The out-of-distribution (OOD) generalization challenge is a longstanding problem in graph learning. Through studying the fundamental cause of data distribution shift, i.e., the changes of environments, significant progress has been achieved in addressing this issue. However, we observe that existing works still fail to effectively address complex environment shifts. Existing practices place excessive attention on extracting causal subgraphs, inevitably treating spurious subgraphs as environment variables. While spurious subgraphs are controlled by environments, the space of environment changes encompass more than the scale of spurious subgraphs. Therefore, existing efforts have a limited inference space for environments, leading to failure under severe environment changes. To tackle this issue, we propose a negative inference graph OOD framework (NeGo) to broaden the inference space for environment factors. Inspired by the successful practice of prompt learning in capturing underlying semantics and causal associations in large language models, we design a negative prompt environment inference to extract underlying environment information. We further introduce the environment-enhanced invariant subgraph learning to effectively exploit inferred environment embedding, ensuring the robust extraction of causal subgraph in the environment shifts. Lastly, we conduct a comprehensive evaluation of NeGo on real-world datasets and synthetic datasets across domains. NeGo outperforms baselines on nearly all datasets, which verify the effectiveness of our framework.

Resource management challenges frequently manifest in systems and networks as tough online decision tasks, for which the proper solution is dependent on an understanding of the workload and environment and facilitates smooth use of mobile edge and cloud resources. Due to the geographical dispersion of resources, constrained resource capacity, unpredictable nature of tasks, and network hierarchy present in such contexts, it is difficult to efficiently schedule jobs in edge environments. Unfortunately, existing heuristic-based methods lack generality and fast adaptability and thus cannot optimally solve such problems. The advantage actor–critic (A2C) method, on the one hand, can quickly adapt to dynamic circumstances based on relatively few data, and deep reinforcement learning (DRL) agents can on the other hand rapidly learn from their experience of environmental interactions to make better judgments. Therefore, we present an A2C-DRL real-time task scheduling technique for stochastic edge–cloud environments that enables decentralized learning and simultaneous work scheduling across multiple servers. With the aim of producing efficient scheduling decisions, we develop reward values for various resources and model the update policy, server resource scheduling method, and policy learning method. The model is adaptive and includes various hyperparameters that can be adjusted in accordance with the application requirements. We evaluate the load balancing capability of the model by introducing a load balancing factor. Experiments on real datasets show that the proposed A2C-DRL method outperforms seven state-of-the-art algorithms in terms of the reward value, task rejection, and the load balancing factor.

Previous running time analyses of evolutionary algorithms (EAs) in noisy environments often studied the one-bit noise model, which flips a randomly chosen bit of a solution before evaluation. In this paper, we study a natural extension of one-bit noise, the bit-wise noise model, which independently flips each bit of a solution with some probability. We analyze the running time of the (1+1)-EA solving OneMax and LeadingOnes under bit-wise noise for the first time, and derive the ranges of the noise level for polynomial and super-polynomial running time bounds. The analysis on LeadingOnes under bit-wise noise can be easily transferred to one-bit noise, and improves the previously known results.

13C metabolic flux analysis (13C-MFA) has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux) in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org) that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species.

Locating lossy nodes in wireless sensor networks (WSNs) is difficult due to the large amount of sensor nodes, and their limited resources. The state-of-the-art work frames lossy node localization in WSNs as an optimal sequential testing problem guided by end-to-end data. It combines both active and passive measurements to minimize testing cost and number of iterations. However, this hybrid approach has many limitations. Inspired by the success of statistic methods in coverage-based software testing, and the similarity between software testing and lossy node localization, we develop an improved approach by employing Chi-square test in WSN lossy node localization. Supported by well-established statistic theories, our elegant approach delivers great performance. Experiments on randomly generated networks and deployed networks show significant performance improvement using the proposed algorithm. We expect to use this approach for other diagnostic problems in WSNs.

In this paper, we firstly modify a parameter in affinity propagation (AP) to improve its convergence ability, and then, we apply it to vector quantization (VQ) codebook design problem. In order to improve the quality of the resulted codebook, we combine the improved AP (IAP) with the conventional LBG algorithm to generate an effective algorithm call IAP-LBG. According to the experimental results, the proposed method not only improves its convergence abilities but also provides higher-quality codebooks than conventional LBG method does.