Learning to count with deep object features

Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene.Comment: This paper has been accepted at Deep Vision Workshop at CVPR 201

Permanent Record, by Edward Snowden

Since 2013 we have become increasingly aware that our data is not private. Tech companies profile their customers and sell sensitive data. Government agencies carry out mass surveillance activity: ..

Primera cita de Arundo donax var. versicolor como alóctona en la península ibérica y en Europa

4 p., fot., dibujos[EN]Arundo donax var. versicolor (Mill.) Stokes is reported for the first time as alien in Spain and Europe.[ES]Se cita por primera vez como alóctona en la península ibérica y Europa a Arundo donax var. versicolor (Mill.) Stokes.Peer reviewe

the united states safe space campus controversy and the paradox of freedom of speech

AbstractThe article examines a remarkable paradox between the overwhelming public view supporting free speech as a principle, as attested by specific polls about the issue, and the fact that most people require establishing boundaries on certain types of speech. Particularly, while American society seems to openly accept free speech and even hate speech, the new generation of America requires 'safe spaces' without offensive speech. This paradox seems to be a blow to the liberal position regarding freedom of speech, specifically to the principle of tolerance. John Milton's call for liberal tolerance based on Voltaire's enlightened conception is explored in contrast to the classical understanding

Event detection in location-based social networks

With the advent of social networks and the rise of mobile technologies, users have become ubiquitous sensors capable of monitoring various real-world events in a crowd-sourced manner. Location-based social networks have proven to be faster than traditional media channels in reporting and geo-locating breaking news, i.e. Osama Bin Laden’s death was first confirmed on Twitter even before the announcement from the communication department at the White House. However, the deluge of user-generated data on these networks requires intelligent systems capable of identifying and characterizing such events in a comprehensive manner. The data mining community coined the term, event detection , to refer to the task of uncovering emerging patterns in data streams . Nonetheless, most data mining techniques do not reproduce the underlying data generation process, hampering to self-adapt in fast-changing scenarios. Because of this, we propose a probabilistic machine learning approach to event detection which explicitly models the data generation process and enables reasoning about the discovered events. With the aim to set forth the differences between both approaches, we present two techniques for the problem of event detection in Twitter : a data mining technique called Tweet-SCAN and a machine learning technique called Warble. We assess and compare both techniques in a dataset of tweets geo-located in the city of Barcelona during its annual festivities. Last but not least, we present the algorithmic changes and data processing frameworks to scale up the proposed techniques to big data workloads.This work is partially supported by Obra Social “la Caixa”, by the Spanish Ministry of Science and Innovation under contract (TIN2015-65316), by the Severo Ochoa Program (SEV2015-0493), by SGR programs of the Catalan Government (2014-SGR-1051, 2014-SGR-118), Collectiveware (TIN2015-66863-C2-1-R) and BSC/UPC NVIDIA GPU Center of Excellence.We would also like to thank the reviewers for their constructive feedback.Peer ReviewedPostprint (author's final draft