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UNO is a popular multiplayer card game. In each turn, a player has to play a card in their hand having the same number or color as the most recently played card. When having few people, adding virtual players to play the game can easily be done in UNO video games. However, this is a challenging task for physical UNO without computers. In this paper, we propose a protocol that can simulate virtual players using only physical cards. In particular, our protocol can uniformly select a valid card to play from each virtual player's hand at random, or report that none exists, without revealing the rest of its hand. The protocol can also be applied to simulate virtual players in other turn-based card or tile games where each player has to select a valid card or tile to play in each turn.

The automatic identification of Magnetic Resonance Imaging (MRI) sequences can streamline clinical workflows by reducing the time radiologists spend manually sorting and identifying sequences, thereby enabling faster diagnosis and treatment planning for patients. However, the lack of standardization in the parameters of MRI scans poses challenges for automated systems and complicates the generation and utilization of datasets for machine learning research. To address this issue, we propose a system for MRI sequence identification using an unsupervised contrastive deep learning framework. By training a convolutional neural network based on the ResNet-18 architecture, our system classifies nine common MRI sequence types as a 9-class classification problem. The network was trained using an in-house internal dataset and validated on several public datasets, including BraTS, ADNI, Fused Radiology-Pathology Prostate Dataset, the Breast Cancer Dataset (ACRIN), among others, encompassing diverse acquisition protocols and requiring only 2D slices for training. Our system achieves a classification accuracy of over 0.95 across the nine most common MRI sequence types.

We present X-ray observations of the upper atmospheric density disturbance caused by the explosive eruption of the Hunga Tonga-Hunga Ha'apai (HTHH) volcano on 15 January 2022. From 14 January to 16 January, the Chinese X-ray astronomy satellite, Insight-HXMT, was observing the supernova remnant Cassiopeia A. The X-ray data obtained during Earth's atmospheric occultations allowed us to measure neutral densities in the altitude range of ~90-150 km. The density profiles above 110 km altitude obtained before the major eruption are in reasonable agreement with expectations by both GAIA and NRLMSIS 2.0 models. In contrast, after the HTHH eruption, a severe density depletion was found up to ~1,000 km away from the epicenter, and a relatively weak depletion extending up to ~7,000 km for over 8 hr after the eruption. In addition, density profiles showed wavy structures with a typical length scale of either ~20 km (vertical) or ~1,000 km (horizontal). This may be caused by Lamb waves or gravity waves triggered by the volcanic eruption., Comment: 14 pages, 4 figures, accepted for publication in Geophysical Research Letters

There is strong motivation to translate C code into Rust code due to the continuing threat of memory safety vulnerabilities in existing C programs and the significant attention paid to Rust as an alternative to the C language. While large language models (LLMs) show promise for automating this translation by generating more natural and safer code than rule-based methods, previous studies have shown that LLM-generated Rust code often fails to compile, even for relatively small C programs, due to significant differences between the two languages and context window limitations. We propose an LLM-based translation scheme that improves the success rate of translating large-scale C code into compilable Rust code. Our approach involves three key techniques: (1) pre-processing the C code to better align its structure and expressions with Rust, (2) segmenting the code into optimally sized translation units to avoid exceeding the LLM's context window limits, and (3) iteratively compiling and repairing errors while maintaining consistency between translation units using context-supplementing prompts. Compilation success is an essential first step in achieving functional equivalence, as only compilable code can be further tested. In experiments with 20 benchmark C programs, including those exceeding 4 kilo lines of code, we successfully translated all programs into compilable Rust code without losing corresponding parts of the original code.

Organ segmentation is a fundamental task in medical imaging since it is useful for many clinical automation pipelines. However, some tasks do not require full segmentation. Instead, a classifier can identify the selected organ without segmenting the entire volume. In this study, we demonstrate a classifier based method to obtain organ labels in real time by using a large context size with a sparse data sampling strategy. Although our method operates as an independent classifier at query locations, it can generate full segmentations by querying grid locations at any resolution, offering faster performance than segmentation algorithms. We compared our method with existing segmentation techniques, demonstrating its superior runtime potential for practical applications in medical imaging., Comment: 11 pages, Organ Classification, Organ Segmentation

We have been developing a high-pressure xenon gas time projection chamber (TPC) to search for neutrinoless double beta ($0\nu\beta\beta$) decay of $^{136}$Xe. The unique feature of this TPC is in the detection part of ionization electrons, called ELCC. ELCC is composed of multiple units, and one unit covers 48.5 $\mathrm{cm}^2$. A 180 L size prototype detector with 12 units, 672 channels, of ELCC was constructed and operated with 7.6 bar natural xenon gas to evaluate the performance of the detector at around the Q value of $^{136}$Xe $0\nu\beta\beta$. The obtained FWHM energy resolution is (0.73 $\pm$ 0.11) % at 1836 keV. This corresponds to (0.60 $\pm$ 0.03) % to (0.70 $\pm$ 0.21) % of energy resolution at the Q value of $^{136}Xe$ $0\nu\beta\beta$. This result shows the scalability of the AXEL detector with ELCC while maintaining high energy resolution. Factors determining the energy resolution were quantitatively evaluated and the result indicates further improvement is feasible. Reconstructed track images show distinctive structures at the endpoint of electron tracks, which will be an important feature to distinguish $0\nu\beta\beta$ signals from gamma-ray backgrounds., Comment: 33 pages, 24 figures, preprint accepted by PTEP

