Detection Of Unexpected Events Research Articles

What does it mean to represent? Mental representations as falsifiable memory patterns

Representation is a key notion in neuroscience and artificial intelligence (AI). However, a longstanding philosophical debate highlights that specifying what counts as representation is trickier than it seems. With this brief opinion paper we would like to bring the philosophical problem of representation into attention and provide an implementable solution. We note that causal and teleological approaches often assumed by neuroscientists and engineers fail to provide a satisfactory account of representation. We sketch an alternative according to which representations correspond to inferred latent structures in the world, identified on the basis of conditional patterns of activation. These structures are assumed to have certain properties objectively, which allows for planning, prediction, and detection of unexpected events. We illustrate our proposal with the simulation of a simple neural network model. We believe this stronger notion of representation could inform future research in neuroscience and AI.

Comparative Study of Ensemble Method vs Deep Learning on Human Activity Recognition for Elderly Care

A drastic increase in healthcare demand has come from the explosive growth of the older population. The elderly are, on average, more vulnerable to health problems than other age groups. Unpredictable events, such as sudden falls, can be avoided with proper monitoring. Activity recognition can help people avoid potentially dangerous behaviours by aiding in the detection of unexpected events. Most of the existing approaches require complex sensors and environment setup, involve data filtering and noise removal steps, and most often the chosen learning models need to be tuned and carefully designed for optimal performance. This study emphasizes light implementation, fast training time, easy experimental setup, and minimal parameter tuning. Human activities are captured using smartphone sensors in this study. Students and senior residents from a local home care facility are among the volunteers for this study. The necessary data sets are obtained from the accelerometer sensor on the smartphone. To provide baseline performance, the traditional instant-based learning architectures k-Nearest Neighbors (kNN) and Support Vector Machine (SVM) are used. To represent the ensemble learning model, the Random Forest (RF) and XGBoost (XGB) are investigated. The Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) are the more advanced deep learning models used in this study (CNN). The results reveal that ensemble methods and deep learning models provide improved accuracy, with ensemble learning models outperforming deep learning models.

Recent Visual Experience Shapes Visual Processing in Rats through Stimulus-Specific Adaptation and Response Enhancement

From an ecological point of view, it is generally suggested that the main goal of vision in rats and mice is navigation and (aerial) predator evasion [1-3]. The latter requires fast and accurate detection of a changein the visual environment. An outstanding question is whether there are mechanisms in the rodent visual system that would support and facilitate visual change detection. An experimental protocol frequently used to investigate change detection in humans is the oddball paradigm, in which a rare, unexpected stimulus is presented in a train of stimulus repetitions [4]. A popular "predictive coding" theory of cortical responses states that neural responses should decrease for expected sensory input and increase for unexpected input [5, 6]. Despite evidence for response suppression and enhancement in noninvasive scalp recordings in humans with this paradigm [7, 8], it has proven challenging to observe both phenomena in invasive action potential recordings in other animals [9-11]. During a visual oddball experiment, we recorded multi-unit spiking activity in rat primary visual cortex (V1) and latero-intermediate area (LI), which is a higher area of the rodent ventral visual stream. In rat V1, there was only evidence for response suppression related to stimulus-specific adaptation, and not for response enhancement. However, higher up in area LI, spiking activity showed clear surprise-based response enhancement in addition to stimulus-specific adaptation. These results show that neural responses along the rat ventral visual stream become increasingly sensitive to changes in the visual environment, suggesting a system specialized in the detection of unexpected events.

Inattentional blindness in older adults: Effects of attentional set and to-be-ignored distractors.

Inattentional blindness (IB) involves failing to detect an unexpected visual stimulus while undertaking another task. Previous research has predominantly investigated IB using young adult samples, with few studies exploring whether or how an observer's age affects their detection of unexpected events. To help address this gap, we compared younger adults (18-25 years of age) and older adults (60-80 years of age) on two IB tasks: one dynamic, one static. In the static task, older age was associated with substantially increased IB rates: 89 % for older adults versus 5 % for younger adults. In the dynamic task, we systematically manipulated the presence of to-be-ignored distractors and whether the unexpected stimulus color matched the observers' attentional set. We found a main effect of age on IB: As in the static task, older age was associated with increased IB rates (38 % for older adults vs. 8 % for younger adults). The presence of to-be-ignored distractors and attentional set mismatch interacted to substantially increase IB rates, but age did not interact with either factor. Overall, the results indicate that older age is associated with large increases in IB rates across a range of tasks. The pattern of results is consistent with attentional capacity models of cognitive aging, suggesting that older adults' reduced cognitive resources result in failure to consciously process stimuli that are inconsistent with their attentional set.

