55 research outputs found
Greening Your Way to Profits: Green Strategies and Green Revenues
We examine hot-debated but underexplored questions of whether and how green strategies affect corporate green revenues. Using a generalized Difference-in-Differences (DiD) framework, we find that green strategies significantly enhance corporate green revenues in the presence of China's Emission Trading Scheme (ETS) pilot. This is consistent with the Porter Hypothesis. Our mechanism analyses document that green strategies increase green revenues by improving green quality and catalyzing environmentally friendly transformation. This study has important implications for policymakers and practitioners, offering new insights into the intended consequences and real outcomes of environmental regulations
On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
Graph neural networks (GNNs) have been widely used in various graph-related
problems such as node classification and graph classification, where the
superior performance is mainly established when natural node features are
available. However, it is not well understood how GNNs work without natural
node features, especially regarding the various ways to construct artificial
ones. In this paper, we point out the two types of artificial node
features,i.e., positional and structural node features, and provide insights on
why each of them is more appropriate for certain tasks,i.e., positional node
classification, structural node classification, and graph classification.
Extensive experimental results on 10 benchmark datasets validate our insights,
thus leading to a practical guideline on the choices between different
artificial node features for GNNs on non-attributed graphs. The code is
available at https://github.com/zjzijielu/gnn-exp/.Comment: This paper has been accepted to the Sixth International Workshop on
Deep Learning on Graphs (DLG-KDD'21) (co-located with KDD'21
Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping
High-resolution 3D object generation remains a challenging task primarily due
to the limited availability of comprehensive annotated training data. Recent
advancements have aimed to overcome this constraint by harnessing image
generative models, pretrained on extensive curated web datasets, using
knowledge transfer techniques like Score Distillation Sampling (SDS).
Efficiently addressing the requirements of high-resolution rendering often
necessitates the adoption of latent representation-based models, such as the
Latent Diffusion Model (LDM). In this framework, a significant challenge
arises: To compute gradients for individual image pixels, it is necessary to
backpropagate gradients from the designated latent space through the frozen
components of the image model, such as the VAE encoder used within LDM.
However, this gradient propagation pathway has never been optimized, remaining
uncontrolled during training. We find that the unregulated gradients adversely
affect the 3D model's capacity in acquiring texture-related information from
the image generative model, leading to poor quality appearance synthesis. To
address this overarching challenge, we propose an innovative operation termed
Pixel-wise Gradient Clipping (PGC) designed for seamless integration into
existing 3D generative models, thereby enhancing their synthesis quality.
Specifically, we control the magnitude of stochastic gradients by clipping the
pixel-wise gradients efficiently, while preserving crucial texture-related
gradient directions. Despite this simplicity and minimal extra cost, extensive
experiments demonstrate the efficacy of our PGC in enhancing the performance of
existing 3D generative models for high-resolution object rendering.Comment: Accepted at ICLR 2024. Project page:
https://fudan-zvg.github.io/PGC-3
Machine unlearning in brain-inspired neural network paradigms
Machine unlearning, which is crucial for data privacy and regulatory compliance, involves the selective removal of specific information from a machine learning model. This study focuses on implementing machine unlearning in Spiking Neuron Models (SNMs) that closely mimic biological neural network behaviors, aiming to enhance both flexibility and ethical compliance of AI models. We introduce a novel hybrid approach for machine unlearning in SNMs, which combines selective synaptic retraining, synaptic pruning, and adaptive neuron thresholding. This methodology is designed to effectively eliminate targeted information while preserving the overall integrity and performance of the neural network. Extensive experiments were conducted on various computer vision datasets to assess the impact of machine unlearning on critical performance metrics such as accuracy, precision, recall, and ROC AUC. Our findings indicate that the hybrid approach not only maintains but in some cases enhances the neural network's performance post-unlearning. The results confirm the practicality and efficiency of our approach, underscoring its applicability in real-world AI systems
Key Information Retrieval to Classify the Unstructured Data Content of Preferential Trade Agreements
With the rapid proliferation of textual data, predicting long texts has
emerged as a significant challenge in the domain of natural language
processing. Traditional text prediction methods encounter substantial
difficulties when grappling with long texts, primarily due to the presence of
redundant and irrelevant information, which impedes the model's capacity to
capture pivotal insights from the text. To address this issue, we introduce a
novel approach to long-text classification and prediction. Initially, we employ
embedding techniques to condense the long texts, aiming to diminish the
redundancy therein. Subsequently,the Bidirectional Encoder Representations from
Transformers (BERT) embedding method is utilized for text classification
training. Experimental outcomes indicate that our method realizes considerable
performance enhancements in classifying long texts of Preferential Trade
Agreements. Furthermore, the condensation of text through embedding methods not
only augments prediction accuracy but also substantially reduces computational
complexity. Overall, this paper presents a strategy for long-text prediction,
offering a valuable reference for researchers and engineers in the natural
language processing sphere.Comment: AI4TS Workshop@AAAI 2024 accepted publicatio
Structural Knowledge Informed Continual Multivariate Time Series Forecasting
Recent studies in multivariate time series (MTS) forecasting reveal that
explicitly modeling the hidden dependencies among different time series can
yield promising forecasting performance and reliable explanations. However,
