Retiring Δ\DeltaDP: New Distribution-Level Metrics for Demographic Parity

Demographic parity is the most widely recognized measure of group fairness in machine learning, which ensures equal treatment of different demographic groups. Numerous works aim to achieve demographic parity by pursuing the commonly used metric $\Delta DP$. Unfortunately, in this paper, we reveal that the fairness metric $\Delta DP$ can not precisely measure the violation of demographic parity, because it inherently has the following drawbacks: i) zero-value $\Delta DP$ does not guarantee zero violation of demographic parity, ii) $\Delta DP$ values can vary with different classification thresholds. To this end, we propose two new fairness metrics, Area Between Probability density function Curves (ABPC) and Area Between Cumulative density function Curves (ABCC), to precisely measure the violation of demographic parity at the distribution level. The new fairness metrics directly measure the difference between the distributions of the prediction probability for different demographic groups. Thus our proposed new metrics enjoy: i) zero-value ABCC/ABPC guarantees zero violation of demographic parity; ii) ABCC/ABPC guarantees demographic parity while the classification thresholds are adjusted. We further re-evaluate the existing fair models with our proposed fairness metrics and observe different fairness behaviors of those models under the new metrics. The code is available at https://github.com/ahxt/new_metric_for_demographic_parityComment: Accepted by TMLR. Code available at https://github.com/ahxt/new_metric_for_demographic_parit

Chasing Fairness in Graphs: A GNN Architecture Perspective

There has been significant progress in improving the performance of graph neural networks (GNNs) through enhancements in graph data, model architecture design, and training strategies. For fairness in graphs, recent studies achieve fair representations and predictions through either graph data pre-processing (e.g., node feature masking, and topology rewiring) or fair training strategies (e.g., regularization, adversarial debiasing, and fair contrastive learning). How to achieve fairness in graphs from the model architecture perspective is less explored. More importantly, GNNs exhibit worse fairness performance compared to multilayer perception since their model architecture (i.e., neighbor aggregation) amplifies biases. To this end, we aim to achieve fairness via a new GNN architecture. We propose \textsf{F}air \textsf{M}essage \textsf{P}assing (FMP) designed within a unified optimization framework for GNNs. Notably, FMP \textit{explicitly} renders sensitive attribute usage in \textit{forward propagation} for node classification task using cross-entropy loss without data pre-processing. In FMP, the aggregation is first adopted to utilize neighbors' information and then the bias mitigation step explicitly pushes demographic group node presentation centers together. In this way, FMP scheme can aggregate useful information from neighbors and mitigate bias to achieve better fairness and prediction tradeoff performance. Experiments on node classification tasks demonstrate that the proposed FMP outperforms several baselines in terms of fairness and accuracy on three real-world datasets. The code is available in {\url{https://github.com/zhimengj0326/FMP}}.Comment: Accepted by AAAI Conference on Artificial Intelligence (AAAI) 2024. arXiv admin note: substantial text overlap with arXiv:2202.0418

Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach

Fairness in machine learning has attracted increasing attention in recent years. The fairness methods improving algorithmic fairness for in-distribution data may not perform well under distribution shifts. In this paper, we first theoretically demonstrate the inherent connection between distribution shift, data perturbation, and model weight perturbation. Subsequently, we analyze the sufficient conditions to guarantee fairness (i.e., low demographic parity) for the target dataset, including fairness for the source dataset, and low prediction difference between the source and target datasets for each sensitive attribute group. Motivated by these sufficient conditions, we propose robust fairness regularization (RFR) by considering the worst case within the model weight perturbation ball for each sensitive attribute group. We evaluate the effectiveness of our proposed RFR algorithm on synthetic and real distribution shifts across various datasets. Experimental results demonstrate that RFR achieves better fairness-accuracy trade-off performance compared with several baselines. The source code is available at \url{https://github.com/zhimengj0326/RFR_NeurIPS23}.Comment: NeurIPS 202

Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model

With the rapid growth in model size, fine-tuning the large pre-trained language model has become increasingly difficult due to its extensive memory usage. Previous works usually focus on reducing the number of trainable parameters in the network. While the model parameters do contribute to memory usage, the primary memory bottleneck during training arises from storing feature maps, also known as activations, as they are crucial for gradient calculation. Notably, neural networks are usually trained using stochastic gradient descent. We argue that in stochastic optimization, models can handle noisy gradients as long as the gradient estimator is unbiased with reasonable variance. Following this motivation, we propose a new family of unbiased estimators called WTA-CRS, for matrix production with reduced variance, which only requires storing the sub-sampled activations for calculating the gradient. Our work provides both theoretical and experimental evidence that, in the context of tuning transformers, our proposed estimators exhibit lower variance compared to existing ones. By replacing the linear operation with our approximated one in transformers, we can achieve up to 2.7$\times$ peak memory reduction with almost no accuracy drop and enables up to $6.4\times$ larger batch size. Under the same hardware, WTA-CRS enables better down-streaming task performance by applying larger models and/or faster training speed with larger batch sizes

Self-compression of stimulated Raman backscattering by flying focus

A novel regime of self-compression is proposed for plasma-based backward Raman amplification(BRA) upon flying focus. By using a pumping focus moving with a speed equal to the group velocity of stimulated Raman backscattering(SRBS), only a short part of SRBS which does always synchronize with the flying focus can be amplified. Due to the asymmetrical amplification, the pulse can be directly compressed in the linear stage of BRA. Therefore, instead of a short pulse, the Raman spontaneous or a long pulse can seed the BRA amplifiers. The regime is supported by the 2D particle-in-cell(PIC) simulation without a seed, presenting that the pump pulse is compressed from 26ps to 116fs, with an output amplitude comparable with the case of a well-synchronized short seed. This method provides a significant way to simplify the Raman amplifiers and overcome the issue of synchronization jitter between the pump and the seed

