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Fecha : Martes 22/05/18
Horario : de 12 a 13:30hs
Lugar : Salón Azul (piso 5, salón 502) – Facultad de Ingeniería, J. Herrera y Reissig 565
In this talk, we will present several results produced at the KAUST Strategic Research Initiative for Uncertainty Quantification. These include, among others, contributions on Multi-level and Multi-index sampling techniques that address both direct and inverse problems. We may also discuss efficient methods for Bayesian Inverse Problems and Optimal Experimental Design.
Raúl Tempone graduated as an industrial engineer in 1995 from the University of the Republic, Montevideo, Uruguay. After graduation, he worked on the optimal dispatch of electricity for the Uruguayan system using techniques from nonlinear stochastic programming and visited the Royal Institute of Technology (KTH) in Stockholm, Sweden, to study numerical analysis. He obtained a Master in Engineering Mathematics in 1999 (inverse problems for incompressible flows, supervised by Jesper Oppelstrup, KTH) and a Ph.D. in Numerical Analysis in 2002 (a posteriori error estimation and control for stochastic differential equations, supervised by Anders Szepessy, KTH). He later moved to ICES, UT Austin, to work as a postdoctoral fellow from 2003 until 2005 in the area of numerical methods for PDEs with random coefficients (supervised by Ivo Babuska and Mary Wheeler). In 2005 he became an assistant professor (joint appointment) with the School of Computational Sciences and the Department of Mathematics at Florida State University, Tallahassee. In 2007 he was awarded the first Dahlquist fellowship by KTH and COMSOL for his contributions to the field of numerical approximation of deterministic and stochastic differential equations. In 2009 he joined King Abdullah University of Science and Technology as Associate Professor (founding faculty) and was promoted in 2015 to the rank of Full Professor in Applied Mathematics. Since 2012, he has been directing the KAUST Strategic Research Center for Uncertainty Quantification. He has received numerous accolades from his peers: he is a highly cited author, is regularly invited as keynote speaker at conferences from several distinct areas and holds honorary appointments as well. For instance, he was elected by Society for Industrial and Applied Mathematics (SIAM) members as the Technical Director of the SIAM Uncertainty Quantification group, a position he held during the term 2013-2014.
Charla de Nori Jacoby : “Perceptual priors on musical rhythm revealed cross-culturally by iterated reproduction”
Martes 8 de mayo 18:00hs, Salón Azul (piso 5, salón 502) – Facultad de Ingeniería, J. Herrera y Reissig 565
El Instituto de Ingeniería Eléctrica de la Facultad de Ingeniería de la Universidad de la República invita a la siguiente charla : “Perceptual priors on musical rhythm revealed cross-culturally by iterated reproduction” por Nori Jacoby
Probability distributions over external states (priors) are essential to the interpretation of sensory signals. In many areas of perception and cognition, humans appear to combine current observations with internal beliefs about the environment (the prior) in a process approximating statistical inference. Priors for cultural artifacts such as music and language remain largely uncharacterized, but critically constrain cultural transmission, because only those signals with high probability under the prior can be reliably reproduced and communicated. We developed a method to estimate priors for rhythm via iterated reproduction of random temporal sequences. Listeners were asked to reproduce random “seed” rhythms; their reproductions were fed back as the stimulus, and over time became dominated by internal biases, such that the prior could be estimated by applying the procedure multiple times.
We measured priors on simple rhythms in residents of the United States as well as members of the Tsimané, an Amazonian society with very limited exposure to Western music. We found that priors in US participants showed peaks at rhythms whose time intervals were related by small integer ratios. The modes of the prior were limited to small integer rhythms prevalent in Western music. Priors in Tsimané participants also exhibited modes at integer ratios, but were otherwise qualitatively different from priors in US participants, in ways that are consistent with the structures prevalent in their music. Our results are consistent with the claim that rhythm perception exhibits universal cognitive constraints favoring integer ratios, but indicate that any such constraints are strongly modulated by experience.
