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

Continual State Representation Learning for Reinforcement Learning using Generative Replay

We consider the problem of building a state representation model in a continual fashion. As the environment changes, the aim is to efficiently compress the sensory state's information without losing past knowledge. The learned features are then fed to a Reinforcement Learning algorithm to learn a policy. We propose to use Variational Auto-Encoders for state representation, and Generative Replay, i.e. the use of generated samples, to maintain past knowledge. We also provide a general and statistically sound method for automatic environment change detection. Our method provides efficient state representation as well as forward transfer, and avoids catastrophic forgetting. The resulting model is capable of incrementally learning information without using past data and with a bounded system size

Microsatellite markers in Spanish lime (Melicoccus bijugatus Jacq., Sapindaceae), a neglected Neotropical fruit crop

Spanish lime (Melicoccus bijugatus Jacq.) is aNeotropical fruit tree cultivated, mainly, in orchards for self-consumption or local sale. The genus Melicoccus includes other nine species with edible fruits, some of these species are at risk of extinction. Like for the vast majority of tropical fruit trees, there is no information on the genetic diversity of Spanish lime and its related species, and this is mostly due to the lack of molecular markers. The objectives of this study were to present the first microsatellite markers developed for Spanish lime, testing its usefulness on a sample of cultivated accessions, as well as its transferability to Huaya India (M. oliviformis). To do this, we performed high-throughput sequencing of microsatellite-enriched libraries of Spanish lime using Roche 454, assembled 9567 DNA contig sequences and identified 10,117 microsatellites. After screening 384 of those microsatellites on four DNA samples, 31 polymorphic markers were used to screen 25 accessions of Spanish lime and five of Huaya India collected in Yucatan, Mexico. Genetic diversity was low in Spanish lime (A = 20.61, HE = 0.38) and similar for both sexes of this species. Neighbor-Joining and PCoA analyses clearly discriminated between the two Melicoccus species studied. Nine of the markers showed unique alleles for Huaya India. The set of microsatellite markers developed has a great potential to generate information in relation to conservation genetics, improvement of elite cultivars and breeding programs for Spanish lime and related species

Positron-neutrino correlations in 32Ar and 33Ar Decays: Probes of Scalar weak currents and nuclear isospin mixing

The positron-neutrino correlation in the 0^+ \to 0^+ \beta decay of ^{32}Ar was measured at ISOLDE by analyzing the effect of lepton recoil on the shape of the narrow proton group following the superallowed decay. Our result is consistent with the Standard Model prediction; for vanishing Fierz interference we find a=0.9989 \pm 0.0052 \pm 0.0036. Our result leads to improved constraints on scalar weak interactions. The positron-neutrino correlation in ^{33}Ar decay was measured in the same experiment; for vanishing Fierz interference we find a=0.944 \pm 0.002 \pm 0.003. The ^{32}Ar and ^{33}Ar correlations, in combination with precision measurements of the half-lives, superallowed branching ratios and beta endpoint energies, will determine the isospin impurities of the superallowed transitions. These will provide useful tests of isospin-violation corrections used in deducing |V_{\rm ud}| which currently indicates non-unitarity of the KM matrix.Comment: 15 pages, 6 figure

Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning

Flatlandis a simple, lightweight environment for fastprototyping and testing of reinforcement learning agents. It is oflower complexity compared to similar 3D platforms (e.g. Deep-Mind Lab or VizDoom), but emulates physical properties of thereal world, such as continuity, multi-modal partially-observablestates with first-person view and coherent physics. We proposeto use it as an intermediary benchmark for problems related toLifelong Learning.Flatlandis highly customizable and offers awide range of task difficulty to extensively evaluate the propertiesof artificial agents. We experiment with three reinforcementlearning baseline agents and show that they can rapidly solvea navigation task inFlatland. A video of an agent acting inFlatlandis available here: https://youtu.be/I5y6Y2ZypdA

