38,042 results on '"Chang, Yu"'
Search Results
2. Masked EEG Modeling for Driving Intention Prediction
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Zhou, Jinzhao, Sia, Justin, Duan, Yiqun, Chang, Yu-Cheng, Wang, Yu-Kai, and Lin, Chin-Teng
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Driving under drowsy conditions significantly escalates the risk of vehicular accidents. Although recent efforts have focused on using electroencephalography to detect drowsiness, helping prevent accidents caused by driving in such states, seamless human-machine interaction in driving scenarios requires a more versatile EEG-based system. This system should be capable of understanding a driver's intention while demonstrating resilience to artifacts induced by sudden movements. This paper pioneers a novel research direction in BCI-assisted driving, studying the neural patterns related to driving intentions and presenting a novel method for driving intention prediction. In particular, our preliminary analysis of the EEG signal using independent component analysis suggests a close relation between the intention of driving maneuvers and the neural activities in central-frontal and parietal areas. Power spectral density analysis at a group level also reveals a notable distinction among various driving intentions in the frequency domain. To exploit these brain dynamics, we propose a novel Masked EEG Modeling framework for predicting human driving intentions, including the intention for left turning, right turning, and straight proceeding. Extensive experiments, encompassing comprehensive quantitative and qualitative assessments on public dataset, demonstrate the proposed method is proficient in predicting driving intentions across various vigilance states. Specifically, our model attains an accuracy of 85.19% when predicting driving intentions for drowsy subjects, which shows its promising potential for mitigating traffic accidents related to drowsy driving. Notably, our method maintains over 75% accuracy when more than half of the channels are missing or corrupted, underscoring its adaptability in real-life driving.
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- 2024
3. Towards Linguistic Neural Representation Learning and Sentence Retrieval from Electroencephalogram Recordings
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Zhou, Jinzhao, Duan, Yiqun, Zhao, Ziyi, Chang, Yu-Cheng, Wang, Yu-Kai, Do, Thomas, and Lin, Chin-Teng
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Decoding linguistic information from non-invasive brain signals using EEG has gained increasing research attention due to its vast applicational potential. Recently, a number of works have adopted a generative-based framework to decode electroencephalogram (EEG) signals into sentences by utilizing the power generative capacity of pretrained large language models (LLMs). However, this approach has several drawbacks that hinder the further development of linguistic applications for brain-computer interfaces (BCIs). Specifically, the ability of the EEG encoder to learn semantic information from EEG data remains questionable, and the LLM decoder's tendency to generate sentences based on its training memory can be hard to avoid. These issues necessitate a novel approach for converting EEG signals into sentences. In this paper, we propose a novel two-step pipeline that addresses these limitations and enhances the validity of linguistic EEG decoding research. We first confirm that word-level semantic information can be learned from EEG data recorded during natural reading by training a Conformer encoder via a masked contrastive objective for word-level classification. To achieve sentence decoding results, we employ a training-free retrieval method to retrieve sentences based on the predictions from the EEG encoder. Extensive experiments and ablation studies were conducted in this paper for a comprehensive evaluation of the proposed approach. Visualization of the top prediction candidates reveals that our model effectively groups EEG segments into semantic categories with similar meanings, thereby validating its ability to learn patterns from unspoken EEG recordings. Despite the exploratory nature of this work, these results suggest that our method holds promise for providing more reliable solutions for converting EEG signals into text.
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- 2024
4. Multi-Modal CLIP-Informed Protein Editing
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Yin, Mingze, Zhou, Hanjing, Zhu, Yiheng, Lin, Miao, Wu, Yixuan, Wu, Jialu, Xu, Hongxia, Hsieh, Chang-Yu, Hou, Tingjun, Chen, Jintai, and Wu, Jian
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Computer Science - Artificial Intelligence - Abstract
Proteins govern most biological functions essential for life, but achieving controllable protein discovery and optimization remains challenging. Recently, machine learning-assisted protein editing (MLPE) has shown promise in accelerating optimization cycles and reducing experimental workloads. However, current methods struggle with the vast combinatorial space of potential protein edits and cannot explicitly conduct protein editing using biotext instructions, limiting their interactivity with human feedback. To fill these gaps, we propose a novel method called ProtET for efficient CLIP-informed protein editing through multi-modality learning. Our approach comprises two stages: in the pretraining stage, contrastive learning aligns protein-biotext representations encoded by two large language models (LLMs), respectively. Subsequently, during the protein editing stage, the fused features from editing instruction texts and original protein sequences serve as the final editing condition for generating target protein sequences. Comprehensive experiments demonstrated the superiority of ProtET in editing proteins to enhance human-expected functionality across multiple attribute domains, including enzyme catalytic activity, protein stability and antibody specific binding ability. And ProtET improves the state-of-the-art results by a large margin, leading to significant stability improvements of 16.67% and 16.90%. This capability positions ProtET to advance real-world artificial protein editing, potentially addressing unmet academic, industrial, and clinical needs., Comment: 13 pages, 7 figures, 5 tables
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- 2024
5. A foundation model approach to guide antimicrobial peptide design in the era of artificial intelligence driven scientific discovery
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Wang, Jike, Feng, Jianwen, Kang, Yu, Pan, Peichen, Ge, Jingxuan, Wang, Yan, Wang, Mingyang, Wu, Zhenxing, Zhang, Xingcai, Yu, Jiameng, Zhang, Xujun, Wang, Tianyue, Wen, Lirong, Yan, Guangning, Deng, Yafeng, Shi, Hui, Hsieh, Chang-Yu, Jiang, Zhihui, and Hou, Tingjun
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Quantitative Biology - Biomolecules - Abstract
We propose AMP-Designer, an LLM-based foundation model approach for the rapid design of novel antimicrobial peptides (AMPs) with multiple desired properties. Within 11 days, AMP-Designer enables de novo design of 18 novel candidates with broad-spectrum potency against Gram-negative bacteria. Subsequent in vitro validation experiments demonstrate that almost all in silico recommended candidates exhibit notable antibacterial activity, yielding a 94.4% positive rate. Two of these candidates exhibit exceptional activity, minimal hemotoxicity, substantial stability in human plasma, and a low propensity of inducing antibiotic resistance as observed in murine lung infection experiments, showcasing their significant efficacy in reducing bacterial load by approximately one hundredfold. The entire process, from in silico design to in vitro and in vivo validation, is completed within a timeframe of 48 days. Moreover, AMP-Designer demonstrates its remarkable capability in designing specific AMPs to target strains with extremely limited labeled datasets. The most outstanding candidate against Propionibacterium acnes suggested by AMP-Designer exhibits an in vitro minimum inhibitory concentration value of 2.0 $\mu$g/ml. Through the integration of advanced machine learning methodologies such as contrastive prompt tuning, knowledge distillation, and reinforcement learning within the AMP-Designer framework, the process of designing AMPs demonstrates exceptional efficiency. This efficiency remains conspicuous even in the face of challenges posed by constraints arising from a scarcity of labeled data. These findings highlight the tremendous potential of AMP-Designer as a promising approach in combating the global health threat of antibiotic resistance., Comment: 43 pages, 6 figures, 5 tables. Due to the limitation "The abstract field cannot be longer than 1,920 characters", the abstract appearing here is slightly shorter than that in the PDF file