I will also present recent results from Botswana, Mali, Brazil, Bolivia, Bulgaria, the United States, South Korea, and Uruguay that suggest that musical exposure, far more than language or geography, profoundly affects the structure of rhythmic perceptual priors. Our method holds promise for characterizing priors in a range of other domains in both audition and vision, including spatial memory, phonetics, and melody.
I’m interested in exploring the role of culture in auditory perception, using iterated learning alongside classical psychophysical methods to characterize perceptual biases in music and speech rhythms in populations around the world. My previous work focused on the mathematical modeling of sensorimotor synchronization in the form of tapping experiments as well as the application of machine-learning techniques to model aspects of musical syntax, including tonal harmony, birdsong, and the perception of musical form. I am currently a Presidential Scholar In Society And Neuroscience at Columbia University. Previously, I was a postdoc at the McDermott Computational Audition Lab at MIT, and a visiting postdoctoral researcher in Tom Griffiths’s Computational Cognitive Science Lab at Berkeley. I completed my Ph.D. at the Edmond and Lily Safra Center for Brain Sciences (ELSC) at the Hebrew University of Jerusalem under the supervision of Naftali Tishby and Merav Ahissar, and hold a M.A. in mathematics from the same institution. My research has been published in journals including Current Biology, Nature, Nature Scientific Reports, Philosophical Transactions B, Journal of Neuroscience, Journal of Vision, and Psychonomic Bulletin and Review.
En el marco del curso Modelado y Agrupamiento de Datos en Alta Dimensión a dictarse a partir del 7 de mayo de 2018 por el Profesor René Vidal (Johns Hopkins University) se dictará la siguiente charla de divulgación:
Métodos automáticos para la interpretación de datos biomédicos
Jueves 10 de mayo, 18hs, salón 102 (junto al Instituto de Ingeniería Eléctrica) Facultad de Ingeniería
En años recientes hemos presenciado una explosión en la disponibilidad de datos biomédicos de múltiples modalidades y escalas. Sin embargo, la falta de métodos automáticos para interpretar tales datos representa un impedimento mayor a la hora de comprender los mecanismos, el diagnóstico y el tratamiento de enfermedades humanas. En esta charla, haré una reseña de nuestro trabajo reciente en el desarrollo de métodos automáticos para la interpretación de datos biomédicos de múltiples modalidades y escalas. A nivel celular, presentaré un método de factorización de matrices estructuradas para segmentar neuronas y encontrar sus patrones de disparo en videos de imagenología de calcio y un método de análisis de formas para clasificar cardiomiocitos embrionales en videos de imagenología óptica. A nivel de órganos, presentaré un marco Riemanniano para el procesamiento de imágenes de resonancia magnética por difusión (dMRI) del cerebro, y un método estocástico de seguimiento para detectar fibras de Purkinje en MRI cardíacos. A nivel de pacientes, presentaré sistemas dinámicos y métodos de aprendizaje automático para reconocer gestos quirúrgicos y evaluar las habilidades de un cirujano a partir de movimientos registrados de un robot médico y filmaciones.
Charla de Luiz Naveda : “Experiments, representation and complexity in cross-cultural studies involving music and dance”
Jueves 5 de abril 17:00hs, Salón Azul (piso 5, salón 502) – Facultad de Ingeniería, J. Herrera y Reissig 565
El Instituto de Ingeniería Eléctrica de la Facultad de Ingeniería de la Universidad de la República invita a la siguiente charla : “Experiments, representation and complexity in cross-cultural studies involving music and dance” por Luiz Naveda
The observation of recurring phenomena is a fundamental scientific practice that allows scientists to uncover mechanisms hidden in the physical, biological, cultural and even artistic contexts. However, the scientific discourse imposes limitations that hide certain types of phenomena and knowledge, such as the recurrent parallelism between music and dance in the cultures. In this exposition, we describe attempts to build datasets, represent and analyze music and movement across different musical cultures. We explore scientific approaches that offer less restrictive conditions for scientific observation and, consequently, result in more complexity and challenges in the representation of the knowledge. These approaches were applied to cross-cultural and gender studies involving Latin American dance and music cultures.
