Priming Neural Networks

Visual priming is known to affect the human visual system to allow detection of scene elements, even those that may have been near unnoticeable before, such as the presence of camouflaged animals. This process has been shown to be an effect of top-down signaling in the visual system triggered by the said cue. In this paper, we propose a mechanism to mimic the process of priming in the context of object detection and segmentation. We view priming as having a modulatory, cue dependent effect on layers of features within a network. Our results show how such a process can be complementary to, and at times more effective than simple post-processing applied to the output of the network, notably so in cases where the object is hard to detect such as in severe noise. Moreover, we find the effects of priming are sometimes stronger when early visual layers are affected. Overall, our experiments confirm that top-down signals can go a long way in improving object detection and segmentation.Comment: fixed error in author nam

Attentive Single-Tasking of Multiple Tasks

In this work we address task interference in universal networks by considering that a network is trained on multiple tasks, but performs one task at a time, an approach we refer to as "single-tasking multiple tasks". The network thus modifies its behaviour through task-dependent feature adaptation, or task attention. This gives the network the ability to accentuate the features that are adapted to a task, while shunning irrelevant ones. We further reduce task interference by forcing the task gradients to be statistically indistinguishable through adversarial training, ensuring that the common backbone architecture serving all tasks is not dominated by any of the task-specific gradients. Results in three multi-task dense labelling problems consistently show: (i) a large reduction in the number of parameters while preserving, or even improving performance and (ii) a smooth trade-off between computation and multi-task accuracy. We provide our system's code and pre-trained models at http://vision.ee.ethz.ch/~kmaninis/astmt/.Comment: CVPR 2019 Camera Read

Computational Creativity

In: Encyclopedia of Systems Biology, W. Dubitzky, O. Wolkenhauer, K-H Cho, H. Yokota (Eds.), Springer 2011Understanding brain processes behind creativity and modeling them using computational means is one of the grand challenges for systems biology. Computational creativity is a new field, inspired by cognitive psychology and neuroscience. In many respects human-level intelligence is far beyond what artificial intelligence can provide now, especially in regard to the high-level functions, involving thinking, reasoning, planning and the use of language. Intuition, insight, imagery and creativity are important aspects of all these functions

A Computational Cognitive Model of User Applying Creativity Technique in Creativity Support Systems

AbstractNumerous creativity techniques have been purposed and applied in creativity support system. Because most creativity techniques are used informally and hardly represented formally in computer, it becomes very difficult to build the computational cognitive model of user applying those techniques. However the model is necessary for creativity support systems to detect or predict the change of user's cognitive state in time and make some adaption to avoid inhibiting creativity of user. In this paper we introduce extension creative idea generation method which has the characteristics of formalization and systematization. The method can be represented by extension rules which provide the precondition to build computational cognitive model of user in creativity support systems. The computational cognitive model of user learning in applying extension creative idea generation method is presented through experiments. The experimental results show how and when the user will develop the application skill of creativity technique and inhibit his creativity

Plugin Networks for Inference under Partial Evidence

In this paper, we propose a novel method to incorporate partial evidence in the inference of deep convolutional neural networks. Contrary to the existing, top performing methods, which either iteratively modify the input of the network or exploit external label taxonomy to take the partial evidence into account, we add separate network modules ("Plugin Networks") to the intermediate layers of a pre-trained convolutional network. The goal of these modules is to incorporate additional signal, ie information about known labels, into the inference procedure and adjust the predicted output accordingly. Since the attached plugins have a simple structure, consisting of only fully connected layers, we drastically reduced the computational cost of training and inference. At the same time, the proposed architecture allows to propagate information about known labels directly to the intermediate layers to improve the final representation. Extensive evaluation of the proposed method confirms that our Plugin Networks outperform the state-of-the-art in a variety of tasks, including scene categorization, multi-label image annotation, and semantic segmentation.Comment: Accepted to WACV 202

Machine Learning Reveals a Non-Canonical Mode of Peptide Binding to MHC class II Molecules

