End-to-End Eye Movement Detection Using Convolutional Neural Networks

Common computational methods for automated eye movement detection - i.e. the task of detecting different types of eye movement in a continuous stream of gaze data - are limited in that they either involve thresholding on hand-crafted signal features, require individual detectors each only detecting a single movement, or require pre-segmented data. We propose a novel approach for eye movement detection that only involves learning a single detector end-to-end, i.e. directly from the continuous gaze data stream and simultaneously for different eye movements without any manual feature crafting or segmentation. Our method is based on convolutional neural networks (CNN) that recently demonstrated superior performance in a variety of tasks in computer vision, signal processing, and machine learning. We further introduce a novel multi-participant dataset that contains scripted and free-viewing sequences of ground-truth annotated saccades, fixations, and smooth pursuits. We show that our CNN-based method outperforms state-of-the-art baselines by a large margin on this challenging dataset, thereby underlining the significant potential of this approach for holistic, robust, and accurate eye movement protocol analysis

On the complexity of resource-bounded logics

We revisit decidability results for resource-bounded logics and use decision problems on vector addition systems with states (VASS) in order to establish complexity characterisations of (decidable) model checking problems. We show that the model checking problem for the logic RB+-ATL is 2EXPTIME-complete by using recent results on alternating VASS (and in EXPTIME when the number of resources is bounded). Moreover, we establish that the model checking problem for RBTL is EXPSPACE-complete. The problem is decidable and of the same complexity for RBTL*, proving a new decidability result as a by-product of the approach. When the number of resources is bounded, the problem is in PSPACE. We also establish that the model checking problem for RB+-ATL*, the extension of RB+-ATL with arbitrary path formulae, is decidable by a reduction to parity games for single-sided VASS (a variant of alternating VASS). Furthermore, we are able to synthesise values for resource parameters. Hence, the paper establishes formal correspondences between model checking problems for resource bounded logics advocated in the AI literature and decision problems on alternating VASS, paving the way for more applications and cross-fertilizations

Competition reaction-based prediction of polyamines' stepwise protonation constants: a case study involving 1,4,7,10-tetraazadecane (2,2,2-tet)

Theoretical prediction of four stepwise protonation constants of 1,4,7,10-tetraazadecane (2,2,2- tet) in correct order and with the smallest (largest) deviation of about 0.1 (–0.8) log unit from experimental values was achieved by an explicit application of a competition reaction (CRn) methodology in discrete-continuum solvation model involving four explicit water molecules. This methodology performs best when (i) tested (L(1)) and reference (L(2)) molecules are structurally similar, (ii) lowest energy conformers (LECs, selected from all possible tautomers) are used and (iii) a CRn, which assures a balanced charge distribution between reactants and products, Hn–1L(1) + HnL(2) = HnL(1) + Hn–1L(2), is implemented. A 5-step EEBGB-protocol was developed to effectively and in shortest time possible select LECs (E, B and G stands for electronic-energy-, Boltzmann-distribution- and Gibbs-free-energy-based stepwise selection of conformers). The EEBGB-protocol (i) reduced (by 94%) the number of conformers subjected to the frequency calculations (to obtain G-values) from 420 MM-selected to 25 used to compute four protonation constants and (ii) is of general-purpose as it is applicable to any flexible and poly-charged molecules. Moreover, in search for LECs, a rapid pre-screening protocol was developed and tested; it was found efficient for the purpose of this study. Additional research protocols, aimed at even better prediction of protonation constants, are also suggested.This work is based on the research supported in part by the National Research Foundation of South Africa (Grant Numbers 87777) and the University of Pretoria.http://link.springer.com/journal/102672017-05-31hb2016Chemistr

On the complexity of resource-bounded logics

We revisit decidability results for resource-bounded logics and use decision problems for vector addition systems with states (VASS) to characterise the complexity of (decidable) model-checking problems. We show that the model-checking problem for the logic RB+-ATL is 2EXPTIME-complete by using recent results on alternating VASS. In addition, we establish that the model-checking problem for RBTL is decidable and has the same complexity as for RBTL* (the extension of RBTL with arbitrary path formulae), namely EXPSPACE-complete, proving a new decidability result as a by-product of the approach. Finally, we establish that the model-checking problem for RB+-ATL* is decidable by a reduction to parity games, and show how to synthesise values for resource parameters

Post-feeding activity of Lucilia sericata (Diptera: Calliphoridae) on common domestic indoor surfaces and its effect on development.