Luiz Naveda is a professor at the State University of Minas Gerais (Brazil). He holds a technical degree in electronics (1994) a bachelor in music (UEMG, 1999) and a master in music performance (UFMG, 2002). During his doctoral and post-doctoral studies in Musicology at Ghent university (2011, promoter: Marc Leman) he worked on the connections between music and dance in the Afro-Brazilian Samba. In the last years, he has published on a range of topics that include musical gesture, dance studies, timing and microtiming, music education, computer music, interactive systems, among others. Luiz also works as an independent artist, in consulting and development of software and hardware for interactive and musical applications, art installations and music research. More information at http://naveda.info
Charla de Andre Holzapfel : “Computer-aided methods in ethnomusicology : Propositions from case studies of Cretan, Indian, Turkish, and Swedish traditional musics”
Jueves 5 de abril 17:00hs, Salón Azul (piso 5, salón 502) – Facultad de Ingeniería, J. Herrera y Reissig 565
El Instituto de Ingeniería Eléctrica de la Facultad de Ingeniería de la Universidad de la República invita a la siguiente charla : “Computer-aided methods in ethnomusicology : Propositions from case studies of Cretan, Indian, Turkish, and Swedish traditional musics” por Andre Holzapfel
Corpora of notated and recorded music can answer a wide range of research questions. In order to obtain an analysis of the data in an efficient way, the development of computational analysis methods has attracted increased intention. This talk will provide examples of the analysis of rhythmic and tonal aspects in corpora from various music cultures. The insights will be related to the theoretical frameworks of the analyzed music cultures. I argue for combining ethnography and computational analysis into an iterative methodology. Corpus studies may provide macro-level information about traits of the music contained in a corpus, approaching music as an acoustic object. On the other hand, ethnography enables close-reading that provides the complementary aspects of music performance and reception, and embeds the acoustic object in its sociocultural context. The iteration between corpus analysis and ethnography may reveal deeper insights into structures and meanings of music as an enacted, cultural process, but the application of such a methodology has been impeded by the lack of truly interdisciplinary research communities.
Andre Holzapfel is Assistant Professor (tenure track) in Media Technology at the Department of Media Technology and Interaction Design (MID) at the School of Electrical Engineering and Computer Science (EECS), KTH, Stockholm, and part of the Sound and Music Computing team. He received his first PhD degree in Computer Science (Univ. of Crete, 2010), and is planning to defend his second PhD in ethnomusicology in 2018 (MIAM Institute, Istanbul). His research topics are the development of analysis algorithms for musical audio signals, music corpus analysis, and interdisciplinary research in computer science and ethnomusicology. Andre Holzapfel is one of the most renowned experts in the computational analysis of rhythm, with about 30 of his scientific publications focusing on this subject, in the contexts of engineering, musicology, music theory, and music perception. Four out of the five and half years as postdoctoral researcher prior to his position at KTH were based on self-acquired funding, such as a Marie Curie IEF grant (grant nr. 328379). Since his arrival at KTH in 2016, he extended his research activities to the analysis of motion capture signals, applications of music in motion capture environments for rehabilitation purposes, and ethical aspects of computational approaches to music. Website : https://www.kth.se/profile/holzap/
Jueves 22 de marzo de 17:30 a 19:30hs, Salón Azul (piso 5, salón 502) – Facultad de Ingeniería, J. Herrera y Reissig 565
El Instituto de Ingeniería Eléctrica de la Facultad de Ingeniería de la Universidad de la República invita al siguiente evento : “Escuchando a las neuronas : La próxima generación de interfaces neurales”. El mismo constará de dos charlas a cargo de destacados expertos internacionales en el área de interfaces neurales. A continuación el resumen de las charlas y la biografía de los conferencistas. El evento cuenta con el apoyo de IEEE-CAS.
