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.

ICSP1997: International Conference on Speech Processing, August 26-28, 1997, Seoul, Korea.

Speech Morphing by Progressive Interpolation of Spectra

In this work we propose a new method to create high density ground maps for autonomous vehicles based on low boundary elevation estimation. Ground maps are created using ray casting of laser beams in 3D LiDAR data accumulated over time and computing the lower elevation data between 3D LiDAR scans. This lower boundary works as a low envelope of the accumulated point cloud. Our lower boundary ground maps approach is not affected by moving objects on the road, and produces high density maps even with coarse vertical resolution 3D LiDARs, works on long curved roads with different elevations, and it is computationally efficient. As proof of concept, we present an application for real-time obstacle and ground segmentation of 3D LiDAR data using our ground maps.

High Density Ground Maps using Low Boundary Height Estimation for Autonomous Vehicles

/pdf/classification-of-speech-under-stress-by-physical-modeling-2dlnva6479.pdf

Classification of speech under stress by physical modeling

This paper addresses the problem of 3D sound representation without sound source localization and proposes a theory based on the ray-space representation of light rays, which is independent of object's specifications An array of beam-formed microphone-arrays (MAs), are set and each MA generates a sound-image (SImage) by scanning the viewing range of a camera in the same location SImage has the same size of an image and contains of blocks of sound wave with duration of one image-frame Captured SImages with the array of MAs generate the sound wave ray-space To make a dense SImage ray-space, we propose to use the geometry compensation of corresponding images in the location of each MA By a dense sound ray-space, any virtual SImage, which corresponds to an arbitrary listening-point, can be generated The listening-point sound is generated by averaging the sound wave in each pixel or group of pixel of the virtual SImage

/pdf/the-sound-wave-ray-space-vlxrhot20g.pdf

The sound wave ray-space

We present a data driven noise suppression filtering system which combines the data clustering and the cumulative histogram equalization techniques.This method uses the SNRGMM index, which has been developed in our previous works, for clustering a speech data into sub-data with the same index. Furthermore,for each sub-data, the cumulative histogram equalization filtering is learned on each the subband log-spectral magnitude domain. The case, when a noisy speech data is not available, is also consdirered in this work. For that case the SNRGMM can be used for the very quick and flexible simulation of a noisy speech data and without any loss of quality in the final system. The experimental evaluation on the AURORA2 Japansese version shows the improvement of the proposed system in both SNR and ASR performances.

Kazuya Takeda

Papers

Speech Morphing by Progressive Interpolation of Spectra

High Density Ground Maps using Low Boundary Height Estimation for Autonomous Vehicles

Classification of speech under stress by physical modeling

The sound wave ray-space

A speech enhancement system based on data clustering and cumulative histogram equalization