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- 2024
6. Token-Mol 1.0: Tokenized drug design with large language model
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Wang, Jike, Qin, Rui, Wang, Mingyang, Fang, Meijing, Zhang, Yangyang, Zhu, Yuchen, Su, Qun, Gou, Qiaolin, Shen, Chao, Zhang, Odin, Wu, Zhenxing, Jiang, Dejun, Zhang, Xujun, Zhao, Huifeng, Wan, Xiaozhe, Wu, Zhourui, Liu, Liwei, Kang, Yu, Hsieh, Chang-Yu, and Hou, Tingjun
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Quantitative Biology - Biomolecules ,Computer Science - Machine Learning - Abstract
Significant interests have recently risen in leveraging sequence-based large language models (LLMs) for drug design. However, most current applications of LLMs in drug discovery lack the ability to comprehend three-dimensional (3D) structures, thereby limiting their effectiveness in tasks that explicitly involve molecular conformations. In this study, we introduced Token-Mol, a token-only 3D drug design model. This model encodes all molecular information, including 2D and 3D structures, as well as molecular property data, into tokens, which transforms classification and regression tasks in drug discovery into probabilistic prediction problems, thereby enabling learning through a unified paradigm. Token-Mol is built on the transformer decoder architecture and trained using random causal masking techniques. Additionally, we proposed the Gaussian cross-entropy (GCE) loss function to overcome the challenges in regression tasks, significantly enhancing the capacity of LLMs to learn continuous numerical values. Through a combination of fine-tuning and reinforcement learning (RL), Token-Mol achieves performance comparable to or surpassing existing task-specific methods across various downstream tasks, including pocket-based molecular generation, conformation generation, and molecular property prediction. Compared to existing molecular pre-trained models, Token-Mol exhibits superior proficiency in handling a wider range of downstream tasks essential for drug design. Notably, our approach improves regression task accuracy by approximately 30% compared to similar token-only methods. Token-Mol overcomes the precision limitations of token-only models and has the potential to integrate seamlessly with general models such as ChatGPT, paving the way for the development of a universal artificial intelligence drug design model that facilitates rapid and high-quality drug design by experts.
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- 2024
7. Breaking the Ceiling of the LLM Community by Treating Token Generation as a Classification for Ensembling
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Yu, Yao-Ching, Kuo, Chun-Chih, Ye, Ziqi, Chang, Yu-Cheng, and Li, Yueh-Se
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Ensembling multiple models has always been an effective approach to push the limits of existing performance and is widely used in classification tasks by simply averaging the classification probability vectors from multiple classifiers to achieve better accuracy. However, in the thriving open-source Large Language Model (LLM) community, ensembling methods are rare and typically limited to ensembling the full-text outputs of LLMs, such as selecting the best output using a ranker, which leads to underutilization of token-level probability information. In this paper, we treat the Generation of each token by LLMs as a Classification (GaC) for ensembling. This approach fully exploits the probability information at each generation step and better prevents LLMs from producing early incorrect tokens that lead to snowballing errors. In experiments, we ensemble state-of-the-art LLMs on several benchmarks, including exams, mathematics and reasoning, and observe that our method breaks the existing community performance ceiling. Furthermore, we observed that most of the tokens in the answer are simple and do not affect the correctness of the final answer. Therefore, we also experimented with ensembling only key tokens, and the results showed better performance with lower latency across benchmarks.
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- 2024
8. Human-level molecular optimization driven by mol-gene evolution
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Fang, Jiebin, Mao, Churu, Zhu, Yuchen, Chen, Xiaoming, Hsieh, Chang-Yu, and Ma, Zhongjun
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Neural and Evolutionary Computing ,Physics - Chemical Physics ,Quantitative Biology - Biomolecules - Abstract
De novo molecule generation allows the search for more drug-like hits across a vast chemical space. However, lead optimization is still required, and the process of optimizing molecular structures faces the challenge of balancing structural novelty with pharmacological properties. This study introduces the Deep Genetic Molecular Modification Algorithm (DGMM), which brings structure modification to the level of medicinal chemists. A discrete variational autoencoder (D-VAE) is used in DGMM to encode molecules as quantization code, mol-gene, which incorporates deep learning into genetic algorithms for flexible structural optimization. The mol-gene allows for the discovery of pharmacologically similar but structurally distinct compounds, and reveals the trade-offs of structural optimization in drug discovery. We demonstrate the effectiveness of the DGMM in several applications.
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- 2024
9. Generalized Jersey Number Recognition Using Multi-task Learning With Orientation-guided Weight Refinement
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Lin, Yung-Hui, Chang, Yu-Wen, Shih, Huang-Chia, and Ogawa, Takahiro
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,Computer Science - Multimedia - Abstract
Jersey number recognition (JNR) has always been an important task in sports analytics. Improving recognition accuracy remains an ongoing challenge because images are subject to blurring, occlusion, deformity, and low resolution. Recent research has addressed these problems using number localization and optical character recognition. Some approaches apply player identification schemes to image sequences, ignoring the impact of human body rotation angles on jersey digit identification. Accurately predicting the number of jersey digits by using a multi-task scheme to recognize each individual digit enables more robust results. Based on the above considerations, this paper proposes a multi-task learning method called the angle-digit refine scheme (ADRS), which combines human body orientation angles and digit number clues to recognize athletic jersey numbers. Based on our experimental results, our approach increases inference information, significantly improving prediction accuracy. Compared to state-of-the-art methods, which can only handle a single type of sport, the proposed method produces a more diverse and practical JNR application. The incorporation of diverse types of team sports such as soccer, football, basketball, volleyball, and baseball into our dataset contributes greatly to generalized JNR in sports analytics. Our accuracy achieves 64.07% on Top-1 and 89.97% on Top-2, with corresponding F1 scores of 67.46% and 90.64%, respectively., Comment: 10 pages, 6 figures, 5 tables
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- 2024
10. Overseas Service-Learning Experiences and Global Perspective Gains for College Students in Taiwan
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Chang, Yu-Heng, Liu, Ruo-Lan, and Li, Yu-Chi
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- 2020
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11. Exosomal miRNA 16-5p/29a-3p from pancreatic cancer induce adipose atrophy by inhibiting adipogenesis and promoting lipolysis.
