Learning control of hearing aid parameter settings

In a hearing aid with a signal processor for signal processing in accordance with selected values of a set of parameters Θ, a method of automatic adjustment of a set z of the signal processing parameters Θ, using a set of learning parameters θ of the signal processing parameters Θ is provided, wherein the method includes extracting signal features u of a signal in the hearing aid, recording a measure r of an adjustment e made by the user of the hearing aid, modifying z by the equation z=u θ+r, and absorbing the user adjustment e in θ by the equation θN=Φ(u,r)+θP, wherein θN is the new values of the learning parameter set θ, θP is the previous values of the learning parameter set θ, and Φ is a function of the signal features u and the recorded adjustment measure r

Asymmetric adjustment

A method of adjusting a signal processing parameter for a first hearing aid and a second hearing aid forming parts of a binaural hearing aid system to be worn by a user is provided. The binaural hearing aid system comprises a user specific model representing a desired asymmetry between a first ear and a second ear of the user. The method includes detecting a request for processing a parameter change at the first hearing aid, adjusting the signal processing parameter in the first hearing aid, and adjusting a processing parameter for the second hearing aid based on the request for processing parameter change and the user specific model.</p

Detecting the most effective cleanup locations using network theory to reduce marine plastic debris: A case study in the Galapagos Marine Reserve

The Galapagos Marine Reserve was established in 1986 to ensure protection of the islands' unique biodiversity. Unfortunately, the islands are polluted by marine plastic debris and the island authorities face the challenge to effectively remove plastic from its shorelines due to limited resources. To optimise efforts, we have identified the most effective cleanup locations on the Galapagos Islands using network theory. A network is constructed from a Lagrangian simulation describing the flow of macroplastic between the various islands within the Galapagos Marine Reserve, where the nodes represent locations along the coastline and the edges the likelihood for plastic to travel from one location and beach at another. We have found four network centralities that provide the best coastline ranking to optimise the cleanup effort based on various impact metrics. In particular locations with a high retention rate are favourable for cleanup. The results indicate that using the most effective centrality for finding cleanup locations is a good strategy for heavily polluted regions if the distribution of marine plastic debris on the coastlines is unknown and limited cleanup resources are available

Direct Interaction between Two Viral Proteins, the Nonstructural Protein 2CATPase and the Capsid Protein VP3, Is Required for Enterovirus Morphogenesis

In spite of decades-long studies, the mechanism of morphogenesis of plus-stranded RNA viruses belonging to the genus Enterovirus of Picornaviridae, including poliovirus (PV), is not understood. Numerous attempts to identify an RNA encapsidation signal have failed. Genetic studies, however, have implicated a role of the non-structural protein 2CATPase in the formation of poliovirus particles. Here we report a novel mechanism in which protein-protein interaction is sufficient to explain the specificity in PV encapsidation. Making use of a novel “reporter virus”, we show that a quasi-infectious chimera consisting of the capsid precursor of C-cluster coxsackie virus 20 (C-CAV20) and the nonstructural proteins of the closely related PV translated and replicated its genome with wild type kinetics, whereas encapsidation was blocked. On blind passages, encapsidation of the chimera was rescued by a single mutation either in capsid protein VP3 of CAV20 or in 2CATPase of PV. Whereas each of the single-mutation variants expressed severe proliferation phenotypes, engineering both mutations into the chimera yielded a virus encapsidating with wild type kinetics. Biochemical analyses provided strong evidence for a direct interaction between 2CATPase and VP3 of PV and CAV20. Chimeras of other C-CAVs (CAV20/CAV21 or CAV18/CAV20) were blocked in encapsidation (no virus after blind passages) but could be rescued if the capsid and 2CATPase coding regions originated from the same virus. Our novel mechanism explains the specificity of encapsidation without apparent involvement of an RNA signal by considering that (i) genome replication is known to be stringently linked to translation, (ii) morphogenesis is known to be stringently linked to genome replication, (iii) newly synthesized 2CATPase is an essential component of the replication complex, and (iv) 2CATPase has specific affinity to capsid protein(s). These conditions lead to morphogenesis at the site where newly synthesized genomes emerge from the replication complex

Assignment ASCI course A12 Neural Computing Systems: car racing with reinforcement learning

rol actions (network outputs) at that time. The immediate cost of the system at this time (the reinforcement) r is expressed as a function of these two variables and the time. The discount factor -/is usually chosen to be 0.95, and accounts (ultimately) for an exponential weighting with recency. Furthermore, credit to each &quot;move&quot; is assigned by looking at the differences in subsequent predic- tions J(k) -- [r(x, uk, k) + &quot;/3(x+, u+, k + 1)] - 3(x, u, k) (2) The cost function associated with a certain policy is being expressed in terms of reinforcement, i.e. a quantitative evaluation of a system&apos;s (mis)behaviour, based on some predefined criteria (e.g. &quot;out of the admissible range&quot;). On the basis of expectations about these reinforcements, actual weight updates take place. This clarifies the need for a reinforcement detector element and a predictor of future reinforcement, based on current system state and current reinforcement (the critic element). When the critic is implemented wi

Support Objects for Domain Approximation

We propose a novel algorithm for extracting samples from a data set supporting the extremal points in the set. Since the density of the data set is not taken into account, the method could enable adaptation to novel (e.g. machine wear) data. Knowledge about the clustering structure of the data can aid in determination of the complexity of the solution. The algorithm is evaluated on its computational feasibility and performance with progressively more dissimilar data. 1 Introduction Automatic recognition of machine wear and failure calls for methods that can deal with small sample sizes in high-dimensional spaces, undersampled fault classes and dynamically changing environments. Since normal machine behaviour is typically determined in a few calibration measurements of extremal operating conditions (e.g. when putting the machine into practice), an accurate but parsimonious description of the borders of the domain in the feature space indicating normal behaviour is expected to emerge. F..