Incremental Neural Implicit Representation with Uncertainty-Filtered Knowledge Distillation

Recent neural implicit representations (NIRs) have achieved great success in the tasks of 3D reconstruction and novel view synthesis. However, they suffer from the catastrophic forgetting problem when continuously learning from streaming data without revisiting the previously seen data. This limitation prohibits the application of existing NIRs to scenarios where images come in sequentially. In view of this, we explore the task of incremental learning for NIRs in this work. We design a student-teacher framework to mitigate the catastrophic forgetting problem. Specifically, we iterate the process of using the student as the teacher at the end of each time step and let the teacher guide the training of the student in the next step. As a result, the student network is able to learn new information from the streaming data and retain old knowledge from the teacher network simultaneously. Although intuitive, naively applying the student-teacher pipeline does not work well in our task. Not all information from the teacher network is helpful since it is only trained with the old data. To alleviate this problem, we further introduce a random inquirer and an uncertainty-based filter to filter useful information. Our proposed method is general and thus can be adapted to different implicit representations such as neural radiance field (NeRF) and neural SDF. Extensive experimental results for both 3D reconstruction and novel view synthesis demonstrate the effectiveness of our approach compared to different baselines

GNeSF: Generalizable Neural Semantic Fields

3D scene segmentation based on neural implicit representation has emerged recently with the advantage of training only on 2D supervision. However, existing approaches still requires expensive per-scene optimization that prohibits generalization to novel scenes during inference. To circumvent this problem, we introduce a generalizable 3D segmentation framework based on implicit representation. Specifically, our framework takes in multi-view image features and semantic maps as the inputs instead of only spatial information to avoid overfitting to scene-specific geometric and semantic information. We propose a novel soft voting mechanism to aggregate the 2D semantic information from different views for each 3D point. In addition to the image features, view difference information is also encoded in our framework to predict the voting scores. Intuitively, this allows the semantic information from nearby views to contribute more compared to distant ones. Furthermore, a visibility module is also designed to detect and filter out detrimental information from occluded views. Due to the generalizability of our proposed method, we can synthesize semantic maps or conduct 3D semantic segmentation for novel scenes with solely 2D semantic supervision. Experimental results show that our approach achieves comparable performance with scene-specific approaches. More importantly, our approach can even outperform existing strong supervision-based approaches with only 2D annotations. Our source code is available at: https://github.com/HLinChen/GNeSF.Comment: NeurIPS 202

The value of IGF-1 and IGFBP-1 in patients with heart failure with reduced, mid-range, and preserved ejection fraction

Background: Previous studies have reported inconsistent results regarding the implications of deranged insulin-like growth factor 1 (IGF-1)/insulin-like growth factor-binding protein 1 (IGFBP-1) axis in patients with heart failure (HF). This study evaluates the roles of IGF1/IGFBP-1 axis in patients with HF with reduced ejection fraction (HFrEF), mid-range ejection fraction (HFmrEF), or preserved ejection fraction (HFpEF). Methods: Consecutive patients with HFrEF, HFmrEF, and HFpEF who underwent comprehensive cardiac assessment were included. The primary endpoint was the composite endpoint of all-cause death and HF rehospitalization at one year. Results: A total of 151 patients with HF (HFrEF: n = 51; HFmrEF: n = 30; HFpEF: n = 70) and 50 control subjects were included. The concentrations of IGFBP-1 (p < 0.001) and IGFBP-1/IGF-1 ratio (p < 0.001) were significantly lower in patients with HF compared to controls and can readily distinguish patients with and without HF (IGFBP-1: areas under the curve (AUC): 0.725, p < 0.001; IGFBP-1/IGF-1 ratio: AUC:0.755, p < 0.001; respectively). The concentrations of IGF-1, IGFBP-1, and IGFBP-1/IGF-1 ratio were similar among HFpEF, HFmrEF, and HFrEF patients. IGFBP-1 and IGFBP-1/IGF-1 ratio positively correlated with N-terminal probrain natriuretic peptide (NT-proBNP) levels (r = 0.255, p = 0.002; r = 0.224, p = 0.007, respectively). IGF-1, IGFBP-1, and IGFBP-1/IGF-1 ratio did not predict the primary endpoint at 1 year for the whole patients with HF and HF subtypes on both univariable and multivariable Cox regression. Conclusion: The concentrations of plasma IGFBP-1 and IGFBP-1/IGF-1 ratio can distinguish patients with and without HF. In HF, IGFBP-1 and IGFBP-1/IGF-1 ratio positively correlated with NT-proBNP levels

Displacement mechanism of polymeric surfactant in chemical cold flooding for heavy oil based on microscopic visualization experiments

 In order to study the microscopic oil displacement mechanism of polymeric surfactant in chemical cold flooding for heavy oil, the indoor microscopic visualization displacement experiments were carried out. The flooding experiment of heavy oil was conducted by using water, osmotic modified oil displacing agent (a kind of polymeric surfactant) and water-in-oil emulsion (obtained by mixing polymeric surfactant and heavy oil) as displacing phases to study the mechanism of polymeric surfactant to enhance oil recovery in heavy oil reservoir. The experimental results show that the polymeric surfactant can increase the viscosity of the water phase, reduce the water-oil mobility ratio, expand the swept area, and there is no obvious fingering phenomenon which occurs during water flooding. The polymeric surfactant has the surfactant characteristics which can reduce the interfacial tension between oil and water to promote the formation of oil droplets with smaller droplet diameter. And the interfacial film composed of polymeric surfactant molecules will be formed on the surface of oil droplets to prevent the coalescence of oil droplets and improve the flow ability of oil phase. The water-in-oil emulsion can be miscible with the oil in heavy oil displacement process, and thus sweeps the areas such as the dead pores which cannot be swept by water and polymeric surfactant flooding, which increases the sweep efficiency to a certain extent.Cited as: Xu, F., Chen, Q., Ma, M., Wang, Y., Yu, F., Li, J. Displacement mechanism of polymeric surfactant in chemical cold flooding for heavy oil based on microscopic visualization experiments. Advances in Geo-Energy Research, 2020, 4(1): 77-85, doi: 10.26804/ager.2020.01.0

Technology adoption in socially sustainable supply chain management: Towards an integrated conceptual framework

This study aims to systematically review existing literature on digital technology adoption for socially sustainable supply chain management (SSSCM) and propose a theoretical framework that outlines the central concepts. A content analysis-based systematic literature review approach was adopted to analyze 49 articles published from 2017 to 2024. The findings of this study identify critical antecedents, barriers, practices, enablers, and outcomes of digital technology adoption for SSSCM. The proposed conceptual model based on technology–organization–environment (TOE) framework and diffusion of innovation (DOI) theory captures these relationships among the identified factors and provides insights into how they can support the development of a socially sustainable supply chain. Furthermore, this study explores the potential positive and negative effects of technology adoption for SSSCM. It highlights the opportunities and challenges that arise from using digital technology in SSSCM, such as the emergence of Industry 4.0 and the need to ensure the ethical use of technology. This study is the first comprehensive review of the role of digital technology in SSSCM. The suggested framework offers guidance for upcoming research in this field, outlining the key areas that require further investigation

Identification and Validation of an 11-Ferroptosis Related Gene Signature and Its Correlation With Immune Checkpoint Molecules in Glioma

BackgroundGlioma is the most common primary malignant brain tumor with significant mortality and morbidity. Ferroptosis, a novel form of programmed cell death (PCD), is critically involved in tumorigenesis, progression and metastatic processes.MethodsWe revealed the relationship between ferroptosis-related genes and glioma by analyzing the mRNA expression profiles from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), GSE16011, and the Repository of Molecular Brain Neoplasia Data (REMBRANDT) datasets. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct a ferroptosis-associated gene signature in the TCGA cohort. Glioma patients from the CGGA, GSE16011, and REMBRANDT cohorts were used to validate the efficacy of the signature. Receiver operating characteristic (ROC) curve analysis was applied to measure the predictive performance of the risk score for overall survival (OS). Univariate and multivariate Cox regression analyses of the 11-gene signature were performed to determine whether the ability of the prognostic signature in predicting OS was independent. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to identify the potential biological functions and pathways of the signature. Subsequently, we performed single sample gene set enrichment analysis (ssGSEA) to explore the correlation between risk scores and immune status. Finally, seven putative small molecule drugs were predicted by Connectivity Map.ResultsThe 11-gene signature was identified to divide patients into two risk groups. ROC curve analysis indicated the 11-gene signature as a potential diagnostic factor in glioma patients. Multivariate Cox regression analyses showed that the risk score was an independent predictive factor for overall survival. Functional analysis revealed that genes were enriched in iron-related molecular functions and immune-related biological processes. The results of ssGSEA indicated that the 11-gene signature was correlated with the initiation and progression of glioma. The small molecule drugs we selected showed significant potential to be used as putative drugs.Conclusionwe identified a novel ferroptosis-related gene signature for prognostic prediction in glioma patients and revealed the relationship between ferroptosis-related genes and immune checkpoint molecules