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Tien, Sui-Chih, Chang, Chin-Chun, Huang, Ching-Hsuan, Peng, Hsuan-Yu, Chang, Yu-Ting, Chang, Ming-Chu, Lee, Wen-Hwa, and Hu, Chun-Mei
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Cell biology ,Functional aspects of cell biology ,Molecular physiology ,Pathophysiology ,Physiology - Abstract
Over 80% of the patients with pancreatic ductal adenocarcinoma (PDAC) have cachexia/wasting syndrome. Cachexia is associated with reduced survival, decreased quality of life, and higher metastasis rates. Here, we demonstrate that fat loss is the earliest feature of PDAC-exosome-induced cachexia. MicroRNA sequencing of exosomal components from normal and cancer-derived exosomes revealed enrichment of miR-16-5p, miR-21-5p, miR-29a-3p, and miR-125b-5p in serum exosomes of mice harboring PDAC and patients with PDAC. Further, miR-16-5p and miR-29a-3p inhibited adipogenesis through decreasing Erlin2 and Cmpk1 expression which downregulates C/EBPβ and PPARγ. Synergistically, miR-29a-3p promotes lipolysis through increasing ATGL expression by suppressing MCT1 expression. Furthermore, PDAC-exosomes deprived of miR-16-5p and miR-29a-3p fail to induce fat loss. Hence, miR-16-5p and miR-29a-3p exosomal miRs are essential for PDAC-induced fat loss. Thus, we unravel that PDAC induces adipose atrophy via exosomal miRs. This knowledge may provide new diagnostic and therapeutic strategies for PDAC-induced cachexia.
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- 2024
12. CHESS: Contextual Harnessing for Efficient SQL Synthesis
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Talaei, Shayan, Pourreza, Mohammadreza, Chang, Yu-Chen, Mirhoseini, Azalia, and Saberi, Amin
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Databases - Abstract
Utilizing large language models (LLMs) for transforming natural language questions into SQL queries (text-to-SQL) is a promising yet challenging approach, particularly when applied to real-world databases with complex and extensive schemas. In particular, effectively incorporating data catalogs and database values for SQL generation remains an obstacle, leading to suboptimal solutions. We address this problem by proposing a new pipeline that effectively retrieves relevant data and context, selects an efficient schema, and synthesizes correct and efficient SQL queries. To increase retrieval precision, our pipeline introduces a hierarchical retrieval method leveraging model-generated keywords, locality-sensitive hashing indexing, and vector databases. Additionally, we have developed an adaptive schema pruning technique that adjusts based on the complexity of the problem and the model's context size. Our approach generalizes to both frontier proprietary models like GPT-4 and open-source models such as Llama-3-70B. Through a series of ablation studies, we demonstrate the effectiveness of each component of our pipeline and its impact on the end-to-end performance. Our method achieves new state-of-the-art performance on the cross-domain challenging BIRD dataset.
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- 2024
13. Probing the impact of radio-mode feedback on the properties of the cool circumgalactic medium
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Chang, Yu-Ling, Lan, Ting-Wen, Prochaska, J. Xavier, Napolitano, Lucas, Anand, Abhijeet, Aguilar, J., Ahlen, S., Brooks, D., Claybaugh, T., de la Macorra, A., Dey, Arjun, Doel, P., Gontcho, S. Gontcho A, Guy, J., Juneau, S., Kisner, T., Lambert, A., Landriau, M., Guillou, L. Le, Manera, M., Martini, P., Meisner, A., Miquel, R., Moustakas, J., Myers, A. D., Nie, J., Poppett, C., Rezaie, M., Rossi, G., Sanchez, E., Schubnell, M., Seo, H., Sprayberry, D., Tarle, G., Weaver, B. A., and Zou, H.
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Astrophysics - Astrophysics of Galaxies - Abstract
We explore the influence of radio-mode feedback on the properties of the cool circumgalactic medium (CGM). To this end, we assemble a statistical sample of approximately 30,000 radio galaxies with background quasars by combining optical spectroscopic measurements of luminous red galaxies (LRGs) and quasars from the year 1 dataset of Dark Energy Spectroscopic Instrument (DESI) and radio sources from the LOw-Frequency ARray Two-metre Sky Survey (LoTSS) DR2 catalog and the Very Large Array Sky Survey (VLASS) quick look catalog. Galaxies with similar optical properties but with no radio counterparts in LoTSS and VLASS are selected as the control group. We measure the cool CGM properties of radio galaxies and their control samples traced by MgII absorption lines, including covering fraction, rest equivalent width, and gas kinematics. Our results show no significant difference in the properties of gas around radio galaxies and their control sample, indicating that the operating radio-mode feedback of massive galaxies does not produce detectable effects on the properties of the cool CGM. Finally, we show that the CGM of radio galaxies contain a non-negligible amount of cool gas with approximately 10^10 solar masses. This abundance can place a stringent constraint on the radio-mode feedback models., Comment: 20 pages, 12 figures
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- 2024
14. Deep Lead Optimization: Leveraging Generative AI for Structural Modification
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Zhang, Odin, Lin, Haitao, Zhang, Hui, Zhao, Huifeng, Huang, Yufei, Huang, Yuansheng, Jiang, Dejun, Hsieh, Chang-yu, Pan, Peichen, and Hou, Tingjun
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Quantitative Biology - Biomolecules ,Computer Science - Artificial Intelligence - Abstract
The idea of using deep-learning-based molecular generation to accelerate discovery of drug candidates has attracted extraordinary attention, and many deep generative models have been developed for automated drug design, termed molecular generation. In general, molecular generation encompasses two main strategies: de novo design, which generates novel molecular structures from scratch, and lead optimization, which refines existing molecules into drug candidates. Among them, lead optimization plays an important role in real-world drug design. For example, it can enable the development of me-better drugs that are chemically distinct yet more effective than the original drugs. It can also facilitate fragment-based drug design, transforming virtual-screened small ligands with low affinity into first-in-class medicines. Despite its importance, automated lead optimization remains underexplored compared to the well-established de novo generative models, due to its reliance on complex biological and chemical knowledge. To bridge this gap, we conduct a systematic review of traditional computational methods for lead optimization, organizing these strategies into four principal sub-tasks with defined inputs and outputs. This review delves into the basic concepts, goals, conventional CADD techniques, and recent advancements in AIDD. Additionally, we introduce a unified perspective based on constrained subgraph generation to harmonize the methodologies of de novo design and lead optimization. Through this lens, de novo design can incorporate strategies from lead optimization to address the challenge of generating hard-to-synthesize molecules; inversely, lead optimization can benefit from the innovations in de novo design by approaching it as a task of generating molecules conditioned on certain substructures.
