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A 35-year-old female presented with headache, photophobia and developed sudden loss of vision after having undergone right-side ophthalmectomy and radiochemotherapy for retinoblastoma in infancy. A neoplastic lesion was found in the left middle cranial fossa and was surgically removed. The diagnosis was radiation-induced osteosarcoma with RB1 gene alteration. Although she received chemotherapy for the residual tumor, it progressed 17 months later. Maximal surgical resection with craniofacial reconstruction was required. We utilized two three-dimensional models for surgical planning. She was discharged without neurological deficits other than loss of light perception subsequent to left ophthalmectomy. In cases where retinoblastoma is treated with radiotherapy, long-term follow-up is necessary to monitor for radiation-induced tumor development.

Geoparsing is a fundamental technique for analyzing geo-entity information in text. We focus on document-level geoparsing, which considers geographic relatedness among geo-entity mentions, and presents a Japanese travelogue dataset designed for evaluating document-level geoparsing systems. Our dataset comprises 200 travelogue documents with rich geo-entity information: 12,171 mentions, 6,339 coreference clusters, and 2,551 geo-entities linked to geo-database entries.

Camera-based person re-identification is a heavily privacy-invading task by design, benefiting from rich visual data to match together person representations across different cameras. This high-dimensional data can then easily be used for other, perhaps less desirable, applications. We here investigate the possibility of protecting such image data against uses outside of the intended re-identification task, and introduce a differential privacy mechanism leveraging both pixelisation and colour quantisation for this purpose. We show its ability to distort images in such a way that adverse task performances are significantly reduced, while retaining high re-identification performances.

Movement Recognition via Channel-Activation-Wise sEMG Attention (accepted by Methods, Elsevier, 2023)

We have constructed Arukikata Travelogue Dataset and released it free of charge for academic research. This dataset is a Japanese text dataset with a total of over 31 million words, comprising 4,672 Japanese domestic travelogues and 9,607 overseas travelogues. Before providing our dataset, there was a scarcity of widely available travelogue data for research purposes, and each researcher had to prepare their own data. This hinders the replication of existing studies and fair comparative analysis of experimental results. Our dataset enables any researchers to conduct investigation on the same data and to ensure transparency and reproducibility in research. In this paper, we describe the academic significance, characteristics, and prospects of our dataset., Comment: The application website for Arukikata Travelogue Dataset: https://www.nii.ac.jp/dsc/idr/arukikata/

We will perform a systematic review and meta-analysis of efficacy, tolerability, and acceptability for therapy using repetitive transcranial magnetic stimulation (rTMS) in the treatment of adult patients with depression in the maintenance phase.

The collection of spatiotemporal tourism information is important in smart tourism and user-generated contents are perceived as reliable information. Participatory sensing is a useful method for collecting such data, and the active contribution of users is an important aspect for continuous and efficient data collection. This study has focused on the impact of task allocation interface design and individual personality on data collection efficiency and their contribution in gamified participatory sensing for tourism. We have designed two types of interfaces: a map-based with active selection and a chat-based with passive selection. Moreover, different levels of elaborateness and indirectness have been introduced into the chat-based interface. We have employed the Gamification User Types Hexad framework to identify the differences in the contributions and interface preferences of different user types. The results of our tourism experiment with 108 participants show that the map-based interface collects more data, while the chat-based interface collects data for spots with higher information demand. We also found that the contribution to sensing behavior and interface preference differed depending on the individual user type., Comment: 21 pages, 15 figures, 5 tables

We present an approach to evaluate argument search techniques in view of their use in argumentative dialogue systems by assessing quality aspects of the retrieved arguments. To this end, we introduce a dialogue system that presents arguments by means of a virtual avatar and synthetic speech to users and allows them to rate the presented content in four different categories (Interesting, Convincing, Comprehensible, Relation). The approach is applied in a user study in order to compare two state of the art argument search engines to each other and with a system based on traditional web search. The results show a significant advantage of the two search engines over the baseline. Moreover, the two search engines show significant advantages over each other in different categories, thereby reflecting strengths and weaknesses of the different underlying techniques., Proceedings of the 12th Language Resources and Evaluation Conference

Recently, due to the drastic increase in foreign tourists coming to Japan, there has been a demand to provide smart tourism services that enable inbound tourists to comfortably enjoy sightseeing. To provide satisfactory experiences for tourists, it is desirable to provide tourist information in a timely manner by considering dynamic information, which is information that changes over time, such as current congestion information in destination spots and travel route information, in addition to static information, such as the preferences and profiles of tourists. However, in many existing systems, serious problems occur, such as (1) a lack of support for on-site use, (2) a lack of consideration of dynamic information, and (3) heavy burden on tourists. In this paper, we propose a novel system that can provide tourism plans for tourism spots in a timely manner. The proposed system consists of the following two key mechanisms: (A) A mechanism for acquiring preference information from tourists (including preference on dynamic information), (B) a curation mechanism for realizing on-site tourism. To demonstrate the effectiveness of the proposed system, we carried out evaluation experiments utilizing real tourism spots and simulations. As a result, we obtained the following primary findings: (1) On-site tourism spot recommendation is effective for tourists who do not make detailed tourism plans before sightseeing, (2) preference information for participants can be reflected in the tourism spot recommendation while massively reducing the burden on participants, (3) it is possible to obtain a higher satisfaction level than is achieved with model courses, which are often used for sightseeing.

Aims and objectives Methods and materials Results Conclusion Personal information References, Aims and objectives: Diffusion-weighted imaging (DWI) is useful for detecting bladder tumors. In DWI, increasing b values suppresses signals from urine in the bladder. However, b values at which signals from the urine are suppressed differ among individuals. At our...