Causal associations between dietary factors and colorectal cancer risk: a Mendelian randomization study

BackgroundPrevious epidemiological studies have found a link between colorectal cancer (CRC) and human dietary habits. However, the inherent limitations and inevitable confounding factors of the observational studies may lead to the inaccurate and doubtful results. The causality of dietary factors to CRC remains elusive.MethodsWe conducted two-sample Mendelian randomization (MR) analyses utilizing the data sets from the IEU Open GWAS project. The exposure datasets included alcoholic drinks per week, processed meat intake, beef intake, poultry intake, oily fish intake, non-oily fish intake, lamb/mutton intake, pork intake, cheese intake, bread intake, tea intake, coffee intake, cooked vegetable intake, cereal intake, fresh fruit intake, salad/raw vegetable intake, and dried fruit intake. In our MR analyses, the inverse variance weighted (IVW) method was employed as the primary analytical approach. The weighted median, MR-Egger, weighted mode, and simple mode were also applied to quality control. Heterogeneity and pleiotropic analyses were implemented to replenish the accuracy of the results.ResultsMR consequences revealed that alcoholic drinks per week [odds ratio (OR): 1.565, 95% confidence interval (CI): 1.068–2.293, p = 0.022], non-oily fish intake (OR: 0.286; 95% CI: 0.095–0.860; p = 0.026), fresh fruit intake (OR: 0.513; 95% CI: 0.273–0.964; p = 0.038), cereal intake (OR: 0.435; 95% CI: 0.253–0.476; p = 0.003) and dried fruit intake (OR: 0.522; 95% CI: 0.311–0.875; p = 0.014) was causally correlated with the risk of CRC. No other significant relationships were obtained. The sensitivity analyses proposed the absence of heterogeneity or pleiotropy, demonstrating the reliability of the MR results.ConclusionThis study indicated that alcoholic drinks were associated with an increased risk of CRC, while non-oily fish intake, fresh fruit intake, cereal intake, and dried fruit were associated with a decreased risk of CRC. This study also indicated that other dietary factors included in this research were not associated with CRC. The current study is the first to establish the link between comprehensive diet-related factors and CRC at the genetic level, offering novel clues for interpreting the genetic etiology of CRC and replenishing new perspectives for the clinical practice of gastrointestinal disease prevention

Design and optimization of an advanced time-of-flight neutron spectrometer for deuterium plasmas of the large helical device

A time-of-flight neutron spectrometer based on the Time-Of-Flight Enhanced Diagnostic (TOFED) concept has been designed and is under development for the Large Helical Device (LHD). It will be the first advanced neutron spectrometer to measure the 2.45 MeV D–D neutrons (DDNs) from helical/stellarator plasmas. The main mission of the new TOFED is to study the supra-thermal deuterons generated from the auxiliary heating systems in helical plasmas by measuring the time-of-flight spectra of DDN. It will also measure the triton burnup neutrons (TBNs) from the d+t reactions, unlike the original TOFED in the EAST tokamak. Its capability of diagnosing the TBN ratios is evaluated in this work. This new TOFED is expected to be installed in the basement under the LHD hall and shares the collimator with one channel of the vertical neutron camera to define its line of sight. The distance from its primary scintillators to the equatorial plane of LHD plasmas is about 15.5 m. Based on Monte Carlo simulation by a GEANT4 model, the resolution of the DDN energy spectra is 6.6%. When projected onto the neutron rates that are typically obtained in LHD deuterium plasmas (an order of 1015 n/s with neutral beam injection), we expect to obtain the DDN and TBN counting rates of about 2.5 · 105 counts/s and 250 counts/s, respectively. This will allow us to analyze the DDN time-of-flight spectra on time scales of 0.1 s and diagnose the TBN emission rates in several seconds with one instrument, for the first time in helical/stellarator plasmas

Low beauvericin concentrations promote PC-12 cell survival under oxidative stress by regulating lipid metabolism and PI3K/AKT/mTOR signaling

Beauvericin (BEA), a naturally occurring cyclic peptide with good pharmacological activity, has been widely explored in anticancer research. Although BEA is toxic, studies have demonstrated its antioxidant activity. However, to date, the antioxidant mechanisms of BEA remain unclear. Herein, we conducted a comprehensive and detailed study of the antioxidant mechanism of BEA using an untargeted metabolomics approach, subsequently validating the results. BEA concentrations of 0.5 and 1 μM significantly inhibited H2O2-induced oxidative stress (OS), decreased reactive oxygen species levels in PC-12 cells, and restored the mitochondrial membrane potential. Untargeted metabolomics indicated that BEA was primarily involved in lipid-related metabolism, suggesting its role in resisting OS in PC-12 cells by participating in lipid metabolism. BEA combated OS damage by increasing phosphatidylcholine, phosphatidylethanolamine, and sphingolipid levels. In the current study, BEA upregulated proteins related to the PI3K/AKT/mTOR pathway, thereby promoting cell survival. These findings support the antioxidant activity of BEA at low concentrations, warranting further research into its pharmacological effects