Streaming Active Learning for Regression Problems Using Regression via Classification

One of the challenges in deploying a machine learning model is that the model's performance degrades as the operating environment changes. To maintain the performance, streaming active learning is used, in which the model is retrained by adding a newly annotated sample to the training dataset if the prediction of the sample is not certain enough. Although many streaming active learning methods have been proposed for classification, few efforts have been made for regression problems, which are often handled in the industrial field. In this paper, we propose to use the regression-via-classification framework for streaming active learning for regression. Regression-via-classification transforms regression problems into classification problems so that streaming active learning methods proposed for classification problems can be applied directly to regression problems. Experimental validation on four real data sets shows that the proposed method can perform regression with higher accuracy at the same annotation cost

Spectrocolorimetric evaluation of repaired articular cartilage after a microfracture

<p>Abstract</p> <p>Background</p> <p>In clinical practice, surgeons differentiate color changes in repaired cartilage compared with surrounding intact cartilage, but cannot quantify these color changes. Objective assessments are required. A spectrocolorimeter was used to evaluate whether intact and repaired cartilage can be quantified.</p> <p>Findings</p> <p>We investigated the use of a spectrocolorimeter and the application of two color models (L* a* b* colorimetric system and spectral reflectance distribution) to describe and quantify articular cartilage. In this study, we measured the colors of intact and repaired cartilage after a microfracture. Histologically, the repaired cartilage was a mixture of fibrocartilage and hyaline cartilage. In the L* a* b* colorimetric system, the L* and a* values recovered to close to the values of intact cartilage, whereas the b* value decreased over time after the operation. Regarding the spectral reflectance distribution at 12 weeks after the operation, the repaired cartilage had a higher spectral reflectance ratio than intact cartilage between wavelengths of 400 to 470 nm.</p> <p>Conclusion</p> <p>This study reports the first results regarding the relationship between spectrocolorimetric evaluation and the histological findings of repair cartilage after a microfracture. Our findings demonstrate the ability of spectrocolorimetric measurement to judge the repair cartilage after treatment on the basis of objective data such as the L*, a* and b* values and the SRP as a coincidence index of the spectral reflectance curve.</p

CAPTDURE: Captioned Sound Dataset of Single Sources

In conventional studies on environmental sound separation and synthesis using captions, datasets consisting of multiple-source sounds with their captions were used for model training. However, when we collect the captions for multiple-source sound, it is not easy to collect detailed captions for each sound source, such as the number of sound occurrences and timbre. Therefore, it is difficult to extract only the single-source target sound by the model-training method using a conventional captioned sound dataset. In this work, we constructed a dataset with captions for a single-source sound named CAPTDURE, which can be used in various tasks such as environmental sound separation and synthesis. Our dataset consists of 1,044 sounds and 4,902 captions. We evaluated the performance of environmental sound extraction using our dataset. The experimental results show that the captions for single-source sounds are effective in extracting only the single-source target sound from the mixture sound.Comment: Accepted to INTERSPEECH202

A Fast Runtime Visualization of a GPU-Based 3D-FDTD Electromagnetic Simulation

In this paper, we present design and implementation of a fast runtime visualizer for a GPU-based 3D-FDTD electromagnetic simulation. We focus on improving the productivity of simulator development without compromising simulation performance. In order to keep the portability, we implemented a visualizer with the MVC model, where simulation kernels and visualization process were completely separated. For high-speed visualization, an interoperability mechanism between OpenGL and CUDA was used in addition to efficient utilization of programmable shaders. We also propose an asynchronous multi-threaded execution with a triple-buffering technique so that developers can concentrate on developing their simulation kernels. As a result of empirical visualization experiments of electromagnetic simulations for practical antenna design, it was revealed that our implementation achieved a rendering throughput of 90 FPS for a view port of 512 x 512 pixels, which corresponds to a 12.9 times speedup compared to when the OpenGL-CUDA interoperability mechanism was not utilized. When a standard visualization throughput of 60 FPS was selected, the performance overhead imposed by the visualization process was 15.8%, which was reasonably low compared to a speedup of the simulation kernel gained by the GPU acceleration

Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques

We present the task description and discussion on the results of the DCASE 2022 Challenge Task 2: ``Unsupervised anomalous sound detection (ASD) for machine condition monitoring applying domain generalization techniques''. Domain shifts are a critical problem for the application of ASD systems. Because domain shifts can change the acoustic characteristics of data, a model trained in a source domain performs poorly for a target domain. In DCASE 2021 Challenge Task 2, we organized an ASD task for handling domain shifts. In this task, it was assumed that the occurrences of domain shifts are known. However, in practice, the domain of each sample may not be given, and the domain shifts can occur implicitly. In 2022 Task 2, we focus on domain generalization techniques that detects anomalies regardless of the domain shifts. Specifically, the domain of each sample is not given in the test data and only one threshold is allowed for all domains. Analysis of 81 submissions from 31 teams revealed two remarkable types of domain generalization techniques: 1) domain-mixing-based approach that obtains generalized representations and 2) domain-classification-based approach that explicitly or implicitly classifies different domains to improve detection performance for each domain.Comment: arXiv admin note: substantial text overlap with arXiv:2106.0449

A Stem Cell Harvesting Manipulator with Flexible Drilling Unit for Bone Marrow Transplantation

Abstract. We present the development of an innovative device (Stem Cell Harvesting Manipulator) to get a donor’s hematopoietic stem cells for bone marrow transplantation with minimal puncture. In this paper, we report the development of a prototype of the manipulator with Flexible Drilling Unit. The manipulator is inserted into the medullary space from the iliac crest and aspirates the cells while an end-mill on the tip of the Flexible Drilling Unit drills through cancellous bone to create a curved path. We found that the manipulator could be inserted into the pig iliac bone 131 mm by 32.1 mm/min. Moreover, the number of harvested nucleated cells per puncture is 6.04 times more than the Aspiration Method. The Aspiration Method, however, could harvest much (3.73 times) denser graft than our method. Further consideration regarding whether or not this device can harvest viable hematopoietic stem cells should be considere

MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task

We present a machine sound dataset to benchmark domain generalization techniques for anomalous sound detection (ASD). To handle performance degradation caused by domain shifts that are difficult to detect or too frequent to adapt, domain generalization techniques are preferred. However, currently available datasets have difficulties in evaluating these techniques, such as limited number of values for parameters that cause domain shifts (domain shift parameters). In this paper, we present the first ASD dataset for the domain generalization techniques, called MIMII DG. The dataset consists of five machine types and three domain shift scenarios for each machine type. We prepared at least two values for the domain shift parameters in the source domain. Also, we introduced domain shifts that can be difficult to notice. Experimental results using two baseline systems indicate that the dataset reproduces the domain shift scenarios and is useful for benchmarking domain generalization techniques