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.

This paper proposes a method of automatically extracting lane change situations from large-scale driving corpora. Naturalistic driving data stored in large-scale corpora has a potential of contributing for developing novel advanced driver-assistance systems based on estimated information about driver's intent and/or potential risk of accidents. However, direct estimation of such kind of information from stream data is difficult. To address the issue, we apply an unsupervised symbolization method and topic representation to driving data. Driving stream data is converted to sequences of discrete symbols by a non-parametric symbolization method, and then the symbols are characterized by topics which represent typical distribution of driving behavior observed during the symbols. Because these symbols are separated on changing points of driving behavior, similar driving situations are effectively retrieved from sequences of the symbols. For evaluating effectiveness of the symbolization approach, we extract lane change situations based on the topic proportions and their temporal patterns. Distinctive elements of topic proportions and their temporal patterns for lane change situations are extracted by AdaBoost classifier. As a result, proposed approach outperforms baselines with neither topic proportions nor their temporal patterns in terms of extracting lane change situations. This result shows effectiveness of symbols with topic proportions for representing characteristics of driving situations.

Automatic lane change extraction based on temporal patterns of symbolized driving behavioral data

Two (linear and nonlinear) interpolation methods of the head‐related transfer function (HRTF) are exploited in order to realize virtual auditory localization. In both methods, HRTFs of the left and right ears are represented by a delay time and a common impulse response, where delay time is determined so that the cross correlation of two HRTFs takes the maximum value. A three‐layer neural network is trained for the nonlinear method, whereas basic linear interpolation is used for the linear method. Evaluation tests are performed by using HRTF prototypes, Web‐published by the MIT Media Lab. The signal‐to‐deviation ratios (SDR) of the measured and interpolated HRTFs are calculated for objective evaluation of the methods. The SDR of the nonlinear method is much better, i.e., 50 to 70 dB, than that of the linear method, i.e., 5 to 30 dB. On the other hand, there is no significant difference in the subjective evaluation of localizing the earphone‐presented sounds generated by the two interpolated HRTFs. Furthermore, the results of the above subjective tests are nearly identical to that of the measured HRTF.

Interpolating HRTF for auditory virtual reality

We propose a data-driven control framework for autonomous driving which combines learning-based risk assessment with personalized, safety-focused, predictive control. Different control strategies are used depending on the detected risk level of the driving situation (risky vs. non-risky). This requires a model which can understand the context of the driving situation. In addition, autonomous driving should also be able to provide various safe and comfortable driving styles customized for various users, which requires a modeling method that can capture individual driving preferences. To achieve this, we propose a novel vehicle control framework in which Model Predictive Control (MPC) is combined with a learning-based risk assessment model. Random Forest (RF) methods are trained to classify driving scenes as risky or not risky, while at the same time capturing individually preferred travel velocities. If driving scenes are classified as risky, then the Safety-focused Model Predictive Control (SMPC) system will be launched to generate control commands satisfying predetermined safety constraints, otherwise, Personalized Model Predictive Control (PMPC) is used instead to track the driver’s individually preferred velocity. We demonstrate experimentally our control framework.

Personalized Safety-focused Control by Minimizing Subjective Risk

In this paper, we investigate a method for detecting risky lane changes using integrated modeling of driver gaze and vehicle operation behavior. Driver gaze direction and vehicle operation behavior are broken down into discrete acts, e.g., looking in the rear view mirror, braking, etc., and sequences of these actions are jointly modeled using multi-stream hidden Markov models (HMMs). Driving data is recorded on expressways as drivers pass leading vehicles, i.e., the drivers make two lane changes, first to pass leading vehicles and then to move back into their original lanes. Since actual driving risk levels are difficult to measure, the risk level of each lane change is rated by subjects, and we assume their scores represent the “ground-truth” risk level. By jointly modeling gaze and vehicle operation behavior, we improve the performance of risky lane change detection. To more accurately evaluate overall risk, we use data from multiple lane change events. We obtain an average correlation coefficient of 0.80 between HMM likelihood scores and subjective risk evaluation scores by accumulating HMM likelihoods for a period of fourteen minutes. By accumulating these indicators for the previous twenty minutes, a 96.0% risky driving detection rate is achieved with a 7.1% false positive rate.

Kazuya Takeda

Papers

Automatic lane change extraction based on temporal patterns of symbolized driving behavioral data

Interpolating HRTF for auditory virtual reality

Personalized Safety-focused Control by Minimizing Subjective Risk

Integrated modeling of driver gaze and vehicle operation behavior to estimate risk level during lane changes

Japanese dictation toolkit: plug-and-play framework for speech recognition R&D