Generator Born from Classifier

In this paper, we make a bold attempt toward an ambitious task: given a pre-trained classifier, we aim to reconstruct an image generator, without relying on any data samples. From a black-box perspective, this challenge seems intractable, since it inevitably involves identifying the inverse function for a classifier, which is, by nature, an information extraction process. As such, we resort to leveraging the knowledge encapsulated within the parameters of the neural network. Grounded on the theory of Maximum-Margin Bias of gradient descent, we propose a novel learning paradigm, in which the generator is trained to ensure that the convergence conditions of the network parameters are satisfied over the generated distribution of the samples. Empirical validation from various image generation tasks substantiates the efficacy of our strategy

Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification

Graph Neural Networks (GNNs) have become the preferred tool to process graph data, with their efficacy being boosted through graph data augmentation techniques. Despite the evolution of augmentation methods, issues like graph property distortions and restricted structural changes persist. This leads to the question: Is it possible to develop more property-conserving and structure-sensitive augmentation methods? Through a spectral lens, we investigate the interplay between graph properties, their augmentation, and their spectral behavior, and found that keeping the low-frequency eigenvalues unchanged can preserve the critical properties at a large scale when generating augmented graphs. These observations inform our introduction of the Dual-Prism (DP) augmentation method, comprising DP-Noise and DP-Mask, which adeptly retains essential graph properties while diversifying augmented graphs. Extensive experiments validate the efficiency of our approach, providing a new and promising direction for graph data augmentation

Theoretical design of an XRF system for environmental measurements of Mercury in fiber banks

This thesis demonstrates the advantages of using the Energy-dispersive X-ray fluorescence (ED-XRF) system to quantify the mercury content in fiber banks at first. The Monte Carlo N-Particle (MCNP) code was then used to simulate the XRF system model with suitable parameters such as the input X-ray energy level, the detector material, and the environmental factor (water depth). The SNR results of the mercury spectrum when applying different parameters were obtained. Then, the limit of detection (LOD) and limit of quantification (LOQ) based on the SNR approach are considered. Finally, system parameters were determined in order to obtain more accurate qualitative and quantitative analysis results for future environmental measurements

To Be or Not to Be? Big Data Business Investment Decision-Making in the Supply Chain

The development of Big Data technology initiates an emerging research question of whether and how to invest in Big Data business for supply chain members to establish sustainable competitive edge. The aim of our study was to assess investment in Big Data business and its sustainable effects on supply chain coordination. We considered a two-stage supply chain with one supplier and one retailer who may or may not invest in Big Data business. Five decision-making modes were proposed based on the investment portfolios. The impacts of Big Data business on the profit of the supply chain and its members were analyzed and it was confirmed that a coordination scheme could achieve supply chain coordination. The results indicated that when the Big Data cost met a certain threshold, the profit of the supply chain and its members would increase whether supply chain members choose to invest in Big Data business individually or jointly. A reasonable cost allocation of Big Data business between supply chain members was provided when both members invest in Big Data. In addition, after the members invested jointly, a revenue-sharing contract could be applied to perfectly coordinate the supply chain

Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms

Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms receive increasing attention. However, OoD generalization algorithms overlook the great variance in the quality of training data, which significantly compromises the accuracy of these methods. In this paper, we theoretically reveal the relationship between training data quality and algorithm performance, and analyze the optimal regularization scheme for Lipschitz regularized invariant risk minimization. A novel algorithm is proposed based on the theoretical results to alleviate the influence of low quality data at both the sample level and the domain level. The experiments on both the regression and classification benchmarks validate the effectiveness of our method with statistical significance