Fast predictive maintenance in Industrial Internet of Things (IIoT) with Deep Learning (DL): A review

Applying Deep Learning in the field of Industrial Internet of Things is a very active research field. The prediction of failures of machines and equipment in industrial environments before their possible occurrence is also a very popular topic, significantly because of its cost saving potential. Predictive Maintenance (PdM) applications can benefit from DL, especially because of the fact that high complex, non-linear and unlabeled (or partially labeled) data is the normal case. Especially with PdM applications being used in connected smart factories, low latency predictions are essential. Because of this real-time processing becomes more important. The aim of this paper is to provide a narrative review of the most current research covering trends and projects regarding the application of DL methods in IoT environments. Especially papers discussing the area of predictions and real-time processing with DL models are selected because of their potential use for PdM applications. The reviewed papers were selected by the authors based on a qualitative rather than a quantitative level

Effect of Pt substitution on the electronic structure of AuTe2

We report a photoemission and x-ray absorption study on Au1-xPtxTe2 (x = 0 and 0.35) triangular lattice in which superconductivity is induced by Pt substitution for Au. Au 4f and Te 3d core-level spectra of AuTe2 suggests a valence state of Au2+(Te2)2-, which is consistent with its distorted crystal structure with Te-Te dimers and compressed AuTe6 otahedra. On the other hand, valence-band photoemission spectra and pre-edge peaks of Te 3d absorption edge indicate that Au 5d bands are almost fully occupied and that Te 5p holes govern the transport properties and the lattice distortion. The two apparently conflicting pictures can be reconciled by strong Au 5d/Au 6s-Te 5p hybridization. Absence of a core-level energy shift with Pt substitution is inconsistent with the simple rigid band picture for hole doping. The Au 4f core-level spectrum gets slightly narrow with Pt substitution, indicating that the small Au 5d charge modulation in distorted AuTe2 is partially suppressed.Comment: 13 pages, 4 figures, accepted by Physical Review

Poverty of the stimulus? A rational approach

The Poverty of the Stimulus (PoS) argument holds that children do not receive enough evidence to infer the exis-tence of core aspects of language, such as the dependence of linguistic rules on hierarchical phrase structure. We reevaluate one version of this argument with a Bayesian model of grammar induction, and show that a rational learner without any initial language-speci¯c biases could learn this dependency given typical child-directed input. This choice enables the learner to master aspects of syn-tax, such as the auxiliary fronting rule in interrogative formation, even without having heard directly relevant data (e.g., interrogatives containing an auxiliary in a relative clause in the subject NP).Amy Perfors, Joshua B. Tenenbaum and Terry Regie

Indirect evidence and the poverty of the stimulus: the case of anaphoric one

It is widely held that children’s linguistic input underdetermines the correct grammar, and that language learning must therefore be guided by innate linguistic constraints. In contrast, a recent counterproposal holds that apparently impoverished input may contain indirect sources of evidence that allow the child to learn without such constraints. Here, we support this latter view by showing that a Bayesian model can learn a standard “poverty-of-stimulus” example, anaphoric one, from realistic input without a constraint traditionally assumed to be necessary, by relying on indirect evidence. Our demonstration does however assume other linguistic knowledge; thus we reduce the problem of learning anaphoric one to that of learning this other knowledge. We discuss whether this other knowledge may itself be acquired without linguistic constraints.Stephani Foraker, Terry Regier, Naveen Khetarpal, Amy Perfors and Joshua B. Tenenbau