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- 2024
15. $L^p$-regularity of a geometrically nonlinear flat Cosserat micropolar model in supercritical dimensions
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Guo, Chang-Yu, Xiang, Chang-Lin, and Liu, Ming-Lun
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Mathematics - Analysis of PDEs ,35B65, 35J47, 35G50 - Abstract
In a recent work [Ann. Inst. H. Poincar\'e C Anal. Non Lin\'eaire 2024], Gastel and Neff introduced an interesting system from a geometrically nonlinear flat cosserat micropolar model and established interior regularity in the critical dimension. Motived by this work, in this article, we establish both interior regularity and sharp $L^p$ regularity for their system in supercritical dimensions., Comment: 21 pages
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- 2024
16. AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation
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Liu, Lijun, Yang, Jiali, Song, Jianfei, Yang, Xinglin, Niu, Lele, Cai, Zeqi, Shi, Hui, Hou, Tingjun, Hsieh, Chang-yu, Shen, Weiran, and Deng, Yafeng
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Computer Science - Artificial Intelligence ,Computer Science - Computational Engineering, Finance, and Science ,Quantitative Biology - Biomolecules - Abstract
Recombinant adeno-associated virus (rAAV) vectors have revolutionized gene therapy, but their broad tropism and suboptimal transduction efficiency limit their clinical applications. To overcome these limitations, researchers have focused on designing and screening capsid libraries to identify improved vectors. However, the large sequence space and limited resources present challenges in identifying viable capsid variants. In this study, we propose an end-to-end diffusion model to generate capsid sequences with enhanced viability. Using publicly available AAV2 data, we generated 38,000 diverse AAV2 viral protein (VP) sequences, and evaluated 8,000 for viral selection. The results attested the superiority of our model compared to traditional methods. Additionally, in the absence of AAV9 capsid data, apart from one wild-type sequence, we used the same model to directly generate a number of viable sequences with up to 9 mutations. we transferred the remaining 30,000 samples to the AAV9 domain. Furthermore, we conducted mutagenesis on AAV9 VP hypervariable regions VI and V, contributing to the continuous improvement of the AAV9 VP sequence. This research represents a significant advancement in the design and functional validation of rAAV vectors, offering innovative solutions to enhance specificity and transduction efficiency in gene therapy applications.
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- 2024
17. Grain boundary metastability controls irradiation resistance in nanocrystalline metals
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El-Atwani, Osman, Barnett, Annie K., Martinez, Enrique, Han, Jian, Leff, Asher C., Hung, Chang-Yu, Nathaniel, James E., He, Sicong, Mang, Emily H., Woryk, Larissa M., Hattar, Khalid, Uberuaga, Blas P., Srolovitz, David J., Falk, Michael L., Marian, Jaime, and Taheri, Mitra L.
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Condensed Matter - Materials Science - Abstract
Grain boundaries (GBs) in polycrystalline materials are powerful sinks for irradiation defects. While standard theories assume that the sink efficiency of a grain boundary is defined solely by its character before irradiation, recent evidence conclusively shows that the irradiation sink efficiency is a highly dynamic property controlled by the intrinsic metastability of GBs under far-from-equilibrium irradiation conditions. In this paper, we reveal that the denuded (i.e., defect-free) zone, typically the signature of a strong sink, can collapse as irradiation damage accumulates. We propose a radiation damage evolution model that captures this behavior based on the emergence of a series of irradiation defect-enabled metastable GB microstate changes that dynamically alter the ability of the GB to absorb further damage. We show that these microstate changes control further defect absorption and give rise to the formation of a defect network that manifests itself as a net Nye-tensor signal detectable via lattice curvature experiments.
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- 2024
18. Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation
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Zhang, Odin, Huang, Yufei, Cheng, Shichen, Yu, Mengyao, Zhang, Xujun, Lin, Haitao, Zeng, Yundian, Wang, Mingyang, Wu, Zhenxing, Zhao, Huifeng, Zhang, Zaixi, Hua, Chenqing, Kang, Yu, Cui, Sunliang, Pan, Peichen, Hsieh, Chang-Yu, and Hou, Tingjun
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Physics - Chemical Physics ,Computer Science - Artificial Intelligence ,Quantitative Biology - Biomolecules - Abstract
Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets. These methods, while effective in designing tightly bound ligands, often overlook other essential properties such as synthesizability. The fragment-wise generation paradigm offers a promising solution. However, a common challenge across both atom-wise and fragment-wise methods lies in their limited ability to co-design plausible chemical and geometrical structures, resulting in distorted conformations. In response to this challenge, we introduce the Deep Geometry Handling protocol, a more abstract design that extends the design focus beyond the model architecture. Through a comprehensive review of existing geometry-related models and their protocols, we propose a novel hybrid strategy, culminating in the development of FragGen - a geometry-reliable, fragment-wise molecular generation method. FragGen marks a significant leap forward in the quality of generated geometry and the synthesis accessibility of molecules. The efficacy of FragGen is further validated by its successful application in designing type II kinase inhibitors at the nanomolar level.
