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.

Genetically diverse populations can increase plant resistance to natural enemies. Yet, beneficial genotype pairs remain elusive due to the occurrence of both positive and negative effects of mixed planting on plant resistance, called associational resistance and susceptibility. We used genome-wide polymorphisms of the plant species Arabidopsis thaliana to identify genotype pairs that enhance associational resistance to herbivory. By quantifying neighbor interactions among 199 genotypes grown in a randomized block design, we predicted that 823 of the 19,701 candidate pairs could reduce herbivory through associational resistance. We planted such pairs with predicted associational resistance in mixtures and monocultures and found a significant reduction in herbivore damage in the mixtures. Our study highlights the potential application to assemble genotype mixtures with positive biodiversity effects.

/pdf/genome-wide-neighbor-effects-predict-genotype-pairs-that-2854mt0k.pdf

Genome-wide neighbor effects predict genotype pairs that reduce herbivory in mixed planting

A model of perceptual distance for group delays based on ellipsoidal mapping

【背景】歯性上顎洞炎は一定の診断基準や治療指針はなく,耳鼻咽喉科・歯科のどちらでも治療し得る疾患で ある。両科の連携も各医師や各施設により異なると思われる。今回,我々は歯性上顎洞炎に診断・治療・連携 に関するアンケート調査を両科の医師に行い,報告する。 【方法】耳鼻咽喉科医と歯科医へWebアンケート調査を行った。調査項目として画像診断や治療方針,診療科 を越えた連携についてとりあげた。 【結果】耳鼻咽喉科医へのアンケートは85名に回答を依頼し50名から回答が得られた。歯科医へのアンケー トでは144名に回答を依頼し78名から回答が得られた。内訳は一般歯科が44名,口腔外科医が29名,その他が 5名であった。耳鼻咽喉科医は歯性上顎洞炎の診断には98%がCTを有用と回答し,歯科医は78%がCTを,22% が歯科用単純X線写真を重視すると回答した。CTを重視する傾向が耳鼻咽喉科医において有意に高かった。ま た耳鼻咽喉科医の62%が歯科と相談し治療方針を決定していた。一方歯科医は耳鼻咽喉科に相談すると回答し たのは22%であり,61%がまず歯科で治療を行うと回答した。 【結語】歯科医の大半が診断にCTを有用と回答したものの,耳鼻咽喉科医と比較するとその割合は少なかっ た。治療方針の決定に関して,耳鼻咽喉科医は歯科と相談する割合が多かったが,歯科医は単独で治療を行う ことが多かった。

A Cross-Sectional Survey on the Diagnosis and Treatment of Odontogenic Maxillary Sinusitis by Otorhinolaryngologists and Dentists

Autonomous driving systems (ADS) have been widely tested in real-world
 environments with operators who must monitor and intervene due to remaining
 technical challenges. However, intervention methods that require operators to
 take over control of the vehicle involve many drawbacks related to human
 performance. ADS consist of recognition, decision, and control modules. The
 latter two phases are dependent on the recognition phase, which still struggles
 with tasks involving the prediction of human behavior, such as pedestrian risk
 prediction. As an alternative to full automation of the recognition task,
 cooperative recognition approaches utilize the human operator to assist the
 automated system in performing challenging recognition tasks, using a
 recognition assistance interface to realize human-machine cooperation. In this
 study, we propose a recognition assistance interface for cooperative recognition
 in order to achieve safer and more efficient driving through improved
 human-automation cooperation. A simulator experiment with 18 participants is
 conducted to evaluate our recognition assistance interface in comparison with a
 conventional control intervention, in terms of driving safety, efficiency, and
 usability. Recognition of pedestrian crossing intention is selected for the
 cooperation task, and driving scenarios in which the automated system cannot
 reliably recognize the crossing intentions of pedestrians at non-signalized
 locations are selected as the driving scenario. Statistical analysis of our
 experimental results reveals that the proposed recognition assistance interface
 allowed more accurate operator intervention, was easier to use, and achieved
 more stable vehicle control than the control intervention. We also found that
 sharing the recognition information of the automated driving system with
 operators could divide their attention, impairing intervention performance. Our
 experimental results suggest that the unifying presentation of the system
 recognition information and the operator’s manipulation target on the
 touchscreen of the user interface addresses this problem.

Recognition Assistance Interface for Human-Automation Cooperation in
 Pedestrian Risk Prediction

Most current research in the field of autonomous vehicle control assumes that all vehicles will follow the same patterns of automated driving behavior, resulting in systems with “conservative” or “average” driving styles. These systems may not be acceptable to drivers who prefer a more aggressive style of driving, however, while extremely cautious drivers may consider the standard outputs to be too aggressive. To address this problem, in this paper, we introduce Risk Sensitive Control (RSC), an inverse optimal control algorithm that estimates risk-sensitive driving features and incorporates them into a receding-horizon controller. RSC uses a meta-learning algorithm to update the parameters of the cost function, continuously improving the controller online as more and more driving data is gathered from the user and subjective risk feedback. An estimator takes into account individual differences in subjective risk analysis, in terms of driving features and surrounding vehicle locations, by adjusting the cost function and its constraints. We test this approach using five lane change scenarios, some safe and some risky, with thirty real drivers in a CARLA simulation environment. Our quantitative and qualitative evaluations demonstrate that the proposed framework is able to generate a user’s preferred driving maneuvers during lane changes, i.e., control commands the user associates with lower subjective risk, outperforming conventional, model-based predictive control methods in terms of replicating the user’s own driving behavior.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09745149.pdf

Kazuya Takeda

Papers

Genome-wide neighbor effects predict genotype pairs that reduce herbivory in mixed planting

A model of perceptual distance for group delays based on ellipsoidal mapping

A Cross-Sectional Survey on the Diagnosis and Treatment of Odontogenic Maxillary Sinusitis by Otorhinolaryngologists and Dentists

Recognition Assistance Interface for Human-Automation Cooperation in Pedestrian Risk Prediction

Data-Driven Risk-Sensitive Control for Personalized Lane Change Maneuvers