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Autonomous driving and advanced driver-assistance systems (ADAS) : applications, development, legal issues, and testing
\"Autonomous Driving and Advanced Driver Assistance Systems (ADAS) outlines the latest research relating to autonomous cars and advanced driver-assistance systems, including the development, testing and verification for real-time situations of sensor fusion, sensor placement, control algorithms, computer vision, and more. With an infinite number of real-time possibilities that need to be addressed, the methods and examples included make this book a valuable source of information for academic and industrial researchers, automotive companies and suppliers\"-- Provided by publisher.
Book
Automated Text Analysis for Consumer Research
The amount of digital text available for analysis by consumer researchers has risen dramatically. Consumer discussions on the internet, product reviews, and digital archives of news articles and press releases are just a few potential sources for insights about consumer attitudes, interaction, and culture. Drawing from linguistic theory and methods, this article presents an overview of automated text analysis, providing integration of linguistic theory with constructs commonly used in consumer research, guidance for choosing amongst methods, and advice for resolving sampling and statistical issues unique to text analysis. We argue that although automated text analysis cannot be used to study all phenomena, it is a useful tool for examining patterns in text that neither researchers nor consumers can detect unaided. Text analysis can be used to examine psychological and sociological constructs in consumer-produced digital text by enabling discovery or by providing ecological validity.
Journal Article
Connected vehicular systems : communication, control, and optimization
\"This book contains our research advances in the past decade in the analysis and synthesis of CAVs systems from all aspects of trajectory planning, cooperative control and communication. The focuses of this book are on the development of mathematical models and methodologies for trajectory optimization and tracking control, communications conflict resolution, cooperative control subject to communication constraints and sensor/actuator failures/faults for CAVs from different perspectives. This book is composed of fourteen Chapters. The contents are divided into three parts, with Chapter 1 - Chapter 5 as Part I, Chapter 6 - Chapter 9 as Part II, and Chapter 10 - Chapter 14 as Part III, respectively, concerned with cooperative vehicular communication and control, performance guarantee under actuator limitations, and speed trajectory planning and tracking control of CAVs.\"-- Provided by publisher.
BOOK
A pathology foundation model for cancer diagnosis and prognosis prediction
Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task
1
,
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. Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations
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. Here, to address this challenge, we devised the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, a general-purpose weakly supervised machine learning framework to extract pathology imaging features for systematic cancer evaluation. CHIEF leverages two complementary pretraining methods to extract diverse pathology representations: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. We developed CHIEF using 60,530 whole-slide images spanning 19 anatomical sites. Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumour origin identification, molecular profile characterization and prognostic prediction. We successfully validated CHIEF using 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals and cohorts internationally. Overall, CHIEF outperformed the state-of-the-art deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations and processed by different slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology evaluation for patients with cancer.
A study describes the development of a generalizable foundation machine learning framework to extract pathology imaging features for cancer diagnosis and prognosis prediction.
Journal Article
Halt the use of facial-recognition technology until it is regulated
Until appropriate safeguards are in place, we need a moratorium on biometric technology that identifies individuals, says Kate Crawford.
Until appropriate safeguards are in place, we need a moratorium on biometric technology that identifies individuals, says Kate Crawford.
“These tools are harmful when they fail and dangerous when they work.”
Journal Article
Myoelectric Pattern Recognition Outperforms Direct Control for Transhumeral Amputees with Targeted Muscle Reinnervation: A Randomized Clinical Trial
Recently commercialized powered prosthetic arm systems hold great potential in restoring function for people with upper-limb loss. However, effective use of such devices remains limited by conventional (direct) control methods, which rely on electromyographic signals produced from a limited set of muscles. Targeted Muscle Reinnervation (TMR) is a nerve transfer procedure that creates additional recording sites for myoelectric prosthesis control. The effects of TMR may be enhanced when paired with pattern recognition technology. We sought to compare pattern recognition and direct control in eight transhumeral amputees who had TMR in a balanced randomized cross-over study. Subjects performed a 6–8 week home trial using direct and pattern recognition control with a custom prostheses made from commercially available parts. Subjects showed statistically better performance in the Southampton Hand Assessment Procedure (p = 0.04) and the Clothespin relocation task (p = 0.02). Notably, these tests required movements along 3 degrees of freedom. Seven of 8 subjects preferred pattern recognition control over direct control. This study was the first home trial large enough to establish clinical and statistical significance in comparing pattern recognition with direct control. Results demonstrate that pattern recognition is a viable option and has functional advantages over direct control.
Journal Article