Uncertainty of Joint Neural Contextual Bandit

Contextual bandit learning is increasingly favored in modern large-scale recommendation systems. To better utlize the contextual information and available user or item features, the integration of neural networks have been introduced to enhance contextual bandit learning and has triggered significant interest from both academia and industry. However, a major challenge arises when implementing a disjoint neural contextual bandit solution in large-scale recommendation systems, where each item or user may correspond to a separate bandit arm. The huge number of items to recommend poses a significant hurdle for real world production deployment. This paper focuses on a joint neural contextual bandit solution which serves all recommending items in one single model. The output consists of a predicted reward $\mu$, an uncertainty $\sigma$ and a hyper-parameter $\alpha$ which balances exploitation and exploration, e.g., $\mu + \alpha \sigma$. The tuning of the parameter $\alpha$ is typically heuristic and complex in practice due to its stochastic nature. To address this challenge, we provide both theoretical analysis and experimental findings regarding the uncertainty $\sigma$ of the joint neural contextual bandit model. Our analysis reveals that $\alpha$ demonstrates an approximate square root relationship with the size of the last hidden layer $F$ and inverse square root relationship with the amount of training data $N$, i.e., $\sigma \propto \sqrt{\frac{F}{N}}$. The experiments, conducted with real industrial data, align with the theoretical analysis, help understanding model behaviors and assist the hyper-parameter tuning during both offline training and online deployment

RISE-Based Integrated Motion Control of Autonomous Ground Vehicles With Asymptotic Prescribed Performance

This article investigates the integrated lane-keeping and roll control for autonomous ground vehicles (AGVs) considering the transient performance and system disturbances. The robust integral of the sign of error (RISE) control strategy is proposed to achieve the lane-keeping control purpose with rollover prevention, by guaranteeing the asymptotic stability of the closed-loop system, attenuating systematic disturbances, and maintaining the controlled states within the prescribed performance boundaries. Three contributions have been made in this article: 1) a new prescribed performance function (PPF) that does not require accurate initial errors is proposed to guarantee the tracking errors restricted within the predefined asymptotic boundaries; 2) a modified neural network (NN) estimator which requires fewer adaptively updated parameters is proposed to approximate the unknown vertical dynamics; and 3) the improved RISE control based on PPF is proposed to achieve the integrated control objective, which analytically guarantees both the controller continuity and closed-loop system asymptotic stability by integrating the signum error function. The overall system stability is proved with the Lyapunov function. The controller effectiveness and robustness are finally verified by comparative simulations using two representative driving maneuvers, based on the high-fidelity CarSim-Simulink simulation

Oxidation behavior of two-phase (γ’+β) Ni-Al coatings doped with Dy and Hf

Dy/Hf co-doped two-phase (γ’+β) Ni-Al coatings were prepared by electron beam physical vapour deposition (EB-PVD). Cyclic oxidation behaviour of the coatings were investigated at 1100℃. The addition of 0.1at% Dy or 0.05at% Dy +0.3at% Hf to two-phase (γ’+β) Ni-Al coating significantly improved cyclic oxidation resistance, while addition of 0.5at% Hf to (γ’+β) Ni-Al coating no obvious effect on scale adhesion. The 0.1at% Dy doped and 0.05at% Dy +0.3at% Hf co-doped two-phase (γ’+β) Ni-Al coatings yielded mass gain of 1.24 mg/cm2 and 1.04 mg/cm2 after 100h cyclic oxidation. The Dy/Hf co-doped coating showed even further lower oxidation rate as compared to the corresponding Dy doped. In order to sufficiently exert reactive element effect (REE), extremely low solubility of the reactive element in each phase of the coatings should be guaranteed

Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series

Multi-modal biomedical time series (MBTS) data offers a holistic view of the physiological state, holding significant importance in various bio-medical applications. Owing to inherent noise and distribution gaps across different modalities, MBTS can be complex to model. Various deep learning models have been developed to learn representations of MBTS but still fall short in robustness due to the ignorance of modal-to-modal variations. This paper presents a multi-scale and multi-modal biomedical time series representation learning (MBSL) network with contrastive learning to migrate these variations. Firstly, MBTS is grouped based on inter-modal distances, then each group with minimum intra-modal variations can be effectively modeled by individual encoders. Besides, to enhance the multi-scale feature extraction (encoder), various patch lengths and mask ratios are designed to generate tokens with semantic information at different scales and diverse contextual perspectives respectively. Finally, cross-modal contrastive learning is proposed to maximize consistency among inter-modal groups, maintaining useful information and eliminating noises. Experiments against four bio-medical applications show that MBSL outperforms state-of-the-art models by 33.9% mean average errors (MAE) in respiration rate, by 13.8% MAE in exercise heart rate, by 1.41% accuracy in human activity recognition, and by 1.14% F1-score in obstructive sleep apnea-hypopnea syndrome.Comment: 4 pages, 3 figures, submitted to ICASSP 202

Educating University Students on Information Security Awareness: A Smartphone Perspective