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- 2024
19. Design of an basis-projected layer for sparse datasets in deep learning training using gc-ms spectra as a case study
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Chang, Yu Tang and Chen, Shih Fang
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Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Signal Processing ,I.2.4 ,J.2 - Abstract
Deep learning (DL) models encompass millions or even billions of parameters and learn complex patterns from big data. However, not all data are initially stored in a suitable formation to effectively train a DL model, e.g., gas chromatography-mass spectrometry (GC-MS) spectra and DNA sequence. These datasets commonly contain many zero values, and the sparse data formation causes difficulties in optimizing DL models. A DL module called the basis-projected layer (BPL) was proposed to mitigate the issue by transforming the sparse data into a dense representation. The transformed data is expected to facilitate the gradient calculation and finetuned process in a DL training process. The dataset, example of a sparse dataset, contained 362 specialty coffee odorant spectra detected from GC-MS. The BPL layer was placed at the beginning of the DL model. The tunable parameters in the layer were learnable projected axes that were the bases of a new representation space. The layer rotated these bases when its parameters were updated. When the number of the bases was the same as the original dimension, the increasing percentage of the F1 scores was 8.56%. Furthermore, when the number was set as 768 (the original dimension was 490), the increasing percentage of the F1 score was 11.49%. The layer not only maintained the model performance and even constructed a better representation space in analyzing sparse datasets., Comment: 5 pages, 2 figures, 2 tables, conference
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- 2024
20. Galled Tree-Child Networks
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Chang, Yu-Sheng, Fuchs, Michael, and Yu, Guan-Ru
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Mathematics - Combinatorics ,05C20, 60C05, 60F05, 92D15 - Abstract
We propose the class of galled tree-child networks which is obtained as intersection of the classes of galled networks and tree-child networks. For the latter two classes, (asymptotic) counting results and stochastic results have been proved with very different methods. We show that a counting result for the class of galled tree-child networks follows with similar tools as used for galled networks, however, the result has a similar pattern as the one for tree-child networks. In addition, we also consider the (suitably scaled) numbers of reticulation nodes of random galled tree-child networks and show that they are asymptotically normal distributed. This is in contrast to the limit laws of the corresponding quantities for galled networks and tree-child networks which have been both shown to be discrete., Comment: Submitted to AofA2024
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- 2024
21. Dendritic cell vaccine for glioblastoma: an updated meta-analysis and trial sequential analysis
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Wong, Chia-En, Chang, Yu, Chen, Pei-Wen, Huang, Yan-Ta, Chang, Yu-Cheng, Chiang, Cho-Han, Wang, Liang-Chao, Lee, Po-Hsuan, Huang, Chi-Chen, Hsu, Heng-Juei, and Lee, Jung-Shun
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- 2024
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22. Enhancing Outcomes in Chronic Fibrotic Interstitial Lung Disease Through Aggressive Management of Nintedanib-Induced Adverse Drug Reactions: A Retrospective Analysis
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Chang, Yu-Wen, Tsai, Meng-Yun, Chang, Yu-Ping, Liao, Chien-Chang, Lin, Yu-Ting, Lai, Chien-Hao, Lin, Meng-Chih, and Huang, Kuo-Tung
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- 2024
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23. The W-theory of Five Elements for Innovative Business Activities with a Case Study of Alibaba Corporation
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Hu, Jin-Li and Chang, Yu-Cheng
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- 2019
24. 'Floating heart' application of holographic 3D imaging in nursing education
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Chang, Yu Mei and Lai, Chin Lun
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- 2018
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25. A Technology Acceptance Model for Deploying Masks to Combat the COVID-19 Pandemic in Taiwan (My Health Bank): Web-Based Cross-sectional Survey Study
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Tsai, Wen-Hsun, Wu, Yi-Syuan, Cheng, Chia-Shiang, Kuo, Ming-Hao, Chang, Yu-Tien, Hu, Fu-Kang, Sun, Chien-An, Chang, Chi-Wen, Chan, Ta-Chien, Chen, Chao-Wen, Lee, Chia-Cheng, and Chu, Chi-Ming
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Computer applications to medicine. Medical informatics ,R858-859.7 ,Public aspects of medicine ,RA1-1270 - Abstract
BackgroundThe successful completion of medical practices often relies on information collection and analysis. Government agencies and medical institutions have encouraged people to use medical information technology (MIT) to manage their conditions and promote personal health. In 2014, Taiwan established the first electronic personal health record (PHR) platform, My Health Bank (MHB), which allows people to access and manage their PHRs at any time. In the face of the COVID-19 pandemic in 2020, Taiwan has used MIT to effectively prevent the spread of COVID-19 and undertaken various prevention measures before the onset of the outbreak. Using MHB to purchase masks in an efficient and orderly way and thoroughly implementing personal protection efforts is highly important to contain disease spread. ObjectiveThis study aims to understand people’s intention to use the electronic PHR platform MHB and to investigate the factors affecting their intention to use this platform. MethodsFrom March 31 to April 9, 2014, in a promotion via email and Facebook, participants were asked to fill out a structured questionnaire after watching an introductory video about MHB on YouTube. The questionnaire included seven dimensions: perceived usefulness, perceived ease of use, health literacy, privacy and security, computer self-efficacy, attitude toward use, and behavioral intention to use. Each question was measured on a 5-point Likert scale ranging from “strongly disagree” (1 point) to “strongly agree” (5 points). Descriptive statistics and structural equation analysis were performed using SPSS 21 and AMOS 21 software. ResultsA total of 350 valid questionnaire responses were collected (female: 219/350, 62.6%; age: 21-30 years: 238/350, 68.0%; university-level education: 228/350, 65.1%; occupation as student: 195/350, 56.6%; average monthly income
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- 2021
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26. Instructing with Cognitive Apprenticeship Programming Learning System (CAPLS) for Novice Computer Science College Freshmen: An Exploration Study
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Chih-Chang Yu and Leon Yufeng Wu
- Abstract
This study presents a new blended learning model that combines a computer-assisted learning system called Cognitive Apprenticeship Programming Learning System (CAPLS) with instructor co-teaching in an introductory programming course. CAPLS, as its unique aspect, functions as a master in cognitive apprenticeship, guiding learners throughout their learning while also assessing their progress. In contrast, the instructor in physical class settings serves a supportive role, monitoring progress and articulating as needed to fill knowledge gaps. To investigate the impact of this learning model on students' motivation, we used the Motivated Strategies for Learning Questionnaire (MSLQ) at the beginning and end of the semester. College Entrance Math score, midterm and final exams were also used to assess student learning outcomes. The study was conducted with first-year students in the Department of Information and Computer Engineering, and two key findings emerged. First, students' programming proficiency was strongly correlated with their College Entrance Math scores. While math ability impacted programming learning, all students improved their final scores compared to their midterms, with high-scoring math students outperforming their peers. Second, the proposed blended cognitive teaching strategy significantly reduced students' extrinsic goal and self-efficacy levels, but their learning outcomes still significantly improved. This suggests that the proposed teaching model promotes more conscious learning. These results can be used as a reference for improving student learning outcomes and experiences with computer-assisted learning systems.
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- 2024
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27. Research Collaboration by Practitioners in Computer Science, Library Science, and Management
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Chang, Yu-Wei
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- 2018
- Full Text
- View/download PDF
28. Tensor modeling of MRSA bacteremia cytokine and transcriptional patterns reveals coordinated, outcome-associated immunological programs
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Chin, Jackson L, Tan, Zhixin Cyrillus, Chan, Liana C, Ruffin, Felicia, Parmar, Rajesh, Ahn, Richard, Taylor, Scott D, Bayer, Arnold S, Hoffmann, Alexander, Fowler, Vance G, Reed, Elaine F, Yeaman, Michael R, Meyer, Aaron S, Rajesh, Parmar, Chan, Liana, Chang, Yu-Ling, Filler, Scott G, Gjertson, David, Medie, Felix, Mitchell, Simon, Rossetti, Maura, Qin, Yan, Sharma, Batu, Sheu, Katherine, Thaden, Joshua, Waring, Alan J, Xiong, Yan Q, and Zheng, Ying
- Subjects
Biological Sciences ,Biomedical and Clinical Sciences ,Clinical Sciences ,Hematology ,Infectious Diseases ,Antimicrobial Resistance ,Emerging Infectious Diseases ,Biodefense ,Genetics ,Human Genome ,Sepsis ,Vaccine Related ,Prevention ,Clinical Research - Abstract
Abstract: Methicillin-resistant Staphylococcus aureus (MRSA) bacteremia is a common and life-threatening infection that imposes up to 30% mortality even when appropriate therapy is used. Despite in vitro efficacy determined by minimum inhibitory concentration (MIC) breakpoints, antibiotics often fail to resolve these infections in vivo, resulting in persistent MRSA bacteremia. Recently, several genetic, epigenetic, and proteomic correlates of persistent outcomes have been identified. However, the extent to which single variables or their composite patterns operate as independent predictors of outcome or reflect shared underlying mechanisms of persistence is unknown. To explore this question, we employed a tensor-based integration of host transcriptional and cytokine datasets across a well-characterized cohort of patients with persistent or resolving MRSA bacteremia outcomes. This method yielded high correlative accuracy with outcomes and immunologic signatures united by transcriptomic and cytokine datasets. Results reveal that patients with persistent MRSA bacteremia exhibit signals of granulocyte dysfunction, suppressed antigen presentation, and deviated lymphocyte polarization. In contrast, patients with resolving bacteremia heterogeneously exhibit correlates of robust antigen-presenting cell trafficking and enhanced neutrophil maturation corresponding to appropriate T lymphocyte polarization and B lymphocyte response. These results suggest that transcriptional and cytokine correlates of persistent versus resolving bacteremia outcomes are complex and may not be disclosed by conventional modeling. In this respect, a tensor-based integration approach may help to reveal consensus molecular and cellular mechanisms and their biological interpretation.
