Neural 3D Scene Reconstruction from Multiple 2D Images without 3D Supervision

Neural 3D scene reconstruction methods have achieved impressive performance when reconstructing complex geometry and low-textured regions in indoor scenes. However, these methods heavily rely on 3D data which is costly and time-consuming to obtain in real world. In this paper, we propose a novel neural reconstruction method that reconstructs scenes using sparse depth under the plane constraints without 3D supervision. We introduce a signed distance function field, a color field, and a probability field to represent a scene. We optimize these fields to reconstruct the scene by using differentiable ray marching with accessible 2D images as supervision. We improve the reconstruction quality of complex geometry scene regions with sparse depth obtained by using the geometric constraints. The geometric constraints project 3D points on the surface to similar-looking regions with similar features in different 2D images. We impose the plane constraints to make large planes parallel or vertical to the indoor floor. Both two constraints help reconstruct accurate and smooth geometry structures of the scene. Without 3D supervision, our method achieves competitive performance compared with existing methods that use 3D supervision on the ScanNet dataset.Comment: 10 pages, 6 figure

A Conceptual Artificial Intelligence Application Framework in Human Resource Management

This study proposes a conceptional framework of artificial intelligence (AI) technology application for human resource management (HRM). Based on the theory of the six basic dimensions of human resource management, which includes human resource strategy and planning, recruitment, training and development process, performance management, salary evaluation, and the employee relationship management, is combine with its potential corresponding AI technology application. With the cases analysis on recruitment of leap.ai and online training of Baidu, the recruitment dimension and training dimension with AI are further explored. Finally, the practical implication and future study are supplemented. This AIHRM conceptual model provides suggestions and directions for the development of AI in enterprise human resource management

Policy decoupling in strategic response to the double world-class project: evidence from elite universities in China

Creating world-class universities (WCUs) has recently become a significant policy and practice in higher education in China under the Double World-Class Project. However, some negative effects have encouraged decoupling from the policy goals. To identify the reasons, we conducted policy document analysis and purposive interviews at three elite universities, focusing on financial funding, discipline development, and human resources. First, the uneven funding plans by central and local governments shape non-competitive environments for universities, hindering the dynamic adjustment of the Double World-Class Project. Second, universities have closed or merged programs in weak academic disciplines to gain legitimacy and stability in conformance to WCU guidelines. Last, as a result of the unbalanced development of universities in east, middle, and west China, universities in the west are facing a serious brain drain. To achieve a higher level of performance, an increasing number of ‘shadow academics’ are being recruited by Chinese universities. These decoupling responses and manipulative strategies result from the dominating constituent in WCUs, ambiguity in policy contents, hierarchical control systems in higher education, and uncertain environments for universities

Research on Factors Affecting the Use of E-commerce Consumer Credit Services: A Study of Ant Check Later

This study uses “Ant Check Later”, the e-commerce consumer credit service of Alibaba, as the artifact and explores factors affecting its use. This study first summarized initiatives that Alibaba has launched to stimulate the use of “Ant Check Later”. Three factors, bonus, quota lifting, and scenario enrichment, were then distinguished from the initiatives using principal component analysis. These factors were anticipated to affect consumers’ intention to use the service. The research model was tested using 373 respondents collected from an online survey. Results indicate that bonus, quota lifting, and scenario enrichment are three predictors of consumers’ intention to continue using the service, and bonus and scenario enrichment positively affect non-users’ intention to use the service. This study found that scenario enrichment is the most important factor among the three factors in boosting consumers’ behavioral intention toward using the service. Keywords E-commerce consumer credit services, bonus, quota lifting, scenario enrichment, acceptance

Exploring Data Geometry for Continual Learning

Continual learning aims to efficiently learn from a non-stationary stream of data while avoiding forgetting the knowledge of old data. In many practical applications, data complies with non-Euclidean geometry. As such, the commonly used Euclidean space cannot gracefully capture non-Euclidean geometric structures of data, leading to inferior results. In this paper, we study continual learning from a novel perspective by exploring data geometry for the non-stationary stream of data. Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data, and prevents forgetting by keeping geometric structures of old data into account. In doing so, making use of the mixed curvature space, we propose an incremental search scheme, through which the growing geometric structures are encoded. Then, we introduce an angular-regularization loss and a neighbor-robustness loss to train the model, capable of penalizing the change of global geometric structures and local geometric structures. Experiments show that our method achieves better performance than baseline methods designed in Euclidean space.Comment: CVPR 202