Open source solution approaches to a class of stochastic supply chain problems

This research proposes a variety of solution approaches to a class of stochastic supply chain problems, with normally distributed demand in a certain period of time in the future. These problems aim to provide the decisions regarding the production levels; supplier selection for raw materials; and optimal order quantity. The typical problem could be formulated as a mixed integer nonlinear program model, and the objective function for maximizing the expected profit is expressed in an integral format. In order to solve the problem, an open source solution package BONMIN is first employed to get the exact optimum result for small scale instances; then according to the specific feature of the problem a tailored nonlinear branch and bound framework is developed for larger scale problems through the introduction of triangular approximation approach and an iterative algorithm. Both open source solvers and commercial solvers are employed to solve the inner problem, and the results to larger scale problems demonstrate the competency of introduced approaches. In addition, two small heuristics are also introduced and the selected results are reported

Policy Analysis of Agricultural Water Fee Collection in China

AbstractThe low collection rate of agricultural irrigation water fee is a common problem facing many developing countries, which has also troubled China since the 2000s. In different areas of China, there are two problem-solving strategies: raising water collection rate or exempting water fee. In this paper, we analyze the dilemma of China's agricultural water fee collection from both practical and theoretical perspective. We argue that China will not follow up one single model in agricultural water fee collection and each local government should explore appropriate policy in line with their own situation

Plug-and-Play Methods Provably Converge with Properly Trained Denoisers

Plug-and-play (PnP) is a non-convex framework that integrates modern denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal algorithms. An advantage of PnP is that one can use pre-trained denoisers when there is not sufficient data for end-to-end training. Although PnP has been recently studied extensively with great empirical success, theoretical analysis addressing even the most basic question of convergence has been insufficient. In this paper, we theoretically establish convergence of PnP-FBS and PnP-ADMM, without using diminishing stepsizes, under a certain Lipschitz condition on the denoisers. We then propose real spectral normalization, a technique for training deep learning-based denoisers to satisfy the proposed Lipschitz condition. Finally, we present experimental results validating the theory.Comment: Published in the International Conference on Machine Learning, 201

A single-chip real-Time range finder

Range finding are widely used in various industrial applications, such as machine vision, collision avoidance, and robotics. Presently most range finders either rely on active transmitters or sophisticated mechanical controllers and powerful processors to extract range information, which make the range finders costly, bulky, or slowly, and limit their applications. This dissertation is a detailed description of a real-time vision-based range sensing technique and its single-chip CMOS implementation. To the best of our knowledge, this system is the first single chip vision-based range finder that doesn't need any mechanical position adjustment, memory or digital processor. The entire signal processing on the chip is purely analog and occurs in parallel. The chip captures the image of an object and extracts the depth and range information from just a single picture. The on-chip, continuous-time, logarithmic photoreceptor circuits are used to couple spatial image signals into the range-extracting processing network. The photoreceptor pixels can adjust their operating regions, simultaneously achieving high sensitivity and wide dynamic range. The image sharpness processor and Winner-Take-All circuits are characterized and analyzed carefully for their temporal bandwidth and detection performance. The mathematical and optical models of the system are built and carefully verified. A prototype based on this technique has been fabricated and tested. The experimental results prove that the range finder can achieve acceptable range sensing precision with low cost and excellent speed performance in short-to-medium range coverage. Therefore, it is particularly useful for collision avoidance

Study of Deformation-Compensated Modeling for Flexible Material Path Processing Based on Fuzzy Neural Network and Fuzzy Clustering

In this paper, the Flexible Material Path Processing (FMPP) deformation compensation modeling method based on T-S fuzzy neural network is proposed. This method combined with T S fuzzy reasoning and fuzzy neural network.Firstly, fuzzy clustering is introduced to extract fuzzy membership functions and the fitness of fuzzy rules of T S fuzzy neural network antecedent from historical processing data; secondly, through back-propagation iteration to calculate connection weights of the network. Processing experiments shows that T S fuzzy neural network modeling in this paper is superior to typical T S model，the angle error and straightness error processing by NTS FNN is decreased than these of STS FNN

MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks

Some recent works revealed that deep neural networks (DNNs) are vulnerable to so-called adversarial attacks where input examples are intentionally perturbed to fool DNNs. In this work, we revisit the DNN training process that includes adversarial examples into the training dataset so as to improve DNN's resilience to adversarial attacks, namely, adversarial training. Our experiments show that different adversarial strengths, i.e., perturbation levels of adversarial examples, have different working zones to resist the attack. Based on the observation, we propose a multi-strength adversarial training method (MAT) that combines the adversarial training examples with different adversarial strengths to defend adversarial attacks. Two training structures - mixed MAT and parallel MAT - are developed to facilitate the tradeoffs between training time and memory occupation. Our results show that MAT can substantially minimize the accuracy degradation of deep learning systems to adversarial attacks on MNIST, CIFAR-10, CIFAR-100, and SVHN.Comment: 6 pages, 4 figures, 2 table

Deformation-compensated modeling of flexible material processing based on T-S fuzzy neural network and fuzzy clustering

According to the factors that influence flexible material processing (FMP), the deformation compensation modeling method based on T-S fuzzy neural network is proposed. This method combines T-S fuzzy reasoning with a fuzzy neural network. Firstly, fuzzy clustering is introduced to extract fuzzy membership functions and the fitness of fuzzy rules of T-S fuzzy neural network antecedent from the past processing data. Secondly, with the steepest descent method, back-propagation iteration is used to calculate the connection weights of the network. The processing of experiments shows that T-S fuzzy neural network modeling is superior to typical T-S model. The angle error and the straightness error processed by NTS-FNN is 40.4 %, 28.8 % lower than those of STS-FNN. The minimum processing time processed by NTS-FNN is lower by 46.1 % than that of STS-FNN

A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing

Language models (LMs) like BERT and GPT have revolutionized natural language processing (NLP). However, privacy-sensitive domains, particularly the medical field, face challenges to train LMs due to limited data access and privacy constraints imposed by regulations like the Health Insurance Portability and Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR). Federated learning (FL) offers a decentralized solution that enables collaborative learning while ensuring the preservation of data privacy. In this study, we systematically evaluate FL in medicine across $2$ biomedical NLP tasks using $6$ LMs encompassing $8$ corpora. Our results showed that: 1) FL models consistently outperform LMs trained on individual client's data and sometimes match the model trained with polled data; 2) With the fixed number of total data, LMs trained using FL with more clients exhibit inferior performance, but pre-trained transformer-based models exhibited greater resilience. 3) LMs trained using FL perform nearly on par with the model trained with pooled data when clients' data are IID distributed while exhibiting visible gaps with non-IID data. Our code is available at: https://github.com/PL97/FedNLPComment: Accepted by KDD 2023 Workshop FL4Data-Minin