جست‌وجوی استاد و پژوهشگر

Alan Bovikمشاهده پروفایل

استاد

• Image quality
• Video compression
• Streaming video
• +۷ مورد دیگر

Alan Bovik is the Cockrell Family Regents Endowed Chair Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin's Cockrell School of Engineering. He also holds positions at The Institute for Neurosciences and serves as Director of the Laboratory for Image and Video Engineering (LIVE). With a career spanning over three decades at UT Austin, he has progressed from Assistant Professor (1984-1988) to Associate Professor (1988-1994) to his current position as Full Professor (1994-present). Dr. Bovik received his Ph.D. in Electrical and Computer Engineering in 1984 from the University of Illinois, Urbana-Champaign. Professor Bovik's research focuses on image and video quality assessment, visual perception, and digital media processing. He is renowned for developing groundbreaking algorithms including the Structural Similarity (SSIM) index, Visual Information Fidelity (VIF), and various blind quality assessment models like BRISQUE and NIQE. His work bridges engineering and neuroscience, creating perception-based models that optimize visual media delivery while reducing bandwidth consumption. These innovations have had profound industry impact, with his algorithms processing a significant proportion of global internet video traffic. His recent publications demonstrate continued leadership in perceptual quality assessment, with increasing focus on AI-generated content, high dynamic range (HDR) video, and novel applications in medical imaging. The research shows a clear trajectory toward more sophisticated, neural network-based quality metrics that better align with human visual perception across diverse content types. Professor Bovik has received numerous prestigious awards recognizing his contributions to the field: John Fritz Medal (2024) IEEE Edison Medal (2022) IAMB BaM Award (2022) Elected to the United States National Academy of Engineering (2022) Technology and Engineering Emmy Award (2021) IEEE Fourier Award for Signal Processing (2019) Progress Medal from The Royal Photographic Society (2019) Named Honorary Fellow of The Royal Photographic Society (2019) Primetime Emmy Award (2015) Edwin H. Land Medal from The Optical Society (2017) As Director of the Laboratory for Image and Video Engineering (LIVE), Professor Bovik has secured substantial research funding from organizations including the National Science Foundation and the National Institute for Standards and Technologies. His lab has produced numerous influential datasets including the LIVE Image and Video Quality Databases. He has mentored many successful students who have gone on to make significant contributions in academia and industry, though specific student names are not provided in the source materials. The Laboratory for Image and Video Engineering (LIVE) under Professor Bovik's direction has become a world-renowned center for research in perceptual image and video quality. The lab maintains close collaborations with major technology companies including Netflix, Amazon, and YouTube, ensuring that research has direct practical applications. LIVE has developed numerous influential tools and databases that are widely used in both academic research and industrial applications worldwide.

Zhiming Caiمشاهده پروفایل

استاد مهمان

• Synthetic Biology
• Genitourinary Oncology
• Genomics
• +۴ مورد دیگر

Zhiming Cai is a Visiting Professor at the National Cancer Institute (NCI) and holds multiple leadership roles in China, including President of Shenzhen Medical Association, Deputy Director of Carlson International Oncology Center at Shenzhen University, and Director of the Guangdong Key Laboratory of Systemic Biology and Synthetic Biology in Genitourinary Oncology. As an Outstanding Principal Investigator at the Chinese Academy of Science's Institute of Synthetic Biology, he leads groundbreaking research in cancer genomics and synthetic biology. Doctor Philosophiae Honoris Causa, The National Academy of Science of Ukraine Fellow, American Institute for Medical and Biological Engineering (AIMBE) His research focuses on synthetic biology applications for cancer therapy , including engineered cell treatments and artificial gene circuits for tumor targeting. Notable work includes the "Remodeling Tumor Suppressor Theory" and CRISPR-based signal transduction systems that earned recognition from Nobel laureates and top-tier journals. With over 330 publications and an H-Index of 36, Cai's work has appeared in Nature , Cell , and European Urology . His research team has successfully treated over 200 advanced tumor patients using synthetic biology approaches, demonstrating clinical translation of cutting-edge technology. Guangdong Medical Natural Science Award (First Class) May 1st Labor Medal (China's highest worker award) State Council Special Allowance Expert National Model Worker International Genetically Engineering Machine Competition Gold Award Cai directs multiple high-profile research institutions including the National Tumor Genome Application Engineering Laboratory and the Academic Committee of the National and Local Joint Medical Synthetic Biology Engineering Laboratory. His work bridges basic research with clinical applications in genitourinary oncology through systematic biology approaches.

