Frozen Gaussian Sampling for Scalar Wave Equations

In this article, we introduce the frozen Gaussian sampling (FGS) algorithm to solve the scalar wave equation in the high-frequency regime. The FGS algorithm is a Monte Carlo sampling strategy based on the frozen Gaussian approximation, which greatly reduces the computation workload in the wave propagation and reconstruction. In this work, we propose feasible and detailed procedures to implement the FGS algorithm to approximate scalar wave equations with Gaussian initial conditions and WKB initial conditions respectively. For both initial data cases, we rigorously analyze the error of applying this algorithm to wave equations of dimensionality $d \geq 3$. In Gaussian initial data cases, we prove that the sampling error due to the Monte Carlo method is independent of the typical wave number. We also derive a quantitative bound of the sampling error in WKB initial data cases. Finally, we validate the performance of the FGS and the theoretical estimates about the sampling error through various numerical examples, which include using the FGS to solve wave equations with both Gaussian and WKB initial data of dimensionality $d = 1, 2$, and $3$

Itinerant Nature of Atom-Magnetization Excitation by Tunneling Electrons

We have performed single-atom magnetization curve (SAMC) measurements and inelastic scanning tunneling spectroscopy (ISTS) on individual Fe atoms on a Cu(111) surface. The SAMCs show a broad distribution of magnetic moments with \unit[3.5]{\mu_{\rm B}} being the mean value. ISTS reveals a magnetization excitation with a lifetime of \unit[200]{fsec} which decreases by a factor of two upon application of a magnetic field of \unit[12]{T}. The experimental observations are quantitatively explained by the decay of the magnetization excitation into Stoner modes of the itinerant electron system as shown by newly developed theoretical modeling.Comment: 3 Figures, Supplement not included, updated version after revisio

BiGSeT: Binary Mask-Guided Separation Training for DNN-based Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) aims to recognize a minority of anomalies that are spectrally different from their surrounding background without prior knowledge. Deep neural networks (DNNs), including autoencoders (AEs), convolutional neural networks (CNNs) and vision transformers (ViTs), have shown remarkable performance in this field due to their powerful ability to model the complicated background. However, for reconstruction tasks, DNNs tend to incorporate both background and anomalies into the estimated background, which is referred to as the identical mapping problem (IMP) and leads to significantly decreased performance. To address this limitation, we propose a model-independent binary mask-guided separation training strategy for DNNs, named BiGSeT. Our method introduces a separation training loss based on a latent binary mask to separately constrain the background and anomalies in the estimated image. The background is preserved, while the potential anomalies are suppressed by using an efficient second-order Laplacian of Gaussian (LoG) operator, generating a pure background estimate. In order to maintain separability during training, we periodically update the mask using a robust proportion threshold estimated before the training. In our experiments, We adopt a vanilla AE as the network to validate our training strategy on several real-world datasets. Our results show superior performance compared to some state-of-the-art methods. Specifically, we achieved a 90.67% AUC score on the HyMap Cooke City dataset. Additionally, we applied our training strategy to other deep network structures, achieving improved detection performance compared to their original versions, demonstrating its effective transferability. The code of our method will be available at https://github.com/enter-i-username/BiGSeT.Comment: 13 pages, 13 figures, submitted to IEEE TRANSACTIONS ON IMAGE PROCESSIN

Spin-polarization of platinum (111) induced by the proximity to cobalt nanostripes

We measured a spin polarization above a Pt (111) surface in the vicinity of a Co nanostripe by spin-polarized scanning tunneling spectroscopy. The spin polarization is exponentially decaying away from the Pt/Co interface and is detectable at distances larger than 1 nm. By performing self-consistent ab-initio calculations of the electronic-structure for a related model system we reveal the interplay between the induced magnetic moments within the Pt surface and the spin-resolved electronic density of states above the surface.Comment: 19 pages, 6 figure

Financial Difficulties at the Private Enterprises in Southern Jiangsu Province

中国江蘇省南部の民営中小企業が直面する資金難について、蘇州市政府が実施した調査をもとに明らかにした。調査によれば、民営中小企業は銀行に高リスクの融資対象だとみなされて高い金利を適用されるだけでなく、さまざまな名目の手数料を負担させられ、融資と見返りの預金や理財商品の購入なども求められる結果、名目的な金利よりもはるかに高い資金コストを負担している。その結果、より金利が高い、規制外の金融に資金を求める企業も少なくない。こうした状況の背景には国有金融機関による金融セクターの独占がある。中小企業の資金調達難を打開するには民間資本の金融業への開放を進めるとともに、既存の銀行の融資姿勢を転換する必要もある。Small and medium-sized private enterprises (SMPEs) in Jiangsu province, China, are facing severe financial troubles. The surveys organized by local government departments show that SMPEs face high obstacles in borrowing money from banks, including high interest rates, requirements from the banks to deposit or to buy investment funds in exchange for receiving loans, and high fees for financial intermediation. Such obstacles lead SMPEs to seek loans from informal financing sources, which may charge even higher interest rates. The main reason for such difficulties experienced by SMPEs in borrowing money is the monopoly of the financial sector by state-owned financial institutions. To solve the financial troubles of SMPEs, the Chinese government needs to open the financial market to non-state financial institutions and reform the credit policy of state-owned banks.特集　中国の地域経済問

Assessing Junior Faculty Research Productivity in the IS Field: Recommendations for Promotion and Tenure Standards for Asian Schools

We gathered information about junior faculty research productivity in the information systems (IS) field in North America and in a set of top Asian schools. Our work complements prior studies on IS faculty research productivity in several ways. First, we focused on junior faculty research productivity, which refers to publication records of current tenure-track assistant professors. To provide statistics with a greater coverage of IS researchers, we also collected information about the pre-tenure publication records of associate professors. Second, we covered IS researchers who obtained their doctoral degrees in or after the year 2000 and counted their publications until 2013 to provide the most up-to-date information about junior faculty research productivity. Third, we collected information about IS researchers’ publications in leading IS journals (based on the AIS Senior Scholar basket of journals) and in elite broader business journals (based on the Financial Times list and UT Dallas list). Finally, examining junior faculty research productivity in the IS field in Asian schools and in North America enabled us to provide recommendations for promotion and tenure standards for Asian schools in light of the research productivity and tenure standards in North America