Due to the widespread of mobile devices such as smartphones, we can get the appropriate information anytime, anywhere according to the surrounding environments and the human conditions, called ``contexts.'' In order to provide context-aware systems in urban spaces, it is indispensable to collect information in a complicated and vast spaces comprehensively, and to recognize the context based on the analysis of collected information. Hence, we define the human-in-the-loop sensing framework which consists of two sensing approaches: the direct urban sensing using sensors embedded on mobile devices, and the indirect urban sensing by utilizing humans as a sensor, as the scope of our study. In this dissertation, we focus on two challenges to realize the human-in-the-loop sensing framework: 1) how to extract various context, and 2) how to operate the proposed framework sustainably. Regarding the first challenge, we have tackled with two different cases. One is a safety assessment system for sidewalks at night based on sensing illuminance of streetlamps. We devise a method to correct data from mobile sensors, which have large differences in characteristics and accuracy, utilizing collective intelligence. Through the experiment, we confirmed the proposed method reduced the estimation error for 90% samples of streetlamps. Another is a psychological context recognition system based on observing the unconscious behavior of tourists. We devise the tourist emotion and satisfaction recognition model by combining multiple modalities collected during sightseeing. With the real-world experiment, we achieved up to 0.50 of average recall score with three-class emotion recognition task. As for the second challenge, we build a mobile participatory sensing platform, which incorporates citizen communities into the ecosystem of human-in-the-loop sensing framework. Especially, we focus on ``civic tech'' which is one of the ways to realize civic cooperation by using ICT, and have placed civic tech communities as the potential user group of our platform. Through 17 case studies with citizens, we have confirmed the usability and availability of the proposed platform.

With the spread of smart devices, people may obtain a variety of information on their surrounding environment thanks to sensing technologies. To design more context-aware systems, psychological user context (e.g., emotional status) is a substantial factor for providing useful information in an appropriate timing. As a typical use case that has a high demand for context awareness but is not tackled widely yet, we focus on the tourism domain. In this study, we aim to estimate the emotional status and satisfaction level of tourists during sightseeing by using unconscious and natural tourist actions. As tourist actions, behavioral cues (eye and head/body movement) and audiovisual data (facial/vocal expressions) were collected during sightseeing using an eye-gaze tracker, physical-activity sensors, and a smartphone. Then, we derived high-level features, e.g., head tilt and footsteps, from behavioral cues. We also used existing databases of emotionally rich interactions to train emotion-recognition models and apply them in a cross-corpus fashion to generate emotional-state prediction for the audiovisual data. Finally, the features from several modalities are fused to estimate the emotion of tourists during sightseeing. To evaluate our system, we conducted experiments with 22 tourists in two different touristic areas located in Germany and Japan. As a result, we confirmed the feasibility of estimating both the emotional status and satisfaction level of tourists. In addition, we found that effective features used for emotion and satisfaction estimation are different among tourists with different cultural backgrounds., publishedVersion

This study examines the relationship between a couple’s labor supply decision and the household family structure. Using data from the Japan General Social Surveys (JGSS) 2010, we estimate their working hours and the husband’s participation in household work. In a household with young children, the wife’s working time is significantly less than that of a household with a married couple without children. Further, we cannot reject the independency between the husband’s participation in household work and the wife’s working hours, while the frequency of a husband’s household work participation is significantly less in a household that includes the parents. In addition, our estimation of a wife’s evaluation of her health status according to the equation controlling household structure and couples’ working time shows that the possibility of a larger investment in health contributes to her positive health evaluation.

This dissertation focuses on ab-initio quantum mechanical calculations of nanoelectronics in three research topics: contact resistance properties of carbon nanotubes and graphenes (Chapters 1 through 3), electrical properties of carbon nanotubes (Chapter 4) and silicon nanowires (Chapter 5). Through all the chapters, the aim of the research is to provide useful guidelines for experimentalists. Chapter 1 presents the contact resistance of metal electrode−carbon nanotube and metal electrode−graphene interfaces for various deposited metals, based on first-principles quantum mechanical density functional and matrix Green’s function methods. Chapters 2 and 3 describe inventive ways to enhance contact resistance properties as well as mechanical stabilities using “molecular anchors” (Chapter 2) or using "end-contacted" (or end-on) electrodes (Chapter 3). Chapters 1 through 3 also provide useful guidelines for nanotube assembly process which is one of the main obstacles in nanoelectronics. Chapter 4 shows accurate and detailed band structure properties of single-walled carbon nanotubes using B3LYP hybrid functional, which are critical parameters in determining the electronic properties such as small band gaps (~0.1 eV) and effective masses. Chapter 5 details both structural and electronic properties of silicon nanowires. These results lead to the findings controlling the diameter and surface coverage by adsorbates (e.g., hydrogen) of silicon nanowires can be effectively used to optimize their properties for various applications. All the theoretical results are compared with other theoretical studies and experimental data. Notably, electronic studies using B3LYP show excellent agreement with experimental studies quantitatively, which previous quantum mechanical calculations had failed. These studies show how quantum mechanical predictions of complex phenomena can be effectively investigated computationally in nanomaterials and nanodevices. Given the difficulty, expense, and time required for experiments, theory may now be useful for high-throughout screening to identify the best conditions and materials before performing experiments.

We apply our first-principles method to simulate the transient electrical response through carbon nanotube based conductors under time-dependent bias voltages, and report the dynamic conductance for a specific system. We find that the electrical response of the carbon nanotube device can be mapped onto an equivalent classical electric circuit. This is confirmed by studying the electric response of a simple model system and its equivalent circuit., 10 pages, 4 figures