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What I want to do is programming an interactive terminal application like htop and vim are. What I mean is not the output of characters which look like boxes or setting colors, this is trivial; also to make the content fit to the window size. What I need is
I really don't want to use ncurses. But of course, if you know which part of ncurses is responsible for these tasks, you're welcome to tell me where in the source code I can find it, so I will study it.
If you're talking about real terminal applications, which run in anxterm, the important thing to note is that many of the portabilityissues concern the terminal, and not the OS. The terminal is controlledby sending different escape sequences. Which ones do what depend on the terminal; the ANSI escape codes are now fairly widespread, however, see _escape_code. These are generally understood by xterm, for example.
Objectives: Real-world evidence (RWE) is essential for the development of pharmaceutical and medical technologies and informs treatment-related decisions by regulatory agencies, payers, healthcare providers, and patients. Given that planning RWE studies present diverse challenges, we developed the RWE Framework, a concise, visual, interactive tool designed to align multidisciplinary stakeholders toward common goals and encourage a methodical approach to RWE study planning.
Conclusions: The RWE Framework is a novel, concise, visual, and interactive tool to inform RWE study planning. It addresses a broad range of real-world study types and research objectives and was found to enhance RWE decision-making by multidisciplinary teams. Further validation is warranted.
A real travel through the research of official, private and industry archives about the development and use of colors on German, Russian USA and British AFVs. 200 pages full of text, color photographs, camouflage schemes, profiles, general camouflage rules, etc. A must book for modellers who like to know more about the real thing and THE definitive guide for WWII AFV Colors.
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They label the left and right paths from the entrance A and B. First, Victor waits outside the cave as Peggy goes in. Peggy takes either path A or B; Victor is not allowed to see which path she takes. Then, Victor enters the cave and shouts the name of the path he wants her to use to return, either A or B, chosen at random. Providing she really does know the magic word, this is easy: she opens the door, if necessary, and returns along the desired path.
Now if we replace Victor with a CRS, Peggy would f.e. enter at entry A and look at a position that is the equivalent of her decision (or where would she look?) whether she should leave the cave at A or B, in this case B. But why doesnt she just look it up beforehand? She has the CRS and she knows where she would have to look at if she took either sides - she can just see that if she goes to A first, she is supposed to come out at B. So instead of really committing to A, she secretly sneaks into B and gets out there without having to pass the magic gate.
For some interesting arguments about this, and a possibly different realization I would suggest reading Cryptography in the Multi-string Model by Groth and Ostrovsky. Especially the introduction is quite interesting and their proposed new setup assumption.
Considering most usages of a CRS in the sense of NI-ZK is that there is usually some property, which is then used in order to prove the ZK property. In regular ZK you give the simulator some kind of advantage, e.g. being able to rewind one step. In this model you use some kind of commitments, which are binding in the real protocol but the advantage of the simulator is that he can decomit his previous commitsments to anything.
The National Park Service Geographic Resources Program hosts an interactive trails map viewer.
Choose El Camino Real de los Tejas National Historic Trail and then zoom in to find the details you need for trip planning.
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Interactive Real Analysis is an online, interactive textbook for Real Analysis or Advanced Calculus in one real variable. It deals with sets, sequences, series, continuity, differentiability, integrability (Riemann and Lebesgue), topology, power series, and more. The text is changing constantly, and your comments are very welcome: please sign our guest book.
Reinforcement learning (RL) enables robots to learn its optimal behavioral strategy in dynamic environments based on feedback. Explicit human feedback during robot RL is advantageous, since an explicit reward function can be easily adapted. However, it is very demanding and tiresome for a human to continuously and explicitly generate feedback. Therefore, the development of implicit approaches is of high relevance. In this paper, we used an error-related potential (ErrP), an event-related activity in the human electroencephalogram (EEG), as an intrinsically generated implicit feedback (rewards) for RL. Initially we validated our approach with seven subjects in a simulated robot learning scenario. ErrPs were detected online in single trial with a balanced accuracy (bACC) of 91%, which was sufficient to learn to recognize gestures and the correct mapping between human gestures and robot actions in parallel. Finally, we validated our approach in a real robot scenario, in which seven subjects freely chose gestures and the real robot correctly learned the mapping between gestures and actions (ErrP detection (90% bACC)). In this paper, we demonstrated that intrinsically generated EEG-based human feedback in RL can successfully be used to implicitly improve gesture-based robot control during human-robot interaction. We call our approach intrinsic interactive RL.
Reinforcement learning (RL) in real-world robotic applications is challenging for different reasons: a) the high-dimensional continuous state and action space, b) high-costs of generating real-world data (e.g., rollouts) and expensive real-world experiences which cannot be replaced by learning in simulation, and c) no straightforward way to specify appropriate reward functions including reward shaping to specify goals, etc1,2,3. These problems scale exponentially with the complexity of the task and the many pitfalls of the real world, which make it oftentimes impossible to decide whether or not an action was successful or failed.
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