Sun Maosongمشاهده پروفایل

استاد

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Prof. Maosong Sun is a Professor at the Department of Computer Science and Technology, Tsinghua University, China. He holds additional leadership roles including Executive Vice Dean of the Institute for Artificial Intelligence and Deputy Director of the National Engineering Laboratory for Cyberlearning and Intelligent Technology. His research focuses on natural language processing (NLP), artificial intelligence, machine learning, and computational education. He leads interdisciplinary projects in computational humanities, knowledge graphs, and MOOC platforms like XuetangX, which has over 58.8 million registered learners. Key contributions include pioneering work in Chinese NLP tools, poetry generation systems like Jiuge, and large-scale research initiatives funded by Chinese and Singaporean programs. Awards include the Tsinghua University Education Award (2019) and the National Outstanding Practitioner Award (2007). Established NLP and Computational Humanities & Social Sciences Lab (2008) Co-director of the Joint Research Center for Extreme Search (2011-present) Over 200 publications with 11,000+ citations (h-index 47)

Pierre Jaïsمشاهده پروفایل

استاد

• Cardiac Arrhythmias
• Atrial Fibrillation
• Catheter Ablation
• +۶ مورد دیگر

Prof. Pierre Jaïs serves as University Professor in Cardiology and Cardiac Electrophysiology at the University of Bordeaux and Head of the Electrophysiology Unit at Bordeaux University Hospital. He concurrently leads the Electrophysiology and Heart Modeling Institute (LIRYC) as CEO since 2021, driving innovation in cardiac rhythm disorder treatments through multidisciplinary research. His research revolutionized cardiac electrophysiology by identifying pulmonary veins as primary sources of atrial fibrillation, establishing pulmonary vein isolation as the global treatment standard. Current work focuses on pulsed field ablation—a non-thermal technique with potential to replace conventional ablation—and developing advanced imaging for precise arrhythmia targeting, reflecting his commitment to translating scientific discovery into clinical solutions. Analysis of recent publications (2017-2021) reveals dominant trends in pulsed field ablation optimization, comparative ablation techniques, and AI integration in cardiovascular imaging. These works consistently address atrial fibrillation treatment efficacy, safety profiles, and technological innovation, positioning him at the forefront of electrophysiology advancement. His distinguished contributions are recognized through prestigious awards including: 2019: Eli S. Gang Most Innovative Abstract Award (Heart Rhythm Society) 2018: Eric N. Prystowsky Lectureship Award 2012: Academy of Medicine Membership (Paris) 2009: Circulation Best Paper Award Multiple National Academy of Medicine honors Prof. Jaïs actively mentors electrophysiology trainees and secures substantial research funding, notably leading an EU-funded randomized trial comparing pulsed field versus thermal ablation. His LIRYC institute integrates cardiology, engineering, and computational expertise to accelerate therapeutic innovation. The LIRYC institute operates as a collaborative hub where clinicians, biomedical engineers, and data scientists develop next-generation electrophysiology tools. Current projects include real-time arrhythmia mapping systems, tissue-selective ablation protocols, and AI-driven predictive models for treatment personalization, fostering seamless translation from bench to bedside.

Leigh Davidمشاهده پروفایل

استاد

• Synthetic molecular machines
• Supramolecular chemistry
• Molecular knots and links
• +۳ مورد دیگر

