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Get PDF奈良先端科学技術大学院大学修士(工学)master thesi
Towards Computational Acoustic Cameras: Neural Deconvolution and Rendering for Synthetic Aperture Sonar
No full textvideo/mp4Acoustic imaging leverages sound to form visual products with applications including biomedical ultrasound and sonar. In particular, synthetic aperture sonar (SAS) has been developed to generate high-resolution imagery of both in-air and underwater environments. In this talk, we explore the application of implicit neural representations and neural rendering for SAS imaging and highlight how such techniques can enhance acoustic imaging for both 2D and 3D reconstructions. Specifically we discuss challenges of neural rendering applied to acoustic imaging especially when handling the phase of reflected acoustic waves that is critical for high spatial resolution in beamforming. We present two recent works on enhanced 2D circular SAS deconvolution in air as well as a general neural rendering framework for 3D volumetric SAS. This research is the starting point for realizing the next generation of acoustic cameras for a variety of applications in air and water environments for the future.講演日: 2024年5月27日講演場所: エーアイ大講義室, AI Inc. Seminar Hall （L1）vide
Loneliness Episodes: A Japanese Dataset for Loneliness Detection and Analysis
No full textLoneliness, a significant public health concern, is closely connected to both physical and mental well-being. Hence, detection and intervention for individuals experiencing loneliness are crucial. Identifying loneliness in text is straightforward when it is explicitly stated but challenging when it is implicit. Detecting implicit loneliness requires a manually annotated dataset because whereas explicit loneliness can be detected using keywords, implicit loneliness cannot be. However, there are no freely available datasets with clear annotation guidelines for implicit loneliness. In this study, we construct a freely accessible Japanese loneliness dataset with annotation guidelines grounded in the psychological definition of loneliness. This dataset covers loneliness intensity and the contributing factors of loneliness. We train two models to classify whether loneliness is expressed and the intensity of loneliness. The model classifying loneliness versus non-loneliness achieves an F1-score of 0.833, but the model for identifying the intensity of loneliness has a low F1-score of 0.400, which is likely due to label imbalance and a shortage of a certain label in the dataset. We validate performance in another domain, specifically X (formerly Twitter), and observe a decrease. In addition, we propose improvement suggestions for domain adaptation.conference pape
An improved method for the highly specific detection of transcription start sites
No full textPrecise detection of the transcriptional start site (TSS) is a key for characterizing transcriptional regulation of genes and for annotation of newly sequenced genomes. Here, we describe the development of an improved method, designated ‘TSS-seq2.’ This method is an iterative improvement of TSS-seq, a previously published enzymatic cap-structure conversion method to detect TSSs in base sequences. By modifying the original procedure, including by introducing split ligation at the key cap-selection step, the yield and the accuracy of the reaction has been substantially improved. For example, TSS-seq2 can be conducted using as little as 5 ng of total RNA with an overall accuracy of 96%; this yield a less-biased and more precise detection of TSS. We then applied TSS-seq2 for TSS analysis of four plant species that had not yet been analyzed by any previous TSS method.journal articl
Estimating work engagement from online chat tools
No full textThe Covid-19 pandemic, caused by the SARS-Cov2- virus, has transformed our lives. To combat the spread of the infection, remote work has become a widespread practice. However, this shift has led to various work-related problems, including prolonged working hours, mental health issues, and communication difficulties. One particular challenge faced by team members is the inability to accurately gauge the work engagement (WE) levels of subordinates, such as their absorption, dedication, and vigor, due to the limited number of in-person interactions that occur in remote work settings. To address this issue, online communication systems utilizing text-based chat tools such as Slack and Microsoft Teams have gained popularity as substitutes for face-to-face communication. In this paper, we propose a novel approach that uses graph neural networks (GNNs) to estimate the work engagement levels (WELs) of users on text-based chat platforms. Specifically, our method involves embedding users in a feature space based solely on the structural information of the utilized communication network, without considering the contents of the conversations that take place. We conduct two studies using Slack data to evaluate our proposal. The first study reveals that the properties of communication networks play a more significant role when estimating WELs than do conversation contents. Building upon this result, the second study involves the development of a machine learning model that estimates WELs using only the architectural features of the employed communication network. In this network representation, each node corresponds to a human user, and edges represent communication logs; i.e., if person A talks to person B, the edge between node A and node B is stretched. Notably, our model achieves a correlation coefficient of 0.60 between the observed and predicted WEL values. Importantly, our proposed approach relies solely on communication network data and does not require linguistic information. This makes it particularly valuable for real-world business situations.journal articl
Continual few-shot patch-based learning for anime-style colorization
No full textThe automatic colorization of anime line drawings is a challenging problem in production pipelines. Recent advances in deep neural networks have addressed this problem; however, collectingmany images of colorization targets in novel anime work before the colorization process starts leads to chicken-and-egg problems and has become an obstacle to using them in production pipelines. To overcome this obstacle, we propose a new patch-based learning method for few-shot anime-style colorization. The learning method adopts an efficient patch sampling technique with position embedding according to the characteristics of anime line drawings. We also present a continuous learning strategy that continuously updates our colorization model using new samples colorized by human artists. The advantage of our method is that it can learn our colorization model from scratch or pre-trained weights using only a few pre- and post-colorized line drawings that are created by artists in their usual colorization work. Therefore, our method can be easily incorporated within existing production pipelines. We quantitatively demonstrate that our colorizationmethod outperforms state-of-the-art methods.journal articl
Scaffold-Hopped Compound Identification by Ligand-Based Approaches with a Prospective Affinity Test
No full textScaffold-hopped (SH) compounds are bioactive compounds structurally different from known active compounds. Identifying SH compounds in the ligand-based approaches has been a central issue in medicinal chemistry, and various molecular representations of scaffold hopping have been proposed. However, appropriate representations for SH compound identification remain unclear. Herein, the ability of SH compound identification among several representations was fairly evaluated based on retrospective validation and prospective demonstration. In the retrospective validation, the combinations of two screening algorithms and four two- and three-dimensional molecular representations were compared using controlled data sets for the early identification of SH compounds. We found that the combination of the support vector machine and extended connectivity fingerprint with bond diameter 4 (SVM-ECFP4) and SVM and the rapid overlay of chemical structures (SVM-ROCS) showed a relatively high performance. The compounds that were highly ranked by SVM-ROCS did not share substructures with the active training compounds, while those ranked by SVM-ECFP4 were mostly recombinant. In the prospective demonstration, 93 SH compounds were prepared by screening the Namiki database using SVM-ROCS, targeting ABL1 inhibitors. The primary screening using surface plasmon resonance suggested five active compounds; however, in the competitive binding assays with adenosine triphosphate, no hits were found.journal articl
Context-Aware Machine Translation with Source Coreference Explanation
No full textDespite significant improvements in enhancing the quality of translation, context-aware machine translation (MT) models underperform in many cases. One of the main reasons is that they fail to utilize the correct features from context when the context is too long or their models are overly complex. This can lead to the explain-away effect, wherein the models only consider features easier to explain predictions, resulting in inaccurate translations. To address this issue, we propose a model that explains the decisions made for translation by predicting coreference features in the input. We construct a model for input coreference by exploiting contextual features from both the input and translation output representations on top of an existing MT model. We evaluate and analyze our method in the WMT document-level translation task of English-German dataset, the English-Russian dataset, and the multilingual TED talk dataset, demonstrating an improvement of over 1.0 BLEU score when compared with other context-aware models.journal articl