Expectation-Maximizing Network Reconstruction and MostApplicable Network Types Based on Binary Time Series Data

Based on the binary time series data of social infection dynamics, we propose a general framework to reconstruct 2-simplex complexes with two-body and three-body interactions by combining the maximum likelihood estimation in statistical inference and introducing the expectation maximization. In order to improve the code running efficiency, the whole algorithm adopts vectorization expression. Through the inference of maximum likelihood estimation, the vectorization expression of the edge existence probability can be obtained, and through the probability matrix, the adjacency matrix of the network can be estimated. We apply a two-step scheme to improve the effectiveness of network reconstruction while reducing the amount of computation significantly. The framework has been tested on different types of complex networks. Among them, four kinds of networks can obtain high reconstruction effectiveness. Besides, we study the influence of noise data or random interference and prove the robustness of the framework, then the effects of two kinds of hyper-parameters on the experimental results are tested. Finally, we analyze which type of network is more suitable for this framework, and propose methods to improve the effectiveness of the experimental results

The failure process of mortars during sulfate attack

External sulfate attacks on concrete structures may cause serious damage and it has attracted a wide range of attention from numerous researchers over the past decades. However, many studies have been concentrated on the sample which has been already destroyed. This paper investigated the entire deterioration process of mortars that were immersed in Na2SO4 solution containing 3 gSO4 2-/L and 33.8g SO4 2-/l at 20 oC up to 600 days. The study on time-varying regularity of expansion, cracks, compressive strength and mineral phases was investigated. Back scattered electron image was used to further examine the evolution of microstructures of the mortars during the attack process. The results showed that damage process of mortars can be described as induction stage, surface damage, bulk damage and then completely damage stage. Fine ettringites that were formed in restricted spaces, approximately 2-5 μm, result in surface damage. At the bulk damage stage, cracking was the main characteristic of mortar which leads to obvious expansion. In this stage, some large ettringite crystals (>20μm) were just deposited in the formed cracks. At the later stage, gypsum can be easily formed at interfacial transition zones as the consumption of calcium hydroxide, which mainly contributed to completely strength failure rather than expansion

Finding emergence in data by maximizing effective information

Quantifying emergence and modeling emergent dynamics in a data-driven manner for complex dynamical systems is challenging due to the lack of direct observations at the micro-level. Thus, it's crucial to develop a framework to identify emergent phenomena and capture emergent dynamics at the macro-level using available data. Inspired by the theory of causal emergence (CE), this paper introduces a machine learning framework to learn macro-dynamics in an emergent latent space and quantify the degree of CE. The framework maximizes effective information, resulting in a macro-dynamics model with enhanced causal effects. Experimental results on simulated and real data demonstrate the effectiveness of the proposed framework. It quantifies degrees of CE effectively under various conditions and reveals distinct influences of different noise types. It can learn a one-dimensional coarse-grained macro-state from fMRI data, to represent complex neural activities during movie clip viewing. Furthermore, improved generalization to different test environments is observed across all simulation data

Automotive Object Detection via Learning Sparse Events by Temporal Dynamics of Spiking Neurons

Event-based sensors, with their high temporal resolution (1us) and dynamical range (120dB), have the potential to be deployed in high-speed platforms such as vehicles and drones. However, the highly sparse and fluctuating nature of events poses challenges for conventional object detection techniques based on Artificial Neural Networks (ANNs). In contrast, Spiking Neural Networks (SNNs) are well-suited for representing event-based data due to their inherent temporal dynamics. In particular, we demonstrate that the membrane potential dynamics can modulate network activity upon fluctuating events and strengthen features of sparse input. In addition, the spike-triggered adaptive threshold can stabilize training which further improves network performance. Based on this, we develop an efficient spiking feature pyramid network for event-based object detection. Our proposed SNN outperforms previous SNNs and sophisticated ANNs with attention mechanisms, achieving a mean average precision (map50) of 47.7% on the Gen1 benchmark dataset. This result significantly surpasses the previous best SNN by 9.7% and demonstrates the potential of SNNs for event-based vision. Our model has a concise architecture while maintaining high accuracy and much lower computation cost as a result of sparse computation. Our code will be publicly available

Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies

Emergence and causality are two fundamental concepts for understanding complex systems. They are interconnected. On one hand, emergence refers to the phenomenon where macroscopic properties cannot be solely attributed to the cause of individual properties. On the other hand, causality can exhibit emergence, meaning that new causal laws may arise as we increase the level of abstraction. Causal emergence theory aims to bridge these two concepts and even employs measures of causality to quantify emergence. This paper provides a comprehensive review of recent advancements in quantitative theories and applications of causal emergence. Two key problems are addressed: quantifying causal emergence and identifying it in data. Addressing the latter requires the use of machine learning techniques, thus establishing a connection between causal emergence and artificial intelligence. We highlighted that the architectures used for identifying causal emergence are shared by causal representation learning, causal model abstraction, and world model-based reinforcement learning. Consequently, progress in any of these areas can benefit the others. Potential applications and future perspectives are also discussed in the final section of the review.Comment: 57 pages, 17 figures, 1 tabl