Millimetre-scale implantable brain machine interfaces
Abstract : Being able to control devices with our thoughts is a concept that has for long captured the imagination. Neural Interfaces or Brain Machine Interfaces (BMIs) are devices that aim to do precisely this. Next generation devices will be distributed like the brain itself. It is currently estimated that if we could record electrical activity simultaneously from between 1,000 and 10,000 neurons, this would enable useful prosthetic control (e.g. of a prosthetic arm). However, rather than relying on a single, highly complex implant and trying to integrate more and more channels in this high density interface (the current paradigm) an emerging trend, and topic of this special session, is to develop a simpler, smaller, safer, and well-engineered primitive, and deploy multiple such devices. It is essential these are each compact, autonomous, calibration-free, and completely wireless. It is envisaged that each device will be mm-scale, and be capable of monitoring fewer sites, but also perform real-time signal processing. This processing will achieve data reduction to wirelessly communicate only useful information, rather than raw data, which can most often be just noise and of no use. Making these underlying devices “simpler” will overcome many of the common challenges that are associated with scaling of neural interfaces, for example, wires breaking, biocompatibility of the packaging, thermal dissipation and yield. By distributing tens to hundreds of these in a “network” of neural interfaces, many of the desirable features of distributed networks come into play; for example, redundancy and robustness to single component failure. Such devices will communicate the neural “control signals” to an external prosthetic device. These can then, for example, be used for: an amputee to control a robotic prosthesis; a paraplegic to control a mobility aid; or an individual with locked-in syndrome to communicate with the outside world.
Biography : Timothy Constandinou received the B.Eng. and Ph.D. degrees in electronic engineering from Imperial College London, in 2001 and 2005, respectively. He is currently a Reader of Neural Microsystems within the Circuits and Systems Group, Department of Electrical and Electronic Engineering at Imperial College London and also the Deputy Director of the Centre for Bio-Inspired Technology. His current research interests include neural microsystems, neural prosthetics, brain machine interfaces, implantable devices, and low-power microelectronics. He leads the Next Generation Neural Interfaces research group at Imperial. The group utilises integrated circuit and microsystem technologies to create advanced neural interfaces that enable new scientific and prosthetic applications. He is a senior member of the IEEE, fellow of the IET, a chartered engineer, and member of the IoP. Within the IEEE, he serves on several committees/panels, regularly contributing to conference organization, technical activities, and governance. He currently serves on the IEEE Circuits & Systems Society (CASS) Board of Governors for the term 2017-19, is associate editor of IEEE Transactions on Biomedical Circuits & Systems (TBioCAS), chairs the IEEE CASS Sensory Systems Technical Committee, and serves on the IEEE BRAIN Initiative Steering Committee and IEEE CASS BioCAS Technical Committee. He was the technical program Co-Chair of the 2010, 2011 and 2018 IEEE BioCAS conferences, General Chair of the BrainCAS 2016 and NeuroCAS 2018 workshops, Special Session Co-Chair of the 2017 IEEE ISCAS Conference, and Demonstrations Co-Chair of the 2017 BioCAS Conference.
Dynamic range considerations for neural recording channels
Abstract : Neural readout microelectronic interfaces are essential in implanted central nerve system prostheses aimed for brain-machine interfaces, the amelioration of disease effects, or the development of robotic mechanisms for the restitution/rehabilitation of abilities lost after injury or disease. Neural signals which can be recorded and used as biomarkers of the brain activity include local field potentials (LFPs) and action potentials (APs). They exhibit small amplitude (typically, below 1mV for LFPs and 100mV for APs) and narrow band characteristics (0.5-200Hz for LFPs and 200Hz-7kHz for APs). A priori, these signals can be easily digitized with low-to-medium resolution ADCs, thus paving the way for neural prostheses with small area and power consumptions. However, along with the biomarkers, strong in band artifacts, which can be much larger that the signals of interest, may contaminate the recording or even preclude it altogether if the front-end saturates. Different causes can be at the origin of artifacts; for instance, they can be motion related or generated by electrical stimulations close to the recording sites. Coping with these large artifacts would demand for high dynamic range (of about 75dB) front-ends and data converters with large effective resolutions (beyond 13-14 bits). However, recent proposals for artifact-aware analog front ends have demonstrated that modest ADCs can still be used for neural recording even in the presence of artifacts. This work reviews these proposals and also presents state-of-the-art techniques for the suppression of differential and common-mode artifacts from neural recordings.