Leigh David is a Professor of Chemistry at the University of Manchester, holding the Sir Samuel Hall Chair since 2014. Previously, he held prestigious roles such as the Forbes Chair of Organic Chemistry at the University of Edinburgh (2001–2012) and Chair of Synthetic Chemistry at the University of Warwick (1998–2001). His research focuses on synthetic molecular machines, supramolecular chemistry, and molecular knots, with notable contributions to the design of molecular motors and catenanes. David earned a BSc (Special Honours) and PhD in Chemistry from the University of Sheffield (1981–1987). He conducted postdoctoral research at the National Research Council of Canada (1987–1989) before joining the University of Manchester Institute of Science and Technology, where he advanced from Lecturer (1989–1996) to Readership (1996–1998). His research interests include developing synthetic strategies for molecular-scale machines, exploring applications in nanotechnology, and investigating dynamic covalent chemistry. Key areas involve creating molecular knots, interlocked structures, and systems capable of programmable motion. David has received numerous accolades, including the Royal Society Bakerian Medal (2013), ERC Advanced Grants (2008, 2014), and the Feynman Prize for Nanotechnology (2007). His work has been recognized globally through fellowships in the Royal Society (2009) and Royal Society of Edinburgh (2005). He leads a research group advancing molecular robotics and has secured major grants, such as the EPSRC Senior Research Fellowship (2005–2010). His lab focuses on translating molecular systems into functional devices with applications in materials science and biotechnology.

• Cybersecurity
• Blockchain
• Machine Learning
• +۱۲ مورد دیگر

Edgar Weippl is a Professor at the Faculty of Computer Science, University of Vienna, where he serves as Vice Dean and Head of the Research Group Security and Privacy. His work spans cybersecurity, blockchain, and machine learning, with teaching roles in information security and software security courses. Current Positions: Vice Dean (Faculty of Computer Science), Head (Security and Privacy Research Group), Deputy Head (Neuroinformatics & Knowledge Engineering Groups) Research Interests: Cybersecurity, blockchain, IoT security, code obfuscation, privacy technologies, reinforcement learning, and socio-technical systems security Selected Publications: Focus on blockchain privacy, VoWiFi security, code obfuscation, and reinforcement learning applications

• Legal Informatics
• Artificial Intelligence and Law
• Data Protection and Privacy
• +۵ مورد دیگر

Erich Schweighofer serves as Associate Professor at the University of Vienna within the Institute for European, International and Comparative Law, specifically affiliated with the Department of International Law and International Relations. His research activities are centered at the Juridicum building (Schottenbastei 10-16, 1010 Vienna), where he maintains an active office presence with scheduled consultation hours. His scholarly focus spans Legal Informatics , Artificial Intelligence and Law , Data Protection , and Legal Knowledge Representation , with particular emphasis on explainable AI systems for legal contexts and formal methodologies for translating legal norms into computational frameworks. This interdisciplinary work bridges jurisprudence and computer science through projects examining biometric regulation, autonomous vehicle governance, and natural language processing applications in legal domains. Analysis of his 2021-2024 publications reveals consistent thematic progression toward operationalizing legal principles in AI systems, with increasing focus on transparency mechanisms, temporal logic for dynamic regulations, and cross-jurisdictional compliance challenges. His work predominantly appears in the International Legal Informatics Symposium (IRIS) proceedings and JURIX conferences, reflecting deep engagement with the legal informatics community. Professor Schweighofer leads a dedicated research team including project assistants Mag. Jessica Fleisch, Mag. Jonas Pfister, Felix Schmautzer, and Mag. Jakob Zanol, while actively participating in the University of Vienna's Working Group on Legal Informatics (Arbeitsgruppe Rechtsinformatik). His collaborative approach extends to organizing the biennial IRIS symposium, which has established itself as a cornerstone event for European legal informatics scholarship since 1998.

Michael Gadermayrمشاهده پروفایل

مدرس ارشد

• Medical Imaging
• Computer Vision
• Deep Learning
• +۴ مورد دیگر

Michael Gadermayr serves as a Senior Lecturer and Head of the Research Group within the Department of Information Technologies and Digitalisation at Salzburg University of Applied Sciences. Based at Campus Urstein (Room 423), he can be contacted via michael.gadermayr@fh-salzburg.ac.at or +43-50-2211-1341. His research focuses on advancing medical imaging through artificial intelligence, with core expertise in deep learning for image segmentation, digital pathology, and cancer diagnosis. Key contributions include multimodal fusion techniques for CT/CBCT integration, synthetic data generation for surgical guidance, and objective wound healing quantification using vision models. His work bridges computer vision and clinical applications to solve real-world healthcare challenges. Analysis of his 15 most recent publications reveals a dominant trend toward leveraging synthetic data and multimodal fusion to enhance segmentation accuracy in oncology and surgical contexts. Over 70% of his work targets CT/CBCT integration for intraoperative navigation, while digital pathology applications (particularly thyroid and breast cancer) constitute 25% of his output. Emerging themes include wound healing quantification using SAM and parameter optimization for MIL-based pathology diagnostics. As Head of the Research Group in Information Technologies and Digitalisation, he leads initiatives focused on translating AI innovations into clinical practice, with emphasis on robustness in medical image analysis and practical deployment of segmentation tools for radiology and pathology workflows.

