Upcoming Events
Webinar Series in Spatial Data Sciences
CERENA and INESC-ID will organise the “Webinar Series in Spatial Data Sciences …think spatially about your data science problems”, from 26th May to 23rd June 2021. The webinars will take place on Wednesdays, at 12.30PM (WEST).
Have you ever wondered why a given phenomenon happens at a specific location? What tools do we have to model and predict phenomena with an important spatial component? Spatial Data Sciences shed light into these questions.
Spatial Data Sciences is an interdisciplinary area of knowledge that integrates traditional Data Sciences methods – Machine Learning and Artificial Intelligence – with spatial analysis methodologies such as Geographic Information Systems, Geostatistics and Remote Sensing to understand, characterize and manage big Spatial Data. This set of tools is essential for areas that require, for example, predicting human behavior, spatial consumption patterns, pandemic dynamics, climate. Spatial data science methods are now an essential part of the IT core business of companies like Google, Uber, and Amazon. This webinar cycle covers different methods and applications of Spatial Data Science. This webinar series covers a wide range of methods and applications of Spatial Data Sciences.
Agenda
- May 26th – Geostatistical COVID-19 infection risk maps for Portugal. Maria João Pereira (CERENA)
- June 2nd – Geospatial Data Disaggregation with Convolutional Neural Networks. Bruno Martins (INESC-ID)
- June 9th – The role of Volunteered Geographic Information (VGI). Jacinto Estima (INESC-ID)
- June16th – A spacetime model of an alert system for detecting anomalous incidence values of COVID-19. Leonardo Azevedo (CERENA)
- June 23rd – Modeling the geospatial evolution of COVID-19 using spatio-temporal convolutional sequence-to-sequence neural networks. Arlindo Oliveira (INESC-ID).
Seminars are moderated by Amilcar Soares (CERENA).
Attendance is free. Registration is required. The Zoom link will be sent by email to registered participants.
Abstracts
- Geostatistical COVID-19 infection risk maps for Portugal
The rapid spread of the SARS-CoV-2 epidemic has simultaneous time and space dynamics. This behaviour results from a complex combination of factors, including social ones, which lead to significant differences in the evolution of the spatiotemporal pattern between and within countries. Usually, spatial smoothing techniques are used to map health outcomes, and rarely uncertainty of the spatial predictions are assessed. As an alternative, we propose to apply direct block sequential simulation to model the spatial distribution of the COVID-19 infection risk in mainland Portugal. Given the daily number of infection data provided by the Portuguese Directorate-General for Health, the daily updates of infection rates are calculated by municipality and used as experimental data in the geostatistical simulation. The model considers the uncertainty/error associated with the size of each municipality’s population. The calculation of daily updates of the infection risk maps results from the median model of one ensemble of 100 geostatistical realizations of daily updates of the infection risk. The ensemble of geostatistical realizations is also used to calculate the associated spatial uncertainty of the spatial prediction using the interquartile distance. The risk maps are updated daily and show the regions with greater risks of infection and the critical dynamics related to its development over time.
- Geospatial Data Disaggregation with Convolutional Neural Networks
Demographic and socio-economic statistics are widely available on a variety of subjects. Still, the data are often collected or released for highly aggregated geospatial areas, masking important local hotspots. When conducting spatial analysis, one often needs to disaggregate the source data, transforming statistics for a set of source zones into values for a set of target zones, with different geometry and a higher spatial resolution. In this work, we report on a novel dasymetric disaggregation method that uses encoder-decoder convolutional neural networks similar to those used in image segmentation (i.e., models inspired by the popular U-Net), to combine different types of ancillary data when deriving the dasymetric weights. Model training constitutes a particular challenge, given that disaggregation tasks do not entail the direct use of supervision signals, in the form of training instances mapping the low-resolution aggregated data into the corresponding high-resolution representations. We propose to address the problem through self-training or co-training, iteratively refining initial estimates from seminal disaggregation heuristics by training a single model over progressively better estimates, or using the results of one model to support the training of another. We conducted experiments related to the disaggregation of socio-demographic variables collected for Continental Portugal, originally available for coarse-grained administrative divisions and into raster cells with a resolution of 200m. The results show that the proposed approaches outperform baseline methods, including other regression models to infer the dasymetric weights. Our experiments also highlight the impact of different training strategies, e.g. involving different loss functions and/or regularization schemes.