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- 2024
29. Canine Schistosomiasis in the West Coast: Heterobilharzia americana in Two Natural Intermediate Hosts Found in the Colorado River, California.
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Baniya, Anil, Goldy, Connor, Ardpairin, Jiranun, Achi, Perla, Chang, Yu, Adrianza, Rose, Vitta, Apichat, and Dillman, Adler
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Colorado River ,Galba cubensis ,Galba humilis ,Heterobilharzia americana ,biodiversity ,canine schistosomiasis ,pathogen ,range expansion ,schistosome - Abstract
The emergence of infectious diseases presents a significant global health, economic, and security risk. Climate change can unexpectedly lead to the spread of pathogens, vectors, or hosts into new areas, contributing to the rise of infectious diseases. Surveillance plays a crucial role in monitoring disease trends and implementing control strategies. In this study, we document the first discovery of Heterobilharzia americana, a parasitic schistosome of mammals and its intermediate hosts Galba cubensis and Galba humilis along the banks of the Colorado River in California. We conducted multiple samplings of snails from various locations in the region with a previous history of canine schistosomiasis. Nucleotide sequencing of the multiple regions of the snails and parasites DNA revealed the coexistence of G. cubensis and G. humilis, both infected with H. americana. Phylogenetic analyses further validate the presence of H. americana in California, suggesting a wider distribution than previously reported. Our findings have implications for public health, veterinary medicine, and biodiversity conservation, contributing to developing effective control strategies to prevent the spread of this emerging infectious disease.
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- 2024
30. The influence of Structural Dynamics in Two-Dimensional Hybrid Organic-Inorganic Perovskites on their Photoluminescence Efficiency -- Neutron scattering analysis
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Rajeev, Haritha Sindhu, Hu, Xiao, Chen, Wei-Liang, Zhang, Depei, Chen, Tianran, Kofu, Maiko, Kajimoto, Ryoichi, Nakamura, Mitsutaka, Chen, Alexander Z., Yoon, Mina, Chang, Yu-Ming, Choi, Joshua J., and Lee, Seung-Hun
- Subjects
Condensed Matter - Materials Science - Abstract
Two-dimensional hybrid organic-inorganic perovskites (HOIPs) have emerged as promising materials for light-emitting diode applications. In this study, by using time-of-flight neutron spectroscopy we identified and quantitatively separated the lattice vibrational and molecular rotational dynamics of two perovskites, butylammonium lead iodide $(\text{BA})_{2}\text{PbI}_{4}$ and phenethyl-ammonium lead iodide $\text{(PEA)}_{2}\text{PbI}_{4}$. By examining the corresponding temperature dependence, we found that the lattice vibrations, as evidenced by neutron spectra, are consistent with the lattice dynamics obtained from Raman scattering. We revealed that the rotational dynamics of organic molecules in these materials tend to suppress their photoluminescence quantum yield while the vibrational dynamics did not show predominant correlations with their optoelectronic properties. This study proposes that the rotational motions of the polarized molecules could significantly interrupt the exciton binding energy potential, cause the exciton dissociations, enhance the non-radiative recombination rates, and hence reduce the photoluminescence yield. These findings provide a deeper understanding of the fundamental interactions in 2D HOIPs and may guide the design of more efficient light-emitting materials for advanced technological applications.
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- 2024
31. Generative AI for Controllable Protein Sequence Design: A Survey
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Zhu, Yiheng, Kong, Zitai, Wu, Jialu, Liu, Weize, Han, Yuqiang, Yin, Mingze, Xu, Hongxia, Hsieh, Chang-Yu, and Hou, Tingjun
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Quantitative Biology - Biomolecules ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
The design of novel protein sequences with targeted functionalities underpins a central theme in protein engineering, impacting diverse fields such as drug discovery and enzymatic engineering. However, navigating this vast combinatorial search space remains a severe challenge due to time and financial constraints. This scenario is rapidly evolving as the transformative advancements in AI, particularly in the realm of generative models and optimization algorithms, have been propelling the protein design field towards an unprecedented revolution. In this survey, we systematically review recent advances in generative AI for controllable protein sequence design. To set the stage, we first outline the foundational tasks in protein sequence design in terms of the constraints involved and present key generative models and optimization algorithms. We then offer in-depth reviews of each design task and discuss the pertinent applications. Finally, we identify the unresolved challenges and highlight research opportunities that merit deeper exploration., Comment: 9 pages
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- 2024
32. Variational post-selection for ground states and thermal states simulation
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Zhang, Shi-Xin, Miao, Jiaqi, and Hsieh, Chang-Yu
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Quantum Physics - Abstract
Variational quantum algorithms (VQAs), as one of the most promising routes in the noisy intermediate-scale quantum (NISQ) era, offer various potential applications while also confront severe challenges due to near-term quantum hardware restrictions. In this work, we propose a framework to enhance the expressiveness of variational quantum ansatz by incorporating variational post-selection techniques. These techniques apply variational modules and neural network post-processing on ancilla qubits, which are compatible with the current generation of quantum devices. Equipped with variational post-selection, we demonstrate that the accuracy of the variational ground state and thermal state preparation for both quantum spin and molecule systems is substantially improved. Notably, in the case of estimating the local properties of a thermalized quantum system, we present a scalable approach that outperforms previous methods through the combination of neural post-selection and a new optimization objective., Comment: 6 pages, 8 figures + supplemental materials
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- 2024
33. Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detection
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Wang, Wei-Yao, Chang, Yu-Chieh, and Peng, Wen-Chih
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Social and Information Networks - Abstract
With the improvements in generative models, the issues of producing hallucinations in various domains (e.g., law, writing) have been brought to people's attention due to concerns about misinformation. In this paper, we focus on neural fake news, which refers to content generated by neural networks aiming to mimic the style of real news to deceive people. To prevent harmful disinformation spreading fallaciously from malicious social media (e.g., content farms), we propose a novel verification framework, Style-News, using publisher metadata to imply a publisher's template with the corresponding text types, political stance, and credibility. Based on threat modeling aspects, a style-aware neural news generator is introduced as an adversary for generating news content conditioning for a specific publisher, and style and source discriminators are trained to defend against this attack by identifying which publisher the style corresponds with, and discriminating whether the source of the given news is human-written or machine-generated. To evaluate the quality of the generated content, we integrate various dimensional metrics (language fluency, content preservation, and style adherence) and demonstrate that Style-News significantly outperforms the previous approaches by a margin of 0.35 for fluency, 15.24 for content, and 0.38 for style at most. Moreover, our discriminative model outperforms state-of-the-art baselines in terms of publisher prediction (up to 4.64%) and neural fake news detection (+6.94% $\sim$ 31.72%)., Comment: EACL 2024 Main Track
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- 2024
34. AMuSE: Adaptive Multimodal Analysis for Speaker Emotion Recognition in Group Conversations
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Devulapally, Naresh Kumar, Anand, Sidharth, Bhattacharjee, Sreyasee Das, Yuan, Junsong, and Chang, Yu-Ping
- Subjects
Computer Science - Sound ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,Computer Science - Multimedia ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
