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arXiv 2023年

作者： Zhang, Ziqi Xu, Jingzehua Zhuang, Zifeng Zhang, Hongyin Liu, Jinxin Wang, Donglin Zhang, Shuai Lyu, Shangke School of Engineering WestLake University China MicroMasters Program in Statistics and Data Science Massachusetts Institute of Technology United States Department of Data Science New Jersey Institute of Technology United States

Proximal Policy Optimization (PPO) has been broadly applied to robotics learning, showcasing stable training performance. However, the fixed clipping bound setting may limit the performance of PPO. Specifically, there is no theoretical proof that the optimal clipping bound remains consistent throughout the entire training process. Meanwhile, previous researches suggest that a fixed clipping bound restricts the policy's ability to explore. Therefore, many past studies have aimed to dynamically adjust the PPO clipping bound to enhance PPO's performance. However, the objective of these approaches are not directly aligned with the objective of reinforcement learning (RL) tasks, which is to maximize the cumulative Return. Unlike previous clipping approaches, we propose a bi-level proximal policy optimization objective that can dynamically adjust the clipping bound to better reflect the preference (maximizing Return) of these RL tasks. Based on this bi-level proximal policy optimization paradigm, we introduce a new algorithm named Preference based Proximal Policy Optimization (Pb-PPO). Pb-PPO utilizes a multi-armed bandit approach to refelect RL preference, recommending the clipping bound for PPO that can maximizes the current Return. Therefore, Pb-PPO results in greater stability and improved performance compared to PPO with a fixed clipping bound. We test Pb-PPO on locomotion benchmarks across multiple environments, including Gym-Mujoco and legged-gym. Additionally, we validate Pb-PPO on customized navigation tasks. Meanwhile, we conducted comparisons with PPO using various fixed clipping bounds and various of clipping approaches. The experimental results indicate that Pb-PPO demonstrates superior training performance compared to PPO and its variants. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Multi-Lingual ESG Impact Duration Inference 7

Multi-Lingual ESG Impact Duration Inference

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Joint Workshop of the 7th Financial Technology and Natural Language Processing, 5th Knowledge Discovery from Unstructured data in Financial Services and 4th Economics and Natural Language Processing, FinNLP-KDF-ECONLP 2024

作者： Chen, Chung-Chi Tseng, Yu-Min Kang, Juyeon Lhuissier, Anaïs Seki, Yohei Lee, Hanwool Day, Min-Yuh Tu, Teng-Tsai Chen, Hsin-Hsi AIST Japan Data Science Degree Program National Taiwan University Academia Sinica Taiwan 3DS Outscale France University of Tsukuba Japan NCSOFT Korea Republic of Graduate Institute of Information Management National Taipei University Taiwan Graduate Institute of International Business National Taipei University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan

ISBN: (纸本)9782493814197

To accurately assess the dynamic impact of a company’s activities on its Environmental, Social, and Governance (ESG) scores, we have initiated a series of shared tasks, named ML-ESG. These tasks adhere to the MSCI guidelines for annotating news articles across various languages. This paper details the third iteration of our series, ML-ESG-3, with a focus on impact duration inference—a task that poses significant challenges in estimating the enduring influence of events, even for human analysts. In ML-ESG-3, we provide datasets in five languages (Chinese, English, French, Korean, and Japanese) and share insights from our experience in compiling such subjective datasets. Additionally, this paper reviews the methodologies proposed by ML-ESG-3 participants and offers a comparative analysis of the models’ performances. Concluding the paper, we introduce the concept for the forthcoming series of shared tasks, namely multi-lingual ESG promise verification, and discuss its potential contributions to the field. © 2024 ELRA Language Resource Association.

