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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： M.H. Ibrahim F.A. Cummins Research and Development Electronic Data Systems Corporation Auburn Hills MI USA

The authors present TARO, an interactive tool to assist linguists in designing, building, testing, and extending natural language processing (NLP) systems. TARO (text analysis and representation with objects) is implemented in KSL, a reflective, object-oriented programming language. The architecture of TARO takes advantage of encapsulation, polymorphism, and inheritance of object-oriented programming to provide an environment in which testing and refinement of complex NLP components can be achieved without major program modifications. It also benefits from the reflectivity of KSL to allow building and extending NLP prototypes dynamically. These qualities, coupled with the ability of TARO to integrate syntactic with semantic analysis, provide the opportunity to achieve greater modularity, extensibility, and integration in the design of natural language systems.< >

关键词： Natural languages Dictionaries Buildings Object oriented programming Natural language processing Encapsulation Testing Reflectivity Programming environments data systems

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High-density recording by two-dimensional signal processing

High-density recording by two-dimensional signal processing

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Optical data Storage 1995

作者： Maeda, T. Sugiyama, H. Saitou, A. Wakabayashi, K. Miyamoto, H. Awan, H. Optical Storage Research and Development Center Advanced Technology Research and Development Division Data Storage and Retrieval Systems Division Hitachi Ltd. 2880 Kozu Odawara-shi Kanagawa256 Japan Central Research Laboratory Hitachi Ltd. 1-280 Higashikoigakubo Kokubunji-shi Tokyo185 Japan

To make higher density on optical disk, a new recording method-two dimensional recording-which can increase the track-density rather than the linear density was proposed. The performance of this method on increasing density is estimated by the simulation and confirmed by measuring the read-out signal. Consequently, it is clarified that this method can attain more than twice the areal density independently and realize the density of 2.2Gbit/in2 which is 8 times as large as that of first generation using 680 nm laser diode. © 1995 SPIE. All rights reserved.

关键词： Equalizers

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Identification of cell type-specific cell-penetrating peptides through in vivo phage display leveraged by next generation sequencing

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Biomedicine and Pharmacotherapy 2025年 182卷 117740-117740页

作者： Kohl, Franziska Laufkötter, Oliver Firth, Mike Krimpenfort, Luc Mangla, Priyanka Ansarizadeh, Mohammadhassan Geylan, Gökçe Eklund, Lauri De Maria, Leonardo Jakobsson, Lars Wiseman, John Centre for Genomics Research Discovery Sciences BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden Department of Life Science Informatics B-IT Rheinische Friedrich-Wilhelms-Universität Bonn Germany Data Sciences and Quantitative Biology Discovery Sciences BioPharmaceuticals R&D AstraZeneca Cambridge United Kingdom Oligonucleotides and Targeted Delivery Discovery Sciences BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden Oulu Center for Cell-Matrix Research University of Oulu Oulu Finland Faculty of Biochemistry and Molecular Medicine University of Oulu Oulu Finland Biocenter Oulu University of Oulu Oulu Finland Molecular AI Discovery Sciences BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden Division of Systems and Synthetic Biology Department of Life Sciences Chalmers University of Technology Gothenburg Sweden Research and Early Development Respiratory & Immunology BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden

