FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee

Algorithmic fairness plays an increasingly critical role in machine learning research. Several group fairness notions and algorithms have been proposed. However, the fairness guarantee of existing fair classification methods mainly depends on specific data distributional assumptions, often requiring large sample sizes, and fairness could be violated when there is a modest number of samples, which is often the case in practice. In this paper, we propose FaiREE, a fair classification algorithm that can satisfy group fairness constraints with finite-sample and distribution-free theoretical guarantees. FaiREE can be adapted to satisfy various group fairness notions (e.g., Equality of Opportunity, Equalized Odds, Demographic Parity, etc.) and achieve the optimal accuracy. These theoretical guarantees are further supported by experiments on both synthetic and real data. FaiREE is shown to have favorable performance over state-of-the-art algorithms.Comment: 45 pages, 9 figure

CloudHealth: A Model-Driven Approach to Watch the Health of Cloud Services

Cloud systems are complex and large systems where services provided by different operators must coexist and eventually cooperate. In such a complex environment, controlling the health of both the whole environment and the individual services is extremely important to timely and effectively react to misbehaviours, unexpected events, and failures. Although there are solutions to monitor cloud systems at different granularity levels, how to relate the many KPIs that can be collected about the health of the system and how health information can be properly reported to operators are open questions. This paper reports the early results we achieved in the challenge of monitoring the health of cloud systems. In particular we present CloudHealth, a model-based health monitoring approach that can be used by operators to watch specific quality attributes. The CloudHealth Monitoring Model describes how to operationalize high level monitoring goals by dividing them into subgoals, deriving metrics for the subgoals, and using probes to collect the metrics. We use the CloudHealth Monitoring Model to control the probes that must be deployed on the target system, the KPIs that are dynamically collected, and the visualization of the data in dashboards.Comment: 8 pages, 2 figures, 1 tabl

Enhanced high-temperature performance and thermal stability of lithium-rich cathode via combining full concentration gradient design with surface spinel modification

Lithium-rich layered oxides (LLOs) are considered as the most promising candidate for the cathode of high energy density lithium-ion batteries. However, the poor cycle stability especially under high temperature is hindering its practical applications. Herein, a full concentration gradient LLO with spinel modification is designed and prepared. This synergistic strategy not only makes full use of high Ni content that improving the discharge voltage but also mitigates the detrimental influence of surface residual alkalis. The surface spinel modified cathode exhibits a higher initial coulombic efficiency of 87.52% with enhanced cycle stability at 55 ?C (191.5mAh/g after 200 cycles at 1C), the average discharge voltage drop is also alleviated to 3.17 mV per cycle (at 55 ?C). Furthermore, it also shows enhanced thermal stability, in which the exothermic onset temperature rises from 265.380 to 295.221 ?C, and the thermal release decreases from 211.525 to 181.181 J/g. This work proposes an integrated strategy to enhance the comprehensive performance of LLOs, thus shed a light on the way for its practical application