Randomized Group-Greedy Method for Large-Scale Sensor Selection Problems

The randomized group-greedy method and its customized method for large-scale sensor selection problems are proposed. The randomized greedy sensor selection algorithm is applied straightforwardly to the group-greedy method, and a customized method is also considered. In the customized method, a part of the compressed sensor candidates is selected using the common greedy method or other low-cost methods. This strategy compensates for the deterioration of the solution due to compressed sensor candidates. The proposed methods are implemented based on the D- and E-optimal design of experiments, and numerical experiments are conducted using randomly generated sensor candidate matrices with potential sensor locations of 10,000--1,000,000. The proposed method can provide better optimization results than those obtained by the original group-greedy method when a similar computational cost is spent as for the original group-greedy method. This is because the group size for the group-greedy method can be increased as a result of the compressed sensor candidates by the randomized algorithm. Similar results were also obtained in the real dataset. The proposed method is effective for the E-optimality criterion, in which the objective function that the optimization by the common greedy method is difficult due to the absence of submodularity of the objective function. The idea of the present method can improve the performance of all optimizations using a greedy algorithm

Data-Driven Sensor Selection Method Based on Proximal Optimization for High-Dimensional Data With Correlated Measurement Noise

The present paper proposes a data-driven sensor selection method for a high-dimensional nondynamical system with strongly correlated measurement noise. The proposed method is based on proximal optimization and determines sensor locations by minimizing the trace of the inverse of the Fisher information matrix under a block-sparsity hard constraint. The proposed method can avoid the difficulty of sensor selection with strongly correlated measurement noise, in which the possible sensor locations must be known in advance for calculating the precision matrix for selecting sensor locations. The problem can be efficiently solved by the alternating direction method of multipliers, and the computational complexity of the proposed method is proportional to the number of potential sensor locations when it is used in combination with a low-rank expression of the measurement noise model. The advantage of the proposed method over existing sensor selection methods is demonstrated through experiments using artificial and real datasets

Seismic Wavefield Reconstruction based on Compressed Sensing using Data-Driven Reduced-Order Model

A seismic wavefield reconstruction framework based on compressed sensing using the data-driven reduced-order model (ROM) is proposed and its characteristics are investigated through numerical experiments. The data-driven ROM is generated from the dataset of the wavefield using the singular value decomposition. The spatially continuous seismic wavefield is reconstructed from the sparse and discrete observation and the data-driven ROM. The observation sites used for reconstruction are effectively selected by the sensor optimization method for linear inverse problems based on a greedy algorithm. The proposed framework was applied to simulation data of theoretical waveform with the subsurface structure of the horizontally-stratified three layers. The validity of the proposed method was confirmed by the reconstruction based on the noise-free observation. Since the ROM of the wavefield is used as prior information, the reconstruction error is reduced to an approximately lower error bound of the present framework, even though the number of sensors used for reconstruction is limited and randomly selected. In addition, the reconstruction error obtained by the proposed framework is much smaller than that obtained by the Gaussian process regression. For the numerical experiment with noise-contaminated observation, the reconstructed wavefield is degraded due to the observation noise, but the reconstruction error obtained by the present framework with all available observation sites is close to a lower error bound, even though the reconstructed wavefield using the Gaussian process regression is fully collapsed. Although the reconstruction error is larger than that obtained using all observation sites, the number of observation sites used for reconstruction can be reduced while minimizing the deterioration and scatter of the reconstructed data by combining it with the sensor optimization method

Observation Site Selection for Physical Model Parameter Estimation toward Process-Driven Seismic Wavefield Reconstruction

The seismic data not only acquired by seismometers but also acquired by vibrometers installed in buildings and infrastructure and accelerometers installed in smartphones will be certainly utilized for seismic research in the near future. Since it is impractical to utilize all the seismic big data in terms of the computational cost, methods which can select observation sites depending on the purpose are indispensable. We propose an observation site selection method for the accurate reconstruction of the seismic wavefield by process-driven approaches. The proposed method selects observation sites suitable for accurately estimating physical model parameters such as subsurface structures and source information to be input into a numerical simulation of the seismic wavefield. The seismic wavefield is reconstructed by the numerical simulation using the parameters estimated based on the observed signals at only observation sites selected by the proposed method. The observation site selection in the proposed method is based on the sensitivity of each observation site candidate to the physical model parameters; the matrix corresponding to the sensitivity is constructed by approximately calculating the derivatives based on the simulations, and then, observation sites are selected by evaluating the quantity of the sensitivity matrix based on the D-optimality criterion proposed in the optimal design of experiments. In the present study, physical knowledge on the sensitivity to the parameters such as seismic velocity, layer thickness, and hypocenter location was obtained by investigating the characteristics of the sensitivity matrix. Furthermore, the effectiveness of the proposed method was shown by verifying the accuracy of seismic wavefield reconstruction using the observation sites selected by the proposed method.Comment: Preprint submitted to Geophysical Journal International on 8-June-202

