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.

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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.

In this research, we proposed an active management method to tele-monitor and tele-operate more autonomous vehicles (AVs) with few observers by adjusting the movement of the AVs actively. A management system is created to get the status from the AVs and separate the monitoring requirement to the observers optimally. When the requirements might be intensive, the management system can also adjust the movements of the AVs actively to distribute the monitoring time, which can make the observers monitor more vehicles. We implement and verify the management system in an autonomous driving simulator - CARLA for the limited number of AVs and observers. Based on the data acquired from the driving simulator, we also create a numerical simulator and tested our method with the pairs of a large number of AVs and observers. The result of both simulators shows that our method can reduce the utilization degree of observers and make them monitor more AVs.

How to monitor multiple autonomous vehicles remotely with few observers: An active management method

It is very important to study the affect of the number of channel signals in the sound field reproduction technique based on wavefield synthesis. Though there are many studies of objective affect such as wavefront accuracy, there are not as many studies of the subjective affect such as sound field perception. Therefore, the subjective assessment was designed to evaluate the effect of the number of channel signals, which were synthesized by convolving a sound source to room transfer functions of free field, on the directional perception. In the low‐reverberation room, a circle loudspeaker array with a radius of 2 m was set on the horizontal plane. The subjects evaluated the directional perception of the sound image reproduced at distances of 3 and 4 m by playing simultaneous channel signals from the loudspeakers set at intervals of 10, 15, 20, 30, and 45 degrees, respectively. As a result, it was subjectively confirmed that 24 channel signals were enough to reproduce the directional perception of 5 degrees...

Subjective assessment for the number of channel signals to realize sound field based on wavefield synthesis

In recent decades, many learning-based autonomous driving systems have been proposed, and researchers have also created toolkits for developing these systems. These toolkits allow developers to train their models easily, and then test them using simulators. Existing toolkits for learning-based autonomous driving systems have some limitations however, which include inability to reuse modules or to perform accurate comparisons with model-based systems, as well as a lack of support for middle-to-middle models. As a solution, in this paper we introduce Deepware, an end-to-end toolkit for developing and evaluating learning-based autonomous driving models. Deepware includes the tools needed for collecting and evaluating datasets, training models, and evaluating models on simulators or in real-world environments using actual vehicles. Unlike existing toolkits, we used ROS as our platform, which is a set of software frameworks for robot software development widely used in autonomous driving systems as middleware, which allows cooperation with model-based systems. This approach also allows system modules to be shared when building models. In addition, it allows the comparison of learning-based and model-based methods under the same conditions. Moreover, by extracting features from model-based systems, our toolkit can also support middle-to-middle models. The proposed Deepware toolkit and dataset are available at: https://github.com/shunchan0677/deepware.

Deepware: An Open-Source Toolkit for Developing and Evaluating Learning-Based and Model-Based Autonomous Driving Models

With the increasing presence of drive recorders and advances in their technology, a large variety of driving data, including video images and sensor signals such as vehicle velocity and acceleration, can be continuously recorded and stored. Although these advances may contribute to traffic safety, the increasing amount of driving data complicates retrieval of desired information from large databases. One of our previous research projects focused on a browsing and retrieval system for driving scenes using driving behavior signals. In order to further its development, in this chapter we propose two driving scene retrieval systems. The first system also measures similarities between driving behavior signals. Experimental results show that a retrieval accuracy of more than 95 % is achieved for driving scenes involving stops, starts, and right and left turns. However, the accuracy is relatively lower for driving scenes of right and left lane changes and going up and down hills. The second system measures similarities between environmental driving signals, focusing on surrounding vehicles and driving road configuration. A subjective score from 1 to 5 is used to indicate retrieval performance, where a score of 1 means that the retrieved scene is completely dissimilar from the query scene and a score of 5 means that they are exactly the same. In a driving scene retrieval experiment, an average score of more than 3.21 is achieved for queries of driving scenes categorized as straight, curve, lane change, and traffic jam, when data from both road configuration and surroundings are employed.

Content-Based Driving Scene Retrieval Using Driving Behavior and Environmental Driving Signals

A method for estimating the sound source depth, i.e., the distance between a source and receiver, using a small-size array is proposed. The proposed method uses the spatial distribution pattern of quasi-independent signal components obtained by the frequency-domain independent component analysis (FDICA) as the cue for depth estimation. The quasi-independent components are calculated by applying FDICA to array signals with very high redundancy, for example, 60 microphone signals for a pair of sources; therefore, signal components associated with reflection signals are obtained even though they are correlated with the direct signal. Experimental evaluation using a small-size microphone array with a large number of elements confirms that the average (RMS) estimation error of the proposed method is 0.33 m, which is sufficiently accurate for our applications.

Kazuya Takeda

Papers

How to monitor multiple autonomous vehicles remotely with few observers: An active management method

Subjective assessment for the number of channel signals to realize sound field based on wavefield synthesis

Deepware: An Open-Source Toolkit for Developing and Evaluating Learning-Based and Model-Based Autonomous Driving Models

Content-Based Driving Scene Retrieval Using Driving Behavior and Environmental Driving Signals

Estimating sound source depth using a small-size array