Achieving non-discrimination in prediction

Discrimination-aware classification is receiving an increasing attention in data science fields. The pre-process methods for constructing a discrimination-free classifier first remove discrimination from the training data, and then learn the classifier from the cleaned data. However, they lack a theoretical guarantee for the potential discrimination when the classifier is deployed for prediction. In this paper, we fill this gap by mathematically bounding the probability of the discrimination in prediction being within a given interval in terms of the training data and classifier. We adopt the causal model for modeling the data generation mechanism, and formally defining discrimination in population, in a dataset, and in prediction. We obtain two important theoretical results: (1) the discrimination in prediction can still exist even if the discrimination in the training data is completely removed; and (2) not all pre-process methods can ensure non-discrimination in prediction even though they can achieve non-discrimination in the modified training data. Based on the results, we develop a two-phase framework for constructing a discrimination-free classifier with a theoretical guarantee. The experiments demonstrate the theoretical results and show the effectiveness of our two-phase framework

Achieving Causal Fairness in Machine Learning

Fairness is a social norm and a legal requirement in today\u27s society. Many laws and regulations (e.g., the Equal Credit Opportunity Act of 1974) have been established to prohibit discrimination and enforce fairness on several grounds, such as gender, age, sexual orientation, race, and religion, referred to as sensitive attributes. Nowadays machine learning algorithms are extensively applied to make important decisions in many real-world applications, e.g., employment, admission, and loans. Traditional machine learning algorithms aim to maximize predictive performance, e.g., accuracy. Consequently, certain groups may get unfairly treated when those algorithms are applied for decision-making. Therefore, it is an imperative task to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also subject to fairness requirements. In the literature, machine learning researchers have proposed association-based fairness notions, e.g., statistical parity, disparate impact, equality of opportunity, etc., and developed respective discrimination mitigation approaches. However, these works did not consider that fairness should be treated as a causal relationship. Although it is well known that association does not imply causation, the gap between association and causation is not paid sufficient attention by the fairness researchers and stakeholders. The goal of this dissertation is to study fairness in machine learning, define appropriate fairness notions, and develop novel discrimination mitigation approaches from a causal perspective. Based on Pearl\u27s structural causal model, we propose to formulate discrimination as causal effects of the sensitive attribute on the decision. We consider different types of causal effects to cope with different situations, including the path-specific effect for direct/indirect discrimination, the counterfactual effect for group/individual discrimination, and the path-specific counterfactual effect for general cases. In the attempt to measure discrimination, the unidentifiable situations pose an inevitable barrier to the accurate causal inference. To address this challenge, we propose novel bounding methods to accurately estimate the strength of unidentifiable fairness notions, including path-specific fairness, counterfactual fairness, and path-specific counterfactual fairness. Based on the estimation of fairness, we develop novel and efficient algorithms for learning fair classification models. Besides classification, we also investigate the discrimination issues in other machine learning scenarios, such as ranked data analysis

Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms

Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information for downstream tasks. In this work, we define a new notion of causal disentanglement from the perspective of independent causal mechanisms. We propose ICM-VAE, a framework for learning causally disentangled representations supervised by causally related observed labels. We model causal mechanisms using learnable flow-based diffeomorphic functions to map noise variables to latent causal variables. Further, to promote the disentanglement of causal factors, we propose a causal disentanglement prior that utilizes the known causal structure to encourage learning a causally factorized distribution in the latent space. Under relatively mild conditions, we provide theoretical results showing the identifiability of causal factors and mechanisms up to permutation and elementwise reparameterization. We empirically demonstrate that our framework induces highly disentangled causal factors, improves interventional robustness, and is compatible with counterfactual generation

An Improved Modeling for Low-grade Organic Rankine Cycle Coupled with Optimization Design of Radial-inflow Turbine