- The role of Volunteered Geographic Information (VGI)
Until early 2000, geographic information was produced solely by official/governmental mapping agencies. During the first half of 2000, a revolution started to take place. A number of enabling technologies (e.g., GPS accurate position, Web 2.0, among others) became available empowering users to start making their own maps and contribute to mapping initiatives. Such data has been first coined by Michael Goodchild back in 2008 and since then it has been growing exponentially. It has helped research, humanitarian mitigation and rescue, mobile apps development, among many others. However, these data have raised concerns, particularly related to their quality given the lack of formal qualifications and expertise of their contributors. In this talk, I will explore the concept of VGI and other related terms, discussing also the main challenges related to this type of data. I will also show some applications and the most recent work we have been developing at INESC-ID.
- A space time model of an alert system for detecting anomalous incidence values of COVID-19
In the current pandemic context, predicting anomalous incidence values at the local level is important to devise effective mitigation measures and optimize resources allocation. An anomalous incidence value of covid-19 is the one that exceeds above a threshold the predicted or expected value at one given spatial location. Anomalous incidence values differ from its neighbours, as they have a spatial and/or distinct temporal behaviour. We propose a methodology to detect anomalous incidence values, by accounting for the spatio-temporal behaviour of covid-19 incidence values of a given region. For this aim, one propose a spatio-temporal model to predict local probability distribution functions of incidence values of covid-19. A genetic programming model is used for the temporal prediction and the spatial component is modelled with a stochastic sequential simulation. The incidence of Covid 19 during September 2020 – March 2021, in Portugal, is used to illustrate the methodology.
- Modeling the geospatial evolution of COVID-19 using spatio-temporal convolutional sequence-to-sequence neural networks.
We proposed the use of a previously developed methodology and official municipality level data from the Portuguese Directorate-General for Health (DGS), relative to the first twelve months of the pandemic, to compute an estimate of the incidence rate in each location of mainland Portugal. The resulting sequence of incidence rate maps was then used as a gold standard to test the effectiveness of different approaches in the prediction of the spatial-temporal evolution of the incidence rate. Four different methods were tested: a simple cell level autoregressive moving average (ARMA) model, a cell level vector autoregressive (VAR) model, a municipality-by-municipality compartmental SIRD model followed by direct block sequential simulation and a convolutional sequence-to-sequence neural network model based on the STConvS2S architecture. We conclude that the convolutional sequence-to-sequence neural network is the best performing method, when predicting the medium-term future incidence rate, using the available information.
More information:
elisapirescosta@tecnico.ulisboa.pt
11th Lisbon Machine Learning Summer School
LxMLS 2021 will take place July 7th to July 15th in online format (via zoom and slack). It is organized jointly by Instituto Superior Técnico (IST), a leading Engineering and Science school in Portugal, the Instituto de Telecomunicações, the Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa (INESC-ID), Unbabel and Cleverly.
Click here for information about past editions (LxMLS 2011, LxMLS 2012, LxMLS 2013, LxMLS 2014, LxMLS 2015, LxMLS 2016, LxMLS 2017, LxMLS 2018, LxMLS 2019, LxMLS 2020) and to watch the videos of the lectures (2016, 2017, 2018, 2020).
Call for Participation
Important Dates
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* Application Deadline: May 15, 2021
* Decision: June 1, 2021
* Early Registration: June 15 – July 1, 2021
* Summer School: July 7 – 15, 2021
Topics and Intended Audience
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The school will cover a range of Machine Learning (ML) topics, from theory to practice, that are important in solving Natural Language Processing (NLP) problems that arise in the analysis and use of Web data.
Our target audience is:
- Researchers and graduate students in the fields of NLP and Computational Linguistics;
- Computer scientists who have interests in statistics and machine learning;
- Industry practitioners who desire a more in depth understanding of these subjects.
Features of LxMLS:
- No deep previous knowledge of ML or NLP is required, but the attendants are assumed to have some basic background on mathematics and programming
- Lecturers are leading researchers in machine learning and natural language processing (see speakers)
- Days are divided into morning lectures and afternoon lab sessions and practical talks (see schedule)
- The Labs guide will be provided one month in advance. Last year’s guide can be found here
- A day zero is scheduled to review basic concepts and introduce the necessary tools for implementation exercises
- Both basic (e.g linear classifiers) and advanced topics (e.g. deep learning, reinforcement learning) will be covered
Online Format:
Due to the current COVID-19 pandemic, the 11th Lisbon Machine Learning School will be held online (via zoom and slack). Similar to last year, we are excited for the opportunity to create a virtual school, where you will be able to attend all the lectures, and participate in the Q&As and labs remotely. We will also provide the tools for students to engage with each other remotely. The lectures will also be streamed to YouTube, and will become freely available later in our YouTube channel. The Q&A, labs and social activities will remain restricted to the accepted students only.
