Xiaoli Li – författare
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22 produkter
22 produkter
Häftad, Engelska, 2026
1 976 kr
Kommande
Advances in Biosystems investigates the latest techniques for evaluating, detecting, measuring, and enhancing the functional bioactivities, chemical composition, and physicochemical characteristics of animal, plant, and environmental biosystems. This comprehensive, interdisciplinary handbook provides detailed explanations for tracking functional molecules during various biological processes, along with practical methodologies for both laboratory and industrial applications. Sections cover cellular biosystems advances, including single-cell microsystems and tissue system biology of plants and animals, alongside biomass microsystems and agricultural biosystems advances, exploring innovations in animal, plant, and crop biosystems, as well as food hydrocolloids and biotechnological advancements.Additional content addresses environmental biosystems, discussing colloidal biosystems, nano and micro-biosystems, chemical toxicology, eco-friendly biosystems, and green chemistry, pharmaceutical biosystems, highlighting medical biosystems, human cellular metabolomics, nutrigenomics, and precision medicine applications. A final section presents in-depth analytical methods for tracking these various biosystems, including high-throughput technologies, deep learning, machine learning, bioinformatics models, and spectroscopic and sensor-based techniques.Delves into an extensive range of biosystems, spanning medical, agricultural, biotechnological, and environmental specialtiesOffers step-by-step guidance for techniques, exploring living systems, intricate cellular processes, and expansive ecological interactionsExplores innovative and sustainable approaches for investigating biosystems, considering eco-friendly and non-invasive methods for studying and improving plant, animal, and environmental biosystemsExamines cutting-edge applications of biosystems in the realm of precision medicine and biotechnology
E-bok
PDF, Engelska, 2026841 kr
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This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advance algorithms that are transforming time series analysis across industries. The authors highlight the use AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time. In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis.TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through unsupervised domain adaptation (UDA) In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like MOIRAI and Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as a supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, climate.
- Nyhet
E-bok
PDF, Engelska, 2026909 kr
Läs direkt efter köp
This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift, and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advanced algorithms that are transforming time series analysis across industries. The authors highlight the use of AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time.In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis. TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through Unsupervised Domain Adaptation (UDA). In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, and climate.
E-bok
Engelska, 2026841 kr
Läs direkt efter köp
This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advance algorithms that are transforming time series analysis across industries. The authors highlight the use AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time. In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis.TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through unsupervised domain adaptation (UDA) In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like MOIRAI and Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as a supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, climate.
- Nyhet
E-bok
Engelska, 2026909 kr
Läs direkt efter köp
This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift, and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advanced algorithms that are transforming time series analysis across industries. The authors highlight the use of AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time.In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis. TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through Unsupervised Domain Adaptation (UDA). In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, and climate.
- Nyhet
Häftad, Engelska, 2026
838 kr
Skickas
This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift, and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advanced algorithms that are transforming time series analysis across industries. The authors highlight the use of AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time.In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis. TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through Unsupervised Domain Adaptation (UDA). In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, and climate.
- Nyhet
Inbunden, Engelska, 2026
2 816 kr
Skickas inom 10-15 vardagar
This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift, and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advanced algorithms that are transforming time series analysis across industries. The authors highlight the use of AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time.In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis. TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through Unsupervised Domain Adaptation (UDA). In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, and climate.
- Nyhet
Häftad, Engelska, 2026
709 kr
Skickas inom 10-15 vardagar
This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advance algorithms that are transforming time series analysis across industries. The authors highlight the use AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time. In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis.TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through unsupervised domain adaptation (UDA) In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like MOIRAI and Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as a supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, climate.
- Nyhet
Inbunden, Engelska, 2026
2 676 kr
Skickas inom 10-15 vardagar
This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advance algorithms that are transforming time series analysis across industries. The authors highlight the use AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time. In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis.TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through unsupervised domain adaptation (UDA) In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like MOIRAI and Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as a supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, climate.
Inbunden, Engelska, 2024
2 367 kr
Skickas inom 10-15 vardagar
This volume provides food researchers with advanced destructive and non-destructive techniques for measuring the chemical composition, functional bioactivities physicochemical changes, and explaining the functional mechanisms for facilitating the tracking of the functional molecules during the different industrial processes. Chapters guide readers through sample preparation emerging extraction technologies protocols analytical methodologies for phytochemicals profiling, multi way models in food data analysis, and the integration of omics for biochemical analyses understanding. Written in the format of the Methods and Protocols in Food Science series, the chapters include an introduction to the respective topic, list necessary materials and reagents, detail well established and validated methods for readily reproducible laboratory protocols and contain notes on how to avoid or solve typical problems.Authoritative and cutting-edge, Plant Chemical Compositions and Bioactivities aims to ensure successful results in the further study of this vital field.
