An Extended TODIM Method for Group Decision Making with the Interval Intuitionistic Fuzzy Sets

For a multiple-attribute group decision-making problem with interval intuitionistic fuzzy sets, a method based on extended TODIM is proposed. First, the concepts of interval intuitionistic fuzzy set and its algorithms are defined, and then the entropy method to determine the weights is put forward. Then, based on the Hamming distance and the Euclidean distance of the interval intuitionistic fuzzy set, both of which have been defined, function mapping is given for the attribute. Finally, to solve multiple-attribute group decision-making problems using interval intuitionistic fuzzy sets, a method based on extended TODIM is put forward, and a case that deals with the site selection of airport terminals is given to prove the method

Some Heronian mean operators with 2-tuple linguistic information and their application to multiple attribute group decision making

With respect to multi-attribute group decision-making problems, in which attribute values take the form of 2-tuple linguistic information, a new decision making method that considers the interrelationships of attribute values is proposed. Firstly, some new aggregation operators of 2-tuple linguistic information based on Heronian mean are proposed, such as 2-tuple linguistic Heronian mean operator (2TLHM) and 2-tuple linguistic weighted Heronian mean operator (2TLWHB), and some desired properties of the proposed operators are studied. Then, a method based on the 2TLHM and 2TLWHB operators for multiple attribute group decision making is developed. In this approach, the interrelationships of attribute values are considered. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness

Multi-criteria decision-making method based on intuitionistic trapezoidal fuzzy prioritised owa operator

In the real decision-making, there are many multiple attribute decision-making (MADM) problems, in which there exists the prioritised relationship among decision-making attributes. In this paper, with respect to the prioritised multi-criteria decision-making problems under intuitionistic trapezoidal fuzzy information, a new decision-making method on the basis of the intuitionistic trapezoidal fuzzy prioritised ordered weighted aggregation operator has been proposed. Firstly, the definitions, operational rules and characteristics of intuitionistic trapezoidal fuzzy numbers and POWA operator have been introduced. Then, intuitionistic trapezoidal fuzzy prioritised ordered weighted aggregation (ITFPOWA) operator has been defined as well as the computational method of associated weight, and some properties have been studied and proved. Furthermore, based on the ITFPOWA operator, an approach to the multi-criteria decision-making with intuitionistic trapezoidal fuzzy numbers has been established. Finally, an illustrative example has been given to prove the evaluation procedures of the developed approach and to demonstrate its practicality and validity

Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability

A trustworthy reinforcement learning algorithm should be competent in solving challenging real-world problems, including {robustly} handling uncertainties, satisfying {safety} constraints to avoid catastrophic failures, and {generalizing} to unseen scenarios during deployments. This study aims to overview these main perspectives of trustworthy reinforcement learning considering its intrinsic vulnerabilities on robustness, safety, and generalizability. In particular, we give rigorous formulations, categorize corresponding methodologies, and discuss benchmarks for each perspective. Moreover, we provide an outlook section to spur promising future directions with a brief discussion on extrinsic vulnerabilities considering human feedback. We hope this survey could bring together separate threads of studies together in a unified framework and promote the trustworthiness of reinforcement learning.Comment: 36 pages, 5 figure

Effects of aluminum diffusion on the adhesive behavior of the Ni(111)/Cr2O3(0001) interface: First principle study

AbstractDensity functional theory was employed to investigate the structure and properties of Ni/Cr2O3 and Ni/Al2O3/Cr2O3. The O-terminated Ni(111)/Cr2O3(0001) interface was firstly found to be the most stable configuration. Based on this construction, the effects of the Al diffusion at the Ni/Cr2O3 interface were further studied. The results of total energies indicate that Al atoms originating from Ni slab prefer to diffuse into Cr2O3 slab through the interface, resulting in the formation of alumina at the Ni/Cr2O3 interface. Due to the presence of Al atoms, there was an amazing increase in the work of adhesion, whereas the Ni/Al2O3/Cr2O3 interface showed the strongest stability. Moreover, this calculated work well agrees with the reported experimental results

lncRNA profile study reveals the mRNAs and lncRNAs associated with docetaxel resistance in breast cancer cells

Abstract Resistance to adjuvant systemic treatment, including taxanes (docetaxel and paclitaxel) is a major clinical problem for breast cancer patients. lncRNAs (long non-coding RNAs) are non-coding transcripts, which have recently emerged as important players in a variety of biological processes, including cancer development and chemotherapy resistance. However, the contribution of lncRNAs to docetaxel resistance in breast cancer and the relationship between lncRNAs and taxane-resistance genes are still unclear. Here, we performed comprehensive RNA sequencing and analyses on two docetaxel-resistant breast cancer cell lines (MCF7-RES and MDA-RES) and their docetaxel-sensitive parental cell lines. We identified protein coding genes and pathways that may contribute to docetaxel resistance. More importantly, we identified lncRNAs that were consistently up-regulated or down-regulated in both the MCF7-RES and MDA-RES cells. The co-expression network and location analyses pinpointed four overexpressed lncRNAs located within or near the ABCB1 (ATP-binding cassette subfamily B member 1) locus, which might up-regulate the expression of ABCB1. We also identified the lncRNA EPB41L4A-AS2 (EPB41L4A Antisense RNA 2) as a potential biomarker for docetaxel sensitivity. These findings have improved our understanding of the mechanisms underlying docetaxel resistance in breast cancer and have provided potential biomarkers to predict the response to docetaxel in breast cancer patients

Accelerated Policy Evaluation: Learning Adversarial Environments with Adaptive Importance Sampling

The evaluation of rare but high-stakes events remains one of the main difficulties in obtaining reliable policies from intelligent agents, especially in large or continuous state/action spaces where limited scalability enforces the use of a prohibitively large number of testing iterations. On the other hand, a biased or inaccurate policy evaluation in a safety-critical system could potentially cause unexpected catastrophic failures during deployment. In this paper, we propose the Accelerated Policy Evaluation (APE) method, which simultaneously uncovers rare events and estimates the rare event probability in Markov decision processes. The APE method treats the environment nature as an adversarial agent and learns towards, through adaptive importance sampling, the zero-variance sampling distribution for the policy evaluation. Moreover, APE is scalable to large discrete or continuous spaces by incorporating function approximators. We investigate the convergence properties of proposed algorithms under suitable regularity conditions. Our empirical studies show that APE estimates rare event probability with a smaller variance while only using orders of magnitude fewer samples compared to baseline methods in both multi-agent and single-agent environments.Comment: 10 pages, 5 figure