Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction

Despite its omnipresence in robotics application, the nature of spatial knowledgeand the mechanisms that underlie its emergence in autonomous agents are stillpoorly understood. Recent theoretical works suggest that the Euclidean structure ofspace induces invariants in an agent’s raw sensorimotor experience. We hypothesizethat capturing these invariants is beneficial for sensorimotor prediction and that,under certain exploratory conditions, a motor representation capturing the structureof the external space should emerge as a byproduct of learning to predict futuresensory experiences. We propose a simple sensorimotor predictive scheme, applyit to different agents and types of exploration, and evaluate the pertinence of thesehypotheses. We show that a naive agent can capture the topology and metricregularity of its sensor’s position in an egocentric spatial frame without any a prioriknowledge, nor extraneous supervision

Sensorimotor Prediction with Neural Networks on Continuous Spaces

In the context of Developmental Robotics, we propose to learn how the sensations of a robot are modified by its action. Many theories of Artificial Intelligence argue that sensorimotor prediction is a fundamental building block of cognition. In this paper, we learn the sensorimotor prediction on data captured by a mobile robot equipped with distance sensors. We show that Neural Networks can learn the sensorimotor regularities and perform sensorimotor prediction on continuous sensor and motor spaces

Image encryption system based on a nonlinear joint transform correlator for the simultaneous authentication of two users

We propose a new encryption system based on a nonlinear joint transform correlator (JTC) using the information of two biometrics (one digital fingerprint for each user) as security keys of the encryption system. In order to perform the decryption and authentication in a proper way, it is necessary to have the two digital fingerprints from the respective users whose simultaneous authentication is pursued. The proposed security system is developed in the Fourier domain. The nonlinearity of the JTC along with the five security keys given by the three random phase masks and the two digital fingerprints of the two users allow an increase of the system security against brute force and plaintext attacks. The feasibility and validity of this proposal is demonstrated using digital fingerprints as biometrics in numerical experiments.Peer ReviewedPostprint (published version

Effects of using an encrypted image corrupted by noise and occlusion in a security system based on joint transform correlator and Gyrator transform

Postprint (published version

Image quality and security through nonlinear joint transform encryption

Postprint (published version

State representation learning with recurrent capsule networks

Unsupervised learning of compact and relevant state representations has beenproved very useful at solving complex reinforcement learning tasks Ha and Schmid-huber (2018). In this paper, we propose a recurrent capsule network Hinton et al.(2011) that learns such representations by trying to predict the future observationsin an agent’s trajector

Symmetry-Based Disentangled Representation Learning requires Interaction with Environments

Finding a generally accepted formal definition of a disentangled representation in the context of an agent behaving in an environment is an important challenge towards the construction of data-efficient autonomous agents. Higgins et al. (2018) recently proposed Symmetry-Based Disentangled Representation Learning, a definition based on a characterization of symmetries in the environment using group theory. We build on their work and make observations, theoretical and empirical, that lead us to argue that Symmetry-Based Disentangled Representation Learning cannot only be based on static observations: agents should interact with the environment to discover its symmetries. Our experiments can be reproduced in Colab and the code is available on GitHub

Testicular fusocellular rhabdomyosarcoma as a metastasis of elbow sclerosing rhabdomyosarcoma: A clinicopathologic, immunohistochemical and molecular study of one case

Sclerosing rhabdomyosarcoma (SRMS) is an infrequent variant of rhabdomyosarcoma characterized by extensive intercellular hyaline fibrosis. We report the case of a 37 year-old male with a 9 × 6 cm SRMS on the right elbow. Histologically, the tumor showed an abundant extracellular hyaline matrix with extratumoral vascular emboli and microscopic foci of fusocellular embryonal rhabdomyosarcoma (FRMS) separated by a fibrotic band from the sclerosing areas. One year later the patient presented with a right intratesticular tumor of 1.2 × 0.8 cm, which was reported as pure FRMS. Immunohistochemically, SRMS was positive only for MyoD1 and Vimentin and negative for Myogenin and Desmin. Both the elbow emboli with the extratumoral foci of FRMS and the intratesticular tumor were positive for Myogenin, MyoD1, Vimentin and Desmin. Using fluorescent in situ hybridization (FISH), the SRMS and the FRMS tumor cells of the elbow and the FRMS tumor cells of the testis were found to be negative for FOXO1A translocation in chromosome 13. PCR chimeric transcriptional products PAX3-FKHR and PAX7-FKHR were not found. Six months following testicular resection, the patient died of multiple metastases in the mediastinum, lung and right thigh