Machines Learning - Towards a New Synthetic Autobiographical Memory

Autobiographical memory is the organisation of episodes and contextual information from an individual’s experiences into a coherent narrative, which is key to our sense of self. Formation and recall of autobiographical memories is essential for effective, adaptive behaviour in the world, providing contextual information necessary for planning actions and memory functions such as event reconstruction. A synthetic autobiographical memory system would endow intelligent robotic agents with many essential components of cognition through active compression and storage of historical sensorimotor data in an easily addressable manner. Current approaches neither fulfil these functional requirements, nor build upon recent understanding of predictive coding, deep learning, nor the neurobiology of memory. This position paper highlights desiderata for a modern implementation of synthetic autobiographical memory based on human episodic memory, and proposes that a recently developed model of hippocampal memory could be extended as a generalised model of autobiographical memory. Initial implementation will be targeted at social interaction, where current synthetic autobiographical memory systems have had success

The results of crossbreeding between chios and the local fat-tail awassi

International audienc

Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland

Automated robotic weeding of grassland will improve the productivity of dairy and sheep farms while helping to conserve their environments. Previous studies have reported results of machine vision methods to separate grass from grassland weeds but each use their own datasets and report only performance of their own algorithm, making it impossible to compare them. A definitive, large-scale independent study is presented of all major known grassland weed detection methods evaluated on a new standardised data set under a wider range of environment conditions. This allows for a fair, unbiased, independent and statistically significant comparison of these and future methods for the first time. We test features including linear binary patterns, BRISK, Fourier and Watershed; and classifiers including support vector machines, linear discriminants, nearest neighbour, and meta-classifier combinations. The most accurate method is found to use linear binary patterns together with a support vector machin

Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland

Automated robotic weeding of grassland will improve the productivity of dairy and sheep farms while helping to conserve their environments. Previous studies have reported results of machine vision methods to separate grass from grassland weeds but each use their own datasets and report only performance of their own algorithm, making it impossible to compare them. A definitive, large-scale independent study is presented of all major known grassland weed detection methods evaluated on a new standardised data set under a wider range of environment conditions. This allows for a fair, unbiased, independent and statistically significant comparison of these and future methods for the first time. We test features including linear binary patterns, BRISK, Fourier and Watershed; and classifiers including support vector machines, linear discriminants, nearest neighbour, and meta-classifier combinations. The most accurate method is found to use linear binary patterns together with a support vector machin

When Should the Chicken Cross the Road? - Game Theory for Autonomous Vehicle - Human Interactions

Autonomous vehicle localization, mapping and planning in un-reactive environments are well-understood, but the human factors of complex interactions with other road users are not yet developed. This study presents an initial model for negotiation between an autonomous vehicle and another vehicle at an unsigned intersections or (equivalently) with a pedestrian at an unsigned road-crossing (jaywalking), using discrete sequential game theory. The model is intended as a basic framework for more realistic and data-driven future extensions. The model shows that when only vehicle position is used to signal intent, the optimal behaviors for both agents must include a non-zero probability of allowing a collision to occur. This suggests extensions to reduce this probability in future, such as other forms of signaling and control. Unlike most Game Theory applications in Economics, active vehicle control requires real-time selection from multiple equilibria with no history, and we present and argue for a novel solution concept, meta-strategy convergence, suited to this task

A heuristic model for pedestrian intention estimation

Understanding pedestrian behaviour and controlling interactions with pedestrians is of critical importance for autonomous vehicles, but remains a complex and challenging problem. This study infers pedestrian intent during possible road-crossing interactions, to assist autonomous vehicle decisions to yield or not yield when approaching them, and tests a simple heuristic model of intent on pedestrian-vehicle trajectory data for the first time. It relies on a heuristic approach based on the observed positions of the agents over time. The method can predict pedestrian crossing intent, crossing or stopping, with 96% accuracy by the time the pedestrian reaches the curbside, on the standard Daimler pedestrian dataset. This result is important in demarcating scenarios which have a clear winner and can be predicted easily with the simple heuristic, from those which may require more complex game-theoretic models to predict and control

Making optical atomic clocks more stable with 10−1610^{-16} level laser stabilization

The superb precision of an atomic clock is derived from its stability. Atomic clocks based on optical (rather than microwave) frequencies are attractive because of their potential for high stability, which scales with operational frequency. Nevertheless, optical clocks have not yet realized this vast potential, due in large part to limitations of the laser used to excite the atomic resonance. To address this problem, we demonstrate a cavity-stabilized laser system with a reduced thermal noise floor, exhibiting a fractional frequency instability of $2 \times 10^{-16}$. We use this laser as a stable optical source in a Yb optical lattice clock to resolve an ultranarrow 1 Hz transition linewidth. With the stable laser source and the signal to noise ratio (S/N) afforded by the Yb optical clock, we dramatically reduce key stability limitations of the clock, and make measurements consistent with a clock instability of $5 \times 10^{-16} / \sqrt{\tau}$

