Attention to the Color of a Moving Stimulus Modulates Motion-Signal Processing in Macaque Area MT: Evidence for a Unified Attentional System

Directing visual attention to spatial locations or to non-spatial stimulus features can strongly modulate responses of individual cortical sensory neurons. Effects of attention typically vary in magnitude, not only between visual cortical areas but also between individual neurons from the same area. Here, we investigate whether the size of attentional effects depends on the match between the tuning properties of the recorded neuron and the perceptual task at hand. We recorded extracellular responses from individual direction-selective neurons in the middle temporal area (MT) of rhesus monkeys trained to attend either to the color or the motion signal of a moving stimulus. We found that effects of spatial and feature-based attention in MT, which are typically observed in tasks allocating attention to motion, were very similar even when attention was directed to the color of the stimulus. We conclude that attentional modulation can occur in extrastriate cortex, even under conditions without a match between the tuning properties of the recorded neuron and the perceptual task at hand. Our data are consistent with theories of object-based attention describing a transfer of attention from relevant to irrelevant features, within the attended object and across the visual field. These results argue for a unified attentional system that modulates responses to a stimulus across cortical areas, even if a given area is specialized for processing task-irrelevant aspects of that stimulus

Observing and recommending from a social web with biases

The research question this report addresses is: how, and to what extent, those directly involved with the design, development and employment of a specific black box algorithm can be certain that it is not unlawfully discriminating (directly and/or indirectly) against particular persons with protected characteristics (e.g. gender, race and ethnicity)?Comment: Technical Report, University of Southampton, March 201

Journée d’étude « Donald Trump et la politique étrangère des Etats-Unis: vers quel (dés)ordre mondial? »

Cette journée d’étude, organisée dans le cadre du laboratoire Center for Research on the English-speaking World (CREW – EA 4399) par Annick Cizel, James Cohen et Jean-Baptiste Velut, a exploré des champs d’études très divers.  De l’histoire diplomatique transatlantique aux dynamiques de pouvoir intérieures et extérieures à la politique environnementale, en passant par l’économie, la diplomatie commerciale ou encore la politique institutionnelle, ce colloque a offert un large panorama des acti..

The Maltreatment : Aggression Link among Prosecuted Males : What about Psychopathy?

Criminal offenders constitute a high-risk sample regarding experiences of childhood maltreatment and engagement in severe aggression. Moreover, psychopathic traits are more common in samples of offenders than non-offenders. Although research has underlined the relationship between childhood maltreatment and adult aggression, the influence of psychopathy on this link is still unclear. We examined the dynamics of maltreatment, aggression, and psychopathy in a mixed sample of 239 male violent, sexual, and other offenders using latent factor structural equation modeling. We found a consistent positive association of maltreatment with aggression. Psychopathy did not mediate this relation. Maltreatment was not associated with psychopathy, although psychopathy had a positive effect on aggressive behavior. These dynamics appeared similar for violent, sexual, and other offenders. However, latent variables were constructed somewhat differently depending on the offender status. For instance, sexual abuse appeared to be of specific importance in sexual offenders. Violent offenders showed high rates of psychopathy compared to sexual and other offenders. The current findings may inspire future research to focus more closely on the different subtypes of psychopathy when examining its role in the prediction of aggression based on childhood maltreatment. Moreover, childhood maltreatment must not be neglected in treatment and prevention approaches aimed at reducing the risk of aggressive behavior

Demographic Parity Inspector: Fairness Audits via the Explanation Space

Even if deployed with the best intentions, machine learning methods can perpetuate, amplify or even create social biases. Measures of (un-)fairness have been proposed as a way to gauge the (non-)discriminatory nature of machine learning models. However, proxies of protected attributes causing discriminatory effects remain challenging to address. In this work, we propose a new algorithmic approach that measures group-wise demographic parity violations and allows us to inspect the causes of inter-group discrimination. Our method relies on the novel idea of measuring the dependence of a model on the protected attribute based on the explanation space, an informative space that allows for more sensitive audits than the primary space of input data or prediction distributions, and allowing for the assertion of theoretical demographic parity auditing guarantees. We provide a mathematical analysis, synthetic examples, and experimental evaluation of real-world data. We release an open-source Python package with methods, routines, and tutorials

On Using Information Retrieval to Recommend Machine Learning Good Practices for Software Engineers

Machine learning (ML) is nowadays widely used for different purposes and in several disciplines. From self-driving cars to automated medical diagnosis, machine learning models extensively support users' daily activities, and software engineering tasks are no exception. Not embracing good ML practices may lead to pitfalls that hinder the performance of an ML system and potentially lead to unexpected results. Despite the existence of documentation and literature about ML best practices, many non-ML experts turn towards gray literature like blogs and Q&A systems when looking for help and guidance when implementing ML systems. To better aid users in distilling relevant knowledge from such sources, we propose a recommender system that recommends ML practices based on the user's context. As a first step in creating a recommender system for machine learning practices, we implemented Idaka. A tool that provides two different approaches for retrieving/generating ML best practices: i) an information retrieval (IR) engine and ii) a large language model. The IR-engine uses BM25 as the algorithm for retrieving the practices, and a large language model, in our case Alpaca. The platform has been designed to allow comparative studies of best practices retrieval tools. Idaka is publicly available at GitHub: https://bit.ly/idaka. Video: https://youtu.be/cEb-AhIPxnM.Comment: Accepted for Publication at ESEC/FSE demonstrations trac

Work on PETS Developed at CIEMAT

CIEMAT has been working on the RF power extractor so-called PETS (Power Extraction and Transfer Structure) for the CLIC Test Facility 3 (CTF3) since 2007. The first contribution has been installed at the Test Beam Line (TBL). Additionally, a new PETS configuration is presently under fabrication at CIEMAT and will be installed in the Test Module at CTF3. This paper describes the PETS prototypes design, fabrication and assembly techniques. The characterization of the devices with low RF power is also described.Comment: 9 pages, 9 figures, 3 tables, 10 references. Work presented in the LCWS1