2,470 research outputs found

    Information content and reward processing in the human striatum during performance of a declarative memory task

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    Negative feedback can signal poor performance, but it also provides information that can help learners reach the goal of task mastery. The primary aim of this study was to test the hypothesis that the amount of information provided by negative feedback during a paired-associate learning task influences feedback-related processing in the caudate nucleus. To do this, we manipulated the number of response options: With two options, positive and negative feedback provide equal amounts of information, whereas with four options, positive feedback provides more information than does negative feedback. We found that positive and negative feedback activated the caudate similarly when there were two response options. With four options, the caudate’s response to negative feedback was reduced. A secondary goal was to investigate the link between brain-based measures of feedback-related processing and behavioral indices of learning. Analysis of the posttest measures showed that trials with positive feedback were associated with higher posttest confidence ratings. Additionally, when positive feedback was delivered, caudate activity was greater for trials with high than with low posttest confidence. This experiment demonstrated the context sensitivity of feedback processing and provided evidence that feedback processing in the striatum can contribute to the strengthening of the representations available within declarative memory

    Kahler Independence of the G2-MSSM

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    The G2-MSSM is a model of particle physics coupled to moduli fields with interesting phenomenology both for colliders and astrophysical experiments. In this paper we consider a more general model - whose moduli Kahler potential is a completely arbitrary G2-holonomy Kahler potential and whose matter Kahler potential is also more general. We prove that the vacuum structure and spectrum of BSM particles is largely unchanged in this much more general class of theories. In particular, gaugino masses are still supressed relative to the gravitino mass and moduli masses. We also consider the effects of higher order corrections to the matter Kahler potential and find a connection between the nature of the LSP and flavor effects.Comment: Final version, matches the version published in JHE

    Nudging Cooperation in a Crowd Experiment

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    We examine the hypothesis that driven by a competition heuristic, people don't even reflect or consider whether a cooperation strategy may be better. As a paradigmatic example of this behavior we propose the zero-sum game fallacy, according to which people believe that resources are fixed even when they are not. We demonstrate that people only cooperate if the competitive heuristic is explicitly overridden in an experiment in which participants play two rounds of a game in which competition is suboptimal. The observed spontaneous behavior for most players was to compete. Then participants were explicitly reminded that the competing strategy may not be optimal. This minor intervention boosted cooperation, implying that competition does not result from lack of trust or willingness to cooperate but instead from the inability to inhibit the competition bias. This activity was performed in a controlled laboratory setting and also as a crowd experiment. Understanding the psychological underpinnings of these behaviors may help us improve cooperation and thus may have vast practical consequences to our society.Fil: Niella, Tamara. Universidad Torcuato di Tella; ArgentinaFil: Stier, Nicolas. Universidad Torcuato di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sigman, Mariano. Universidad Torcuato di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

    Isoperimetric inequalities for some integral operators arising in potential theory

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    In this paper we review our previous isoperimetric results for the logarithmic potential and Newton potential operators. The main reason why the results are useful, beyond the intrinsic interest of geometric extremum problems, is that they produce a priori bounds for spectral invariants of operators on arbitrary domains. We demonstrate these in explicit examples.Comment: This conference paper gives a review of our previous results in the subjec

    EACVI survey on radiation exposure in interventional echocardiography.

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    AIMS: The European Association of Cardiovascular Imaging (EACVI) Scientific Initiatives Committee performed a global survey on radiation exposure in interventional echocardiography. The survey aimed to collect data on local practices for radioprotection in interventional echocardiography and to assess the awareness of echocardiography operators about radiation-related risks. METHODS AND RESULTS: A total of 258 interventional echocardiographers from 52 different countries (48% European) responded to the survey. One hundred twenty-two (47%) participants were women. Two-thirds (76%) of interventional echocardiographers worked in tertiary care/university hospitals. Interventional echocardiography was the main clinical activity for 34% of the survey participants. The median time spent in the cath-lab for the echocardiographic monitoring of structural heart procedures was 10 (5-20) hours/month. Despite this, only 28% of interventional echocardiographers received periodic training and certification in radioprotection and 72% of them did not know their annual radiation dose. The main adopted personal protection devices were lead aprons and thyroid collars (95% and 92% of use, respectively). Dedicated architectural protective shielding was not available for 33% of interventional echocardiographers. Nearly two-thirds of responders thought that the radiation exposure of interventional echocardiographers was higher than that of interventional cardiologists and 72% claimed for an improvement in the radioprotection measures. CONCLUSION: Radioprotection measures for interventional echocardiographers are widely variable across centres. Radioprotection devices are often underused by interventional echocardiographers, portending an increased radiation-related risk. International scientific societies working in the field should collaborate to endorse radioprotection training, promote reliable radiation dose assessment, and support the adoption of radioprotection shielding dedicated to interventional echocardiographers

    Identifying the Machine Learning Family from Black-Box Models

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    [EN] We address the novel question of determining which kind of machine learning model is behind the predictions when we interact with a black-box model. This may allow us to identify families of techniques whose models exhibit similar vulnerabilities and strengths. In our method, we first consider how an adversary can systematically query a given black-box model (oracle) to label an artificially-generated dataset. This labelled dataset is then used for training different surrogate models (each one trying to imitate the oracle¿s behaviour). The method has two different approaches. First, we assume that the family of the surrogate model that achieves the maximum Kappa metric against the oracle labels corresponds to the family of the oracle model. The other approach, based on machine learning, consists in learning a meta-model that is able to predict the model family of a new black-box model. We compare these two approaches experimentally, giving us insight about how explanatory and predictable our concept of family is.This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-17-1-0287, the EU (FEDER), and the Spanish MINECO under grant TIN 2015-69175-C4-1-R, the Generalitat Valenciana PROMETEOII/2015/013. F. Martinez-Plumed was also supported by INCIBE under grant INCIBEI-2015-27345 (Ayudas para la excelencia de los equipos de investigacion avanzada en ciberseguridad). J. H-Orallo also received a Salvador de Madariaga grant (PRX17/00467) from the Spanish MECD for a research stay at the CFI, Cambridge, and a BEST grant (BEST/2017/045) from the GVA for another research stay at the CFI.Fabra-Boluda, R.; Ferri Ramírez, C.; Hernández-Orallo, J.; Martínez-Plumed, F.; Ramírez Quintana, MJ. (2018). Identifying the Machine Learning Family from Black-Box Models. 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