Pervasive Developmental Disorders and Autism Spectrum Disorders: Are These Disorders One and the Same?

The concept of pervasive developmental disorders (PDD) and autism spectrum disorders (ASD) closely resemble each other. Both ICD-10 and DSM-IV use the term PDD. The authors surveyed the perception of PDD/ASD and attitudes toward terminology. The subjects of this study were 205 medical/social-welfare professionals working in fields relating to developmental disorders. Questionnaires were mailed to site investigators at the collaborating institutes. With regard to what the scope of ASD and PDD encompasses, the answers were almost equally divided among three views: ASD and PDD are the same, PDD is wider in scope and ASD is wider. The terms PDD and autism were used in slightly different ways depended upon the situation. Our results demonstrate that the parameters of PDD and ASD are unclear and that the terms related to PDD/ASD are often used differently. Further studies are required to develop more clear and reliable diagnostic criteria for PDD

マイクロ波CTマンモグラフィの開発

24個の固定ダイポールアンテナを用いて三次元マイクロ波CT実験を行い，スーパーコンピュータを用いてForward-Backward Time Stepping（FBTS）法によるCT計算を行った．その結果得られた知見は，FBTS法が雑音に強いこと，及び計算の初期設定やキャリブレーション設定が精度向上に重要なことである．また計算モデル化が容易な広帯域平面アンテナの開発も行った．これらの知見を生かし，FBTS法マイクロ波CTマンモグラフィ装置の概念設計を行った

Trapping of CDC42 C-terminal variants in the Golgi drives pyrin inflammasome hyperactivation

CDC42-C末端異常症に於ける炎症病態を解明 --ゴルジ体への異常蓄積がパイリンインフラマソーム形成を過剰促進--. 京都大学プレスリリース. 2022-05-02.Mutations in the C-terminal region of the CDC42 gene cause severe neonatal-onset autoinflammation. Effectiveness of IL-1β–blocking therapy indicates that the pathology involves abnormal inflammasome activation; however, the mechanism underlying autoinflammation remains to be elucidated. Using induced-pluripotent stem cells established from patients carrying CDC42[R186C], we found that patient-derived cells secreted larger amounts of IL-1β in response to pyrin-activating stimuli. Aberrant palmitoylation and localization of CDC42[R186C] protein to the Golgi apparatus promoted pyrin inflammasome assembly downstream of pyrin dephosphorylation. Aberrant subcellular localization was the common pathological feature shared by CDC42 C-terminal variants with inflammatory phenotypes, including CDC42[*192C*24] that also localizes to the Golgi apparatus. Furthermore, the level of pyrin inflammasome overactivation paralleled that of mutant protein accumulation in the Golgi apparatus, but not that of the mutant GTPase activity. These results reveal an unexpected association between CDC42 subcellular localization and pyrin inflammasome activation that could pave the way for elucidating the mechanism of pyrin inflammasome formation

New Approach to Teaching Japanese Pronunciation in the Digital Era - Challenges and Practices

Pronunciation has been a black hole in the L2 Japanese classroom on account of a lack of class time, teacher\u2019s confidence, and consciousness of the need to teach pronunciation, among other reasons. The absence of pronunciation instruction is reported to result in fossilized pronunciation errors, communication problems, and learner frustration. With an intention of making a contribution to improve such circumstances, this paper aims at three goals. First, it discusses the importance, necessity, and e ectiveness of teaching prosodic aspects of Japanese pronunciation from an early stage in acquisition. Second, it shows that Japanese prosody is challenging because of its typological rareness, regardless of the L1 backgrounds of learners. Third and finally, it introduces a new approach to teaching L2 pronunciation with the goal of developing L2 comprehensibility by focusing on essential prosodic features, which is followed by discussions on key issues concerning how to implement the new approach both inside and outside the classroom in the digital era