MHC class II molecules play a fundamental role in the cellular immune system: they load short peptide fragments derived from extracellular proteins and present them on the cell surface. It is currently thought that the peptide binds lying more or less flat in the MHC groove, with a fixed distance of nine amino acids between the first and last residue in contact with the MHCII. While confirming that the great majority of peptides bind to the MHC using this canonical mode, we report evidence for an alternative, less common mode of interaction. A fraction of observed ligands were shown to have an unconventional spacing of the anchor residues that directly interact with the MHC, which could only be accommodated to the canonical MHC motif either by imposing a more stretched out peptide backbone (an 8mer core) or by the peptide bulging out of the MHC groove (a 10mer core). We estimated that on average 2% of peptides bind with a core deletion, and 0·45% with a core insertion, but the frequency of such non‐canonical cores was as high as 10% for certain MHCII molecules. A mutational analysis and experimental validation of a number of these anomalous ligands demonstrated that they could only fit to their MHC binding motif with a non‐canonical binding core of length different from nine. This previously undescribed mode of peptide binding to MHCII molecules gives a more complete picture of peptide presentation by MHCII and allows us to model more accurately this event.Fil: Andreatta, Massimo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Jurtz, Vanessa I.. Technical University of Denmark; DinamarcaFil: Kaever, Thomas. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Sette, Alessandro. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Nielsen, Morten. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina. Technical University of Denmark; Dinamarc

Emotion and Creativity: Hacking into Cognitive Appraisal Processes to Augment Creative Ideation

Creativity thrives when people experience positive emotions. How to design an interactive system that can effectively make use of this potential is, however, still an unanswered question. In this paper, we propose one approach to this problem that relies on hacking into the cognitive appraisal processes that form part of positive emotions. To demonstrate our approach we have conceived, made, and evaluated a novel interactive system that influences an individual’s appraisals of their own idea generation processes by providing real-time and believable feedback about the originality of their ideas. The system can be used to manipulate this feedback to make the user’s ideas appear more or less original. This has enabled us to test experimentally the hypothesis that providing more positive feedback, rather than neutral, or more negative feedback than the user is expecting, causes more positive emotion, which in turn causes more creativity during idea generation. The findings demonstrate that an interactive system can be designed to use the function of cognitive appraisal processes in positive emotion to help people to get more out of their own creative capabilities

Multimodal deep learning for point cloud panoptic segmentation of railway environments

The demand for transportation asset digitalisation has significantly increased over the years. For this purpose, mobile mapping systems (MMSs) are among the most popular technologies that allow capturing high precision three-dimensional point clouds of the infrastructure. In this paper, a multimodal deep learning methodology is presented for panoptic segmentation of the railway infrastructure. The methodology takes advantage of image rasterisation of the point clouds to perform a rough segmentation and discard more than 80% of points that are not relevant to the infrastructure. With this approach, the computational requirements for processing the remaining point cloud are highly reduced, allowing the process of dense point clouds in short periods of time. A 90 km-long railway scenario was used for training and testing. The proposed methodology is two times faster than the current state-of-the-art for the same point cloud density, and pole-like object segmentation metrics are improved.Fundación BBVAAgencia Estatal de Investigación | Ref. PID2019-108816RB-I00Ministerio de Universidades | Ref. FPU20/01024Universidade de Vigo/CISU

MACHINE LEARNING AND SOFTWARE SOLUTIONS FOR DATA QUALITY ASSESSMENT IN CERN’S ATLAS EXPERIMENT

The Large Hadron Collider (LHC) is home to multiple particle physics experiments designed to verify the standard model and push our understanding of the universe to its limits. The ATLAS detector is one of the large general-purpose experiments that make use of the LHC and generates a significant amount of data as part of its regular operations. Prior to physics analysis, this data is cleaned through a data assessment process which involves significant operator resources. With the evolution of the field of machine learning and anomaly detection, there is great opportunity to upgrade the ATLAS Data Quality Monitoring Framework to include automated, machine learning based solutions to reduce operator requirements and improve data quality for physics analysis. This thesis provides an infrastructure, theoretical foundation and a unique machine learning approach to automate this process. It accomplishes this by combining 2 heavily documented algorithms (Autoencoders and DBScan) and organizing the dataset around geometric descriptor features. The results of this work are released as code and software solutions for the benefit of current and future data quality assessment, research, and collaborations in the ATLAS experiment