关键词： Iterative methods

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The Impact of Different Modes of Augmented Reality Information in Assisted Aircraft Cable Assembly 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Ariansyah, Dedy Xi, Khaizuo Erkoyuncu, John Ahmet Pardamean, Bens Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia School of Aerospace Transport and Manufacturing Cranfield University BedfordMK43 0AL United Kingdom Centre of Digital Engineering and Manufacturing School of Aerospace Transport and Manufacturing Cranfield University BedfordMK43 0AL United Kingdom Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Aircraft avionics systems are complicated systems which involves high number of components and complex cable assembly procedure. To deal with this challenge, Augmented Reality (AR) has been proposed to be an effective method to improve assembly efficiency and to reduce error rates. In this paper, the authors developed AR-based guidance cable assembly and compare the impact of different modes of AR information. The results show that although AR outperformed the paper document assembly guidance, the design of AR information has distinct influence on the successful completion of the cables assembly process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Augmented reality

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ASS1 metabolically contributes to the nuclear and cytosolic p53-mediated DNA damage response (04 July, 10.1038/s42255-024-01090-z)

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NATURE METABOLISM 2024年第7期6卷 1417-1417页

作者： Lim, Lisha Qiu Jin Adler, Lital Hajaj, Emma Soria, Leandro R. Perry, Rotem Ben-Tov Darzi, Naama Brody, Ruchama Furth, Noa Lichtenstein, Michal Bab-Dinitz, Elizabeta Porat, Ziv Melman, Tevie Brandis, Alexander Malitsky, Sergey Itkin, Maxim Aylon, Yael Ben-Dor, Shifra Orr, Irit Pri-Or, Amir Seger, Rony Shaul, Yoav Ruppin, Eytan Oren, Moshe Perez, Minervo Meier, Jordan Brunetti-Pierri, Nicola Shema, Efrat Ulitsky, Igor Erez, Ayelet Department of Molecular Cell Biology Weizmann Institute of Science Rehovot Israel Department of Medicine D Beilinson Hospital Petah Tikva Israel Telethon Institute of Genetics and Medicine Pozzuoli Italy Department of Translational Medicine Medical Genetics University of Naples Federico II Naples Italy Scuola Superiore Meridionale (SSM School of Advanced Studies) Genomics and Experimental Medicine Program University of Naples Federico II Naples Italy Department of Immunology and Regenerative Biology Weizmann Institute of Science Rehovot Israel Department of Molecular Neuroscience Weizmann Institute of Science Rehovot Israel Department of Biochemistry and Molecular Biology The Institute for Medical Research Israel-Canada Faculty of Medicine Hebrew University of Jerusalem Jerusalem Israel Department of Life Sciences Core Facilities Weizmann Institute of Science Rehovot Israel The De Botton Protein Profiling Institute of the Nancy and Stephen Grand Israel National Center for Personalized Medicine Weizmann Institute of Science Rehovot Israel Cancer Data Science Lab Center for Cancer Research National Cancer Institute National Institute of Health Bethesda MD USA

Downregulation of the urea cycle enzyme argininosuccinate synthase (ASS1) in multiple tumors is associated with a poor prognosis partly because of the metabolic diversion of cytosolic aspartate for pyrimidine synthesis, supporting proliferation and mutagenesis owing to nucleotide imbalance. Here, we find that prolonged loss of ASS1 promotes DNA damage in colon cancer cells and fibroblasts from subjects with citrullinemia type I. Following acute induction of DNA damage with doxorubicin, ASS1 expression is elevated in the cytosol and the nucleus with at least a partial dependency on p53; ASS1 metabolically restrains cell cycle progression in the cytosol by restricting nucleotide synthesis. In the nucleus, ASS1 and ASL generate fumarate for the succination of SMARCC1, destabilizing the chromatin-remodeling complex SMARCC1–SNF5 to decrease gene transcription, specifically in a subset of the p53-regulated cell cycle genes. Thus, following DNA damage, ASS1 is part of the p53 network that pauses cell cycle progression, enabling genome maintenance and survival. Loss of ASS1 contributes to DNA damage and promotes cell cycle progression, likely contributing to cancer mutagenesis and, hence, adaptability potential.

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis

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Procedia Computer science 2025年 253卷 84-93页

作者： Anak Agung Ayu Putri Ardyanti Zhiqiang Xie Andrea Stevens Karnyoto Bens Pardamean Ni Made Estiyanti School of Computer Science and Technology Harbin University of Science and Technology Harbin 150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Doctoral School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions.