Cerebral fat embolism and the "starfield" pattern: a case report

Nearly all long-bone fractures are accompanied by some form of fat embolism. The rare complication of clinically significant fat embolism syndrome, however, occurs in only 0.9-2.2% of cases. The clinical triad of fat embolism syndrome consists of respiratory distress, altered mental status, and petechial rash. Cerebral fat embolism causes the neurologic involvement seen in fat embolism syndrome. A 19-year-old African-American male was admitted with gunshot wounds to his right hand and right knee. He had diffuse hyperactive deep tendon reflexes, bilateral ankle clonus and decerebrate posturing with a Glasgow Coma Scale (GCS) score of 4T. Subsequent MRI of the brain showed innumerable punctate areas of restricted diffusion consistent with "starfield" pattern. On a 10-week follow up he has a normal neurological examination and he is discharged home. Despite the severity of the neurologic insult upon initial presentation, the majority of case reports on cerebral fat embolism illustrate that cerebral dysfunction associated with cerebral fat embolism is reversible. When neurologic deterioration occurs in the non-head trauma patient, then a systemic cause such as fat emboli should be considered. We describe a patient with non-head trauma who demonstrated the classic "starfield" pattern on diffusion-weighted MRI imaging

Legal framework for small autonomous agricultural robots

Legal structures may form barriers to, or enablers of, adoption of precision agriculture management with small autonomous agricultural robots. This article develops a conceptual regulatory framework for small autonomous agricultural robots, from a practical, self-contained engineering guide perspective, sufficient to get working research and commercial agricultural roboticists quickly and easily up and running within the law. The article examines the liability framework, or rather lack of it, for agricultural robotics in EU, and their transpositions to UK law, as a case study illustrating general international legal concepts and issues. It examines how the law may provide mitigating effects on the liability regime, and how contracts can be developed between agents within it to enable smooth operation. It covers other legal aspects of operation such as the use of shared communications resources and privacy in the reuse of robot-collected data. Where there are some grey areas in current law, it argues that new proposals could be developed to reform these to promote further innovation and investment in agricultural robots

Cost-effectiveness Analysis of Rivaroxaban in the Secondary Prevention of Acute Coronary Syndromes in Sweden.

BACKGROUND: Worldwide, coronary heart disease accounts for 7 million deaths each year. In Sweden, acute coronary syndrome (ACS) is a leading cause of hospitalization and is responsible for 1 in 4 deaths. OBJECTIVE: The aim of this analysis was to assess the cost-effectiveness of rivaroxaban 2.5 mg twice daily (BID) in combination with standard antiplatelet therapy (ST-APT) versus ST-APT alone, for the secondary prevention of ACS in adult patients with elevated cardiac biomarkers without a prior history of stroke/transient ischemic attack (TIA), from a Swedish societal perspective, based on clinical data from the global ATLAS ACS 2-TIMI 51 trial, literature-based quality of life data and costs sourced from Swedish national databases. METHODS: A Markov model was developed to capture rates of single and multiple myocardial infarction (MI), ischemic and hemorrhagic stroke, thrombolysis in myocardial infarction (TIMI) major, minor, and "requiring medical attention" bleeds, revascularization events, and associated costs and utilities in patients who were stabilized after an initial ACS event. Efficacy and safety data for the first 2 years came from the ATLAS ACS 2-TIMI 51 trial. Long-term probabilities were extrapolated using safety and effectiveness of acetylsalicylic acid data, which was estimated from published literature, assuming constant rates in time. Future cost and effects were discounted at 3.0%. Univariate and probabilistic sensitivity analyses were conducted. RESULTS: In the base case, the use of rivaroxaban 2.5 mg BID was associated with improvements in survival and quality-adjusted life years (QALYs), yielding an incremental cost per QALY of 71,246 Swedish Krona (SEK) (€8045). The outcomes were robust to changes in inputs. The probabilistic sensitivity analysis demonstrated rivaroxaban 2.5 mg BID to be cost-effective in >99.9% of cases, assuming a willingness-to-pay threshold of SEK 500,000 (€56,458). CONCLUSION: Compared with ST-APT alone, the use of rivaroxaban 2.5 mg BID in combination with ST-APT can be considered a cost-effective treatment option for ACS patients with elevated cardiac biomarkers without a prior history of stroke/TIA in Sweden. FUNDING: Bayer Pharma AG