Social Bias Meets Data Bias: The Impacts of Labeling and Measurement Errors on Fairness Criteria

Although many fairness criteria have been proposed to ensure that machine learning algorithms do not exhibit or amplify our existing social biases, these algorithms are trained on datasets that can themselves be statistically biased. In this paper, we investigate the robustness of a number of existing (demographic) fairness criteria when the algorithm is trained on biased data. We consider two forms of dataset bias: errors by prior decision makers in the labeling process, and errors in measurement of the features of disadvantaged individuals. We analytically show that some constraints (such as Demographic Parity) can remain robust when facing certain statistical biases, while others (such as Equalized Odds) are significantly violated if trained on biased data. We also analyze the sensitivity of these criteria and the decision maker's utility to biases. We provide numerical experiments based on three real-world datasets (the FICO, Adult, and German credit score datasets) supporting our analytical findings. Our findings present an additional guideline for choosing among existing fairness criteria, or for proposing new criteria, when available datasets may be biased

LightSleepNet: Design of a Personalized Portable Sleep Staging System Based on Single-Channel EEG

This paper proposed LightSleepNet - a light-weight, 1-d Convolutional Neural Network (CNN) based personalized architecture for real-time sleep staging, which can be implemented on various mobile platforms with limited hardware resources. The proposed architecture only requires an input of 30s single-channel EEG signal for the classification. Two residual blocks consisting of group 1-d convolution are used instead of the traditional convolution layers to remove the redundancy in the CNN. Channel shuffles are inserted into each convolution layer to improve the accuracy. In order to avoid over-fitting to the training set, a Global Average Pooling (GAP) layer is used to replace the fully connected layer, which further reduces the total number of the model parameters significantly. A personalized algorithm combining Adaptive Batch Normalization (AdaBN) and gradient re-weighting is proposed for unsupervised domain adaptation. A higher priority is given to examples that are easy to transfer to the new subject, and the algorithm could be personalized for new subjects without re-training. Experimental results show a state-of-the-art overall accuracy of 83.8% with only 45.76 Million Floating-point Operations per Second (MFLOPs) computation and 43.08 K parameters.Comment: 5 pages, 3 figures, published by IEEE TCAS-I

KwaiYiiMath: Technical Report

Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning. In this report, we introduce the KwaiYiiMath which enhances the mathematical reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT) and Reinforced Learning from Human Feedback (RLHF), including on both English and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale Chinese primary school mathematics test set (named KMath), consisting of 188 examples to evaluate the correctness of the problem-solving process generated by the models. Empirical studies demonstrate that KwaiYiiMath can achieve state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with the similar size models, respectively.Comment: technical report. arXiv admin note: text overlap with arXiv:2306.16636 by other author

Cost-effectiveness analysis of vaccinations and decision makings on vaccination programmes in Hong Kong: a systematic review

Objectives: To describe and systematically review the modelling and reporting of cost-effectiveness analysis of vaccination in Hong Kong, and to identify areas for quality enhancement in future cost-effectiveness analyses. Methods: We conducted a comprehensive and systematic review of cost-effectiveness studies related to vaccination and government immunisation programmes in Hong Kong published from 1990 to 2015, through database search of Pubmed, Web of Science, Embase, and OVID Medline. Methodological quality of selected studies was assessed using Consolidated Health Economic Evaluation Reporting Standards checklist (CHEERS). Decision making of vaccination was obtained from Scientific Committee on Vaccine Preventable Diseases (SCVPD) and Department of Health in Hong Kong. Results: Nine eligible studies reporting twelve comparative cost-effectiveness comparisons of vaccination programme for influenza (n = 2), pneumococcal disease (n = 3), influenza plus pneumococcal disease (n = 1), chickenpox (n = 2), Haemophilus influenzae b (n = 1), hepatitis A (n = 1), cervical cancer (n = 1) and rotavirus (n = 1) were identified. Ten comparisons (83.3%) calculated the incremental cost-effectiveness ratio (ICER) of a vaccination strategy versus status quo as outcomes in terms of cost in USD per life-years, cost per quality-adjusted life-years, or cost per disability-adjusted life-years. Among those 10 comparisons in base-case scenario, 4 evaluated interventions were cost-saving relative to status quo while the ICER estimates in 3 of the 6 remaining comparisons were far below commonly accepted threshold and WHO willingness-to-pay threshold, suggestive of very cost-effective. Seven studies were of good quality based on the CHEERS checklist; one was of moderate quality; and one was of excellent quality. The common methodological problems were characterisation of heterogeneity and reporting of study parameters. Conclusions: There was a paucity of cost-effectiveness models evaluating vaccination targeted to the Hong Kong population. All evaluated vaccinations and immunisation interventions in Hong Kong, except for Haemophilus influenzae b, hepatitis A and HPV vaccinations, were considered either cost-saving or very cost-effective when compared to status quo

