In 2016, Beberok obtained an explicit formula for the Bergman kernel of a specific Reinhardt domain in ℂ3, defined by |ζ1|2<(1−|z|2)q,|ζ2|2<(1−|z|2)r where q,r>0. We derive explicit formulas for the Bergman kernels of a wider class of domains, expressed in terms of polylogarithm functions. This class includes Beberok's domain as a special case.

The attempt to utilize machine learning in procedural content generation (PCG) has been made in the past. In this survey paper, we investigate how generative artificial intelligence (AI), which saw a significant increase in interest in the mid-2010s, is being used for PCG. We review applications of generative AI for the creation of various types of content, including terrains, items, and even storylines. While generative AI is effective for PCG, building high-performance models requires not only handling customized content and ensuring quality and diversity, but also securing sufficient training data. For PCG research to advance further, addressing these challenges is essential. Thus, we also give special consideration to research that explores innovative generation techniques, model architectures, and approaches suited for limited-data scenarios.

Let w be an Erdös-type weight on ℝ and let {pn} be the orthonormal polynomials with respect to w. We denote by {xj,n} the zeros of pn. For an interger ν≥2 and a continuous function f on ℝ, let Hn(ν;f,·) be the ν-th order Hermite–Fejér interpolation polynomial for f based at {xj,n}, that is, Hn(ν;f,xj,n)=f(xj,n) and Hn(k)(ν;f,xj,n) = 0 hold for j=1,2,…,n and k=1,2,…,ν-1. We discuss a uniform norm of Hn(ν;f,·)-f on ℝ and give quantitative interpolation estimates.

Electromagnetic levitation combined with high-speed imaging enables direct in-situ observation of crystal growth dynamics in high-temperature melts, providing crucial insights for materials synthesis. However, high-speed imaging generates massive datasets, often exceeding tens of thousands of frames per experiment, which poses significant challenges for traditional manual characterization methods. We demonstrate the evolution from computer vision methods to machine learning approaches using U-Net through three experimental studies of aluminum nitride (AlN) crystal growth in Ni–Al and Fe–Al systems at 1850–2030 K. Our methodological progression from basic computer-vision image processing to machine learning establishes increasingly robust frameworks for quantifying nucleation events, AlN crystal growth rates, and morphological evolution. When applied to electromagnetic levitation experiments with synchronized dual-camera imaging, these automated techniques revealed quantitative relationships between thermodynamic driving forces and crystal orientation that would be impractical to extract manually. Notably, the deep learning approach in this work achieved 95% IoU in detecting crystal formation area while reducing analysis time to minutes. This deep learning framework is applicable beyond crystal growth studies, offering a template for automated analysis in other in-situ characterization techniques where rapid dynamic processes generate substantial datasets.

Case Western Reserve University (CWRU) and Tohoku University (TU) have been exchanging academic and research knowledge and experience focused on data science over the past 10 years. During this decade, our exchange activities have been continuing and extending year by year. Our extensive exchanges are characterized by more than ten joint symposia organized alternately in CWRU and TU, research collaborations, and the acceptance of visiting professors. The data science education at TU has been constantly inspired by CWRU, which has been conducting cross-disciplinary data science education for undergraduate as well as graduate students since the very beginning of "the Data Era." Now, resources and know-how of the data science education program are shared with the other graduate schools at TU. Similar growth has been experienced at CWRU in Applied Data Science at the undergraduate and graduate levels, where recently a new graduate certificate was developed with CWRU's Mandel School of Applied Social Sciences focusing on Data Science for Social Impact. In addition to data science education, unprecedented-scale data produced by a large-scale measurement facility such as a synchrotron is a common target for data science research focus of CWRU and TU. Such a coincidence of research could suggest perspectives for our collaborations in the next decade.

Neutrophils are the major populations of white blood cells and have been reported to facilitate cancer metastasis. Meanwhile, emerging evidence has recently suggested the anti-cancer role of neutrophils. Our previous study revealed that CB-839 and 5-FU-treated colorectal cancer (CRC) tumors recruited neutrophils and induced neutrophil extracellular traps (NETs). Cathepsin G (CTSG), which is released during NET formation, enters CRC cells through the receptor for advanced glycation end products (RAGE) and cleaves 14–3-3ε to promote apoptosis. However, the detailed mechanism underlying CTSG’s anti-tumor function remains less studied. In this study, we report that CTSG enters CRC cells through RAGE-mediated endocytosis. Knocking out RAGE or inhibiting endocytosis blocks CTSG from entering CRC cells and attenuates CTSG-induced apoptosis. Furthermore, the clathrin coat assembly complex and SNARE proteins were enriched in an arrayed CRISPR/Cas9 screening targeting human membrane trafficking genes. Knocking out SNARE protein STX1A prevents the spread of CTSG in CRC cells and the induction of cleaved PARP. A pooled genome-wide CRISPR/Cas9 screening further identifies the role of CDK1 in the NET-induced killing of CRC cells. Inhibiting CDK1 protected CRC cells from killing by CTSG. Our study reveals novel mechanisms by which CTSG enters and kills CRC cells.

Bin-picking is a problem of an object to be automatically picked up from a randomly stacked pile. When considering the complex light reflection scenes, Light Transport Matrix (LTM) estimation based 3D measurement method achieves high accuracy and robustness; however, it is computationally expensive. To achieve the bin-picking such a real-time application for complex light reflection scenes, we propose a new learning-based 3D object recognition and pose estimation method. We leverage a neural network for learning features of point clouds in order to detect and estimate 3D position of the object. We develop a deep learning model which is trained by using the synthetic point cloud data. The key idea of our method is to separate translation estimation and rotation estimation, and introduce the attention mechanism to aggregate the pair-wise feature and the point-wise feature. We train the network using the dataset from a simulation, and test this trained network on the real scene. We also integrate the LTM estimation-based 3D measurement and proposed object detection and pose estimaition with a robot system to achieve the bin-picking task.

Traditional scientific investigations often have poor or non-existent metadata and data management plans, which poses many challenges for the efficiency, transparency, and reproducibility of research studies. Due to these practices, historical data are rarely leveraged in future studies, but rather are frequently forgotten on a local computer or hardware. We describe a data-driven study protocol design to overcome such challenges in research investigations and to garner the maximum value of available and existing data. A study protocol focuses on the importance and lifespan of data beyond a study where data is re-used to generate new insights. We outline management strategies to ensure that data and metadata, including historical results, are properly linked and recorded using the domain-driven ontologies and FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles.