Formalizing Consistency and Coherence of Representation Learning

In the study of reasoning in neural networks, recent efforts have sought to improve consistency and coherence of sequence models, leading to important developments in the area of neuro-symbolic AI. In symbolic AI, the concepts of consistency and coherence can be defined and verified formally, but for neural networks these definitions are lacking. The provision of such formal definitions is crucial to offer a common basis for the quantitative evaluation and systematic comparison of connectionist, neuro-symbolic and transfer learning approaches. In this paper, we introduce formal definitions of consistency and coherence for neural systems. To illustrate the usefulness of our definitions, we propose a new dynamic relation-decoder model built around the principles of consistency and coherence. We compare our results with several existing relation-decoders using a partial transfer learning task based on a novel data set introduced in this paper. Our experiments show that relation-decoders that maintain consistency over unobserved regions of representation space retain coherence across domains, whilst achieving better transfer learning performance

Efeito do encharcamento e de alturas de corte sobre a taxa de expansão foliar em plantas de Brachiaria brizantha cv. Marandu.

Objetivou-se de contribuir para o entendimento dos fatores que possam estar envolvidos na morte de capim-braquiarão (Brachiaria brizantha cv Marandu), através da análise de encharcamento. Segundo Dias-Filho (Informação pessoal), o excesso de umidade é um dos principais causadores deste fenômeno

Predicting recovery following stroke: deep learning, multimodal data and feature selection using explainable AI

Machine learning offers great potential for automated prediction of post-stroke symptoms and their response to rehabilitation. Major challenges for this endeavour include the very high dimensionality of neuroimaging data, the relatively small size of the datasets available for learning, and how to effectively combine neuroimaging and tabular data (e.g. demographic information and clinical characteristics). This paper evaluates several solutions based on two strategies. The first is to use 2D images that summarise MRI scans. The second is to select key features that improve classification accuracy. Additionally, we introduce the novel approach of training a convolutional neural network (CNN) on images that combine regions-of-interest extracted from MRIs, with symbolic representations of tabular data. We evaluate a series of CNN architectures (both 2D and a 3D) that are trained on different representations of MRI and tabular data, to predict whether a composite measure of post-stroke spoken picture description ability is in the aphasic or non-aphasic range. MRI and tabular data were acquired from 758 English speaking stroke survivors who participated in the PLORAS study. The classification accuracy for a baseline logistic regression was 0.678 for lesion size alone, rising to 0.757 and 0.813 when initial symptom severity and recovery time were successively added. The highest classification accuracy 0.854 was observed when 8 regions-of-interest was extracted from each MRI scan and combined with lesion size, initial severity and recovery time in a 2D Residual Neural Network.Our findings demonstrate how imaging and tabular data can be combined for high post-stroke classification accuracy, even when the dataset is small in machine learning terms. We conclude by proposing how the current models could be improved to achieve even higher levels of accuracy using images from hospital scanners

Coherent and Consistent Relational Transfer Learning with Auto-encoders

Human defined concepts are inherently transferable, but it is not clear under what conditions they can be modelled effectively by non-symbolic artificial learners. This paper argues that for a transferable concept to be learned, the system of relations that define it must be coherent across domains and properties. That is, they should be consistent with respect to relational constraints, and this consistency must extend beyond the representations encountered in the source domain. Further, where relations are modelled by differentiable functions, their gradients must conform – the functions must at times move together to preserve consistency. We propose a Partial Relation Transfer (PRT) task which exposes how well relation-decoders model these properties, and exemplify this with ordinality prediction transfer task, including a new data set for the transfer domain. We evaluate this on existing relation-decoder models, as well as a novel model designed around the principles of consistency and gradient conformity. Results show that consistency across broad regions of input space indicates good transfer performance, and that good gradient conformity facilitates consistency

Parallaxes of southern extremely cool objects III : 118 L and T dwarfs

We present new results from the Parallaxes of Southern Extremely Cool dwarfs program to measure parallaxes, proper motions and multiepoch photometry of L and early T dwarfs. The observations were made on 108 nights over the course of 8 yr using the Wide Field Imager on the ESO 2.2m telescope. We present 118 new parallaxes of L and T dwarfs of which 52 have no published values and 24 of the 66 published values are preliminary estimates from this program. The parallax precision varies from 1.0 to 15.5mas with a median of 3.8mas. We find evidence for two objects with long term photometric variation and 24 new moving group candidates. We cross-match our sample to published photometric catalogues and find standard magnitudes in up to 16 pass-bands from which we build spectral energy distributions and H-R diagrams. This allows us to confirm the theoretically anticipated minimum in radius between stars and brown dwarfs across the hydrogen burning minimum mass. We find the minimum occurs between L2 and L6 and verify the predicted steep dependence of radius in the hydrogen burning regime and the gentle rise into the degenerate brown dwarf regime. We find a relatively young age of ~2 Gyr from the kinematics of our sample.Peer reviewedFinal Accepted Versio

Unimodal late fusion for NIST i-vector challenge on speaker detection

Speaker detection is a very interesting machine learning task for which the latest i-vector challenge has been coordinated by the National Institute of Standards and Technology (NIST). A simple late fusion approach for the speaker detection task on the i-vector challenge is presented. The approach is based on the late fusion of scores from the cosine distance method (the baseline) and the scores obtained from linear discriminant analysis. The results show that by adapting the simple late fusion approach, the framework can outperform the baseline score for the decision cost function on the NIST i-vector machine learning challenge