This document is the Accepted Manuscript of the following article: Lijing Zhai, Guoqiang Xu, Jie Wen, Yongkai Quan, Jian Fu, Hongwei Wu, and Tingting Li, ‘An improved modeling for low-grade organic Rankine cycle coupled with optimization design of radial-inflow turbine’, Energy Conversion and Management, Vol. 153: 60-70, December 2017. Under embargo. Embargo end date: 10 October 2018. The final, published version is available online at DOI: https://doi.org/10.1016/j.enconman.2017.09.063. Published by Elsevier Ltd.Organic Rankine cycle (ORC) has been proven to be an effective and promising technology to convert low-grade heat energy into power, attracting rapidly growing interest in recent years. As the key component of the ORC system, turbine significantly influences the overall cycle performance and its efficiency also varies with different working fluids as well as in different operating conditions. However, turbine efficiency is generally assumed to be constant in the conventional cycle design. Aiming at this issue, this paper couples the ORC system design with the radial-inflow turbine design to investigate the thermodynamic performance of the ORC system and the aerodynamic characteristics of radial-inflow turbine simultaneously. The constrained genetic algorithm (GA) is used to optimize the radial-inflow turbine with attention to six design parameters, including degree of reaction, velocity ratio, loading coefficient, flow coefficient, ratio of wheel diameter, and rotational speed. The influence of heat source outlet temperature on the performance of the radial-inflow turbine and the ORC system with constant mass flow rate of the heat source and constant heat source inlet temperature is investigated for four kinds of working fluids. The net electrical powers achieved are from few tens kWs to one hundred kWs. The results show that the turbine efficiency decreases with increasing heat source outlet temperature and that the decreasing rate of turbine efficiency becomes faster in the high temperature region. The optimized turbine efficiency varies from 88.06% (using pentane at the outlet temperature of 105 ºC) to 91.01% (using R245fa at the outlet temperature of 80 ºC), which appears much higher compared to common values reported in the literature. Furthermore, the cycle efficiency increases monotonously with the growth of the heat source outlet temperature, whereas the net power output has the opposite trend. R123 achieves the maximum cycle efficiency of 12.21% at the heat source outlet temperature of 110 ºC. Based on the optimized results, the recommended ranges of the key design parameters for ORC radial-inflow turbine are presented as well.Peer reviewe

Performance analysis of a new deep super-cooling two-stage organic Rankine cycle

This document is the Accepted Manuscript version of the following article: Y. Yuan, G. Xu, Y. Quan, H. Wu, G. Song, W. Gong, and X. Luo, ‘Performance analysis of a new deep super-cooling two-stage organic Rankine cycle’, Energy Conversion and Management, Vol. 148: 305-316, September 2017. The final, definitive version is available online at doi:https://doi.org/10.1016/j.enconman.2017.06.006. Published by Elsevier.In this article, a new deep super-cooling two-stage organic Rankine cycle (DTORC) is proposed and evaluated at high temperature waste heat recovery in order to increase the power output. A thermodynamic model of recuperative organic rankine cycle (ORC) is also established for the purpose of comparison. Furthermore, a new evaluation index, effective heat source utilization, is proposed to reflect the relationship among the heat source, power output and consumption of the waste heat carrier. A simulation model is formulated and analysed under a wide range of operating conditions with the heat resource temperature fixed at 300℃. Hexamethyldisiloxane (MM) and R245fa are used as the working fluid for DTORC, and MM for ORC. In the current work, the comparisons of heat source utilization, net thermal efficiency as well as the total surface area of the heat exchangers between DTORC and RC are discussed in detail. Results show that the DTORC performs better than ORC at high temperature waste heat recovery and it could increase the power output by 150%. Moreover, the maximum net thermal efficiency of DTORC can reach to 23.5% and increased by 30.5% compared with that using ORC, whereas the total surface areas of the heat exchangers are nearly the same.Peer reviewe

SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models

Mixture-of-Experts (MoE) has emerged as a favorable architecture in the era of large models due to its inherent advantage, i.e., enlarging model capacity without incurring notable computational overhead. Yet, the realization of such benefits often results in ineffective GPU memory utilization, as large portions of the model parameters remain dormant during inference. Moreover, the memory demands of large models consistently outpace the memory capacity of contemporary GPUs. Addressing this, we introduce SiDA (Sparsity-inspired Data-Aware), an efficient inference approach tailored for large MoE models. SiDA judiciously exploits both the system's main memory, which is now abundant and readily scalable, and GPU memory by capitalizing on the inherent sparsity on expert activation in MoE models. By adopting a data-aware perspective, SiDA achieves enhanced model efficiency with a neglectable performance drop. Specifically, SiDA attains a remarkable speedup in MoE inference with up to 3.93X throughput increasing, up to 75% latency reduction, and up to 80% GPU memory saving with down to 1% performance drop. This work paves the way for scalable and efficient deployment of large MoE models, even in memory-constrained systems

Pressure-induced Superconductivity and Structure Phase Transition in SnAs-based Zintl Compound SrSn2As2

Layered SnAs-based Zintl compounds exhibit a distinctive electronic structure, igniting extensive research efforts in areas of superconductivity, topological insulators and quantum magnetism. In this paper, we systematically investigate the crystal structures and electronic properties of the Zintl compound SrSn2As2 under high-pressure. At approximately 20.8 GPa, pressure-induced superconductivity is observed in SrSn2As2 with a characteristic dome-like evolution of Tc. Theoretical calculations together with high pressure synchrotron X-ray diffraction and Raman spectroscopy have identified that SrSn2As2 undergoes a structural transformation from a trigonal to a monoclinic structure. Beyond 28.3 GPa, the superconducting transition temperature is suppressed due to a reduction of the density of state at the Fermi level. The discovery of pressure-induced superconductivity, accompanied by structural transitions in SrSn2As2, greatly expands the physical properties of layered SnAs-based compounds and provides a new ground states upon compression.Comment: 15 pages, 6 figures. arXiv admin note: text overlap with arXiv:2307.1562