In this study, we investigated the ability of the large language model (LLM) to enhance healthcare data interoperability. We leveraged the LLM to convert clinical texts into their corresponding FHIR resources. Our experiments, conducted on 3,671 snippets of clinical text, demonstrated that the LLM not only streamlines the multi-step natural language processing and human calibration processes but also achieves an exceptional accuracy rate of over 90% in exact matches when compared to human annotations., Comment: Submitted to 2024 AMIA IS

Effective communication between humans and intelligent agents has promising applications for solving complex problems. One such approach is visual dialogue, which leverages multimodal context to assist humans. However, real-world scenarios occasionally involve human mistakes, which can cause intelligent agents to fail. While most prior research assumes perfect answers from human interlocutors, we focus on a setting where the agent points out unintentional mistakes for the interlocutor to review, better reflecting real-world situations. In this paper, we show that human answer mistakes depend on question type and QA turn in the visual dialogue by analyzing a previously unused data collection of human mistakes. We demonstrate the effectiveness of those factors for the model's accuracy in a pointing-human-mistake task through experiments using a simple MLP model and a Visual Language Model., Comment: Accepted at ICCVW 2023

We propose a method to match anatomical locations between pairs of medical images in longitudinal comparisons. The matching is made possible by computing a descriptor of the query point in a source image based on a hierarchical sparse sampling of image intensities that encode the location information. Then, a hierarchical search operation finds the corresponding point with the most similar descriptor in the target image. This simple yet powerful strategy reduces the computational time of mapping points to a millisecond scale on a single CPU. Thus, radiologists can compare similar anatomical locations in near real-time without requiring extra architectural costs for precomputing or storing deformation fields from registrations. Our algorithm does not require prior training, resampling, segmentation, or affine transformation steps. We have tested our algorithm on the recently published Deep Lesion Tracking dataset annotations. We observed more accurate matching compared to Deep Lesion Tracker while being 24 times faster than the most precise algorithm reported therein. We also investigated the matching accuracy on CT and MR modalities and compared the proposed algorithm's accuracy against ground truth consolidated from multiple radiologists., Comment: Registration Methods, 9 pages, MTSAIL & LEAF 2023

A conversational recommender system (CRS) is a practical application for item recommendation through natural language conversation. Such a system estimates user interests for appropriate personalized recommendations. Users sometimes have various interests in different categories or genres, but existing studies assume a unique user interest that can be covered by closely related items. In this work, we propose to model such multiple user interests in CRS. We investigated its effects in experiments using the ReDial dataset and found that the proposed method can recommend a wider variety of items than that of the baseline CR-Walker., Comment: Accepted as a conference paper at IWSDS 2023

In this paper, we introduce a kind of decomposition of a finite group called a uniform group factorization, as a generalization of exact factorizations of a finite group. A group $G$ is said to admit a uniform group factorization if there exist subgroups $H_1, H_2, \ldots, H_k$ such that $G = H_1 H_2 \cdots H_k$ and the number of ways to represent any element $g \in G$ as $g = h_1 h_2 \cdots h_k$ ($h_i \in H_i$) does not depend on the choice of $g$. Moreover, a uniform group factorization consisting of cyclic subgroups is called a uniform cyclic group factorization. First, we show that any finite solvable group admits a uniform cyclic group factorization. Second, we show that whether all finite groups admit uniform cyclic group factorizations or not is equivalent to whether all finite simple groups admit uniform group factorizations or not. Lastly, we give some concrete examples of such factorizations., Comment: 10 pages. To appear in Communications in Algebra

Magnetic resonance imaging (MRI) is one of the most commonly applied tests in neurology and neurosurgery. However, the utility of MRI is largely limited by its long acquisition time, which might induce many problems including patient discomfort and motion artifacts. Acquiring fewer k-space sampling is a potential solution to reducing the total scanning time. However, it can lead to severe aliasing reconstruction artifacts and thus affect the clinical diagnosis. Nowadays, deep learning has provided new insights into the sparse reconstruction of MRI. In this paper, we present a new approach to this problem that iteratively fuses the information of k-space and MRI images using novel dual Squeeze-Excitation Networks and Cross-Iteration Residual Connections. This study included 720 clinical multi-coil brain MRI cases adopted from the open-source deidentified fastMRI Dataset. 8-folder downsampling rate was applied to generate the sparse k-space. Results showed that the average reconstruction error over 120 testing cases by our proposed method was 2.28%, which outperformed the existing image-domain prediction (6.03%, p<0.001), k-space synthesis (6.12%, p<0.001), and dual-domain feature fusion (4.05%, p<0.001)., Comment: 4 pages, 5 figures, 2 tables