modeling variable dependencies remains underexplored when MTS is continuously
accumulated under different regimes (stages). Due to the potential distribution
and dependency disparities, the underlying model may encounter the catastrophic
forgetting problem, i.e., it is challenging to memorize and infer different
types of variable dependencies across different regimes while maintaining
forecasting performance. To address this issue, we propose a novel Structural
Knowledge Informed Continual Learning (SKI-CL) framework to perform MTS
forecasting within a continual learning paradigm, which leverages structural
knowledge to steer the forecasting model toward identifying and adapting to
different regimes, and selects representative MTS samples from each regime for
memory replay. Specifically, we develop a forecasting model based on graph
structure learning, where a consistency regularization scheme is imposed
between the learned variable dependencies and the structural knowledge while
optimizing the forecasting objective over the MTS data. As such, MTS
representations learned in each regime are associated with distinct structural
knowledge, which helps the model memorize a variety of conceivable scenarios
and results in accurate forecasts in the continual learning context. Meanwhile,
we develop a representation-matching memory replay scheme that maximizes the
temporal coverage of MTS data to efficiently preserve the underlying temporal
dynamics and dependency structures of each regime. Thorough empirical studies
on synthetic and real-world benchmarks validate SKI-CL's efficacy and
advantages over the state-of-the-art for continual MTS forecasting tasks
基于CIM技术的智慧交通项目管理建设方案研究
This paper takes smart transportation as the research object, selects the construction of project management as the research problem, and proposes to use CIM as the supporting technology. From the perspective of various digital fusion technologies, it emphasizes the information reliability of multi-dimensional model calculation, which is used to guide the management of urban transportation hub projects
Precise emergency load shedding approach for distributed network considering response time requirements
Emergency load shedding (ELS) is a vital measure for power systems to manage extreme events, ensuring the safety, stability, and economic operation of the grid. The integration of distributed energy resources and controllable devices in modern power systems has bolstered grid flexibility. Consequently, developing precise load shedding strategies to balance economic and security goals has emerged as a prominent subject in power system optimization. However, existing methods exhibit inadequacies, including overlooking practical operability, privacy concerns, and a lack of adaptability to response time requirements. To address these gaps, this paper introduces a precise ELS approach for distributed networks with a focus on response time needs. Contributions encompass designing load shedding processes for various response times, integrating demand response, and partitioning networks for optimized load shedding. Through validation using standard test cases, the proposed approach effectively utilizes response time and demand-side resources for precise ELS control in distribution networks. It accommodates different scenarios, offering a robust solution for rapid and accurate load shedding during emergencies
SPI1-induced downregulation of FTO promotes GBM progression by regulating pri-miR-10a processing in an m6A-dependent manner
As one of the most common post-transcriptional modifications of mRNAs and noncoding RNAs, N6-methyladenosine (m6A) modification regulates almost every aspect of RNA metabolism. Evidence indicates that dysregulation of m6A modification and associated proteins contributes to glioblastoma (GBM) progression. However, the function of fat mass and obesity-associated protein (FTO), an m6A demethylase, has not been systematically and comprehensively explored in GBM. Here, we found that decreased FTO expression in clinical specimens correlated with higher glioma grades and poorer clinical outcomes. Functionally, FTO inhibited growth and invasion in GBM cells in vitro and in vivo. Mechanistically, FTO regulated the m6A modification of primary microRNA-10a (pri-miR-10a), which could be recognized by reader HNRNPA2B1, recruiting the microRNA microprocessor complex protein DGCR8 and mediating pri-miR-10a processing. Furthermore, the transcriptional activity of FTO was inhibited by the transcription factor SPI1, which could be specifically disrupted by the SPI1 inhibitor DB2313. Treatment with this inhibitor restored endogenous FTO expression and decreased GBM tumor burden, suggesting that FTO may serve as a novel prognostic indicator and therapeutic molecular target of GBM.publishedVersio
The dual role of glioma exosomal microRNAs: glioma eliminates tumor suppressor miR-1298-5p via exosomes to promote immunosuppressive effects of MDSCs
Clear evidence shows that tumors could secrete microRNAs (miRNAs) via exosomes to modulate the tumor microenvironment (TME). However, the mechanisms sorting specific miRNAs into exosomes are still unclear. In order to study the biological function and characterization of exosomal miRNAs, we performed whole-transcriptome sequencing in 59 patients’ whole-course cerebrospinal fluid (CSF) small extracellular vesicles (sEV) and matched glioma tissue samples. The results demonstrate that miRNAs could be divided into exosome-enriched miRNAs (ExomiRNAs) and intracellular-retained miRNAs (CLmiRNAs), and exosome-enriched miRNAs generally play a dual role. Among them, miR-1298-5p was enriched in CSF exosomes and suppressed glioma progression in vitro and vivo experiments. Interestingly, exosomal miR-1298-5p could promote the immunosuppressive effects of myeloid-derived suppressor cells (MDSCs) to facilitate glioma. Therefore, we found miR-1298-5p had different effects on glioma cells and MDSCs. Mechanically, downstream signaling pathway analyses showed that miR-1298-5p plays distinct roles in glioma cells and MDSCs via targeting SETD7 and MSH2, respectively. Moreover, reverse verification was performed on the intracellular-retained miRNA miR-9-5p. Thus, we confirmed that tumor-suppressive miRNAs in glioma cells could be eliminated through exosomes and target tumor-associated immune cells to induce tumor-promoting phenotypes. Glioma could get double benefit from it. These findings uncover the mechanisms that glioma selectively sorts miRNAs into exosomes and modulates tumor immunity.publishedVersio
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