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Abstract The utility of the newly prepared catalysts is demonstrated by their application in the transformation of various allenoic acids to five‐ or six‐membered ring lactones.
The capability of a reinforcement learning (RL) agent heavily depends on the diversity of the learning scenarios generated by the environment. Generation of diverse realistic scenarios is challenging for real-time strategy (RTS) environments. The RTS environments are characterized by intelligent entities/non-RL agents cooperating and competing with the RL agents with large state and action spaces over a long period of time, resulting in an infinite space of feasible, but not necessarily realistic, scenarios involving complex interaction among different RL and non-RL agents. Yet, most of the existing simulators rely on randomly generating the environments based on predefined settings/layouts and offer limited flexibility and control over the environment dynamics for researchers to generate diverse, realistic scenarios as per their demand. To address this issue, for the first time, we formally introduce the benefits of adopting an existing formal scenario specification language, SCENIC, to assist researchers to model and generate diverse scenarios in an RTS environment in a flexible, systematic, and programmatic manner. To showcase the benefits, we interfaced SCENIC to an existing RTS environment Google Research Football (GRF) simulator and introduced a benchmark consisting of 32 realistic scenarios, encoded in SCENIC, to train RL agents and testing their generalization capabilities. We also show how researchers/RL practitioners can incorporate their domain knowledge to expedite the training process by intuitively modeling stochastic programmatic policies with SCENIC.
This paper describes the geometric structure of a strange attractor observed from the simplest known chaotic electronic circuit which the author conceived and proposed to Professor Matsumoto from Waseda University in 1983. Computer simulation of this circuit by Professor Matsumoto in October 1983 confirmed its rich chaotic dynamics [1]. Subsquent laboratory experiments using an operational amplifier circuit further confirmed the robust nature of the chaotic attractor [2], and its rich bifurcation phenomena [3]. The strange attractor is now widely known as the double scroll [4] because it resembles, from a distance, a pair of Saturn plants attached to the opposite ends of a tube formed by rolling two long strips of paper together into a scroll whose cross section consists of two tightly wound spirals.
OBJECTIVE: Therapist use of memory support (MS) alongside treatment-as-usual, with the goal of enhancing patient recall of treatment contents, has been of recent interest as a novel pathway to improve treatment outcome. The memory support intervention (MSI) involves treatment providers using 8 specific MS strategies to promote patient memory for treatment. The present study examines to what extent therapist use of MS strategies and bundles improves patient recall of treatment contents and treatment outcome. METHOD: The data were drawn from a pilot RCT reported elsewhere. Participants were 48 adults (mean age = 44.27 years, 29 females) with major depressive disorder (MDD), randomized to receive 14 sessions of either CT + Memory Support (n = 25) or CT-as-usual (n = 23). Therapist use of MS was coded using the Memory Support Rating Scale. Patient memory and treatment outcomes were assessed at baseline, midtreatment (patient recall only), posttreatment, and 6-month follow-up. RESULTS: Participants in CT + Memory Support received significantly higher amount of MS relative to CT-as-usual. Although not reaching statistical significance, small-to-medium effects were observed between MS strategies and patient recall in the expected direction. Although MS variables were not significantly associated with changes in continuous depressive symptoms, MS was associated with better global functioning. MS also exhibited small to medium effects on treatment response and recurrence in the expected direction but not on remission, though these effects did not reach statistical significance. CONCLUSIONS: These results provide initial empirical evidence supporting an active method for therapists to implement MS strategies. (PsycINFO Database Record
ADVERTISEMENT RETURN TO ISSUEEditorialNEXTThree Future Directions for Metal–Organic FrameworksLaura Gagliardi*Laura GagliardiDepartment of Chemistry, Pritzker School of Molecular Engineering, James Franck Institute, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois 60637, United States, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, Illinois 60439, United States*Email: [email protected]More by Laura Gagliardihttps://orcid.org/0000-0001-5227-1396 and Omar M. Yaghi*Omar M. YaghiDepartment of Chemistry, Kavli Energy Nanoscience Institute, and Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, United States*Email: [email protected]More by Omar M. Yaghihttps://orcid.org/0000-0002-5611-3325Cite this: Chem. Mater. 2023, 35, 15, 5711–5712Publication Date (Web):August 8, 2023Publication History Received7 July 2023Published online8 August 2023Published inissue 8 August 2023https://pubs.acs.org/doi/10.1021/acs.chemmater.3c01706https://doi.org/10.1021/acs.chemmater.3c01706editorialACS PublicationsCopyright © 2023 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views8572Altmetric-Citations5LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (784 KB) Get e-AlertscloseSUBJECTS:Computational chemistry,Crystal structure,Electronic structure,Metal organic frameworks,Molecules Get e-Alerts