Jean Ponceمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Robotics
• +۳ مورد دیگر

Jean Ponce is a Professor of Computer Science at Ecole Normale Superieure (ENS) in Paris and a Part-Time Global Distinguished Professor at New York University's Courant Institute of Mathematical Sciences and Center for Data Science (CDS). He previously served as Director of the ENS Computer Science Department (2011-2017) and held positions at Inria (2017-2022), University of Illinois at Urbana-Champaign (1998-2006), MIT, Stanford, and Inria (1982-1985). Academic Leadership: Scientific Director of PRAIRIE Interdisciplinary AI Research Institute in Paris Startup Involvement: Co-founder and CEO of Enhance Lab (2022) Editorial Roles: Senior Editor-in-Chief of International Journal of Computer Vision (2019-2022) Conference Leadership: Chair of IEEE CVPR (1997,2000), ECCV (2008), and upcoming ICCV (2023) Research Focus: Computer vision, machine learning, robotics, and AI with applications in exoplanet imaging, 3D reconstruction, and image quality assessment. His work bridges statistical learning and deep learning approaches. Awards: IEEE Fellow (2003) ELLIS Fellow (2019) ERC Advanced Grant (2011) IEEE CVPR Longuet-Higgins Prizes (2016,2020) ICML Test-of-Time Award (2019) Patents & Publications: Co-author of influential textbook Computer Vision: A Modern Approach (translated into Chinese, Japanese, Russian). Holds two US patents and one pending French patent. Google Scholar h-index of 78 with over 55,000 citations.

Andreas Stadlbauerمشاهده پروفایل

استاد مدعو

• Biomedical Imaging
• Medical Physics
• Artificial Intelligence in Medicine
• +۷ مورد دیگر

Prof. Dr. Andreas Stadlbauer is a medical physicist affiliated with the Clinical Institute for Diagnostic and Interventional Radiology at St. Pölten University Hospital and the Department of Neurosurgery at Friedrich-Alexander University Erlangen-Nuremberg. He holds an adjunct professorship at the University of Erlangen-Nuremberg and contributes to both clinical and academic research in biomedical imaging and AI applications in oncology. University: Friedrich-Alexander University Erlangen-Nuremberg Hospital Affiliation: St. Pölten University Hospital Department: Department of Neurosurgery Academic Rank: Adjunct Professor His research focuses on advanced MRI techniques, particularly physio-metabolic imaging of brain tumors, oxygen metabolism, and the integration of artificial intelligence in clinical diagnostics. He has led research on glioma classification, tumor microenvironment characterization, and deep learning models for radiomic analysis. The recent publications highlight a strong trend toward AI-driven diagnostic tools in neuro-oncology, particularly in differentiating glioblastomas from metastases and predicting genetic mutations using machine learning. His work emphasizes the clinical translation of complex imaging data into actionable insights. Artificial Intelligence in Oncology Medical Imaging and Radiomics Brain Tumor Metabolism Deep Learning for MRI Analysis Oxygen Metabolism Imaging Clinical Decision Support Systems Prof. Stadlbauer has been involved in seed-funded research projects developing deep learning algorithms for clinical integration. He collaborates extensively with neurosurgeons, radiologists, and oncologists across institutions, contributing to multidisciplinary tumor boards and translational research initiatives. He completed his doctorate in medical physics in 2004, habilitation in 2008, and an MBA in Health Management in 2010. His academic journey reflects a blend of technical expertise and leadership in healthcare innovation.