Biography : Manuel Delgado-Restituto (IEEE M’96–SM’12) received the M.S. degree in physics and the Ph.D. degree (with honors) in physics-electronics from the University of Seville, Seville, Spain, in 1988 and 1996, respectively. He is a Senior Research Scientist of the Institute of Microelectronics of Seville (IMSE-CNM/CSIC), Spain, where he currently heads a research group on low-power medical microelectronics and works in the design of silicon microsystems to understanding biological neural systems, the development of neural prostheses and brain-machine interfaces, the implementation of wireless Body Area Network transceivers and the realization of RFID transponders with biomedical sensing capabilities.He has coauthored two books; more than 20 chapters in contributed books, including original tutorials on chaotic integrated circuits, design of data converters, and chips for bioengineering and neuroscience; and some 150 articles in peer-review specialized publications. Dr. Delgado-Restituto served as an Associate Editor for the IEEE TRANSACTIONS on CIRCUITS AND SYSTEMS—II: EXPRESS BRIEFS (2006–2007) and for the IEEE TRANSACTIONS on CIRCUITS AND SYSTEMS—I: REGULAR PAPERS (2008–2011). He served as Deputy Editor-in-Chief (2011–2013) and as Editor-in-Chief (2014–2015) for the IEEE JOURNAL on EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS. Currently, he is Vice President for Publications of IEEE CAS (2016–). He is in the committee of different international conferences and has served as technical program chair in different international IEEE conferences.
Viernes 9 de marzo 15:00hs, Laboratorio de Software del IIE – Facultad de Ingeniería, J. Herrera y Reissig 565
“Presentación de la Licenciatura en Ingeniería Biológica”, a cargo de Ricardo Armentano, Ingeniero en Electrónica, Profesor distinguido en Ingeniería Cardiovascular, Dr en Física área Biomecánica, Dr en Fisiología área Cardiovascular, abierta a todo público
Charlas de Leonardo Nunes : “Deep learning for image understanding” y “Real-time video understanding”
Lunes 19 de febrero 16:00hs, Salón Azul (piso 5, salón 502) – Facultad de Ingeniería, J. Herrera y Reissig 565
El Instituto de Ingeniería Eléctrica de la Facultad de Ingeniería de la Universidad de la República invita a la siguiente charla : “Deep learning for image understanding” por Leonardo Nunes
In this lecture, a brief introduction to deep learning is performed using image understanding to motivate and illustrate possible applications. Initially, different image understanding tasks will be presented, then an introduction to deep neural network image classification is described, as well as some of the most popular layers and architecures for image classification. At the end, a more advanced architectures for object localization will be detailed as well as an example of how the architectures shown can be extended to different domains, such as audio classification.
Martes 20 de febrero 09:00hs, Sala de reuniones de ICT4V, Av Italia 6201 – Edificio Los Nogales
El Instituto de Ingeniería Eléctrica de la Facultad de Ingeniería de la Universidad de la República invita a la siguiente charla : “Real-time video understanding” por Leonardo Nunes
In this lecture the work being performed by Microsoft’s Advanced Technology Labs in Rio de Janeiro, Brazil will be described. In particular, recent advances in real-time video understanding for event detection on the cloud will be detailed, with the processing pipeline being presented as well as a top-down view of the whole system architecture running on the cloud. Finally, real world examples and use cases will be shown.
