Ciclo de Semin�rios PESC

O Ciclo de Semin�rios PESC tem como objetivo trazer palestras acess�veis a um p�blico mais amplo ministradas por pesquisadores e professores mais experientes. As palestras ao longo do ano ter�o tema e foco variados podendo ser mais espec�ficas (ex. avan�os no contexto de um problema espec�fico) ou mais abrangentes (ex. desafios de uma �rea). A apresenta��o e discuss�o de ideias novas e antigas de diferentes temas contribui de maneira fundamental para a forma��o e pesquisa desenvolvida por alunos e professores.

As palestras do Ciclo de Semin�rios PESC ocorrem em geral uma vez ao m�s, mas sempre quarta-feira �s 11h na sala H-324B. Segue a programa��o confirmada para 2019. N�o percam!

NetMicroscope: Passive Measurements of Residential Internet Performance
Renata Cruz Teixeira, Senior Researcher, Inria Paris, Fran�a
27 de fevereiro (quarta), 11h

Cartaz - Resumo - Slides

Biografia resumida

Open Science - Challenges and Opportunities
Claudia Bauzer Medeiros, Professor Titular, UNICAMP
13 de maio (segunda), 11h

Cartaz - Resumo - Slides - Video

Biografia resumida

Graph Representation Learning: Where Probability Theory, Data Mining, and Neural Networks Meet
Bruno Ribeiro, Assistant Professor, Purdue University, USA
17 de maio (sexta), 10h

Cartaz - Resumo - Slides - Video

Biografia resumida

Integer Linear Programming Tricks with DEA (Data Envelopment Analysis) Applications
Mehdi Toloo, Full Professor, Technical University of Ostrava, Czech Republic
19 de junho (quarta), 11h

Cartaz - Resumo - Slides

Biografia resumida

Competition in randomly growing processes
Alexandre Stauffer, Associate Professor, Universit� Roma Tre (Italy) and Reader, University of Bath (England)
17 de julho (quarta), 11h

Cartaz - Resumo - Slides - Video

Biografia resumida

The Future Quantum Internet: Research Challenges
Don Towsley, Distinguished University Professor, University of Massachusetts Amherst, USA
21 de agosto (quarta), 11h, sala G-122

Cartaz - Resumo - Slides - Video - Nota de Imprensa

Biografia resumida

Modelling and Optimization of Non-linear Complex Systems
Elizabeth Wanner, Senior Lecturer, Aston University, England
28 de agosto (quarta), 11h

Cartaz - Resumo - Slides - Video

Biografia resumida

Sistemas Brasileiros de Ensino Superior, Ci�ncia e Tecnologia: fatos e experi�ncias pessoais
Cl�udia Linhares Sales, Professora Titular, Universidade Federal do Cear�
23 de outubro (quarta), 13h

Cartaz - Resumo - Slides

Biografia resumida

Relato do Processo no USPTO da Patente: "Hyperbolic Smoothing Clustering and Minimum Distance Methods"
Adilson Elias Xavier, Professor Colaborador, PESC/COPPE/UFRJ
11 de dezembro, 11h

Cartaz - Resumo - Slides - Video

Hyperbolic Smoothing Clustering Methods (HSCM), muito mais do que novos algoritmos, apresenta um diferenciado enfoque completamente diferenci�vel, absolutamente inovador no t�pico Clustering Analysis. Gra�as � essa caracter�stica, o HSCM oferece a vantagem singular de poder resolver uma amplitude de formula��es de clustering, definidas por minimiza��o de soma de avalia��es de uma arbitr�ria fun��o mon�tona crescente de dist�ncias, as de cada observa��o ao centroide mais pr�ximo, calculadas segundo diferentes m�tricas, tais como as mais utilizadas: Euclidiana, Manhattan, Minkowski e Chebychev. O outro componente b�sico, a estrat�gia de particionamento Boundary Band Zone and Gravitational Regions Partition, possibilita uma simplifica��o expressiva do trabalho computacional, pois reduz o conjunto de observa��es inicial a um conjunto efetivo com somente um pequeno percentual delas. Para problemas de grandes dimens�es, essa redu��o do trabalho computacional chega � ordem de 99,9% do total de observa��es. Ademais, quanto maior o problema, maior o fator de redu��o. A inovadora articula��o da suaviza��o hiperb�lica com a estrat�gia de parti��o acima descrita, aplicada � especifica��o mais usual minimum sum-of-squares clustering problem produz resultados computacionais superiores ao k-means segundo quatro diferentes crit�rios: de acur�cia, de velocidade, de consist�ncia e de escalabilidade. Igualmente, outras especifica��es alternativas, como a "minimum sum-of-distances clustering problem", s�o tratadas com igual sucesso. Finalmente, a metodologia HSCM tem a diferenciada capacidade de tanto poder produzir resultados segundo o enfoque tradicional hard, como tamb�m segundo o fuzzy, fato igualmente inaudito na literatura e nos softwares oferecidos no mercado. A palestra apresentar� toda a s�rie de perip�cias ocorridas at� a aprova��o da patente, ap�s nove anos de tramita��o no United States Patent and Trademark Office.

Biografia resumida

Understanding and Modelling Consciousness
David Gamez, Lecturer at Middlesex University, England
16 de dezembro (segunda), 11h

Cartaz - Resumo - Slides - Video

The first part of my talk will explain how our modern concept of consciousness emerged. In the 17th Century many people used the properties of invisible atoms to explain regularities in the world. This led to a distinction between primary qualities, such as size, which were properties of the atoms, and secondary qualities, such as colour, which appeared when atoms interacted with our senses. Secondary qualities were real non-physical properties that had to be accommodated somewhere. Galileo's and Locke's solution was to locate secondary qualities in consciousness. While modern science has more elaborate physical descriptions, consciousness continues to be a placeholder for experiences that cannot be captured by a physical description.

Philosophers often study the relationship between consciousness and the physical world using thought experiments. For example, in the hard problem of consciousness, philosophers imagine a red object, imagine activity in a grey neuron, and try (and fail) to imagine the relationship between the two. A more promising methodology is to measure consciousness, measure the physical world and look for correlations between the two sets of data. Using this scientific approach, we might be able to discover precise mathematical relationships between measurements of consciousness and measurements of the physical world, which could be used to make accurate predictions about conscious states.

The last part of my talk will discuss how scientific research on consciousness connects with models of consciousness and conscious machines. Many people have built neural and cognitive models of the correlates of consciousness, which have been used to improve our understanding of consciousness in the brain and to build intelligent machines. Models of conscious experience have also been used to control robots. There are no grounds for believing that systems based on these types of models have real conscious experiences. However, if science could discover mathematical relationships between consciousness and the physical world, then it would become possible to build machines with specific states of consciousness and to make accurate predictions about the consciousness of machines.

Biografia resumida

Entre em contato e envie seus coment�rios e sugest�es, inclusive potenciais palestrantes.
Organizado por Daniel R. Figueiredo.

Anos anteriores: 2014, 2015, 2016, 2017, 2018

Ciclo de Semin�rios

Menu principal