Tag Archives: ownwork

N. Tran Quang – When is Network Lasso Accurate: The Vector Case

Nguyen Tran Quang (Aalto) Thursday 2017-11-23 13.15 – 14.00 Seminar room 1021-1022, TUAS-building When is Network Lasso Accurate: The Vector Case A recently proposed learning algorithm for massive network-structured data sets (big data over networks) is the network Lasso (nLasso), which extends the well-known Lasso estimator from sparse models to network-structured datasets. Efficient implementations of… Read More »

C. Gershenson – Improving Urban Mobility with Self-organization

C. Gershenson (IIMAS, UNAM, Mexico) Thursday 2017-11-02 12.00 – 12.30 Seminar room T5, T-building Improving Urban Mobility with Self-organization Urban mobility is non-stationary, i.e. the precise number of vehicles or passengers is changing constantly, and with a limited predictability. Transportation systems will be more effective if they can adapt at the same timescales at which… Read More »

C. Pineda – The random nature of rank dynamics

C. Pineda (IF, UNAM, Mexico) Thursday 2017-11-02 12.30 – 13.00 Seminar room T5, T-building The random nature of rank dynamics Any set can be ranked by comparing a common property, such as size, age, or wealth. Ranks indicate who does one object compare to others of the same set. People have analyzed the rank distribution… Read More »

O. Korhonen – Regions of Interest as nodes of dynamic functional brain networks

Onerva Korhonen (Aalto) Thursday 2017-11-09 12.15 – 13.00 Seminar room T5, T-building Regions of Interest as nodes of dynamic functional brain networks The properties of functional brain networks strongly depend on how their nodes are chosen. Commonly, nodes are defined by Regions of Interest (ROIs), pre-determined groupings of fMRI measurement voxels. Earlier, we have demonstrated… Read More »

J. Török – Age prediction using egocentric network

János Török (BME) Thursday 2017-07-27 14.15 – 15.00 Lecture room AS3, TUAS-building Age prediction using egocentric network Prediction is often done by supervised learning where the many parameter model (neural network) is trained by the some part of data, and the accuracy of the prediction is then verified on the rest. The problem with these… Read More »

J. Török – Cascading collapse of online social networks

János Török (BME) Thursday 2017-07-18 14.15 – 15.00 Lecture room AS3, TUAS-building Cascading collapse of online social networks Online social networks have increasing influence on our society, they may play decisive roles in politics and can be crucial for the fate of companies. Such services compete with each other and some may even break down… Read More »

T. Shimada – SOC and non-SOC behaviors in simple models of evolving systems

Takashi Shimada (University of Tokyo) Thursday 2017-07-20 14.15 – 14.45 Lecture room AS3, TUAS-building SOC and non-SOC behaviors in simple models of evolving systems For understanding of evolving systems, the robustness is one of the most essential property. The self-organized-criticality, which argue the system’s nature to evolve toward a critical state, is a famous example.… Read More »

F. Ogushi – Bidirectional interaction enhances the robustness of the system

Fumiko Ogushi (Tohoku University) Thursday 2017-05-04 14.45 – 15.15 Lecture room AS3, TUAS-building Bidirectional interaction enhances the robustness of the system An essential feature of complex systems like ecological, biological and social systems is that they are open. These systems can exist with inclusion of new elements and disappearance of old elements. On the other… Read More »

J. Kertész – The hybrid percolation transition

János Kertész (CEU, BME, Aalto) Tuesday 2017-07-25 14.15 – 15.00 Lecture room AS3, TUAS-building The hybrid percolation transition Percolation is a paradigmatic example of second order phase transitions where the order parameter changes continuously (in contrast to first order transitions). However, there are dynamic versions of the percolation model, where the order parameter has a… Read More »