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A major limitations for many heat engines is that their functioning demands on-line control, and/or an external fitting between environmental parameters (e.g. temperatures of thermal baths) and internal parameters of the engine. We study a model for an adaptive heat engine, where---due to feedback from the functional part---the engine's structure adapts to given thermal baths. Hence no on-line control and no external fitting are needed. The engine can employ unknown resources, it can also adapt to results of its own functioning that makes the bath temperatures closer. We determine thermodynamic costs of adaptation and relate them to the prior information available about the environment. We also discuss informational constraints on the structure-function interaction that are necessary for adaptation.

In this work, the relationship between Carnot engine and Szilárd engine was discussed. By defining the available information about the temperature difference between two heat reservoirs, the Carnot engine was found to have a same physical essence with Szilárd engine: lossless conversion of available information. Thus, a generalized Carnot's theorem for wider scope of application can be described as "all the available information is 100% coded into work".

In this work, we introduce the concept of the fractional quantum heat engine. We examine the space-fractional quantum Szilard heat engine as an example to show that the space-fractional quantum heat engines can produce higher efficiency than the conventional quantum heat engines.

We report on a lossless information engine that converts nearly all available information from an error-free feedback protocol into mechanical work. Combining high-precision detection at resolution of 1 nm with ultrafast feedback control, the engine is tuned to extract the maximum work from information on the position of a Brownian particle. We show that the work produced by the engine achieves a bound set by a generalized second law of thermodynamics, demonstrating for the first time the sharpness of this bound. We validate a generalized Jarzynski equality for error-free feedback-controlled information engines.

We propose quantum engines powered entirely by the quantum measurement process. Our theoretical construction of the engine requires no work from the system Hamiltonian, and takes energy only from the process of observation to move a particle against a force. We present results for the work done and the efficiency for different values of the engine parameters. Feedback is required for optimal performance. We find that unit efficiency can be approached when one measurement outcome prepares the initial state of the next engine cycle, while the other outcomes leave the original state nearly unchanged.

We present a theoretical and numerical analysis of a quantum system that is capable of functioning as a heat engine. This system could be realized experimentally using cold bosonic atoms confined to a double well potential that is created by splitting a harmonic trap with a focused laser. The system shows thermalization, and can model a reversible heat engine cycle. This is the first demonstration of the operation of a heat engine with a finite quantum heat bath.

In order to build a quantum analog of traditional Carnot engine, a common choice is replacing the two thermodynamic adiabatic processes with two quantum mechanical adiabatic processes. In general, such quantum Carnot engine has six strokes. We analyze the efficiency of such six-stroke quantum Carnot engine in a perturbative way.

Recent work has uncovered Schottky-like peaks in the temperature dependence of key specific heats of certain black hole thermodynamic systems. They signal a finite window of available energy states for the underlying microscopic degrees of freedom. This paper reports on new families of peaks, found for the Kerr and Reissner-Nordstrom black holes in a spacetime with positive cosmological constant. It is known that a system with a highest energy, when coupled to two distinct heat baths, can naturally generate a thermodynamic instability, population inversion, a channel for work output. It is noted that these features are all present for de Sitter black holes. It is shown that there are trajectories in parameter space where they behave as generalized masers, operating as continuous heat engines, doing work by shedding angular momentum. It is suggested that bounds on efficiency due to the second law of thermodynamics for general de Sitter black hole solutions could provide powerful consistency checks.

Using a game engine, we have developed a virtual environment which models important aspects of critical incident scenarios. We focused on modelling phenomena relating to the identification and gathering of key forensic evidence, in order to develop and test a system which can handle chemical, biological, radiological/nuclear or explosive (CBRNe) events autonomously. This allows us to build and validate AI-based technologies, which can be trained and tested in our custom virtual environment before being deployed in real-world scenarios. We have used our virtual scenario to rapidly prototype a system which can use simulated Remote Aerial Vehicles (RAVs) to gather images from the environment for the purpose of mapping. Our environment provides us with an effective medium through which we can develop and test various AI methodologies for critical incident scene assessment, in a safe and controlled manner

In order to expand their reach and increase website ad revenue, media outlets have started using clickbait techniques to lure readers to click on articles on their digital platform. Having successfully enticed the user to open the article, the article fails to satiate his curiosity serving only to boost click-through rates. Initial methods for this task were dependent on feature engineering, which varies with each dataset. Industry systems have relied on an exhaustive set of rules to get the job done. Neural networks have barely been explored to perform this task. We propose a novel approach considering different textual embeddings of a news headline and the related article. We generate sub-word level embeddings of the title using Convolutional Neural Networks and use them to train a bidirectional LSTM architecture. An attention layer allows for calculation of significance of each term towards the nature of the post. We also generate Doc2Vec embeddings of the title and article text and model how they interact, following which it is concatenated with the output of the previous component. Finally, this representation is passed through a neural network to obtain a score for the headli

Hi HN, I'm Hugo. I've been building Rocky over the past month, shipping fast in the open. The binary is on GitHub Releases, `dagster-rocky` on PyPI, and the VS Code extension on the Marketplace. I held off on a broader announcement until the trust-system surface was coherent enough to talk about as one thing. The governance waveplan — col

Received by email tonight about two hours ago:Dear Tindie Community,My name is Gongyu Su, and I am writing on behalf of the new Tindie ownership team.First, we sincerely apologize for the recent downtime and the disruption it caused. We understand that many buyers and community members were left without clear information during the transition, and