Q-Refine: A Perceptual Quality Refiner for AI-Generated Image

With the rapid evolution of the Text-to-Image (T2I) model in recent years, their unsatisfactory generation result has become a challenge. However, uniformly refining AI-Generated Images (AIGIs) of different qualities not only limited optimization capabilities for low-quality AIGIs but also brought negative optimization to high-quality AIGIs. To address this issue, a quality-award refiner named Q-Refine is proposed. Based on the preference of the Human Visual System (HVS), Q-Refine uses the Image Quality Assessment (IQA) metric to guide the refining process for the first time, and modify images of different qualities through three adaptive pipelines. Experimental shows that for mainstream T2I models, Q-Refine can perform effective optimization to AIGIs of different qualities. It can be a general refiner to optimize AIGIs from both fidelity and aesthetic quality levels, thus expanding the application of the T2I generation models.Comment: 6 pages, 5 figure

Effects of biochar-amended alkali-activated slag on the stabilization of coral sand in coastal areas

Coral sand is widely encountered in coastal areas of tropical and subtropical regions. Compared with silica sand, it usually exhibits weaker performance from the perspective of engineering geology. To improve the geomechanical performance of coral sand and meet the requirement of foundation construction in coastal areas, a novel alkali activation-based sustainable binder was developed. The alkali-activated slag (AAS) binder material was composed of ground granulated blast-furnace slag (GGBS) and hydrated lime with the amendment of biochar, an agricultural waste-derived material. The biochar-amended AAS stabilized coral sand was subjected to a series of laboratory tests to determine its mechanical, physicochemical, and microstructural characteristics. Results show that adding a moderate amount of biochar in AAS could improve soil strength, elastic modulus, and water holding capacity by up to 20%, 70%, and 30%, respectively. Moreover, the addition of biochar in AAS had a marginal effect on the sulfate resistance of the stabilized sand, especially at high biochar content. However, the resistance of the AAS stabilized sand to wet-dry cycles slightly deteriorated with the addition of biochar. Based on these observations, a conceptual model showing biochar-AAS-sand interactions was proposed, in which biochar served as an internal curing agent, micro-reinforcer, and mechanically weak point

Properties and Microstructure of Na2CO3-Activated Binders Modified with Ca(OH)2 and Mg(OH)2

Delayed strength development and long setting times are the main disadvantageous properties of Na2CO3-activated slag cements. In this work, combined auxiliary activators of Ca(OH)2 and Mg(OH)2 were incorporated in one-part Na2CO3-activated slag binders to accelerate the kinetics of alkali activation. The properties and microstructure evolution were investigated to clarify the reaction mechanism. The results showed that the additions of auxiliary activators promoted the hardening of the pastes within 2 h. The 28 days compressive strengths were in the range of 39.5–45.5 MPa, rendering the binders practical cementitious materials in general construction applications. Ca(OH)2 was more effective than Mg(OH)2 in accelerating the kinetics of alkali activation. The dissolution of Ca(OH)2 released more OH− and Ca2+ ions in the aqueous phase to increase alkalinity in the aqueous phase and promote the formation of the main binding gel phase of calcium-aluminosilicate hydrate (C-A-S-H). An increase in the Ca(OH)2/Mg(OH)2 ratios increased autogenous shrinkage and decreased drying shrinkage of the binders. The formation of a compact pore structure restricted the water evaporation from the binders during the drying procedure

The E-Bayesian Estimation for Lomax Distribution Based on Generalized Type-I Hybrid Censoring Scheme

This article studies the E-Bayesian estimation of the unknown parameter of Lomax distribution based on generalized Type-I hybrid censoring. Under square error loss and LINEX loss functions, we get the E-Bayesian estimation and compare its effectiveness with Bayesian estimation. To measure the error of E-Bayesian estimation, the expectation of mean square error (E-MSE) is introduced. With Markov chain Monte Carlo technology, E-Bayesian estimations are computed. Metropolis–Hastings algorithm is applied within the process. Similarly, the credible interval for the parameter is calculated. Then, we can compare the MSE and E-MSE to evaluate whose result is more effective. For the purpose of illustration in real datasets, cases of generalized Type-I hybrid censored samples are presented. In order to judge whether the sample data can be directly fitted by the Lomax distribution, we adopt the Kolmogorov–Smirnov tests for evaluation. Finally, we can get the conclusion after comparing the results of E-Bayesian and Bayesian estimation

Neural Information Squeezer for Causal Emergence

Conventional studies of causal emergence have revealed that stronger causality can be obtained on the macro-level than the micro-level of the same Markovian dynamical systems if an appropriate coarse-graining strategy has been conducted on the micro-states. However, identifying this emergent causality from data is still a difficult problem that has not been solved because the appropriate coarse-graining strategy can not be found easily. This paper proposes a general machine learning framework called Neural Information Squeezer to automatically extract the effective coarse-graining strategy and the macro-level dynamics, as well as identify causal emergence directly from time series data. By using invertible neural network, we can decompose any coarse-graining strategy into two separate procedures: information conversion and information discarding. In this way, we can not only exactly control the width of the information channel, but also can derive some important properties analytically. We also show how our framework can extract the coarse-graining functions and the dynamics on different levels, as well as identify causal emergence from the data on several exampled systems