Adaptation Speed Analysis for Fairness-aware Causal Models

For example, in machine translation tasks, to achieve bidirectional translation between two languages, the source corpus is often used as the target corpus, which involves the training of two models with opposite directions. The question of which one can adapt most quickly to a domain shift is of significant importance in many fields. Specifically, consider an original distribution p that changes due to an unknown intervention, resulting in a modified distribution p*. In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable. To explore this scenario, we examine a simple structural causal model (SCM) with a cause-bias-effect structure, where variable A acts as a sensitive variable between cause (X) and effect (Y). The two models, respectively, exhibit consistent and contrary cause-effect directions in the cause-bias-effect SCM. After conducting unknown interventions on variables within the SCM, we can simulate some kinds of domain shifts for analysis. We then compare the adaptation speeds of two models across four shift scenarios. Additionally, we prove the connection between the adaptation speeds of the two models across all interventions.Comment: CIKM 202

Pursuing Counterfactual Fairness via Sequential Autoencoder Across Domains

Recognizing the prevalence of domain shift as a common challenge in machine learning, various domain generalization (DG) techniques have been developed to enhance the performance of machine learning systems when dealing with out-of-distribution (OOD) data. Furthermore, in real-world scenarios, data distributions can gradually change across a sequence of sequential domains. While current methodologies primarily focus on improving model effectiveness within these new domains, they often overlook fairness issues throughout the learning process. In response, we introduce an innovative framework called Counterfactual Fairness-Aware Domain Generalization with Sequential Autoencoder (CDSAE). This approach effectively separates environmental information and sensitive attributes from the embedded representation of classification features. This concurrent separation not only greatly improves model generalization across diverse and unfamiliar domains but also effectively addresses challenges related to unfair classification. Our strategy is rooted in the principles of causal inference to tackle these dual issues. To examine the intricate relationship between semantic information, sensitive attributes, and environmental cues, we systematically categorize exogenous uncertainty factors into four latent variables: 1) semantic information influenced by sensitive attributes, 2) semantic information unaffected by sensitive attributes, 3) environmental cues influenced by sensitive attributes, and 4) environmental cues unaffected by sensitive attributes. By incorporating fairness regularization, we exclusively employ semantic information for classification purposes. Empirical validation on synthetic and real-world datasets substantiates the effectiveness of our approach, demonstrating improved accuracy levels while ensuring the preservation of fairness in the evolving landscape of continuous domains

Dynamical Behaviors of Impulsive Stochastic Reaction-Diffusion Neural Networks with Mixed Time Delays

We discuss the dynamical behaviors of impulsive stochastic reaction-diffusion neural networks (ISRDNNs) with mixed time delays. By using a well-known L-operator differential inequality with mixed time delays and combining with the Lyapunov-Krasovkii functional approach, as well as linear matrix inequality (LMI) technique, some novel sufficient conditions are derived to ensure the existence, uniqueness, and global exponential stability of the periodic solutions for ISRDNNs with mixed time delays in the mean square sense. The obtained sufficient conditions depend on the reaction-diffusion terms. The results of this paper are new and improve some of the previously known results. The proposed model is quite general since many factors such as noise perturbations, impulsive phenomena, and mixed time delays are considered. Finally, two numerical examples are provided to verify the usefulness of the obtained results

Adsorption of Indium(III) Ions from an Acidic Solution by Using UiO-66

Considering environmental friendliness and economic factors, the separation and extraction of indium under acidic conditions are of great significance. In this research, metal-organic frameworks (MOFs) of UiO-66 were successfully prepared and used for the separation and adsorption of indium. The properties of UiO-66 were structurally characterized using powder X-ray diffraction (XRD), Fourier-Transform Infrared Spectroscopy (FTIR), Brunauer-Emmett-Teller surface area analyzer (BET), thermogravimetric analysers (TGA) and Scanning Electron Microscope (SEM). The results show that UiO-66 can resist acid and keep its structure unchanged, even at a strong acidity of pH 1. The adsorption performance of UiO-66 to indium (III) was also evaluated. The results show that the adsorption process of indium ions was by the Langmuir adsorption isotherm, with a maximum adsorption capacity of 11.75 mg·g−1 being recorded. The adsorption kinetics experiment preferably fits the second-order kinetic model. A possible mechanism for the adsorption of In(III) by UiO-66 was explored through X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared analysis(FT-IR). It was concluded that the C=O of free –COOH of UiO-66 was involved in the adsorption of In(III) by cation exchange. This study indicates, for the first time, that UiO-66 can be applied as an acid-resistant adsorbent to recover indium (III)

Insights into the Structures, Inhibitors, and Improvement Strategies of Glucose Oxidase

Glucose oxidase, which uses molecular oxygen as an electron acceptor to specifically catalyze the conversion of β-d-glucose to gluconic acid and hydrogen peroxide (H2O2), has been considered an important enzyme in increasing environmental sustainability and food security. However, achieving the high yield, low price and high activity required for commercial viability remains challenging. In this review, we first present a brief introduction, looking at the sources, characteristics, catalytic process, and applications of glucose oxidase. Then, the predictive structures of glucose oxidase from two different sources are comparatively discussed. We summarize the inhibitors of glucose oxidase. Finally, we highlight how the production of glucose oxidase can be improved by optimizing the culture conditions and microbial metabolic engineering

傅里叶望远镜激光发射系统性能分析

The performance of Fourier telescope transmitting system directly affects the image resolution and quality. The parameters and performance requirements of laser are analyzed. The design outline of fiber laser is presented. Meanwhile, the point is put forward that the laser coherence length need to be at least 1.6 times of the residual optical path. The stability of laser power and frequency is simulated. To ensure the stability of the laser, some methods are put forward from the points of design, algorithm and practice. Furthermore, combined with the results of simulation and experiment, the influences of the transmitter performance on image quality is analyzed, from the aspects of transmitting aperture layout, aperture amount, position precision and the beam pointing error. And point out that baseline redundancy, image resolution, target spectrum and image quality are the factors should be synthetically considered when arranging transmitter array. The formula for calculating the number of transmitting apertures is obtained based on the two-dimensional sampling theorem and the statistical analysis results. The transmitting aperture number is calculated for the Fourier telescope whose resolution would be 5 cm for objects in 1000 km low earth orbit. In addition, it is concluded that the transmitting aperture position error should be less than 5% of the minimum aperture spacing. ©, 2015, Chinese Optical Society. All right reserved