关键词： integrated scheduling assembly job shop scheduling heuristic algorithm genetic algorithm bibliometric analysis

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Total Nitrogen and Total Phosphorus Pollution Reshaped the Relationship Between Water Supply and Demand in the Huaihe River Watershed, China

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Chinese Geographical science 2023年第3期33卷 512-530页

作者： LU Yan LIU Lyuyi QIN Fen WANG Jiayao LIU Jianzhong LI Yingjie WAN Luwen School of Geoscience and Technology Zhengzhou UniversityZhengzhou 450001China Henan Provincial Academy of Eco-Environmental Sciences Zhengzhou 450003China Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Ministry of EducationHenan UniversityKaifeng 475000China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan UniversityKaifeng 475000China College of Geography and Environmental Science Henan UniversityKaifeng 475000China Environmental Science and Policy Program Michigan State UniversityEast LansingMI 48824USA

As total nitrogen(TN)and total phosphorus(TP)pollution is the main source of water pollution in the Huaihe River watershed in China,it is important to understand how TN and TP pollution affect the relationship between water supply and *** their impacts and describing the spatiotemporal distribution of these relationships are necessary for furtherly deepening the theory of TN and TP pollution on water bodies,and this information is also particularly essential for managing water resources *** this study,based on the potential water supply,the water demand and the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)water purification models,we estimated the TN and TP pollution from agricultural fertilizer,livestock and poultry breeding,and rural residents in the Huaihe River watershed and simulated TN and TP impacts on the relationship between water supply and *** found that if the impact of TN and TP pollution on water supply was not taken into account,on average,there was excess water supply in 79.20%of the watershed and excess demand in 20.80%of the rest during 1980–*** the TN concentration limit,Grade-Ⅱ(The water quality meets the secondary level of water body qualified in GB3838–2002,classified as Grade-II)water was the main watersupply type in 1980–2018,followed by Grade-Ⅰ and Grade-Ⅲ *** total water shortage showed an inverted V-shaped trend:first increasing and then decreasing at the same *** proportion of the water shortage of Grade-I water in the total water shortage was the largest,followed by Grade-Ⅱ and Grade-III *** with excess demand were located on the north bank of Wang-Beng,Yishuhe,and Huxi regions,although the water in these sub-watersheds met the water quality standards of Grade-Ⅰ *** the TP concentration limit,Grade-Ⅱ and Grade-Ⅰ water were the main water-supply *** overall water shortage trend first increased and then decreased,exhibiting an inverted V-shape from

关键词： watershed breeding poultry

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EARLY-STAGE DETECTION OF COGNITIVE IMPAIRMENT BY HYBRID QUANTUM-CLASSICAL ALGORITHM USING RESTING-STATE FUNCTIONAL MRI TIME-SERIES

arXiv

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arXiv 2024年

作者： Choi, Junggu Park, Daniel K. Lee, Seung-Koo Hur, Tak Shin, Na-Young Lee, Hakbae Han, Sanghoon Yonsei Graduate program in Cognitive science Yonsei University Seoul Korea Republic of Department of Applied Statistics Department of Statistics and Data Science Yonsei University Seoul Korea Republic of Department of Radiology College of Medicine Yonsei University Seoul Korea Republic of Department of Statistics and Data Science Yonsei University Seoul Korea Republic of Department of Psychology Yonsei Graduate program in Cognitive science Yonsei University Seoul Korea Republic of

Following the recent development of quantum machine learning techniques, the literature has reported several quantum machine learning algorithms for disease detection. This study explores the application of a hybrid quantum-classical algorithm for classifying region-of-interest time-series data obtained from resting-state functional magnetic resonance imaging in patients with early-stage cognitive impairment based on the importance of cognitive decline for dementia or aging. Classical one-dimensional convolutional layers are used together with quantum convolutional neural networks in our hybrid algorithm. In the classical simulation, the proposed hybrid algorithms showed higher balanced accuracies than classical convolutional neural networks under the similar training conditions. Moreover, a total of nine brain regions (left precentral gyrus, right superior temporal gyrus, left rolandic operculum, right rolandic operculum, left parahippocampus, right hippocampus, left medial frontal gyrus, right cerebellum crus, and cerebellar vermis) among 116 brain regions were found to be relatively effective brain regions for the classification based on the model performances. The associations of the selected nine regions with cognitive decline, as found in previous studies, were additionally validated through seed-based functional connectivity analysis. We confirmed both the improvement of model performance with the quantum convolutional neural network and neuroscientific validities of brain regions from our hybrid quantum-classical model. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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ZIP-FIT: EMBEDDING-FREE data SELECTION VIA COMPRESSION-BASED ALIGNMENT

