There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target-domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning research studies, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey article reviews more than 40 representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over 20 representative transfer learning models are used for experiments. The models are performed on three different data sets, that is, Amazon Reviews, Reuters-21578, and Office-31, and the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.

A Comprehensive Survey on Transfer Learning

Forecasting with artificial neural networks: the state of the art

Optimal tension control in parallel mechanisms for balancing wrench at the end‐effector introduces a complex model of the overall system Moreover, actuator saturations such as motor current and angular velocity limits impose that a non‐linear system be controlled Such a system is usually controlled sub‐optimally due to the complexity of its model In haptic applications where transparency and stability are both critical parameters, optimal performance should be achieved This paper introduces an enhanced FPID auto‐tuning based on Extremum Seeking‐Tuning (ES‐Tuning) which uses a different cost function to find the local optimal solution for haptic rendering and a correction on noise measurement for a high order complex system The solution of this optimization gives a direct measure of the reel dynamic transparency evaluated by the proposed cost function

/pdf/extremum-seeking-tuning-for-reel-tension-control-in-haptic-1qiwblafwr.pdf

Extremum Seeking Tuning for Reel Tension Control in Haptic Application

/pdf/disscussion-kinematics-of-the-translational-3-urc-mechanism-1sjfuw8xl2.pdf

Disscussion: “Kinematics of the Translational 3-URC Mechanism” [Di Gregorio, R., 2004, ASME J. Mech. Des., 126, pp. 1113–1117]

http://asmedigitalcollection.asme.org/IDETC-CIE/proceedings-pdf/doi/10.1115/DETC2000/MECH-14107/6610100/523_1.pdf

Dynamic Modeling and Simulation of Parallel Mechanisms Using the Virtual Spring Approach

https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings-pdf/DETC-CIE96/97577/V02AT02A015/6644156/v02at02a015-96-detc-mech-1563.pdf

Modeling and Identification of Non-Geometric Parameters in Semi-Flexible Serial Robotic Mechanisms

A mechanism comprises a first Halbach cylinder having an inner cavity, the first Halbach cylinder magnetized to produce a first magnetic flux concentrated circumferentially inside the inner cavity. A second Halbach cylinder is concentrically received in the inner cavity of the first Halbach cylinder to concurrently form a rotational joint having a rotational axis. One of the Halbach cylinders is a rotor and the other of the Halbach cylinders is a stator, the second Halbach cylinder magnetized to produce a second magnetic flux concentrated circumferentially outwardly. An output is connected to the rotor to rotate therewith relative to the stator, the output applying a gravity load on the rotor, the gravity load being offset from the rotational axis, whereby the magnetic flux of the first Halbach cylinder and the second Halbach cylinder cooperatively produce a torque against the gravity load caused by the output.

Statically-balanced mechanism using halbach cylinders

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