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

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

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

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.

Methylglyoxal reduces molecular responsiveness to 4 weeks of endurance exercise in mouse plantaris muscle

Endurance exercise triggers skeletal muscle adaptations, including enhanced insulin signaling, glucose metabolism, and mitochondrial biogenesis. However, exercise-induced skeletal muscle adaptations may not occur in some cases, a condition known as exercise-resistance. Methylglyoxal (MG) is a highly reactive dicarbonyl metabolite and has detrimental effects on the body such as causing diabetic complications, mitochondrial dysfunction, and inflammation. This study aimed to clarify the effect of methylglyoxal on skeletal muscle molecular adaptations following endurance exercise. Mice were randomly divided into 4 groups (n = 12 per group): sedentary control group, voluntary exercise group, MG-treated group, and MG-treated with voluntary exercise group. Mice in the voluntary exercise group were housed in a cage with a running wheel, while mice in the MG-treated groups received drinking water containing 1% MG. Four weeks of voluntary exercise induced several molecular adaptations in the plantaris muscle, including increased expression of peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC1α), mitochondria complex proteins, toll-like receptor 4 (TLR4), 72-kDa heat shock protein (HSP72), hexokinase II, and glyoxalase 1; this also enhanced insulin-stimulated Akt Ser473 phosphorylation and citrate synthase activity. However, these adaptations were suppressed with MG treatment. In the soleus muscle, the exercise-induced increases in the expression of TLR4, HSP72, and advanced glycation end products receptor 1 were inhibited with MG treatment. These findings suggest that MG is a factor that inhibits endurance exercise-induced molecular responses including mitochondrial adaptations, insulin signaling activation, and the upregulation of several proteins related to mitochondrial biogenesis, glucose handling, and glycation in primarily fast-twitch skeletal muscle

Application of X-Ray Clumpy Torus Model (XCLUMPY) to 10 Obscured Active Galactic Nuclei Observed with Suzaku and NuSTAR

We apply XCLUMPY, an X-ray spectral model from a clumpy torus in an active galactic nucleus (AGN), to the broadband X-ray spectra of 10 obscured AGNs observed with both Suzaku and NuSTAR. The infrared spectra of these AGNs were analyzed with the CLUMPY code. Since XCLUMPY adopts the same clump distribution as that in the CLUMPY, we can directly compare the torus parameters obtained from the X-ray spectra and those from the infrared ones. The torus angular widths determined from the infrared spectra ($\sigma_{\mathrm{IR}}$) are systematically larger than those from the X-ray data ($\sigma_{\mathrm{X}}$); the difference ($\sigma_{\mathrm{IR}}-\sigma_{\mathrm{X}}$) correlates with the inclination angle determined from the X-ray spectrum. These results can be explained by the contribution from dusty polar outflows to the observed infrared flux, which becomes more significant at higher inclinations (more edge-on views). The ratio of the hydrogen column density and V-band extinction in the line of sight absorber shows large scatter ($\simeq$1 dex) around the Galactic value, suggesting that a significant fraction of AGNs have dust-rich circumnuclear environments.Comment: 17 pages, 3 figures, accepted for publication in Ap

Glycative stress and skeletal muscle dysfunctions: as an inducer of "Exercise-Resistance."

Skeletal muscle, the largest tissue in the body, is often overlooked for its role as a locomotor organ, however over the past few decades it has been revealed that it also has an important role as a metabolic organ. In recent years, its role as an endocrine organ that controls the homeostatic functions of organs throughout the body mediated by myokine secretion has come under close scrutiny. Skeletal muscle is indispensable for our daily life activities, and in order to maintain its function, it is necessary to understand the factors that deteriorate muscle function and establish a countermeasure. Glycative stress has recently received attention as a factor that impairs skeletal muscle function. Accumulation of advanced glycation end products (AGEs) in skeletal muscle impairs contractile function and myogenic potential. Furthermore, AGEs in the blood elicit inflammatory signals through binding to RAGE (Receptor for AGEs) expressed on muscle cells, resulting in muscle proteolysis. Habitual exercise is important to mitigate the negative effects of such glycative stress on skeletal muscle. On the other hand, it is known that the beneficial effects of exercise vary among individuals. The state in which the effects of exercise are difficult to obtain is called "exercise-resistance, " and we hypothesize that glycative stress may be one of the causes of exercise-resistance. In this paper, we will discuss the possibility of glycative stress as an inducer of exercise resistance and summarize its impacts on skeletal muscle