arXiv

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arXiv 2024年

作者： Obbad, Elyas Mlauzi, Iddah Miranda, Brando Schaeffer, Rylan Obbad, Kamal Bedi, Suhana Koyejo, Sanmi Department of Computer Science Stanford University United States Department of Biomedical Data Science Stanford School of Medicine United States Stanford Biophysics Program Stanford School of Medicine United States

data selection is crucial for optimizing language model (LM) performance on specific tasks, yet most existing methods fail to effectively consider the target task distribution. Current approaches either ignore task-specific requirements entirely or rely on approximations that fail to capture the nuanced patterns needed for tasks like Autoformalization or code generation. Methods that do consider the target distribution often rely on simplistic, sometimes noisy, representations, like hashed n-gram features, which can lead to collisions and introduce noise. We introduce ZIP-FIT, a data selection framework that uses gzip compression to directly measure alignment between potential training data and the target task distribution. Our key insight is that compression-based similarity captures both syntactic and structural patterns relevant to the target task, enabling more precise selection of truly task-relevant data. In extensive evaluations on Autoformalization and Python code generation, ZIP-FIT significantly outperforms leading baselines like DSIR and D4. Models trained on ZIP-FIT-selected data achieve their lowest cross-entropy loss up to 85.1% faster than baselines, demonstrating that better task alignment leads to more efficient learning. In addition, ZIP-FIT performs selection up to 65.8% faster than DSIR and two orders of magnitude faster than D4. Notably, ZIP-FIT shows that smaller, well-aligned datasets often outperform larger but less targeted ones, demonstrating that a small amount of higher quality data is superior to a large amount of lower quality data. Our results imply that task-aware data selection is crucial for efficient domain adaptation, and that compression offers a principled way to measure task alignment. By showing that targeted data selection can dramatically improve task-specific performance, our work provides new insights into the relationship between data quality, task alignment, and model learning efficiency. © 2024, CC BY.

关键词： data consistency

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Exome-wide variation in a diverse barley panel reveals genetic associations with ten agronomic traits in Eastern landraces

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Journal of Genetics and Genomics 2023年第4期50卷 241-252页

作者： June-Sik Kim Kotaro Takahagi Komaki Inoue Minami Shimizu Yukiko Uehara-Yamaguchi Asaka Kanatani Daisuke Saisho Ryuei Nishii Alexander ELipka Takashi Hirayama Kazuhiro Sato Keiichi Mochida Bioproductivity Informatics Research Team RIKEN Center for Sustainable Resource ScienceYokohama 230-0045Japan Institute of Plant Science and Resources Okayama UniversityKurashiki 710-0046Japan Kihara Institute for Biological Research Yokohama City UniversityYokohama 244-0813Japan Graduate School of Nanobioscience Yokohama City UniversityYokohama 236-0027Japan School of Information and Data Sciences Nagasaki UniversityNagasaki 852-8521Japan Department of Crop Sciences University of IllinoisUrbanaIL 61801USA Microalgae Production Control Technology Laboratory RIKEN Baton Zone ProgramRIKEN Cluster for ScienceTechnology and Innovation HubYokohama 230-0045Japan

Barley(Hordeum vulgare ***)is one of the first crops to be domesticated and is adapted to a wide range of *** barley germplasm collections possess valuable allelic variations that could further improve barley *** barley genomics has offered a global picture of allelic variation among varieties and its association with various agronomic traits,polymorphisms from East Asian varieties remain *** this study,we analyze exome polymorphisms in a panel of 274 barley varieties collected worldwide,including 137 varieties from East Asian countries and *** reveal the underlying population structure and conduct genome-wide association studies for 10 agronomic ***,we examin genome-wide associations for traits related to grain size such as awn length and glume *** results demonstrate the value of diverse barley germplasm panels containing Eastern varieties,highlighting their distinct genomic signatures relative to Western subpopulations.

关键词： Barley Exome-seq Genome-wide association study(GWAS) Population structure Allelic variation

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ConvNeXt Model for Breast Cancer Image Classification

ConvNeXt Model for Breast Cancer Image Classification

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Devin Setiawan Andrea Stevens Karnyoto Indo Intan Bens Pardamean Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83.

关键词： Support vector machines Accuracy Head Computational modeling Transfer learning Breast cancer Magnetic heads data models Reliability Image classification

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