• Cyber-Physical Systems
• Machine Learning
• Autonomous Systems
• +۵ مورد دیگر

Radu Grosu is a Professor at Technische Universität Wien (TU Wien), leading the Forschungsbereich Cyber-Physical Systems . His research focuses on Cyber-Physical Systems (CPS), Machine Learning, and autonomous robotics, with notable contributions to neural network architectures like Liquid Time-Constant Networks (LTC) and their applications in robotics and medical imaging. He is affiliated with the Network Lab and has supervised numerous PhD and Master's students, including Sebastian Michael Bittner, Daniel Scheuchenstuhl, and Sophie Neubauer. His work spans topics such as reinforcement learning, autonomous driving, and IoT ecosystems. Recent projects include developing robust AI systems for healthcare and robotics, such as tumor delineation using PET imaging and neuromorphic IoT architectures for smart villages. Grosu has published extensively on CPS, with over 146 contributions across peer-reviewed journals and conferences. His research emphasizes bridging theory and practice, addressing challenges in safety, scalability, and real-time control in autonomous systems. Key research interests include robotic perception, neural network robustness, and CPS/IoT integration. He has pioneered methods like DeepSTL for translating temporal logic requirements into neural network training objectives and developed frameworks like NimbleAI for neuromorphic sensing-processing systems. His team also explores distributed control algorithms for multi-agent systems, such as flocking drones and formation control using relative distance measurements. Recent work examines the generalization properties of deep filters in CNNs and quantum-classical reinforcement learning models for game AI. Grosu has advised over 20 students on topics ranging from deep learning in wafer defect analysis to bio-inspired neural circuits for auditable autonomy. His lab collaborates on interdisciplinary projects, such as applying AI to battery health estimation and prostate cancer diagnostics. He actively contributes to academic communities, editing special issues on AI in healthcare and CPS resilience, and has organized summer schools on CPS and IoT systems.

Cezary Kaliszykمشاهده پروفایل

استاد

• Theoretical Computer Science
• Automated Reasoning
• Formal Methods
• +۱۳ مورد دیگر

Cezary Kaliszyk is a Professor in Theoretical Computer Science at the University of Melbourne. His research focuses on automated reasoning, formal methods, and learning for reasoning, particularly in the context of interactive theorem proving and formalized mathematics. He leads the ERC project 'FormalWeb3' and has been involved in several other significant research initiatives. Theoretical Computer Science Automated Reasoning Formal Methods Interactive Theorem Proving Machine Learning for Theorem Proving Formalized Mathematics Proof Guidance Learning for Reasoning His research explores the integration of machine learning techniques with formal reasoning systems to enhance automation in theorem proving. This includes developing systems like CoqHammer and Tactician, advancing premise selection, proof guidance, and learning-based proof search strategies. His work bridges logical foundations with practical AI-driven tools for formal verification. The recent publications demonstrate a consistent focus on advancing automated and interactive theorem proving through learning techniques, formalization of mathematical concepts (like surreal numbers), and improving reasoning systems (e.g., Prover9, tableaux methods). There is a strong emphasis on practical system development, formalization projects, and learning-based enhancements to reasoning. ERC project 'FormalWeb3' - Principal Investigator Cost Action EuroProofNet - WG5 Leader until 2024 FWF project P26201 - developing HOL(y)Hammer Other projects: JSPS P10044, NWO MathWiki, SURF WebDed, ProofWeb He has advised several PhD students to completion, including Michael Färber, Thibault Gauthier, Yutaka Nagashima, Stanisław Purgał, and Liao Zhang, and is currently supervising Daniel Ranalter and Neil Vyas. He has not received any explicitly mentioned scientific awards in the provided text. His work involves leadership in collaborative systems such as ProofWeb and HOL Import, and participation in major formalization efforts including the Mizar library integration with Isabelle. He is actively involved in the development of tool ecosystems for formal mathematics and automated reasoning.

• Environmental Anthropology
• Anthropology of the Body
• Amazonian Anthropology
• +۴ مورد دیگر

Aníbal Arregui is a Lecturer at the Department of Social and Cultural Anthropology within the Faculty of Social Sciences at the University of Vienna . He also holds a Post-Doc position at the Department of Social Anthropology , University of Barcelona (since 2019), with prior postdoctoral work at the University of Rostock and Charles University/CEFRES . Research Focus : Environmental anthropology, anthropology of the body, multi-species ethnography, and Amazonian anthropology. Key Projects : Long-term ethnographic work with Amazonian ribeirinho and quilombola communities (since 2006), and the Wild Boar Project in Barcelona (since 2017). Publications : His work intersects environmental anthropology, multi-species relations, and technoecological politics, with recent 2024 book Infraespecie and peer-reviewed articles spanning 2015–2022. Teaching : Courses available via u:find at Vienna, focusing on ethnographic methods and theory. His research explores corporeal interplays between humans and nonhumans, emphasizing ecological transformation through multi-species ethnography in both Amazonian and European contexts.

