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International Symposium on Chinese Spoken Language Processing

作者： Li-Jen Yang I-Ping Yeh Jen-Tzung Chien Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan Graduate Degree Program of Cybersecurity National Yang Ming Chiao Tung University Taiwan

ISBN: (纸本)9798350397970

Speech synthesis has been successfully exploited for mapping from text sequence to speech waveform where high-resource languages have been well studied and learned from a large amount of text-speech paired data in public-domain corpora. However, developing speech synthesis under low-resource languages is challenging for speech communication in local regions since the collection of training data is expensive. In particular, the speaker-aware speech generation under low-resource settings is crucial in real world. Such a problem is increasingly difficult in case of very limited speaker-specific data. This paper presents a speaker-aware speech synthesis under low-resource settings based on an encoder-decoder framework by using transformer. Knowledge transfer is performed by incorporating a speaker-aware embedding through first learning a pretrained transformer from multi-speaker data of a low-populated spoken language and then fine-tuning the transformer to a target speaker with very limited speaker-specific embeddings. Experiments on low-resource Taiwanese speech synthesis are evaluated to show the merit of speaker-aware transformer in terms of Mel cepstral distortion and mean opinion score.

关键词： Vocoders Synthesizers Training data Process control Oral communication Transformers Distortion

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Chang, Shun-Cheng Yu, Hsin-Pei Hsieh, Yi-Hsien Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan National Taiwan University Hospital Division of Gastroenterology and Hepatology Department of Internal Medicine Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350351552

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag. © 2024 IEEE.

关键词： Convolutional neural networks

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging 46

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Chang, Shun-Cheng Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350371499

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages. © 2024 IEEE.

关键词： Distribution transformers

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Multiscale Numerical Modeling for Near-Field Microwave Impedance Microscopy

Multiscale Numerical Modeling for Near-Field Microwave Imped...

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CLEO: science and Innovations, S and I 2022

作者： Tami, Diego Ohlberg, Douglas A.A. Ramirez, Jhonattan C. do Rego, Cássio Gonçalves Medeiros-Ribeiro, Gilberto Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Brazil Microscopy Center Universidade Federal de Minas Gerais Brazil Department of Electronic Engineering Universidade Federal de Minas Gerais Brazil Computer Science Department Universidade Federal de Minas Gerais Brazil

In this work, we analyzed numerically a multiscale nanosystem based on sMIM on TBG. Spontaneous formation of a water-meniscus by the approximation between the tip-sample concentrates the microwave fields, reaching res...

ISBN: (纸本)9781557528209

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Phortex: Physically-Informed Operational Robotic Trajectories for Scientific Expeditions

IEEE Transactions on Field Robotics

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IEEE Transactions on Field Robotics 2024年 1卷 446-475页

作者： Victoria L. Preston Genevieve E. Flaspohler John W. Fisher Anna P. M. Michel Nicholas Roy Applied Ocean Physics and Engineering Massachusetts Institute of Technology and Woods Hole Oceanographic Institution Joint Program Cambridge MA USA Olin College of Engineering Needham MA USA Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Rhiza Research Oakland CA USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution Woods Hole MA USA

Mobile robots are increasingly used to collect valuable in situ samples during scientific expeditions. However, many phenomena of scientific interest—deep-sea hydrothermal plumes, algal blooms, warm-core eddies, and lava flows—are spatiotemporal distributions that evolve on spatial and temporal scales that complicate sample collection. Here, we consider the problem of charting the space-time dynamics of deep-sea hydrothermal plumes with the state-of-the-art autonomous underwater vehicle (AUV) Sentry. In the hydrothermal plume charting problem, the plume state is driven by complicated and unobserved dynamics in the deep sea. To effectively sample the moving plume, an autonomy system must infer plume dynamics from sparse, point observations, while respecting operational constraints of AUV Sentry that restrict the set of possible trajectories to nonadaptive, uniform-coverage patterns. We frame the plume charting problem as a sequential decision-making problem, and formulate a mission planner Phortex: physically-informed operational robotic trajectories for expeditions that strategically designs full mission trajectories for Sentry, where each mission plan is informed by the observations of the last. Phortex is composed of a trajectory optimizer that maximizes expected samples collected within a moving plume, and Phumes: physically-informed uncertainty models for environment spatiotemporality, a modeling framework that leverages an embedded simulator of idealized plume physics as an inductive bias to enable dynamics learning from extreme partial observations and few Sentry deployments. In both simulation and in field trials at a hydrothermal site in the Gulf of California, we demonstrate that Sentry using Phortex learns to track a moving hydrothermal plume and gather samples that significantly improve upon baseline spatial and temporal diversity for use in downstream science tasks.

