312 research outputs found
Learning policies through argumentation-derived evidence (extended abstract)
(c) IFAAMASPublisher PD
Learning policy constraints through dialogue
Publisher PD
Learning How a Tool Affords by Simulating 3D Models from the Web
Thanks to: UoAs ABVenture Zone, N. Petkov, K. Georgiev, B. Nougier, S. Fichtl, S. Ramamoorthy, M. Beetz, A. Haidu, J. Alexander, M. Schoeler, N. Pugeault, D. Cruickshank, M. Chung and N. Khan. Paulo Abelha is on a PhD studentship supported by the Brazilian agency CAPES through the program Science without Borders. Frank Guerin received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Published in: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) DOI: 10.1109/IROS.2017.8206372 Date of Conference: 24-28 Sept. 2017 Conference Location: Vancouver, BC, Canada.Postprin
ACL Anthology Helper: A Tool to Retrieve and Manage Literature from ACL Anthology
The ACL Anthology is an online repository that serves as a comprehensive
collection of publications in the field of natural language processing (NLP)
and computational linguistics (CL). This paper presents a tool called ``ACL
Anthology Helper''. It automates the process of parsing and downloading papers
along with their meta-information, which are then stored in a local MySQL
database. This allows for efficient management of the local papers using a wide
range of operations, including "where," "group," "order," and more. By
providing over 20 operations, this tool significantly enhances the retrieval of
literature based on specific conditions. Notably, this tool has been
successfully utilised in writing a survey paper (Tang et al.,2022a). By
introducing the ACL Anthology Helper, we aim to enhance researchers' ability to
effectively access and organise literature from the ACL Anthology. This tool
offers a convenient solution for researchers seeking to explore the ACL
Anthology's vast collection of publications while allowing for more targeted
and efficient literature retrieval
Uncertainty and trade-offs in quantum multiparameter estimation
Uncertainty relations in quantum mechanics express bounds on our ability to simultaneously obtain knowledge about expectation values of non-commuting observables of a quantum system. They quantify trade-offs in accuracy between complementary pieces of information about the system. In quantum multiparameter estimation, such trade-offs occur for the precision achievable for different parameters characterizing a density matrix: an uncertainty relation emerges between the achievable variances of the different estimators. This is in contrast to classical multiparameter estimation, where simultaneous optimal precision is attainable in the asymptotic limit. We study trade-off relations that follow from known tight bounds in quantum multiparameter estimation. We compute trade-off curves and surfaces from Cramer-Rao type bounds which provide a compelling graphical representation of the information encoded in such bounds, and argue that bounds on simultaneously achievable precision in quantum multiparameter estimation should be regarded as measurement uncertainty relations. From the state-dependent bounds on the expected cost in parameter estimation, we derive a state-independent uncertainty relation between the parameters of a qubit system
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