Probabilistic representations should be defined by their computational role: reply to Rahnev et al. 2021

Note: This is a reply to the Generative Adversarial Collaboration "Is perception probabilistic? Clarifying the definitions" by Rahnev, Block, Denison, and Jehee (DOI 10.31234/osf.io/f8v5r). Probabilistic representations promise to shed light on how humans and other organisms cope with uncertainty, but debates about them have been hindered by unclear definitions and a lack of testable predictions. We argue that neither camp in this GAC has offered a plausible way out of this situation. We describe problems with their attempts to define probabilistic representations and sketch an alternative view, in which the defining feature of a probabilistic representation is how it is used.

On the Green function of the killed fractional Laplacian on the periodic domain

We give a very simple proof of the positivity and unimodality of the Green function for the killed fractional Laplacian on the periodic domain. The argument relies on the Jacobi triple product and a probabilistic representation of the Green function. We also show by a contour integration that the Green function is completely monotone on the positive part of the periodic domain.

Application of non-positional representation of information in environmental monitoring systems

Developing forward-looking and advanced information systems requires the creation of a unified architecture, with unified hardware and software based on comprehensive integration of components of natural and technical information systems not only at the technical, but also at the functional level, The implementation of the above structure leads to a multiple increase in the volume of calculations on large-bit data arrays carried out in real time, as well as to the complexity of computational algorithms. It results in sharp contradictions between hardware costs, speed, accuracy and fault tolerance. The work is concerned with the formation of a methodology for the probabilistic representation and information transformation and the development on its basis of techniques, methods and algorithms for the synthesis of computing devices and components for advanced and existing information systems built on the domestic element base, which becomes an effective and high-tech means of overcoming these contradictions.

Coupling Intent and Action for Pedestrian Crossing Behavior Prediction

Accurate prediction of pedestrian crossing behaviors by autonomous vehicles can significantly improve traffic safety. Existing approaches often model pedestrian behaviors using trajectories or poses but do not offer a deeper semantic interpretation of a person's actions or how actions influence a pedestrian's intention to cross in the future. In this work, we follow the neuroscience and psychological literature to define pedestrian crossing behavior as a combination of an unobserved inner will (a probabilistic representation of binary intent of crossing vs. not crossing) and a set of multi-class actions (e.g., walking, standing, etc.). Intent generates actions, and the future actions in turn reflect the intent. We present a novel multi-task network that predicts future pedestrian actions and uses predicted future action as a prior to detect the present intent and action of the pedestrian. We also designed an attention relation network to incorporate external environmental contexts thus further improve intent and action detection performance. We evaluated our approach on two naturalistic driving datasets, PIE and JAAD, and extensive experiments show significantly improved and more explainable results for both intent detection and action prediction over state-of-the-art approaches. Our code is available at: https://github.com/umautobots/pedestrian_intent_action_detection

Geologist in the Loop: A Hybrid Intelligence Model for Identifying Geological Boundaries from Augmented Ground Penetrating Radar

Common industry practice means that geological or stratigraphic boundaries are estimated from exploration drill holes. While exploration holes provide opportunities for accurate data at a high resolution down the hole, their acquisition is cost-intensive, which can result in the number of holes drilled being reduced. In contrast, sampling with ground-penetrating radar (GPR) is cost-effective, non-destructive, and compact, allowing for denser, continuous data acquisition. One challenge with GPR data is the subjectivity and challenges associated with interpretation. This research presents a hybrid model of geologist and machine learning for the identification of geological boundaries in a lateritic deposit. This model allows for an auditable, probabilistic representation of geologists’ interpretations and can feed into exploration planning and optimising drill campaigns in terms of the density and location of holes.

Neuronal variability reflects probabilistic inference tuned to natural image statistics

Neuronal activity in sensory cortex fluctuates over time and across repetitions of the same input. This variability is often considered detrimental to neural coding. The theory of neural sampling proposes instead that variability encodes the uncertainty of perceptual inferences. In primary visual cortex (V1), modulation of variability by sensory and non-sensory factors supports this view. However, it is unknown whether V1 variability reflects the statistical structure of visual inputs, as would be required for inferences correctly tuned to the statistics of the natural environment. Here we combine analysis of image statistics and recordings in macaque V1 to show that probabilistic inference tuned to natural image statistics explains the widely observed dependence between spike count variance and mean, and the modulation of V1 activity and variability by spatial context in images. Our results show that the properties of a basic aspect of cortical responses—their variability—can be explained by a probabilistic representation tuned to naturalistic inputs.