关键词： Trajectory Robots Robot sensing systems Vehicle dynamics Spatiotemporal phenomena Predictive models Decision making Uncertainty Data models

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Demonstration of a CAFQA-bootstrapped Variational Quantum Eigensolver on a trapped-ion quantum computer

arXiv

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arXiv 2024年

作者： Wang, Qingfeng Zhukas, Liudmila Miao, Qiang Dalvi, Aniket S. Love, Peter J. Monroe, Christopher Chong, Frederic T. Ravi, Gokul Subramanian Duke Quantum Center Departments of Electrical and Computer Engineering and Physics Duke University Durham United States Chemical Physics Program Institute for Physical Science and Technology University of Maryland College ParkMD United States Duke Quantum Center Duke University Durham United States Department of Electrical and Computer Engineering Duke University Durham United States Department of Physics and Astronomy Tufts University Medford United States Department of Computer Science University of Chicago Chicago United States Department of Computer Science and Engineering University of Michigan Ann Arbor United States

To enhance the variational quantum eigensolver (VQE), the CAFQA method can utilize classical computational capabilities to identify a better initial state than the Hartree-Fock method. Previous research has demonstrated that the initial state provided by CAFQA recovers more correlation energy than that of the Hartree-Fock method and results in faster convergence. In the present study, we advance the investigation of CAFQA by demonstrating its advantages on a high-fidelity trapped-ion quantum computer located at the Duke Quantum Center—this is the first experimental demonstration of CAFQA-bootstrapped VQE on a TI device and on any academic quantum device. In our VQE experiment, we use LiH and BeH2 as test cases to show that CAFQA achieves faster convergence and obtains lower energy values within the specified computational budget limits. To ensure the seamless execution of VQE on this academic device, we develop a novel hardware-software interface framework that supports independent software environments for both the circuit and hardware end. This mechanism facilitates the automation of VQE-type job executions as well as mitigates the impact of random hardware interruptions. This framework is versatile and can be applied to a variety of academic quantum devices beyond the trapped-ion quantum computer platform, with support for integration with customized packages. © 2024, CC BY.

关键词： Quantum computers

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AV Navigation in 3D Urban Environments with Curriculum-based Deep Reinforcement Learning

AV Navigation in 3D Urban Environments with Curriculum-based...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Kevin Braathen de Carvalho Iure Rosa L. de Oliveira Alexandre S. Brandão Department of Electrical Engineering Graduate Program in Computer Science Nucleus of Specialization in Robotics Universidade Federal de Viçosa Viçosa MG Brazil Department of Electrical Engineering Federal University of Viçosa Viçosa MG Brazil

Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires robust and efficient decision-making algorithms. This paper presents a novel approach to UAV navigation in 3D environments using a Curriculum-based Deep Reinforcement Learning (DRL) approach. The proposed method utilizes a deep neural network to model the UAV’s decision-making process and to learn a mapping from the state space to the action space. The learning process is guided by a reinforcement signal that reflects the performance of the UAV in terms of reaching its target while avoiding obstacles and with energy efficiency. Simulation results show that the proposed method has a positive trade off when compared to the baseline algorithm. The proposed method was able to perform well in environments with a state space size of 22 millions, allowing the usage in big environments or in maps with high resolution. The results demonstrate the potential of DRL for enabling UAVs to operate effectively in complex environments.

