Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

The European Position Paper on Rhinosinusitis and Nasal Polyps 2020 is the update of similar evidence based position papers published in 2005 and 2007 and 2012. The core objective of the EPOS2020 guideline is to provide revised, up-to-date and clear evidence-based recommendations and integrated care pathways in ARS and CRS. EPOS2020 provides an update on the literature published and studies undertaken in the eight years since the EPOS2012 position paper was published and addresses areas not extensively covered in EPOS2012 such as paediatric CRS and sinus surgery. EPOS2020 also involves new stakeholders, including pharmacists and patients, and addresses new target users who have become more involved in the management and treatment of rhinosinusitis since the publication of the last EPOS document, including pharmacists, nurses, specialised care givers and indeed patients themselves, who employ increasing self-management of their condition using over the counter treatments. The document provides suggestions for future research in this area and offers updated guidance for definitions and outcome measurements in research in different settings. EPOS2020 contains chapters on definitions and classification where we have defined a large number of terms and indicated preferred terms. A new classification of CRS into primary and secondary CRS and further division into localized and diffuse disease, based on anatomic distribution is proposed. There are extensive chapters on epidemiology and predisposing factors, inflammatory mechanisms, (differential) diagnosis of facial pain, allergic rhinitis, genetics, cystic fibrosis, aspirin exacerbated respiratory disease, immunodeficiencies, allergic fungal rhinosinusitis and the relationship between upper and lower airways. The chapters on paediatric acute and chronic rhinosinusitis are totally rewritten. All available evidence for the management of acute rhinosinusitis and chronic rhinosinusitis with or without nasal polyps in adults and children is systematically reviewed and integrated care pathways based on the evidence are proposed. Despite considerable increases in the amount of quality publications in recent years, a large number of practical clinical questions remain. It was agreed that the best way to address these was to conduct a Delphi exercise . The results have been integrated into the respective sections. Last but not least, advice for patients and pharmacists and a new list of research needs are included. The full document can be downloaded for free on the website of this journal: http://www.rhinologyjournal.com.

/pdf/european-position-paper-on-rhinosinusitis-and-nasal-polyps-mj734vsl7p.pdf

European Position Paper on Rhinosinusitis and Nasal Polyps 2020

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

Independent Component Analysis.

We present multichannel speech enhancement method based on MAP speech spectral magnitude estimation using a generalized gamma model of speech prior distribution, where the model parameters are adapted from actual noisy speech in a frame-by-frame manner. The utilization of a more general prior distribution with its online estimation is shown to be effective for speech spectral estimation. We tested the proposed algorithm in an in-car speech database and obtained significant improvements on the speech recognition performance, particularly under nonstationary noise conditions such as music, air-conditioner and open window.

Multichannel Speech Enhancement Based on Speech Spectral Magnitude Estimation Using Generalized Gamma Prior Distribution

/pdf/a-traffic-flow-simulation-framework-for-learning-driver-2d4n3n67b7.pdf

A Traffic Flow Simulation Framework for Learning Driver Heterogeneity from Naturalistic Driving Data using Autoencoders

The separation and localization of sound source signals are important techniques for many applications, such as highly realistic communication and speech recognition systems These systems are expected to work without such prior information as the number of sound sources and the environmental conditions In this paper, we developed a dodecahedral microphone array and proposed a novel separation method with our developed device This method refers to human sound localization cues and uses acoustical characteristics obtained by the shape of the dodecahedral microphone array Moreover, this method includes an estimation method of the number of sound sources that can operate without prior information The sound source separation performances were evaluated under simulated and actual reverberant conditions, and the results were compared with the conventional method The experimental results showed that our separation performance outperformed the conventional method

Blind Source Separation Using Dodecahedral Microphone Array under Reverberant Conditions

An Advanced Japanese Speech Corpus for In-car Spoken Dialogue Research

In this paper, we describe a multichannel method of noisy speech recognition that can adapt to various in-car noise situations during driving. The method allows us to estimate the log spectrum of speech at a close-talking microphone based on the multiple regression of the log spectra (MRLS) of noisy signals captured by multiple distributed microphones. Through clustering of the spatial noise distributions under various driving conditions, the regression weights for MRLS are effectively adapted to the driving conditions. The experimental evaluation shows an average error rate reduction of 43 % in isolated word recognition under 15 different driving conditions.

Kazuya Takeda

Papers

Multichannel Speech Enhancement Based on Speech Spectral Magnitude Estimation Using Generalized Gamma Prior Distribution

A Traffic Flow Simulation Framework for Learning Driver Heterogeneity from Naturalistic Driving Data using Autoencoders

Blind Source Separation Using Dodecahedral Microphone Array under Reverberant Conditions

An Advanced Japanese Speech Corpus for In-car Spoken Dialogue Research

In-car speech recognition using distributed microphones-adapting to automatically detected driving conditions