Analyzing individual emotions during group conversation is crucial in developing intelligent agents capable of natural human-machine interaction. While reliable emotion recognition techniques depend on different modalities (text, audio, video), the inherent heterogeneity between these modalities and the dynamic cross-modal interactions influenced by an individual's unique behavioral patterns make the task of emotion recognition very challenging. This difficulty is compounded in group settings, where the emotion and its temporal evolution are not only influenced by the individual but also by external contexts like audience reaction and context of the ongoing conversation. To meet this challenge, we propose a Multimodal Attention Network that captures cross-modal interactions at various levels of spatial abstraction by jointly learning its interactive bunch of mode-specific Peripheral and Central networks. The proposed MAN injects cross-modal attention via its Peripheral key-value pairs within each layer of a mode-specific Central query network. The resulting cross-attended mode-specific descriptors are then combined using an Adaptive Fusion technique that enables the model to integrate the discriminative and complementary mode-specific data patterns within an instance-specific multimodal descriptor. Given a dialogue represented by a sequence of utterances, the proposed AMuSE model condenses both spatial and temporal features into two dense descriptors: speaker-level and utterance-level. This helps not only in delivering better classification performance (3-5% improvement in Weighted-F1 and 5-7% improvement in Accuracy) in large-scale public datasets but also helps the users in understanding the reasoning behind each emotion prediction made by the model via its Multimodal Explainability Visualization module.
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- 2024
35. Optimal higher regularity for biharmonic maps via quantitative stratification
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Guo, Chang-Yu, Jiang, Gui-Chun, Xiang, Chang-Lin, and Zheng, Gao-Feng
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Mathematics - Analysis of PDEs ,Mathematics - Differential Geometry ,53C43, 35J48 - Abstract
This little note is devoted to refining the almost optimal regularity results of Breiner and Lamm \cite{Breiner-Lamm-2015} on minimizing and stationary biharmonic maps via the powerful quantitative stratification method introduced by Cheeger and Naber \cite{Cheeger-Naber-2013} and further developed by Naber and Valtorta \cite{Naber-V-2017,Naber-V-2018} for harmonic maps. In particular, we obtain an optimal regularity results for minimizing biharmonic maps.
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- 2024
36. Counting Phylogenetic Networks with Few Reticulation Vertices: Galled and Reticulation-Visible Networks
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Chang, Yu-Sheng and Fuchs, Michael
- Subjects
Mathematics - Combinatorics ,05A15, 92B05 - Abstract
We give exact and asymptotic counting results for the number of galled networks and reticulation-visible networks with few reticulation vertices. Our results are obtained with the component graph method, which was introduced by L. Zhang and his coauthors, and generating function techniques. For galled networks, we in addition use analytic combinatorics. Moreover, in an appendix, we consider maximally reticulated reticulation-visible networks and derive their number, too., Comment: Revised version; accepted for publication in Bull. Math. Biol
- Published
- 2024
37. Contrastive learning-based agent modeling for deep reinforcement learning
- Author
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Ma, Wenhao, Chang, Yu-Cheng, Yang, Jie, Wang, Yu-Kai, and Lin, Chin-Teng
- Subjects
Computer Science - Multiagent Systems ,Computer Science - Artificial Intelligence - Abstract
Multi-agent systems often require agents to collaborate with or compete against other agents with diverse goals, behaviors, or strategies. Agent modeling is essential when designing adaptive policies for intelligent machine agents in multiagent systems, as this is the means by which the ego agent understands other agents' behavior and extracts their meaningful policy representations. These representations can be used to enhance the ego agent's adaptive policy which is trained by reinforcement learning. However, existing agent modeling approaches typically assume the availability of local observations from other agents (modeled agents) during training or a long observation trajectory for policy adaption. To remove these constrictive assumptions and improve agent modeling performance, we devised a Contrastive Learning-based Agent Modeling (CLAM) method that relies only on the local observations from the ego agent during training and execution. With these observations, CLAM is capable of generating consistent high-quality policy representations in real-time right from the beginning of each episode. We evaluated the efficacy of our approach in both cooperative and competitive multi-agent environments. Our experiments demonstrate that our approach achieves state-of-the-art on both cooperative and competitive tasks, highlighting the potential of contrastive learning-based agent modeling for enhancing reinforcement learning., Comment: 8 pages, 6 figures
- Published
- 2023
38. Global Infectious Disease Surveillance and Case Tracking System for COVID-19: Development Study
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Lee, Hsiu-An, Kung, Hsin-Hua, Lee, Yuarn-Jang, Chao, Jane C-J, Udayasankaran, Jai Ganesh, Fan, Hueng-Chuen, Ng, Kwok-Keung, Chang, Yu-Kang, Kijsanayotin, Boonchai, Marcelo, Alvin B, and Hsu, Chien-Yeh
- Subjects
Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
BackgroundCOVID-19 has affected more than 180 countries and is the first known pandemic to be caused by a new virus. COVID-19’s emergence and rapid spread is a global public health and economic crisis. However, investigations into the disease, patient-tracking mechanisms, and case report transmissions are both labor-intensive and slow. ObjectiveThe pandemic has overwhelmed health care systems, forcing hospitals and medical facilities to find effective ways to share data. This study aims to design a global infectious disease surveillance and case tracking system that can facilitate the detection and control of COVID-19. MethodsThe International Patient Summary (IPS; an electronic health record that contains essential health care information about a patient) was used. The IPS was designed to support the used case scenario for unplanned cross-border care. The design, scope, utility, and potential for reuse of the IPS for unplanned cross-border care make it suitable for situations like COVID-19. The Fast Healthcare Interoperability Resources confirmed that IPS data, which includes symptoms, therapies, medications, and laboratory data, can be efficiently transferred and exchanged on the system for easy access by physicians. To protect privacy, patient data are deidentified. All systems are protected by blockchain architecture, including data encryption, validation, and exchange of records. ResultsTo achieve worldwide COVID-19 surveillance, a global infectious disease information exchange must be enacted. The COVID-19 surveillance system was designed based on blockchain architecture. The IPS was used to exchange case study information among physicians. After being verified, physicians can upload IPS files and receive IPS data from other global cases. The system includes a daily IPS uploading and enhancement plan, which covers real-time uploading through the interoperation of the clinic system, with the module based on the Open Application Programming Interface architecture. Through the treatment of different cases, drug treatments, and the exchange of treatment results, the disease spread can be controlled, and treatment methods can be funded. In the Infectious Disease Case Tracking module, we can track the moving paths of infectious disease cases. The location information recorded in the blockchain is used to check the locations of different cases. The Case Tracking module was established for the Centers for Disease Control and Prevention to track cases and prevent disease spread. ConclusionsWe created the IPS of infectious diseases for physicians treating patients with COVID-19. Our system can help health authorities respond quickly to the transmission and spread of unknown diseases, and provides a system for information retrieval on disease transmission. In addition, this system can help researchers form trials and analyze data from different countries. A common forum to facilitate the mutual sharing of experiences, best practices, therapies, useful medications, and clinical intervention outcomes from research in various countries could help control an unknown virus. This system could be an effective tool for global collaboration in evidence-based efforts to fight COVID-19.
