Algorithmic Randomness for Infinite Time Register Machines

A concept of randomness for infinite time register machines (ITRMs), resembling Martin-L\"of-randomness, is defined and studied. In particular, we show that for this notion of randomness, computability from mutually random reals implies computability and that an analogue of van Lambalgen's theorem holds

Self-supervised learning of a facial attribute embedding from video

We propose a self-supervised framework for learning facial attributes by simply watching videos of a human face speaking, laughing, and moving over time. To perform this task, we introduce a network, Facial Attributes-Net (FAb-Net), that is trained to embed multiple frames from the same video face-track into a common low-dimensional space. With this approach, we make three contributions: first, we show that the network can leverage information from multiple source frames by predicting confidence/attention masks for each frame; second, we demonstrate that using a curriculum learning regime improves the learned embedding; finally, we demonstrate that the network learns a meaningful face embedding that encodes information about head pose, facial landmarks and facial expression, i.e. facial attributes, without having been supervised with any labelled data. We are comparable or superior to state-of-the-art self-supervised methods on these tasks and approach the performance of supervised methods.Comment: To appear in BMVC 2018. Supplementary material can be found at http://www.robots.ox.ac.uk/~vgg/research/unsup_learn_watch_faces/fabnet.htm

Generalized Effective Reducibility

We introduce two notions of effective reducibility for set-theoretical statements, based on computability with Ordinal Turing Machines (OTMs), one of which resembles Turing reducibility while the other is modelled after Weihrauch reducibility. We give sample applications by showing that certain (algebraic) constructions are not effective in the OTM-sense and considerung the effective equivalence of various versions of the axiom of choice

Predictive Modeling of Cholera Outbreaks in Bangladesh

Despite seasonal cholera outbreaks in Bangladesh, little is known about the relationship between environmental conditions and cholera cases. We seek to develop a predictive model for cholera outbreaks in Bangladesh based on environmental predictors. To do this, we estimate the contribution of environmental variables, such as water depth and water temperature, to cholera outbreaks in the context of a disease transmission model. We implement a method which simultaneously accounts for disease dynamics and environmental variables in a Susceptible-Infected-Recovered-Susceptible (SIRS) model. The entire system is treated as a continuous-time hidden Markov model, where the hidden Markov states are the numbers of people who are susceptible, infected, or recovered at each time point, and the observed states are the numbers of cholera cases reported. We use a Bayesian framework to fit this hidden SIRS model, implementing particle Markov chain Monte Carlo methods to sample from the posterior distribution of the environmental and transmission parameters given the observed data. We test this method using both simulation and data from Mathbaria, Bangladesh. Parameter estimates are used to make short-term predictions that capture the formation and decline of epidemic peaks. We demonstrate that our model can successfully predict an increase in the number of infected individuals in the population weeks before the observed number of cholera cases increases, which could allow for early notification of an epidemic and timely allocation of resources.Comment: 43 pages, including appendices, 5 figures, 1 table in the main tex

Where and When: {S}pace-Time Attention for Audio-Visual Explanations

Explaining the decision of a multi-modal decision-maker requires to determine the evidence from both modalities. Recent advances in XAI provide explanations for models trained on still images. However, when it comes to modeling multiple sensory modalities in a dynamic world, it remains underexplored how to demystify the mysterious dynamics of a complex multi-modal model. In this work, we take a crucial step forward and explore learnable explanations for audio-visual recognition. Specifically, we propose a novel space-time attention network that uncovers the synergistic dynamics of audio and visual data over both space and time. Our model is capable of predicting the audio-visual video events, while justifying its decision by localizing where the relevant visual cues appear, and when the predicted sounds occur in videos. We benchmark our model on three audio-visual video event datasets, comparing extensively to multiple recent multi-modal representation learners and intrinsic explanation models. Experimental results demonstrate the clear superior performance of our model over the existing methods on audio-visual video event recognition. Moreover, we conduct an in-depth study to analyze the explainability of our model based on robustness analysis via perturbation tests and pointing games using human annotations

Plant water uptake by hard red winter wheat (Triticum aestivum L.) genotypes at 2°C and low light intensity

BACKGROUND: Hard red winter wheat (HRWW; Triticum aestivm L.) plants from genotypes selected in the Northern Great Plains of the U.S. have less tissue water after exposure to cool autumn temperatures than plants from the Southern Great Plains. It is generally assumed that the reduced tissue water content of northern compared to southern cultivars is due to an impedance to water uptake by northern plants as a result of the low autumn temperatures. We hypothesize that if low temperature impedes water uptake then less soil water would be removed by northern than by southern-selected cultivars. This hypothesis was tested by comparing plant water uptake of a northern (FR) and a southern (FS) cultivar in relation to their foliage water content at 2°C. RESULTS: At 2°C foliage water content of FR plants decreased more rapidly than that of FS plants, similar to field results in the fall. During 6 wk, foliage water content of FR plants decreased 20 to 25% of the pre-treatment value, compared to only 5 to 10% by FS plants. Plant water uptake was about 60 g H(2)O·g FDW(-1) by FS plants, while FR plants maintained plant water uptake in excess of 100 g H(2)O·g FDW(-1) during the 6 wk period at 2°C. When four other northern genotypes of equal freeze resistance were studied, foliage water content and plant water uptake change were similar to FR plants. CONCLUSION: In these northern-selected HRWW cultivars foliage water content reduction resulting from cold acclimation is not due to impedance to plant water uptake