This study examines the information security awareness of university students, focusing on their knowledge, awareness, and practices related to smartphone usage. Using a 35-question survey, data were collected from 1364 students across six universities in China. Descriptive statistics, cross-analysis, and exploratory factor analysis were employed to assess students' security awareness levels. Results indicated that most students preferred Android, expressed concerns about personal finance and privacy risks, and primarily acquired security knowledge through internet media. Despite a satisfactory overall awareness, gaps were found in password setting and Wi-Fi connection practices. Male students performed better in knowledge and practice, while no significant differences were observed across grades. These insights are crucial for enhancing information security education programs

Optimism Based Exploration in Large-Scale Recommender Systems

Bandit learning algorithms have been an increasingly popular design choice for recommender systems. Despite the strong interest in bandit learning from the community, there remains multiple bottlenecks that prevent many bandit learning approaches from productionalization. Two of the most important bottlenecks are scaling to multi-task and A/B testing. Classic bandit algorithms, especially those leveraging contextual information, often requires reward for uncertainty estimation, which hinders their adoptions in multi-task recommender systems. Moreover, different from supervised learning algorithms, bandit learning algorithms emphasize greatly on the data collection process through their explorative nature. Such explorative behavior induces unfair evaluation for bandit learning agents in a classic A/B test setting. In this work, we present a novel design of production bandit learning life-cycle for recommender systems, along with a novel set of metrics to measure their efficiency in user exploration. We show through large-scale production recommender system experiments and in-depth analysis that our bandit agent design improves personalization for the production recommender system and our experiment design fairly evaluates the performance of bandit learning algorithms

Influence of Gd2O3 and Yb2O3 Co-doping on Phase Stability, Thermo-physical Properties and Sintering of 8YSZ

AbstractThe role of multicomponent rare earth oxides in phase stability, thermo-physical properties and sintering for ZrO2-based thermal barrier coatings (TBCs) materials is investigated. 8YSZ co-doped with 3 mol(Gd2O3 and 3 mol% Yb2O3 (GYb-YSZ) powders are synthesized by solid state reaction for 24 h at various temperatures. As temperature increases, stabilizers are dissolved into zirconia matrix gradually. Synthesized at 1 500 °C, GYb-YSZ is basically composed of cubic phase. GYb-YSZ exhibits excellent phase stability and sinters lower than 8YSZ by nearly three times. The thermal conductivity of GYb-YSZ is much lower than that of 8YSZ, and the thermal expansion coefficient of GYb-YSZ is comparable to that of 8YSZ. The influence of Gd2O3 and Yb2O3 co-doping on phase stability, thermal conductivity and sintering of 8YSZ is discussed

The role of reactive elements in improving the cyclic oxidation performance of Β- NiAl coatings

the bond coat in thermal barrier coating (TBC) system. However, the oxide scale grown on NiAl spalls readily during high-temperature cyclic oxidation. Reactive elements (REs) as well as their oxides dispersions were investigated to improve the cyclic oxidation performance. In this work, the effects of several REs on the adherence of Al2O3/NiAl interface were investigated by first principles theory calculations and experiments. We find that the solubility of the REs in NiAl alloy arrive at an order of Hf \u3eZr\u3eDy\u3eY\u3eLa, all the REs exhibit an affinity for sulfur, with an order of La\u3eDy\u3eY\u3eZr\u3eHf, and direct effects of the REs on the Al2O3/NiAl interface exhibit an order of Hf\u3eY\u3eHf\u3eZr\u3eclean interface\u3eLa. Combined with experimental results, we provide some suggestions on how to choose an appropriate RE. Co-doping of appropriate REs exhibits promising potential in improving the oxide scale adherence but also in reducing the growth rate of the oxides formed on the NiAl alloy or coating as compared to the single RE doping

CMAS-resistance of a yttria graded thermal barrier coating fabricated by plasma activated EB-PVD

EB-PVD yttria stabilized zirconia (YSZ) thermal barrier coatings (TBCs) are susceptible to calcia-magnesia-aluminum-silicate (CMAS) corrosion. The service lifetime of typical 8YSZ TBCs can be significantly reduced by CMAS attack. Currently, composition and microstructure modifications are the most commonly used methods for CMAS infiltration resistance. It has been reported by previous researchers that reactive elements, including Y, Gd, La, and etc., doped in TBCs can promote the formation of a dense protective layer by a sacrificing reaction with CMAS. It is therefore that the CMAS infiltration can be retarded. Besides, tailored columnar grains of TBCs are are also proved to be effective for CMAS mitigation. In this work, TBCs specimens with graded microstructure were fabricated by EB-PVD. The upper region of the TBC was doped with a higher Y2O3 content up to 25 wt.%, compared with the conventional 8YSZ composition. Besides, plasma activation was also introduced in the EB-PVD process to yield a tailored coating morphology and prosity. The coating specimens were tested at 1250 oC for evaluating CMAS resistance. Conventional YSZ coatings and graded coatings without plasma activation were also investigated for comparison