Developmental data of forensically important blowflies used by entomologists to estimate minimum post mortem interval (mPMI) are established under controlled laboratory conditions for various temperature ranges throughout the stages of egg, 1st-3rd instar, puparia, and adult fly emergence. However, environmental conditions may influence the patterns of development and behaviour of blowflies, potentially impacting on these established development rates. Previous studies investigating indoor colonisation have focused on the delay to oviposition, with behaviour during the post-feeding phase in this setting often overlooked. The environment in which third instar larvae disperse when searching for a pupariation site may vary drastically at both outdoor and indoor scenarios, influencing the activity and distance travelled during this phase and possibly affecting developmental rates. This study investigated the effect of eight common domestic indoor surfaces on dispersal time, distance travelled, and behaviour of post-feeding Lucilia sericata as well as any resulting variation in development. It was found that pupariation and puparia length within a pupariation medium of sawdust (often used in laboratory settings) produced comparable results with that of carpeted environments (those deemed to be 'enclosed'). Non-carpeted environments (those which were 'exposed') produced a delay to pupariation likely due to increased activity and energy expenditure in searching for pupariation sites which enabled burial. In addition, the observed speed of travel during dispersal was seen via time lapse photography to be greater within 'exposed' conditions. Larvae which dispersed upon burnt laminate flooring were observed to travel faster than in all other conditions and showed the only significant variation (P=0.04) in the day of emergence in comparison to the control condition of sawdust. This study has demonstrated that wandering phase activity is affected by the environmental surface which has potential implications for estimating both the distance travelled by dispersing larvae in indoor conditions and with further research, may be a consideration in mPMI calculations.This work was supported by the University of Derby Undergraduate Research Scholarly Scheme (grant number URSS056)

Real-time estimation of horizontal gaze angle by saccade integration using in-ear electrooculography

The manuscript proposes and evaluates a real-time algorithm for estimating eye gaze angle based solely on single-channel electrooculography (EOG), which can be obtained directly from the ear canal using conductive ear moulds. In contrast to conventional high-pass filtering, we used an algorithm that calculates absolute eye gaze angle via statistical analysis of detected saccades. The estimated eye positions of the new algorithm were still noisy. However, the performance in terms of Pearson product-moment correlation coefficients was significantly better than the conventional approach in some instances. The results suggest that in-ear EOG signals captured with conductive ear moulds could serve as a basis for lightweight and portable horizontal eye gaze angle estimation suitable for a broad range of applications. For instance, for hearing aids to steer the directivity of microphones in the direction of the user’s eye gaze

A study on the natural history of scanning behaviour in patients with visual field defects after stroke

BACKGROUND: A visual field defect (VFD) is a common consequence of stroke with a detrimental effect upon the survivors' functional ability and quality of life. The identification of effective treatments for VFD is a key priority relating to life post-stroke. Understanding the natural evolution of scanning compensation over time may have important ramifications for the development of efficacious therapies. The study aims to unravel the natural history of visual scanning behaviour in patients with VFD. The assessment of scanning patterns in the acute to chronic stages of stroke will reveal who does and does not learn to compensate for vision loss. METHODS/DESIGN: Eye-tracking glasses are used to delineate eye movements in a cohort of 100 stroke patients immediately after stroke, and additionally at 6 and 12 months post-stroke. The longitudinal study will assess eye movements in static (sitting) and dynamic (walking) conditions. The primary outcome constitutes the change of lateral eye movements from the acute to chronic stages of stroke. Secondary outcomes include changes of lateral eye movements over time as a function of subgroup characteristics, such as side of VFD, stroke location, stroke severity and cognitive functioning. DISCUSSION: The longitudinal comparison of patients who do and do not learn compensatory scanning techniques may reveal important prognostic markers of natural recovery. Importantly, it may also help to determine the most effective treatment window for visual rehabilitation.Tobias Loetscher, Celia Chen, Sophie Wignall, Andreas Bulling, Sabrina Hoppe, Owen Churches, Nicole A Thomas, Michael E R Nicholls and Andrew Le

Human Activity Recognition Using Deep Models and Its Analysis from Domain Adaptation Perspective

© 2019, Springer Nature Switzerland AG. Human activity recognition (HAR) is a broad area of research which solves the problem of determining a user’s activity from a set of observations recorded on video or low-level sensors (accelerometer, gyroscope, etc.) HAR has important applications in medical care and entertainment. In this paper, we address sensor-based HAR, because it could be deployed on a smartphone and eliminates the need to use additional equipment. Using machine learning methods for HAR is common. However, such, methods are vulnerable to changes in the domain of training and test data. More specifically, a model trained on data collected by one user loses accuracy when utilised by another user, because of the domain gap (differences in devices and movement pattern results in differences in sensors’ readings.) Despite significant results achieved in HAR, it is not well-investigated from domain adaptation (DA) perspective. In this paper, we implement a CNN-LSTM based architecture along with several classical machine learning methods for HAR and conduct a series of cross-domain tests. The result of this work is a collection of statistics on the performance of our model under DA task. We believe that our findings will serve as a foundation for future research in solving DA problem for HAR