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Highly Flexible Piezoelectric Pressure Sensor at Extremely High-Temperature Range Using Ultrawide-Bandgap III-N Thin Films

Highly Flexible Piezoelectric Pressure Sensor at Extremely H...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Nam-In Kim Miad Yarali Muhammad Aqib Jae-Hyun Ryou Department of Mechanical Engineering Texas Center for Superconductivity at UH Advanced Manufacturing Institute University of Houston USA Materials Science and Engineering Program University of Houston USA Department of Electrical and Computer Engineering University of Houston USA

Pressure monitoring in extreme conditions, such as in industrial, aerospace, and radioactive atmosphere, is crucial. Our focus is on developing and researching piezoelectric sensors that are highly flexible for detecting gas pressures at extremely high temperatures. We develop the flexible device using epitaxial III -nitride film, which is single-crystalline, by layer-transfer method. The sensor shows a sensitive and stable operation for extended periods. We use software simulation and relevant formulas to support our experimental findings.

关键词： Temperature sensors Pressure sensors X-ray scattering Sensor phenomena and characterization Software Semiconductor process modeling III-V semiconductor materials

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Cutting corners to suppress high-order modes in Mie resonator arrays

arXiv

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arXiv 2023年

作者： Haddadin, Zaid Khan, Shahrose Poulikakos, Lisa V. Department of Electrical & Computer Engineering UC San Diego United States Department of Mechanical & Aerospace Engineering UC San Diego United States Materials Science & Engineering Program UC San Diego United States

Mie resonators as lattice resonant metasurfaces have the capability to produce structural colour. However, design criteria for these metasurfaces are still being investigated. In this work, we numerically examine how the two-dimensional nanostructure shape in a lattice array affects the colorimetric response of the metasurface under linearly polarised light excitation. First, the transformation from a square-shaped to rectangle-shaped nanostructure array resulted in polarisation-sensitive metasurfaces with colorimetric outputs bound along a line on the CIE 1931 2-degree Standard Observer colour space. The bounds of the colorimetry line were tuneable to any desired chromatic range. Second, the removal of the corners in square- or rectangle-shaped nanostructures to create t-shaped nanostructure arrays displayed a dampening effect on the high-order resonance. Finally, we analytically determined that the colour saturation could increase when moving from rectangle-shaped to t-shaped nanostructure arrays. From these results, we present two design guidelines for lattice resonant metasurfaces: (1) Constructing the nanostructure to support fundamental resonances at different wavelengths enables two-colour-bound movement when excited by successive angles of linearly polarised light;(2) Removing portions of the nanostructure that only support high-order resonances dampens these modes while maintaining support for fundamental resonances. These results present first-principles guidelines for engineering nanoparticles in lattice resonant metasurfaces, offering a new toolbox for polarised-light sensing and colorimetric applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Nanostructures

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Investigating the Impact of Data Augmentation for Fine-Grained Grocery Product Classification Based on Vision Transformer

Investigating the Impact of Data Augmentation for Fine-Grain...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Rissa Rahmania Yaya Heryadi Lukas Lukas Wayan Suparta Ilvico Sonata Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Group (CEG) Faculty of Engineering Universitas Katolik Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

The existence of fine-grained image classification supporting smart retail provides effectiveness in recognizing products with high similarity. However, the generic classification method performs poorly in identifying products from a subordinate category. This paper aims to identify augmentation techniques to leverage the Vision Transformer (ViT) model to classify the fine-grained grocery product, which involves embedding patches and transformer encoders to extract the main features. First, we develop a fine-grained image dataset with ColorJitter, CutOut, and combining both augmentations. Secondly, we perform experiments and analysis of ViT size, embedded patch size and image size in the patch embedding process. Lastly, the ViT model are evaluated according to the image sizes 224, 384, and 512 in accuracy, loss, and confusion matrix. The highest accuracy was obtained at 0.9922. The ColorJitter and CutOut improved the confusion matrix in ViT-B/16 and ViT-L/16 with an image size of 384 and 512. The results show that both augmentations in the ViT model are able to distinguish fine-grained grocery products.

关键词： Surveys computer vision Accuracy Image recognition Transformers Feature extraction Data augmentation Artificial intelligence Image classification

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