Hierarchical Clustering of Ensemble Prediction Using LOOCV Predictable Horizon for Chaotic Time Series

Recently, we have presented a method of ensemble prediction of chaotic time series. The method employs strong learners capable of making predictions with small error, where usual ensemble mean does not work well owing to the long term unpredictability of chaotic time series. Thus, we have developed a method to select a representative prediction from a set of plausible predictions by means of using LOOCV (leave-one-out cross-validation) measure to estimate predictable horizon. Although we have shown the effectiveness of the method, it sometimes fails to select the representative prediction with long predictable horizon. In order to cope with this problem, this paper presents a method to select multiple candidates of representative prediction by means of employing hierarchical K-means clustering with K = 2. From numerical experiments, we show the effectiveness of the method and an analysis of the property of LOOCV predictable horizon.The 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), November 27 to December 1, 2017, Honolulu, Hawaii, US

Grading Fruits and Vegetables Using RGB-D Images and Convolutional Neural Network

This paper presents a method for grading fruits and vegetables by means of using RGB-D (RGB and depth) images and convolutional neural network (CNN). Here, we focus on grading according to the size of objects. First, the method transforms positions of pixels in RGB image so that the center of the object in 3D space is placed at the position equidistant from the focal point by means of using the corresponding depth image. Then, with the transformed RGB images involving equidistant objects, the method uses CNN for learning to classify the objects or fruits and vegetables in the images for grading according to the size, where the CNN is structured for achieving both size sensitivity for grading and shift invariance for reducing position error involved in images. By means of numerical experiments, we show the effectiveness and the analysis of the present method.The 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), November 27 to December 1, 2017, Honolulu, Hawaii, US

Performance improvement via bagging in probabilistic prediction of chaotic time series using similarity of attractors and LOOCV predictable horizon

Recently, we have presented a method of probabilistic prediction of chaotic time series. The method employs learning machines involving strong learners capable of making predictions with desirably long predictable horizons, where, however, usual ensemble mean for making representative prediction is not effective when there are predictions with shorter predictable horizons. Thus, the method selects a representative prediction from the predictions generated by a number of learning machines involving strong learners as follows: first, it obtains plausible predictions holding large similarity of attractors with the training time series and then selects the representative prediction with the largest predictable horizon estimated via LOOCV (leave-one-out cross-validation). The method is also capable of providing average and/or safe estimation of predictable horizon of the representative prediction. We have used CAN2s (competitive associative nets) for learning piecewise linear approximation of nonlinear function as strong learners in our previous study, and this paper employs bagging (bootstrap aggregating) to improve the performance, which enables us to analyze the validity and the effectiveness of the method

Probabilistic Prediction in Multiclass Classification Derived for Flexible Text-Prompted Speaker Verification

So far, we have presented amethod for text-promptedmultistep speaker verification using GEBI (Gibbs-distribution based extended Bayesian inference) for reducing single-step verification error, where we use thresholds for acceptance and rejection but the tuning is not so easy and affects the performance of verification. To solve the problem of thresholds, this paper presents a method of probabilistic prediction in multiclass classification for solving verification problem.We also present loss functions for evaluating the performance of probabilistic prediction. By means of numerical experiments using recorded real speech data, we examine the properties of the present method using GEBI and BI (Bayesian inverence) and show the effectiveness and the risk of probability loss in the present method.22nd International Conference on Neural Information Processing, ICONIP 2015, November 9-12, 2015, Istanbul, Turke

HLA-Haploidentical Peripheral Blood Stem Cell Transplantation with Post-Transplant Cyclophosphamide after Busulfan-Containing Reduced-Intensity Conditioning