Vascular anomalies (VA) refer to abnormal blood or lymphatic vessel architecture, most often as a result of dysregulated growth. Venous malformations (VM), a subgroup of VAs, are triggered by activating mutations in the Angiopoietin/TIE2-PI3K/AKT/mTOR signaling pathway with TIE2 L914F (gene name TEK) being one of the most frequent mutations in patients with VMs. Although systemic targeting of the overactivated pathway is possible, it would be a therapeutic advantage to restrict treatment to only the affected lesions. To identify peptides with potential selective binding to TIE2 L914F lesions we applied in vivo phage display to TIE2 L914F-overexpressing endothelial cells (ECs) in a subcutaneous matrigel xenograft mouse model of VMs. By panning for lesion-targeting phages in combination with subcellular fractionation, a screen for cell-penetrating candidate phages was established. Employing Next Generation Sequencing (NGS) and a refined bioinformatic analysis we were able to identify many novel cell-penetrating peptides (CPPs). To pinpoint the most selective and viable CCP candidates a hierarchical clustering algorithm was utilized. This method aggregated CPPs with highly similar sequences into a small number of clusters from which consensus sequences could be derived. Selected candidate CPPs exhibited uptake in TIE2 L914F-expressing human umbilical vein endothelial cells (HUVEC) in culture and were able to deliver siRNA into these cells. In conclusion, our NGS bioinformatic-supported approach led to the identification of novel and selective CPPs capable of transporting a siRNA cargo into targeted cells. © 2024

关键词： Bioinformatic analysis Cell-penetrating peptides In vivo phage display Next generation sequencing Vascular anomalies

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Classifying Spend Descriptions with Off-the-Shelf Learning Components

Classifying Spend Descriptions with Off-the-Shelf Learning C...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Saikat Mukherjee Dmitriy Fradkin Michael Roth Integrated Data Systems Department Siemens AG Corporate Research and Development USA

Analyzing spend transactions is essential to organizations for understanding their global procurement. Central to this analysis is the automated classification of these transactions to hierarchical commodity coding systems. Spend classification is challenging due not only to the complexities of the commodity coding systems but also because of the sparseness and quality of each individual transaction text description and the volume of such transactions in an organization. In this paper, we demonstrate the application of off-the-shelf machine learning tools to address the challenges in spend classification. We have built a system using off-the-shelf SVM, logistic regression, and language processing toolkits and describe the effectiveness of these different learning techniques for spend classification.

关键词： Support vector machines Support vector machine classification Logistics Machine learning Procurement Software tools Humans Error correction Artificial intelligence data systems

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Loop-shaping controller design with the RBode plot for hard disk drives

Loop-shaping controller design with the RBode plot for hard ...

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作者： Atsumi, Takenori Messner, William C. Research and Development Group Hitachi Ltd. 1 Kirihara Fujisawa Kanagawa 252-8588 Japan Data Storage Systems Center Carnegie Mellon University Pittsburgh PA 15213 United States

ISBN: (纸本)9781424474264

The controller for the head positioning system of a hard disk drive must have both high performance and high robustness. This paper presents a user-friendly loop-shaping design method employing the Robust Bode (RBode) plot for the head positioning system. By using the RBode plot, control engineers can easily design controllers that can suppress disturbances and account for perturbations of the controlled object with frequency response data alone. A transfer function-based nominal model is not required. As a result, the engineer can avoid the excessively conservative designs while accommodating perturbations in the plant and improve the controller performance. The design results for a benchmark hard disk drive problem show the usefulness of proposed method. © 2010 AACC.

关键词： Controllers

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Transfer learning versus multi-agent learning regarding distributed decision-making in highway traffic 10

Transfer learning versus multi-agent learning regarding dist...

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10th International Workshop on Agents in Traffic and Transportation, ATT 2018

作者： Schutera, Mark Goby, Niklas Neumann, Dirk Reischl, Markus Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Chair for Information Systems Research University of Freiburg Germany IT Innovation Chapter Data Science ZF Friedrichshafen AG Germany Research and Development ZF Friedrichshafen AG Germany

Transportation and traffic are currently undergoing a rapid increase in terms of both scale and complexity. At the same time, an increasing share of traffic participants are being transformed into agents driven or supported by artificial intelligence resulting in mixed-intelligence traffic. This work explores the implications of distributed decision-making in mixed-intelligence traffic. The investigations are carried out on the basis of an online-simulated highway scenario, namely the MIT DeepTraffic simulation. In the first step traffic agents are trained by means of a deep reinforcement learning approach, being deployed inside an elitist evolutionary algorithm for hyperparameter search. The resulting architectures and training parameters are then utilized in order to either train a single autonomous traffic agent and transfer the learned weights onto a multi-agent scenario or else to conduct multi-agent learning directly. Both learning strategies are evaluated on different ratios of mixed-intelligence traffic. The strategies are assessed according to the average speed of all agents driven by artificial intelligence. Traffic patterns that provoke a reduction in traffic flow are analyzed with respect to the different strategies. © 2018 CEUR-WS. All rights reserved.

