An Algorithmic Framework for Computing Validation Performance Bounds by Using Suboptimal Models

Practical model building processes are often time-consuming because many different models must be trained and validated. In this paper, we introduce a novel algorithm that can be used for computing the lower and the upper bounds of model validation errors without actually training the model itself. A key idea behind our algorithm is using a side information available from a suboptimal model. If a reasonably good suboptimal model is available, our algorithm can compute lower and upper bounds of many useful quantities for making inferences on the unknown target model. We demonstrate the advantage of our algorithm in the context of model selection for regularized learning problems

Development of the Micro Pixel Chamber with resistive electrodes

We developed a novel design of a Micro Pixel Chamber ($\mu$-PIC) with resistive electrodes for a charged-particle-tracking detector in high-rate applications. Diamond-Like Carbon (DLC) thin film is used for the cathodes. The resistivity can be controlled flexibly ($\mathrm{10^{5-7}k\Omega/sq.}$) at high uniformity. The fabrication-process was greatly improved and the resistive $\mu$-PIC could be operated at 10$\times$10 $\mathrm{cm^2}$. Resistors for the HV bias and capacitors for the AC coupling were completely removed by applying PCB and carbon-sputtering techniques, and the resistive $\mu$-PIC became a very compact detector. The performances of our new resistive $\mu$-PIC were measured in various ways. Consequently, it was possible to attain high gas gains ($\mathrm{> 10^{4}}$), high detection efficiency, and position resolution exceeding 100 $\mu$m. The spark current was suppressed, and the new resistive $\mu$-PIC was operated stably under fast-neutrons irradiation. These features offer solutions for a charged-particle-tracking detector in future high-rate applications.Comment: 37pages, 40figures, To be submitted to Nucl. Instrum. Methods Phys. Res.

Efficacy and Safety of Pancreas-Targeted Hydrodynamic Gene Delivery in Rats

新潟大学博士（医学）Development of an effective, safe, and convenient method for gene delivery to the pancreas is a critical step toward gene therapy for pancreatic diseases. Therefore, we tested the possibility of applying the principle of hydrodynamic gene delivery for successful gene transfer to pancreas using rats as a model. The established procedure involves the insertion of a catheter into the superior mesenteric vein with temporary blood flow occlusion at the portal vein and hydrodynamic injection of DNA solution. We demonstrated that our procedure achieved efficient pancreas-specific gene expression that was 2,000-fold higher than that seen in the pancreas after the systemic hydrodynamic gene delivery. In addition, the level of gene expression achieved in the pancreas by the pancreas-specific gene delivery was comparable to the level in the liver achieved by a liver-specific hydrodynamic gene delivery. The optimal level of reporter gene expression in the pancreas requires an injection volume equivalent to 2.0% body weight with flow rate of 1 mL/s and plasmid DNA concentration at 5 mg/mL. With the exception of transient expansion of intercellular spaces and elevation of serum amylase levels, which recovered within 3 days, no permanent tissue damage was observed. These results suggest that pancreas-targeted hydrodynamic gene delivery is an effective and safe method for gene delivery to the pancreas and clinically applicable.学位の種類: 博士（医学）. 報告番号: 甲第4391号. 学位記番号: 新大院博（医）甲第790号. 学位授与年月日: 平成30年3月23日Molecular Therapy Nucleic Acids. 2017. 9, 80-88.新大院博（医）甲第790号thesi

Aharonov-Bohm Oscillations in Photoluminescence from Charged Exciton in Quantum Tubes

The oscillation of photoluminescence peak energies is observed in InAs quantum tubes depending on the magnetic flux through the tube. The oscillation is shown to be due to the Aharonov-Bohm effect of a charged exciton in a quantum tube. No quadratic shift in photoluminescence peak energies is observed, which is a characteristic feature of a thin quantum tube with a single channel surrounding the magnetic flux through the tube.Comment: 14 pages, 4 figure

Effects of the Behavior of a Para-operated Robot on the Impression of the Operator: a Preliminary Online Study Considering the Robot-operator Distance

While research and applications of operated social robots that can be used as operators’ avatars have been conducted, a para-operated social robot has also been proposed, in which the robot and its operator exist in the same space, and the operator, robot, and interlocutor can interact with each other. From the findings of human-human-robot interaction research, it is known that the robot’s behavior influences the interpersonal relationships of the people involved in the dialog. However, there is no knowledge of how the operator is perceived by the interlocutor when the operations by the operator are unveiled. This paper reports preliminary results from two online surveys conducted to investigate the following question considering the robot-operator distance; Does the impression of the operator improve in unveiled para-operation when the operator operates the robot and has it say favorable utterances? The results of the two video surveys conducted at different distances between the robot and the operator show that the impression of the operator was improved by the robot’s favorable humorous utterances when the distance was close, but not when the distance was further apart. We consider that the findings contribute to the potential application of unveiled para-operated robots and suggest the importance of considering the distance factor in the influence of robots on human-human relationships.11th Conference on Human-Agent InteractionIn (HAL2023), December 4-11, 2023, Gothenburg, Swedenconference pape

Remixing-based Unsupervised Source Separation from Scratch

We propose an unsupervised approach for training separation models from scratch using RemixIT and Self-Remixing, which are recently proposed self-supervised learning methods for refining pre-trained models. They first separate mixtures with a teacher model and create pseudo-mixtures by shuffling and remixing the separated signals. A student model is then trained to separate the pseudo-mixtures using either the teacher's outputs or the initial mixtures as supervision. To refine the teacher's outputs, the teacher's weights are updated with the student's weights. While these methods originally assumed that the teacher is pre-trained, we show that they are capable of training models from scratch. We also introduce a simple remixing method to stabilize training. Experimental results demonstrate that the proposed approach outperforms mixture invariant training, which is currently the only available approach for training a monaural separation model from scratch.Comment: Interspeech2023, 5pages, 2figures, 2table

Self-Remixing: Unsupervised Speech Separation via Separation and Remixing

We present Self-Remixing, a novel self-supervised speech separation method, which refines a pre-trained separation model in an unsupervised manner. The proposed method consists of a shuffler module and a solver module, and they grow together through separation and remixing processes. Specifically, the shuffler first separates observed mixtures and makes pseudo-mixtures by shuffling and remixing the separated signals. The solver then separates the pseudo-mixtures and remixes the separated signals back to the observed mixtures. The solver is trained using the observed mixtures as supervision, while the shuffler's weights are updated by taking the moving average with the solver's, generating the pseudo-mixtures with fewer distortions. Our experiments demonstrate that Self-Remixing gives better performance over existing remixing-based self-supervised methods with the same or less training costs under unsupervised setup. Self-Remixing also outperforms baselines in semi-supervised domain adaptation, showing effectiveness in multiple setups.Comment: Accepted by ICASSP2023, 5pages, 2figures, 2table