Leonardo Nunes is the lead researcher of Microsoft’s Advanced Technology Labs in Brazil where he investigates algorithms for real-time video understanding and event detection. He received his B.Sc. degree in electronics and computer engineering and his M.Sc. and D.Sc. degrees in electrical engineering, from the Federal University of Rio de Janeiro (UFRJ). Dr. Nunes research focus in signal processing and machine learning, with special focus in audio and video signals, and computationally efficient methods. Prior to working to Microsoft, he was a substitute professor at the Federal University of Rio de Janeiro and a principal scientist with Halliburton’s Applied Photonics Center. He received a top 10% award from the IEEE Workshop on Multimedia Signal Processing in 2009. Dr. Nunes is an IEEE member.
Conferencia de Jean-Michel Morel : “Cómo las imágenes determinan nuestra forma de interpretar el mundo en la era digital”
Martes 27 de febrero 19:00hs, Alianza Francesa, Bvar Gral Artigas 1271
La embajada de Francia tiene el agrado de invitarle a la conferencia del matemático francés Jean-Michel Morel
A su vez el viernes 23 de febrero a las 18:00 horas, en la Sala Maggiolo del Edificio Central de la UdelaR (Av 18 de Julio 1824, planta alta), se hará entrega del Doctor Honoris Causa de la Universidad de la República, al reconocido académico francés
Martes 19 de diciembre 16:00hs, Salón Azul (piso 5, salón 502) – Facultad de Ingeniería, J. Herrera y Reissig 565
El Instituto de Ingeniería Eléctrica de la Facultad de Ingeniería de la Universidad de la República invita a la siguiente charla : “Network topology inference from spectral templates” por Gonzalo Mateos
Advancing a holistic theory of networks necessitates fundamental breakthroughs in modeling, identiﬁcation, and controllability of distributed network processes – often conceptualized as signals deﬁned on the vertices of a graph. Under the assumption that the signal properties are related to the topology of the graph where they are supported, the goal of graph signal processing (GSP) is to develop algorithms that fruitfully leverage this relational structure, and can make inferences about these relationships when they are only partially observed.
After presenting the fundamentals of GSP, we leverage these ideas to address the problem of network topology inference from graph signal observations. It is assumed that the unknown graph encodes direct relationships between signal elements, which we aim to recover from observable indirect relationships generated by a diffusion process on the graph. The innovative approach is to consider the Graph Fourier Transform of the acquired signals associated with an arbitrary graph and, among all the feasible networks, search for one that endows the resulting transforms with target spectral properties and the sought graph with appealing physical characteristics such as sparsity. Leveraging results from GSP and sparse recovery, efficient topology inference algorithms with theoretical guarantees are put forth. Numerical tests corroborate de effectiveness of the proposed algorithms when used to recover social and structural brain networks from synthetically-generated signals, as well as to identify the structural properties of proteins.
Gonzalo Mateos earned the B.Sc. degree from Universidad de la Republica, Uruguay, in 2005, and the M.Sc. and Ph.D. degrees from the University of Minnesota, Twin Cities, in 2009 and 2011, all in electrical engineering. He joined the University of Rochester, Rochester, NY, in 2014, where he is currently an Assistant Professor with the Department of Electrical and Computer Engineering, as well as a member of the Goergen Institute for Data Science. During the 2013
academic year, he was a visiting scholar with the Computer Science Department at Carnegie Mellon University. From 2004 to 2006, he worked as a Systems Engineer at Asea Brown Boveri (ABB), Uruguay. His research interests lie in the areas of statistical learning from Big Data, network science, decentralized optimization, and graph signal processing, with applications in dynamic network health monitoring, social, power grid, and Big Data analytics. Dr. Mateos received the 2017 IEEE Signal Processing Society Young Author Best Paper Award (as senior co-author) as well as the Best Student Paper Award at the 2012 IEEE Workshop on Signal Processing Advances in Wireless Communications
(SPAWC) and the 2016 IEEE Statistical Signal Processing (SSP) Workshop (as senior co-author). His doctoral work has been recognized with the 2013 University of Minnesota’s Best Dissertation Award (Honorable Mention) across all Physical Sciences and Engineering areas.