关键词： Reinforcement learning

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Expert System for Manufacturing Order Determination in Hot-Rolling Process

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IFAC Proceedings Volumes 1992年第17期25卷 71-77页

作者： H. Fujimoto S. Arai K. Sanou N. Fukaya Systems Laboratory Systems Planning and Data Processing Department Kawasaki Steel Corporation Tokyo Japan Systems Engineering Division Kawasaki Steel Systems Research and Development Corporation Chita Japan

We have developed an expert system for determining manufacturing order in the hot-rolling process of seamless steel pipe based on the knowledge engineering. The purpose of this expert system is to decide optimal manufacturing order, satisfying various requirements. This kind of decision making is one of the synthesis type problems which are known as the hardest one for a practical use. We paid close attention to the professionals for their ability of optimized problem solving using various broadened and unstructured standards, and then attended to systematize with the knowledge-based system technology. In this development, we confirmed effectiveness of the expert system in relation to problems involved in sequence composition, and developed its inference method.

关键词： Artificial intelligence expert systems synthesis methods inference processes production control heuristic programming steel industry

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Should the limits of liability be changed

Should the limits of liability be changed

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2005 International Oil Spill Conference, IOSC 2005

作者： Hartley III, D.S. Beckerman, Barbara G. Oak Ridge National Laboratory Department of Energy Facilities Center for Modeling Simulation and Gaming College on Simulation InfORMS Data Systems Research and Development Program Lockheed Martin Energy Systems Lockheed Martin Energy Systems Inc. 1099 Commerce Park Oak Ridge TN 37830

The U.S. Coast Guard commissioned an evaluation of the appropriateness of adjusting the current limits of liability for oil spills from transportation-related onshore facilities, based on the requirements of the Oil Pollution Act of 1990. Extensive data collection and analysis revealed that the type and quality of the data needed to support recommendations for setting limits of liability for oil spills from specific categories of facilities were either not available or not reliable. Therefore, no recommendation can be made regarding a change in the limits of liability. Further, the lack of accessible data cripples decision making in the entire subject of oil spills. Recommendations are made regarding the data acquisition process and the systems needed to support it.

关键词： Oil spills

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Organic Pie Charts

Organic Pie Charts

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IEEE International Conference on data Mining (ICDM)

作者： Fabian Moerchen Integrated Data Systems Siemens AG Corporate Research and Development Princeton NJ USA

We present a new visualization of the distance and cluster structure of high dimensional data. It is particularly well suited for analysis tasks of users unfamiliar with complex data analysis techniques as it builds on the well known concept of pie charts. The non-linear projection capabilities of Emergent Self-Organizing Maps (ESOM) are used to generate a topology-preserving ordering of the data points on a circle. The distance structure within the high dimensional space is visualized on the circle analogously to the U-Matrix method for two-dimensional SOM. The resulting display resembles pie charts but has an organic structure that naturally emerges from the data. Pie segments correspond to groups of similar data points. Boundaries between segments represent low density regions with larger distances among neighboring points in the high dimensional space. The representation of distances in the form of a periodic sequence of values makes time series segmentation applicable to automated clustering of the data that is in sync with the visualization. We discuss the usefulness of the method on a variety of data sets to demonstrate the applicability in applications such as document analysis or customer segmentation.

关键词： Distance measurement Training data mining data visualization Equations Continuous wavelet transforms Trajectory

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ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

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