Social Bias Meets Data Bias: The Impacts of Labeling and Measurement Errors on Fairness Criteria

Although many fairness criteria have been proposed to ensure that machine learning algorithms do not exhibit or amplify our existing social biases, these algorithms are trained on datasets that can themselves be statistically biased. In this paper, we investigate the robustness of existing (demographic) fairness criteria when the algorithm is trained on biased data. We consider two forms of dataset bias: errors by prior decision makers in the labeling process, and errors in the measurement of the features of disadvantaged individuals. We analytically show that some constraints (such as Demographic Parity) can remain robust when facing certain statistical biases, while others (such as Equalized Odds) are significantly violated if trained on biased data. We provide numerical experiments based on three real-world datasets (the FICO, Adult, and German credit score datasets) supporting our analytical findings. While fairness criteria are primarily chosen under normative considerations in practice, our results show that naively applying a fairness constraint can lead to not only a loss in utility for the decision maker, but more severe unfairness when data bias exists. Thus, understanding how fairness criteria react to different forms of data bias presents a critical guideline for choosing among existing fairness criteria, or for proposing new criteria, when available datasets may be biased

Protective Effects of Intravitreal Injection of the Rho-Kinase Inhibitor Y-27632 in a Rodent Model of Nonarteritic Anterior Ischemic Optic Neuropathy (rAION)

Purpose. We sought to explore the effects of intravitreal injection of the Rho-kinase inhibitor Y-27632 in a rodent model of nonarteritic anterior ischemic optic neuropathy (rAION). Methods. The rAION model was established by using laser-induced photoactivation of intravenously administered Rose Bengal in rats. The rats received intravitreal injections of Y-27632 or PBS 1, 3, and 6 days after rAION induction. Optical coherence tomography (OCT) was performed at 2 days and 4 weeks after induction. Visual evoked potential (VEP) was used to evaluate the visual function at 4 weeks. Brn3a immunofluorescence staining of surviving RGCs and apoptosis assays of RGCs were performed at 4 weeks. Results. Optic nerve head (ONH) width was significantly reduced in the Y-27632 group compared with that in the PBS group at 2 days after induction (p<0.05). At 4 weeks, the P1 amplitude of flash-VEP (FVEP) in the Y-27632 group was significantly higher than that of the PBS group (p<0.05). The RGC densities in the central and midperipheral retinas in the Y-27632 group were significantly higher than those in the PBS group (p<0.05). Furthermore, there was a significant decrease in apoptotic RGCs in the Y-27632 group than in the PBS group (p<0.05). Conclusions. Intravitreal injection of Y-27632 had neuroprotective effects on ONH edema, RGC survival, and visual function preservation in rAION

Variations in microbial diversity and chemical components of raw dark tea under different relative humidity storage conditions

Raw dark tea (RDT) usually needs to be stored for a long time to improve its quality under suitable relative humidity (RH). However, the impact of RH on tea quality is unclear. In this study, we investigated the metabolites and microbial diversity, and evaluated the sensory quality of RDT stored under three RH conditions (1%, 57%, and 88%). UHPLC-Q-TOF-MS analysis identified 144 metabolites, including catechins, flavonols, phenolic acids, amino acids, and organic acids. 57% RH led to higher levels of O-methylated catechin derivatives, polymerized catechins, and flavonols/flavones when compared to 1% and 88% RH. The best score in sensory evaluation was also obtained by 57% RH. Aspergillus, Gluconobacter, Kluyvera, and Pantoea were identified as the core functional microorganisms in RDT under different RH storage conditions. Overall, the findings provided new insights into the variation of microbial communities and chemical components under different RH storage conditions

Design of a Flexible Wearable Smart sEMG Recorder Integrated Gradient Boosting Decision Tree Based Hand Gesture Recognition

This paper proposed a wearable smart sEMG recorder integrated gradient boosting decision tree (GBDT) based hand gesture recognition. A hydrogel-silica gel based flexible surface electrode band is used as the tissue interface. The sEMG signal is collected using a neural signal acquisition analog front end (AFE) chip. A quantitative analysis method is proposed to balance the algorithm complexity and recognition accuracy. A parallel GBDT implementation is proposed featuring a low latency. The proposed GBDT based neural signal processing unit (NSPU) is implemented on an FPGA near the AFE. A RF module is used for wireless communication. A hand gesture set including 12 gestures is designed for human-computer interaction. Experimental results show an overall hand gesture recognition accuracy of 91