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- 2020
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39. Survival dynamics of starving bacteria are determined by ion homeostasis that maintains plasmolysis
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Schink, Severin, Polk, Mark, Athaide, Edward, Mukherjee, Avik, Ammar, Constantin, Liu, Xili, Oh, Seungeun, Chang, Yu-Fang, and Basan, Markus
- Published
- 2024
- Full Text
- View/download PDF
40. An improved tuna swarm optimization algorithm based on behavior evaluation for wireless sensor network coverage optimization
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Chang, Yu, He, Dengxu, and Qu, Liangdong
- Published
- 2024
- Full Text
- View/download PDF
41. Risk, Predictive, and Preventive Factors for Noninfectious Ventriculitis and External Ventricular Drain Infection
- Author
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Huang, Tzu-Fen, Su, Yu-Kai, Su, I-Chang, Yeh, Yun-Kai, Liu, Heng-Wei, Kan, I-Hung, Lu, Yu-Chun, Chang, Yu-Pei, Lin, Chien-Min, Tu, Yong-Kwang, and Tseng, Chien-Hua
- Published
- 2024
- Full Text
- View/download PDF
42. Comparison of Drug-Free Remission after the End of Phase III Trials of Three Different Anti-IL-23 Inhibitors in Psoriasis
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Hsieh, Chang-Yu, Hsu, Francis Li-Tien, and Tsai, Tsen-Fang
- Published
- 2024
- Full Text
- View/download PDF
43. Self-pressurizing nanoscale capsule catalysts for CO2 electroreduction to acetate or propanol
- Author
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Cai, Yanming, Yang, Ruixin, Fu, Jiaju, Li, Zhe, Xie, Liangyiqun, Li, Kai, Chang, Yu-Chung, Ding, Shichao, Lyu, Zhaoyuan, Zhang, Jian-Rong, Zhu, Jun-Jie, Lin, Yuehe, and Zhu, Wenlei
- Published
- 2024
- Full Text
- View/download PDF
44. Exome sequencing and genome-wide association analyses unveils the genetic predisposition in hydroxychloroquine retinopathy
- Author
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Chiu, Hsun-I, Cheng, Hui-Chen, Wu, Chih-Chiau, Chen, Shih-Jen, Hwang, De-Kuang, Huang, Yi-Ming, Chou, Yu-Bai, Lin, Po-Kang, Lin, Tai-Chi, Chen, Ko-Hua, Lin, Pei-Yu, Chang, Yu-Fan, and Wang, An-Guor
- Published
- 2024
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45. The nematode-trapping fungus Arthrobotrys oligospora detects prey pheromones via G protein-coupled receptors
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Kuo, Chih-Yen, Tay, Rebecca J., Lin, Hung-Che, Juan, Sheng-Chian, Vidal-Diez de Ulzurrun, Guillermo, Chang, Yu-Chu, Hoki, Jason, Schroeder, Frank C., and Hsueh, Yen-Ping
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- 2024
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46. Exploring consumers’ adoption and recommendation in smart retailing: a cognitive absorption perspective
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Tan, Lingbo, Ren, Chenxue, Zhan, Youya, Chang, Yu-Wei, Chen, Jiahe, and Hsu, Ming-Chia
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- 2024
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47. Microstructures and mechanical properties of laser-directed energy deposited CrCoNi medium-entropy alloy
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Zhao, Wen-Jie, Liu, Chang-Yu, Che, Peng-Cheng, Ning, Zhi-Liang, Fan, Hong-Bo, Sun, Jian-Fei, Huang, Yong-Jiang, and Ngan, Alfonso H. W.
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- 2024
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48. Solid solution strategy modulated defects engineering of (Cr1-xVx)2AlC MAX phase toward superior electromagnetic wave absorption
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Gou, Ji-Lin, Chang, Yu-Kai, Liu, Shu, Li, Peng-Hui, Cui, Ping-Hao, Hu, Qian-Ku, Wang, Li-Bo, Zhang, Xin, Wang, Jun-Kai, Xia, Qi-Xun, and Zhou, Ai-Guo
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- 2024
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49. Dynamic change of metabolic dysfunction-associated steatotic liver disease in patients with hepatitis C virus infection after achieving sustained virologic response with direct-acting antivirals
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Liu, Chen-Hua, Chang, Yu-Ping, Fang, Yu-Jen, Cheng, Pin-Nan, Chen, Chi-Yi, Kao, Wei-Yu, Lin, Chih-Lin, Yang, Sheng-Shun, Shih, Yu-Lueng, Peng, Cheng-Yuan, Tsai, Ming-Chang, Huang, Shang-Chin, Su, Tung-Hung, Tseng, Tai-Chung, Liu, Chun-Jen, Chen, Pei-Jer, and Kao, Jia-Horng
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- 2024
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50. Recent advances in near-infrared I/II persistent luminescent nanoparticles for biosensing and bioimaging in cancer analysis
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Chan, Ming-Hsien and Chang, Yu-Chan
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- 2024
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