Generative Models from the perspective of Continual Learning

Which generative model is the most suitablefor Continual Learning? This paper aims at evaluating andcomparing generative models on disjoint sequential imagegeneration tasks. We investigate how several models learn andforget, considering various strategies: rehearsal, regularization,generative replay and fine-tuning. We used two quantitativemetrics to estimate the generation quality and memory ability.We experiment with sequential tasks on three commonly usedbenchmarks for Continual Learning (MNIST, Fashion MNISTand CIFAR10). We found that among all models, the originalGAN performs best and among Continual Learning strategies,generative replay outperforms all other methods. Even ifwe found satisfactory combinations on MNIST and FashionMNIST, training generative models sequentially on CIFAR10is particularly instable, and remains a challenge. Our code isavailable online

Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning

Flatlandis a simple, lightweight environment for fastprototyping and testing of reinforcement learning agents. It is oflower complexity compared to similar 3D platforms (e.g. Deep-Mind Lab or VizDoom), but emulates physical properties of thereal world, such as continuity, multi-modal partially-observablestates with first-person view and coherent physics. We proposeto use it as an intermediary benchmark for problems related toLifelong Learning.Flatlandis highly customizable and offers awide range of task difficulty to extensively evaluate the propertiesof artificial agents. We experiment with three reinforcementlearning baseline agents and show that they can rapidly solvea navigation task inFlatland. A video of an agent acting inFlatlandis available here: https://youtu.be/I5y6Y2ZypdA

EFICÁCIA DE COLOIDES VS CRISTALOIDES NA REPOSIÇÃO VOLÉMICA A NÍVEL HEPÁTICO EM PACIENTES HEMORRÁGICOS

Apesar dos avanços na sua abordagem terapêutica, a hemorragia severa continua a ser a principal causa de morbilidade e mortalidade em animais vítimas de trauma ou sujeitos a intervenção cirúrgica. A causa das lesões decorrentes, ou da morte consequente, deve-se ao deficit de volume de fluidos intravasculares e subsequente desenvolvimento do estado hipovolémico.O controlo da hipovolémia passa pela resolução da hemorragia e pela correção do deficit de volume intravascular causado e envolve, obrigatoriamente, o recurso à administração de fluidos intravenosos. A escolha do tipo de fluido mais adequado para a terapia intravenosa (IV), em cada ocorrência, é uma tarefa que exige reflexão e ponderação. O objetivo deste trabalho consistiu na avaliação do tipo de fluido mais adequado e eficiente para efetuar a reposição volémica em pacientes hemorrágicos, em particular no respeitante a qual deles acarreta menor comprometimento da função hepática. As lesões histopatológicas avaliadas nos diferentes grupos deste estudo não revelaram diferenças estatisticamente significativas entre os 3 grupos, o que validou a possibilidade de se usar qualquer um dos fluidos testados. Contudo, foi notória uma maior intensidade da gravidade das lesões no grupo 2. Assim, foi possível concluir que o uso do hidroxietilamido 130/0,4, em detrimento do lactato de Ringer em situações de hipovolémia, contribui para a preservação da integridade histológica e fisiológica hepática.COMPETE - FCOMP-01-0124-FEDER-009525; FCT/CI&DETS (PEst- OE/CED/UI4016/2011

On the Sensory Commutativity of Action Sequences for Embodied Agents

Perception of artificial agents is one the grand challenges of AI research. Deep Learning and data-driven approaches are successful on constrained problems where perception can be learned using supervision, but do not scale to open-worlds. In such case, for autonomous embodied agents with first-person sensors, perception can be learned end-to-end to solve particular tasks. However, literature shows that perception is not a purely passive compression mechanism, and that actions play an important role in the formulation of abstract representations. We propose to study perception for these embodied agents, under the mathematical formalism of group theory in order to make the link between perception and action. In particular, we consider the commutative properties of continuous action sequences with respect to sensory information perceived by such an embodied agent. We introduce the Sensory Commutativity Probability (SCP) criterion which measures how much an agent's degree of freedom affects the environment in embodied scenarios. We show how to compute this criterion in different environments, including realistic robotic setups. We empirically illustrate how SCP and the commutative properties of action sequences can be used to learn about objects in the environment and improve sample-efficiency in Reinforcement Learning