BALANCING THE DEEP LOOSENING MACHINE WITH ACTIVE FURROWS
نویسندگان
چکیده
منابع مشابه
The furrows of Rhinolophidae revisited.
Rhinolophidae, a family of echolocating bats, feature very baroque noseleaves that are assumed to shape their emission beam. Zhuang & Muller (Zhuang & Muller 2006 Phys. Rev. Lett. 97, 218701 (doi:10.1103/PhysRevLett.97.218701); Zhuang & Muller 2007 Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 76(Pt. 1), 051902 (doi:10.1103/PhysRevE.76.051902)) have proposed, based on finite element simulations,...
متن کاملThe balancing effect in brain-machine interaction
The meta-analysis of Intangible Brain-Machine Interaction (IMMI) data with random number generators (RNG’s) (Bösch, 2006) is re-evaluated through the application of rigorous and recognised mathematical tools. The current analysis shows that the statistical average of the true RNG-IMMI data is not shifted from chance by direct mental intervention, thus refuting the IMMI hypothesis. A facet of th...
متن کاملPolarizing for furrows
S plitting mitotic cells in two is not the one-way signaling road it once seemed, based on evidence from Hu et al. The group identifi es a positive feedback loop that creates a furrow at cytokinesis. The fi ndings also throw a wrench in the well-accepted dogma of microtubule dynamic instability. Hu and colleagues devised a monopolar HeLa system to determine how the cytokinetic furrow is created...
متن کاملThe Active Element Machine
We present a new computing machine, called an active element machine (AEM), and the AEM programming language. This computing model is motivated by the positive aspects of dendritic integration, inspired by biology, and traditional programming languages based on the register machine. Distinct from the traditional register machine, the fundamental computing elements – active elements – compute si...
متن کاملModeling Documents with a Deep Boltzmann Machine
We introduce a type of Deep Boltzmann Machine (DBM) that is suitable for extracting distributed semantic representations from a large unstructured collection of documents. We overcome the apparent difficulty of training a DBM with judicious parameter tying. This enables an efficient pretraining algorithm and a state initialization scheme for fast inference. The model can be trained just as effi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Engineering Studies and Research
سال: 2016
ISSN: 2068-7559
DOI: 10.29081/jesr.v20i4.49