up to 1.1 billion nodes and 3.7 billion edges, xFraud is able to outperform various baseline models in many evaluation metrics while remaining scalable in distributed settings. In addition, we show that xFraud explainer can generate reasonable explanations to significantly assist the business analysis via both quantitative and qualitative evaluations.Comment: This is the extended version of a full paper to appear in PVLDB 15 (3) (VLDB 2022

The nuclear hormone receptor gene Nr2c1 (Tr2) is a critical regulator of early retina cell patterning.

Nuclear hormone receptors play a major role in the development of many tissues. This study uncovers a novel role for testicular receptor 2 (Tr2, Nr2c1) in defining the early phase of retinal development and regulating normal retinal cell patterning and topography. The mammalian retina undergoes an overlapping yet biphasic period of development to generate all seven retinal cell types. We discovered that Nr2c1 expression coincides with development of the early retinal cells. Loss of Nr2c1 causes a severe vision deficit and impacts early, but not late retina cell types. Retinal cone cell topography is disrupted with an increase in displaced amacrine cells. Additionally, genetic background significantly impacts phenotypic outcome of cone photoreceptor cells but not amacrine cells. Chromatin-IP experiments reveal NR2C1 regulates early cell transcription factors that regulate retinal progenitor cells during development, including amacrine (Satb2) and cone photoreceptor regulators thyroid and retinoic acid receptors. This study supports a role for Nr2c1 in defining the biphasic period of retinal development and specifically influencing the early phase of retinal cell fate