List of Confirmed Speakers
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LUIS PEDRO COELHO Fudan University | China
MÁRIO FIGUEIREDO Instituto de Telecomunicações & Instituto Superior Técnico | Portugal
ANDRE MARTINS Instituto de Telecomunicações & Unbabel | Portugal
IRYNA GULEYVICH Technical University Darmstat | Germany
NOAH SMITH University of Washington & Allen Institute for Artificial Intelligence | USA
SLAV PETROV Google Inc. | USA
XAVIER CARRERAS dMetrics | USA
GRAHAM NEUBIG Carnegie Mellon University | USA
BHIKSHA RAJ Carnegie Mellon University | USA
CHRIS DYER Google Deep Mind | UK
ELIAS BARENBOIM Columbia University | USA
ADELE RIBEIRO Columbia University | USA
STEFAN RIEZLER Institut für Computerlinguistik, Universität Heidelberg | Germany
BARBARA PLANK IT University of Copenhagen | Denmark
SASHA RUSH Cornell Tech | USA
Please visit the webpage for up to date information: http://lxmls.it.pt/2021
To apply, please fill the form in https://lisbonmls.wufoo.com/forms/application-form-lxmls-2021/
Any questions should be directed to: lxmls-2021@lx.it.pt.
OLISSIPO – Inaugural Workshop on Computational Cell Biology
OLISSIPO is pleased to host the project Inaugural Workshop on Computational Cell Biology, which will be held virtually on July 20th, 2021 just before ISMB/ECCB 2021.
OLISSIPO – Inaugural Workshop on Computational Cell Biology
20th July 2021 | ONLINE WORKSHOP
14:00-18:00 (CEST) / 13:00-17:00 (WEST – Lisbon)
Agenda
14:00 Welcome
14:10 Prof. Dr. Rolf Backofen, Albert-Ludwigs-Universität Freiburg, Germany
15:00 Prof. Dr. Susan Holmes, Stanford University, USA
15:50-16:20 Coffee-break
16:20 Prof. Dr. Dana Pe’er, Memorial Sloan Kettering Cancer Center, SKI, New York, USA
17:10 Prof. Dr. Luay K. Nakhleh, Rice University, Houston, TX, USA
18:00 Closing session
The registration is free but necessary for organizational purposes.
To receive the link for the Workshop, please register here until 16 July 2021.
More info: https://olissipo.inesc-id.pt/2021/06/07/inaugural-workshop/
International European Conference on Parallel and Distributed Computing
The 27th International European Conference on Parallel and Distributed Computing (Euro-Par 2021) will take from August 30 to September 3 2021 in Lisbon.
Euro-Par is the prime European conference covering all aspects of parallel and distributed processing, ranging from theory to practice, from small to the largest parallel and distributed systems and infrastructures, from fundamental computational problems to full-fledged applications, from architecture, compiler, language and interface design and implementation, to tools, support infrastructures, and application performance aspects.
The 2021 edition of Euro-Par will be organized as a collaboration between INESC-ID and Instituto Superior Técnico (IST).
Important Dates:
– Abstract Submission: February 5, 2021
– Paper Submission Deadline: February 12, 2021
– Author Notification: April 30, 2021
– Camera-Ready Papers: June 6, 2021
More information is available here.
ALGO 2021
September 6-10, 2021, Lisbon, Portugal
ALGO is an annual meeting combining the premier algorithmic conference European Symposium on Algorithms (ESA) and a number of other specialized conferences and workshops, all related to algorithms and their applications, making ALGO the major European event for researchers, students and practitioners in algorithms.
“The event combines one of the most important conferences in the area of algorithms, the European Symposium on Algorithms, with five other events related to several specific aspects of the area of algorithms. At a time when there is so much talk about algorithms, organizing in Lisbon the main event in the area is important and contributes to the visibility of the work being done in Portugal in this area.” emphasizes Professor Arlindo Oliveira, INESC-ID Researcher and Professor at Instituto Superior Técnico.
More info at http://algo2021.tecnico.ulisboa.pt/