Yun Fuمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +۲ مورد دیگر

Yun Fu is a tenured Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a joint appointment in the Khoury College of Computer Science. He has established himself as a leading researcher in Artificial Intelligence, with over 500 publications in top-tier venues including IEEE/ACM transactions and major AI conferences. His work spans both theoretical foundations and practical applications, with significant impact in computer vision and machine learning. Professor Fu earned his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. His academic career progressed from Assistant Professor at SUNY Buffalo to his current position as tenured Professor at Northeastern University, where he has held appointments since 2012. His educational background includes a Beckman Graduate Fellowship at UIUC (2007-2008). His research focuses on advancing Artificial Intelligence with particular emphasis on Computer Vision, Pattern Recognition, and Machine Learning. His seminal work includes the "Residual Dense Network for Image Super-Resolution" presented at CVPR 2018, which was ranked among the Top 10 Most Influential CVPR papers. His research interests span image processing, anomaly detection, multimodal learning, and trajectory prediction, with applications ranging from healthcare to consumer technology. Analysis of his recent publications reveals a strong trend toward developing efficient and robust AI systems that bridge computer vision with language understanding. His work increasingly focuses on multimodal learning, trajectory prediction for multi-agent systems, anomaly detection in complex environments, and model validation techniques for black-box systems, while maintaining practical applications in real-world scenarios. Professor Fu's extensive recognition includes: Fellow of IEEE (2018), OSA (2019), SPIE (2018), IAPR (2016), AAIA (2021), and AAAI (2025) Member of Academia Europaea (2022) and European Academy of Sciences and Arts (2023) Fellow of National Academy of Inventors (2023) Multiple Young Investigator Awards from NAE, ONR, ARO, IEEE, ACM, and INNS 12 Best Paper Awards from major conferences Industrial Research Awards from Google, Amazon, Samsung, JPMorgan, and others Professor Fu has successfully mentored numerous Ph.D. students who now hold prominent positions in academia and industry at institutions including Amazon, Microsoft, Meta, Adobe, and major universities. His entrepreneurial ventures include founding Giaran (acquired by Shiseido in 2017) and co-founding TVision Insights, demonstrating his commitment to translating research into real-world impact. He has secured significant research funding from both government agencies and industry partners. As the PI and Founding Director of the SmiLe Lab at Northeastern University, Professor Fu leads a dynamic research group focused on advancing the state-of-the-art in AI and Computer Vision. The lab fosters interdisciplinary collaboration across computer science, electrical engineering, and applied mathematics, with ongoing projects in efficient deep learning, multimodal understanding, and practical AI applications.

Miriam Buttمشاهده پروفایل

استاد

• Morphology
• Lexical semantics
• Digital humanities
• +۳ مورد دیگر

Miriam Butt is a Professor (W3) for General and Computational Linguistics at the Department of Linguistics, University of Konstanz since 2019. Previously, she held a Professor (C3) position at the same department from 2003 to 2019. Earlier roles include a Lecturer at the Centre for Computational Linguistics, UMIST (2002-2003), and Research Scientist positions at the University of Konstanz, Universität Stuttgart, and Universität Tübingen. Education: PhD and MA in Linguistics from Stanford University (1988-1993), BA in Language Studies and Computer Science from Wellesley College (1984-1987). Her research focuses span Morphology, Lexical Semantics, Digital Humanities, Historical Linguistics, Computational Linguistics, and Syntax. She has contributed to projects such as SFB471 (Variation and Change in the Lexicon), ParGram (grammar development for German), and Verbmobil (tense analysis in German-English translation). She has served on advisory boards and consulted for projects including NSF's Hindi/Urdu treebank initiative and CRULP's Urdu machine translation system.