AbstractAllogeneic hematopoietic stem cell transplantation (allo-SCT) using post-transplant cyclophosphamide (PTCy) is increasingly performed. We conducted a multicenter phase II study to evaluate the safety and efficacy of PTCy-based HLA-haploidentical peripheral blood stem cell transplantation (PTCy-haploPBSCT) after busulfan-containing reduced-intensity conditioning. Thirty-one patients were enrolled; 61% patients were not in remission and 42% patients had a history of prior allo-SCT. Neutrophil engraftment was achieved in 87% patients with a median of 19 days. The cumulative incidence of grades II to IV and III to IV acute graft-versus-host disease (GVHD) and chronic GVHD at 1 year were 23%, 3%, and 15%, respectively. No patients developed severe chronic GVHD. Day 100 nonrelapse mortality (NRM) rate was 19.4%. Overall survival, relapse, and disease-free survival rates were 45%, 45%, and 34%, respectively, at 1 year. Subgroup analysis showed that patients who had a history of prior allo-SCT had lower engraftment, higher NRM, and lower overall survival than those not receiving a prior allo-SCT. Our results suggest that PTCy-haploPBSCT after busulfan-containing reduced-intensity conditioning achieved low incidences of acute and chronic GVHD and NRM and stable donor engraftment and low NRM, particularly in patients without a history of prior allo-SCT

Kinetic analysis of poplar wood properties by thermal modification in conventional oven

The kinetics of several poplar (Populus alba L.) wood properties during thermal modification conducted in conventional oven with air recirculation were analysed and modelled in this paper. A wide range of properties was assessed, such as: equilibrium moisture content, sorption diagram, shrinkage coefficients, specific shrinkage coefficients, mass loss, modulus of elasticity, strength and colour. The tests were executed at different temperatures ranging from 90°C to 180°C and with different durations. The time-temperature equivalency was checked and property modifications over time analysed through master curves in order to obtain a general model connecting together properties, treatment temperature and duration. Different activation energies arising from each property evolution with treatment temperature and duration are provided showing that every modification could occur with different kinetics

Recurrent malignant melanoma of the palate successfully treated by gamma knife radiosurgery

The prognosis of oral malignant melanoma is reported to be extremely poor. In this report, a patient with recurrent oral melanoma in the skull base that was successfully treated by gamma knife radiosurgery (GKS) is described. A 53-year-old man was referred with a chief complaint of a mass of the hard palate. The histological diagnosis of a biopsy specimen was malignant melanoma. He underwent a wide local resection with bilateral neck dissection, followed by immunochemotherapy with DAV-Feron. At 13 months postoperatively, a recurrent tumor was found in the posterior lower region of the nasal septum. The patient underwent resection of the lesion, followed by immunochemotherapy with DAC-Tam-Feron. However, at 9 months after the last chemotherapy, local recurrence occurred again in the skull base, and he underwent GKS. The recurrent tumor disappeared completely and he is well with no signs of recurrence or metastasis at 57 months after GKS

RUNX1 transactivates BCR-ABL1 expression in Philadelphia chromosome positive acute lymphoblastic leukemia

The emergence of tyrosine kinase inhibitors as part of a front-line treatment has greatly improved the clinical outcome of the patients with Ph⁺ acute lymphoblastic leukemia (ALL). However, a portion of them still become refractory to the therapy mainly through acquiring mutations in the BCR-ABL1 gene, necessitating a novel strategy to treat tyrosine kinase inhibitor (TKI)-resistant Ph⁺ ALL cases. In this report, we show evidence that RUNX1 transcription factor stringently controls the expression of BCR-ABL1, which can strategically be targeted by our novel RUNX inhibitor, Chb-M'. Through a series of in vitro experiments, we identified that RUNX1 binds to the promoter of BCR and directly transactivates BCR-ABL1 expression in Ph⁺ ALL cell lines. These cells showed significantly reduced expression of BCR-ABL1 with suppressed proliferation upon RUNX1 knockdown. Moreover, treatment with Chb-M' consistently downregulated the expression of BCR-ABL1 in these cells and this drug was highly effective even in an imatinib-resistant Ph⁺ ALL cell line. In good agreement with these findings, forced expression of BCR-ABL1 in these cells conferred relative resistance to Chb-M'. In addition, in vivo experiments with the Ph⁺ ALL patient-derived xenograft cells showed similar results. In summary, targeting RUNX1 therapeutically in Ph⁺ ALL cells may lead to overcoming TKI resistance through the transcriptional regulation of BCR-ABL1. Chb-M' could be a novel drug for patients with TKI-resistant refractory Ph⁺ ALL