Introduction to Quantum Thermodynamics: History and Prospects

Quantum Thermodynamics is a continuous dialogue between two independent theories: Thermodynamics and Quantum Mechanics. Whenever the two theories addressed the same phenomena new insight has emerged. We follow the dialogue from equilibrium Quantum Thermodynamics and the notion of entropy and entropy inequalities which are the base of the II-law. Dynamical considerations lead to non-equilibrium thermodynamics of quantum Open Systems. The central part played by completely positive maps is discussed leading to the Gorini-Kossakowski-Lindblad-Sudarshan GKLS equation. We address the connection to thermodynamics through the system-bath weak-coupling-limit WCL leading to dynamical versions of the I-law. The dialogue has developed through the analysis of quantum engines and refrigerators. Reciprocating and continuous engines are discussed. The autonomous quantum absorption refrigerator is employed to illustrate the III-law. Finally, we describe some open questions and perspectives

Open dialogue peer review: a response to Morag Stuart

Morag Stuart is right that how best to teach reading has been debated for years and we need clarity about what reading involves and how this develops in beginning readers. I also like her emphasis on teaching and on the importance of teaching phonics early and in a systematic way. The history of 'reading wars' has been unhelpful for researchers, policy-makers, teachers and, most importantly, children. We need to ensure that the debates this time around are more complex and measured. This means that, first, it is important to recognise the socio-cultural basis of literacy. Second, I prefer not to talk in terms of convincing anyone of the 'sense' of one view, but in terms of exploring how different views shed light on the actual task to be achieved - children who can, and do, read

Disfluency in dialogue:an intentional signal from the speaker?

Disfluency is a characteristic feature of spontaneous human speech, commonly seen as a consequence of problems with production. However, the question remains open as to why speakers are disfluent: Is it a mechanical by-product of planning difficulty, or do speakers use disfluency in dialogue to manage listeners' expectations? To address this question, we present two experiments investigating the production of disfluency in monologue and dialogue situations. Dialogue affected the linguistic choices made by participants, who aligned on referring expressions by choosing less frequent names for ambiguous images where those names had previously been mentioned. However, participants were no more disfluent in dialogue than in monologue situations, and the distribution of types of disfluency used remained constant. Our evidence rules out at least a straightforward interpretation of the view that disfluencies are an intentional signal in dialogue. © 2012 Psychonomic Society, Inc

Deep Active Learning for Dialogue Generation

We propose an online, end-to-end, neural generative conversational model for open-domain dialogue. It is trained using a unique combination of offline two-phase supervised learning and online human-in-the-loop active learning. While most existing research proposes offline supervision or hand-crafted reward functions for online reinforcement, we devise a novel interactive learning mechanism based on hamming-diverse beam search for response generation and one-character user-feedback at each step. Experiments show that our model inherently promotes the generation of semantically relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational styles.Comment: Accepted at 6th Joint Conference on Lexical and Computational Semantics (*SEM) 2017 (Previously titled "Online Sequence-to-Sequence Active Learning for Open-Domain Dialogue Generation" on ArXiv