E-bok
Engelska, 20242 130 kr
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This volume provides food researchers with advanced destructive and non-destructive techniques for measuring the chemical composition, functional bioactivities physicochemical changes, and explaining the functional mechanisms for facilitating the tracking of the functional molecules during the different industrial processes. Chapters guide readers through sample preparation emerging extraction technologies protocols analytical methodologies for phytochemicals profiling, multi way models in food data analysis, and the integration of omics for biochemical analyses understanding. Written in the format of the Methods and Protocols in Food Science series, the chapters include an introduction to the respective topic, list necessary materials and reagents, detail well established and validated methods for readily reproducible laboratory protocols and contain notes on how to avoid or solve typical problems.
Authoritative and cutting-edge, Plant Chemical Compositions and Bioactivities aims to ensure successful results in the further study of this vital field.
Häftad, Engelska, 2025
1 723 kr
Skickas inom 10-15 vardagar
This volume provides food researchers with advanced destructive and non-destructive techniques for measuring the chemical composition, functional bioactivities physicochemical changes, and explaining the functional mechanisms for facilitating the tracking of the functional molecules during the different industrial processes. Chapters guide readers through sample preparation emerging extraction technologies protocols analytical methodologies for phytochemicals profiling, multi way models in food data analysis, and the integration of omics for biochemical analyses understanding. Written in the format of the Methods and Protocols in Food Science series, the chapters include an introduction to the respective topic, list necessary materials and reagents, detail well established and validated methods for readily reproducible laboratory protocols and contain notes on how to avoid or solve typical problems.Authoritative and cutting-edge, Plant Chemical Compositions and Bioactivities aims to ensure successful results in the further study of this vital field.
Inbunden, Engelska, 2016
1 995 kr
Skickas inom 10-15 vardagar
This book reviews cutting-edge developments in neural signalling processing (NSP), systematically introducing readers to various models and methods in the context of NSP.Neuronal Signal Processing is a comparatively new field in computer sciences and neuroscience, and is rapidly establishing itself as an important tool, one that offers an ideal opportunity to forge stronger links between experimentalists and computer scientists. This new signal-processing tool can be used in conjunction with existing computational tools to analyse neural activity, which is monitored through different sensorssuch as spike trains, local filed potentials and EEG. The analysis of neural activity can yield vital insights into the function of the brain. This book highlights the contribution of signal processing in the area of computational neuroscience by providing a forum for researchers in this field to share their experiences to date.
E-bok
PDF, Engelska, 20162 599 kr
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This book reviews cutting-edge developments in neural signalling processing (NSP), systematically introducing readers to various models and methods in the context of NSP.
Neuronal Signal Processing is a comparatively new field in computer sciences and neuroscience, and is rapidly establishing itself as an important tool, one that offers an ideal opportunity to forge stronger links between experimentalists and computer scientists. This new signal-processing tool can be used in conjunction with existing computational tools to analyse neural activity, which is monitored through different sensorssuch as spike trains, local filed potentials and EEG. The analysis of neural activity can yield vital insights into the function of the brain. This book highlights the contribution of signal processing in the area of computational neuroscience by providing a forum for researchers in this field to share their experiences to date.
Häftad, Engelska, 2018
1 995 kr
Skickas inom 10-15 vardagar
This book reviews cutting-edge developments in neural signalling processing (NSP), systematically introducing readers to various models and methods in the context of NSP.Neuronal Signal Processing is a comparatively new field in computer sciences and neuroscience, and is rapidly establishing itself as an important tool, one that offers an ideal opportunity to forge stronger links between experimentalists and computer scientists. This new signal-processing tool can be used in conjunction with existing computational tools to analyse neural activity, which is monitored through different sensorssuch as spike trains, local filed potentials and EEG. The analysis of neural activity can yield vital insights into the function of the brain. This book highlights the contribution of signal processing in the area of computational neuroscience by providing a forum for researchers in this field to share their experiences to date.
Inbunden, Engelska, 2021
1 580 kr
Skickas inom 3-6 vardagar
Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning technology can be a very good candidate for improving sensing capabilities.In this edited volume, we aim to narrow the gap between humans and machines by showcasing various deep learning applications in the area of sensing. The book will cover the fundamentals of deep learning techniques and their applications in real-world problems including activity sensing, remote sensing and medical sensing. It will demonstrate how different deep learning techniques help to improve the sensing capabilities and enable scientists and practitioners to make insightful observations and generate invaluable discoveries from different types of data.
