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.

To improve speech recognition performance, feature transformation based on discriminant analysis has been widely used for its effectiveness and tractability. For the same purpose, discriminative training of HMMs has also been used. The feature transformation technique and the discriminative training technique improve speech recognition performance independently. Therefore, the combination of these two techniques can yield better speech recognition performance. In this paper we investigate the effectiveness of these two techniques and their combinations. For feature transformation techniques, we evaluate not only the traditional techniques, such as linear discriminant analysis and heteroscedastic discriminant analysis, used in speech recognition but also state-of-the-art techniques. In addition, we investigate the robustness of matched and mismatched noise conditions between training and evaluation environments.

Evaluation of Discriminant Analysis-based Feature Transformation and Discriminative Training for Speech Recognition

Estimation of speaker and listener positions in a car using binaural signals

Skilled interception behavior often relies on accurate predictions of external objects because of a large delay in our sensorimotor systems. To deal with the sensorimotor delay, the brain predicts future states of the target based on the current state available, but it is still debated whether internal representations acquired from prior experience are used as well. Here we estimated the predictive manner by analyzing the response behavior of a pursuer to a sudden directional change of the evasive target, providing strong evidence that prediction of target motion by the pursuer was incompatible with a linear extrapolation based solely on the current state of the target. Moreover, using neural network models, we validated that nonlinear extrapolation as estimated was computationally feasible and useful even against unknown opponents. These results support the use of internal representations in predicting target motion, suggesting the usefulness and versatility of predicting external object motion through internal representations.

/pdf/flexible-prediction-of-opponent-motion-with-internal-2lwdiwyeul.pdf

Flexible prediction of opponent motion with internal representation in interception behavior

━━ Background. Alectinib is an effective and well-tolerated agent for treating anaplastic lymphoma kinase (ALK) fusion gene-positive lung adenocarcinoma. However, treatment with alectinib results in interstitial lung disease (ILD) as an adverse effect, thereby precluding therapy continuation. Several other ALK inhibitors have been studied in clinical trials

/pdf/two-cases-of-alk-fusion-gene-positive-lung-adenocarcinoma-1ltt9xcc.pdf

Two Cases of ALK Fusion Gene-positive Lung Adenocarcinoma Treated with Brigatinib After Alectinib-induced Interstitial Lung Disease

An acoustic measure for predicting the degradation of speech recognition performance due to noise contamination is developed. The merits of the proposed measure over using conventional SNR are that (1) the measure does not require the original clean signal as a reference signal (2) the measure takes the spectral shape of the noise into account and, (3) the measure can predict recognition performance directly. The basic idea of the measure is to utilize the dynamic range of the sub-band signals as an estimate of SNR in the corresponding subband and, to predict the degradation of the recognition performance by taking a product of the recognition accuracy of each sub-band. The proposed measure is tested through experimental evaluation using white Gaussian and human speech like (HSL) noise. In the experiment, the correlation between the predicted and the actual recognition accuracies are 0.96 and 0.99 for white and HSL noise respectively. From the results, the effectiveness of the proposed measure is confirmed.

Kazuya Takeda

Papers

Evaluation of Discriminant Analysis-based Feature Transformation and Discriminative Training for Speech Recognition

Estimation of speaker and listener positions in a car using binaural signals

Flexible prediction of opponent motion with internal representation in interception behavior

Two Cases of ALK Fusion Gene-positive Lung Adenocarcinoma Treated with Brigatinib After Alectinib-induced Interstitial Lung Disease

An acoustic measure for predicting recognition performance degradation