Inbunden, Engelska, 2024
2 164 kr
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3D deep learning is a rapidly evolving field that has the potential to transform various industries. This book provides a comprehensive overview of the current state-of-the-art in 3D deep learning, covering a wide range of research topics and applications. It collates the most recent research advances in 3D deep learning, including algorithms and applications, with a focus on efficient methods to tackle the key technical challenges in current 3D deep learning research and adoption, therefore making 3D deep learning more practical and feasible for real-world applications.This book is organized into five sections, each of which addresses different aspects of 3D deep learning. Section I: Sample Efficient 3D Deep Learning, focuses on developing efficient algorithms to build accurate 3D models with limited annotated samples. Section II: Representation Efficient 3D Deep Learning, deals with the challenge of developing efficient representations for dynamic 3D scenes and multiple 3D modalities. Section III: Robust 3D Deep Learning, presents methods for improving the robustness and reliability of deep learning models in real-world applications. Section IV: Resource Efficient 3D Deep Learning, explores ways to reduce the computation cost of 3D models and improve their efficiency in resource-limited environments. Section V: Emerging 3D Deep Learning Applications, showcases how 3D deep learning is transforming industries and enabling new applications for healthcare and manufacturing.This collection is a valuable resource for researchers and practitioners interested in exploring the potential of 3D deep learning.
Inbunden, Engelska, 2024
1 374 kr
Skickas inom 3-6 vardagar
Over the past few decades, the field of machine learning has made remarkable strides, surpassing human performance in tasks like voice and object recognition, as well as mastering various complex games. Despite these accomplishments, a critical challenge remains: the absence of general intelligence. Achieving artificial general intelligence (AGI) requires the development of learning agents that can continually adapt and learn throughout their existence, a concept known as lifelong learning.In contrast to machines, humans possess an extraordinary capacity for continuous learning throughout their lives. Drawing inspiration from human learning, there is immense potential to enable artificial learning agents to learn and adapt continuously. Recent advancements in continual learning research have opened up new avenues to pursue this objective.This book is a comprehensive compilation of diverse methods for continual learning, crafted by leading researchers in the field, along with their practical applications. These methods encompass various approaches, such as adapting existing paradigms like zero-shot learning and Bayesian learning, leveraging the flexibility of network architectures, and employing replay mechanisms to enable learning from streaming data without catastrophic forgetting of previously acquired knowledge.This book is tailored for researchers, practitioners, and PhD scholars working in the realm of Artificial Intelligence (AI). It particularly targets those envisioning the implementation of AI solutions in dynamic environments where data continually shifts, leading to challenges in maintaining model performance for streaming data.
Häftad, Engelska, 2021
565 kr
Skickas inom 10-15 vardagar
This book constitutes refereed proceedings of the Second International Workshop on Deep Learning for Human Activity Recognition, DL-HAR 2020, held in conjunction with IJCAI-PRICAI 2020, in Kyoto, Japan, in January 2021. Due to the COVID-19 pandemic the workshop was postponed to the year 2021 and held in a virtual format. The 10 presented papers were thorougly reviewed and included in the volume. They present recent research on applications of human activity recognition for various areas such as healthcare services, smart home applications, and more.
734 kr
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This book constitutes refereed proceedings of the Second International Workshop on Deep Learning for Human Activity Recognition, DL-HAR 2020, held in conjunction with IJCAI-PRICAI 2020, in Kyoto, Japan, in January 2021. Due to the COVID-19 pandemic the workshop was postponed to the year 2021 and held in a virtual format. The 10 presented papers were thorougly reviewed and included in the volume. They present recent research on applications of human activity recognition for various areas such as healthcare services, smart home applications, and more.
Del 8 - Science, Engineering, And Biology Informatics
Biological Data Mining And Its Applications In Healthcare
Inbunden, Engelska, 2014
2 194 kr
Skickas inom 3-6 vardagar
Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to disease pathways and electronic health records. To exploit these data for discovering new knowledge that can be translated into clinical applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues such as handling noisy and incomplete data, processing compute-intensive tasks, and integrating various data sources, are new challenges faced by biologists in the post-genome era. This book will cover the fundamentals of state-of-the-art data mining techniques which have been designed to handle such challenging data analysis problems, and demonstrate with real applications how biologists and clinical scientists can employ data mining to enable them to make meaningful observations and discoveries from a wide array of heterogeneous data from molecular biology to pharmaceutical and clinical domains.
Inbunden, Engelska, 2026
1 209 kr
Kommande
This book establishes a shift towards sustainable AI by providing researchers with alternatives that optimize energy consumption and reduce the environmental footprint of artificial intelligence. Sustainable AI addresses these challenges through a novel approach focused on the inversely proportional relationship between computational efficiency and impactful outcomes.While generative AI has significantly transformed various domains, the challenge lies in the extensive computational resources required for its operation; its environmental impact associated with high energy consumption and carbon emissions has inevitably made the sustainability of AI a growing concern. This book comprehensively addresses the technical challenges inherent in AI's current research and adoption while surveying the fundamental capabilities of sustainable AI—specifically data-efficient learning and resource-efficient learning. These insights transform sustainable AI into a more viable and realistic solution for various scientific applications, such as semiconductor failure detection, remote sensing, autonomous navigation, and more. Readers will find useful roadmaps for balancing high-performance computing with environmental responsibility in real-world scenarios.