Apples to Oranges

Inattentional blindness research suggests that individuals are not “blind” to unexpected events; instead, unexpected information is perceived and processed, and can reach attention, based on the information contained within these events. Factors that have been shown to influence further processing and subsequent attention include luminance (Most, Simons, Scholl, and Chabris, 2000) and color (Welk, Creager, and Gillan, 2014). The current research examined how semantic content of unexpected events influenced the processing of these events in an inattentional dual task. Participants performed a dynamic, computer-based visual task, in which unexpected events occasionally occurred; semantic relevance of these unexpected events was manipulated across trials. Results indicated that the degree of semantic relatedness of unexpected events did not significantly affect primary task performance or the detection of unexpected events. This suggests that semantic content is processed differently from perceptual content in a visual inattention task. The authors propose that the processing of semantic information in visual inattention tasks may be analogous to the processing that takes place in dichotic listening tasks; specifically that the processing of semantic content may be more dependent on the availability of attentional resources than perceptual content. Future directions are discussed.

Mediofrontal event‐related potentials following observed actions reflect an action prediction error

Both execution and observation of erroneous actions have been shown to increase the activity of the anterior cingulate cortex (ACC) as reflected in characteristic event-related potential (ERP) components labelled error-related negativity (ERN) and observer error-related negativity (oERN), respectively. Whereas these labels implicate a modulation of both components by response accuracy, recent findings suggest a more general involvement of the ACC in the detection of unexpected events. In previous studies, a lower frequency of erroneous as compared with correct observed actions resulted in lower expectation of erroneous actions. The present study investigates whether ERPs following observed actions are modulated by response accuracy or violation of expectation. Sixteen human subjects observed a virtual person whose actions in a game were expected or unexpected. Action expectation was independent of accuracy. In both conditions, subjects observed correct and incorrect actions equally often. Whereas ERPs were not modulated by accuracy, we found an enhanced amplitude of a negative frontocentral ERP component in the time window of the oERN for unexpected as compared with expected observed actions, which we suggest reflects an action prediction error. These results propose that the function of the ACC in performance monitoring depends less on accuracy of actions but rather on predictions and their violations. Future research will have to clarify whether the present ERP modulations revealed a feature of the oERN or whether they represent a distinct component.

Telehealth Ontario detection of gastrointestinal illness outbreaks.

Prompt detection of infectious disease outbreaks and rapid introduction of mitigation strategies is a primary concern for public health, emergency and security management organizations. Traditional surveillance methods rely on astute clinical detection and reporting of disease or laboratory confirmation. Although effective, these methods are slow, dependent on physician compliance and delay timely, effective intervention. To address these issues, syndromic surveillance programs have been integrated into the health care system at the earliest points of access; in Ontario, these points are primary care providers, emergency departments (ED), and Telehealth Ontario. This study explores the role of Telehealth Ontario, a telephone helpline, as an early warning system for detection of gastrointestinal (GI) illness. Retrospective time-series analysis of the National Ambulatory Care Reporting System (NACRS) ED discharges and Telehealth Ontario data for GI illness from June 1, 2004 to March 31, 2006. Telehealth Ontario recorded 184,904 calls and the NACRS registered 34,499 ED visits for GI illness. The Spearman rank correlation coefficient was calculated to be 0.90 (p < 0.0001). Time-series analysis resulted in significant correlation at lag (weekly) 0 indicating that increases in Telehealth Ontario call volume correlate with increases in NACRS data for GI illness. Telehealth Ontario call volume fluctuation reflects directly on ED GI visit data on a provincial basis. Telehealth Ontario GI call complaints are a timely, novel and representative data stream that shows promise for integration into a real-time syndromic surveillance system for detection of unexpected events.

A Statistical Video Content Recognition Method Using Invariant Features on Object Trajectories

This work is dedicated to a statistical trajectory-based approach addressing two issues related to dynamic video content understanding: recognition of events and detection of unexpected events. Appropriate local differential features combining curvature and motion magnitude are defined and robustly computed on the motion trajectories in the image sequence. These features are invariant to image translation, in-the-plane rotation and spatial scaling. The temporal causality of the features is then captured by hidden Markov models dedicated to trajectory description, whose states are properly quantized values. The similarity between trajectories is expressed by exploiting this quantization-based HMM framework. Moreover statistical techniques have been developed for parameter estimations. Evaluations of the method have been conducted on several data sets including real trajectories obtained from sport videos, especially Formula One and ski TV program. The novel method compares favorably with other methods including feature histogram comparisons, HMM/GMM modeling and SVM classification.

Chirurgie ambulatoire : sécurité, qualité

Demands related to regulation of ambulatory surgery in France and to expectations of the patients imply that we can be able to proof that ambulatory organization guarantees total patient care with optimal safety conditions and that detection of unexpected events is the first step in a larger approach to continue quality improvement and minimize risks for patients. To achieve these goals, we suggest a list of means based on international ambulatory surgery definitions, including clinical indicators, frequent asked questions and measurement of patient satisfaction.

Adaptive Pattern Recognition

A long term goal of research in artificial intelligence is to determine and to implement principles which permit a movable machine to direct its actions depending upon sensory feed-back from its environment. This paper concentrates on spatial sensors which input images (2-dimensional arrays). A proposal is put forward in which the machine adapts to the actual data, and examples are given of input prediction, of detection of unexpected events, and of recognition of spatial patterns. The image sequence is locally partitioned into temporally contiguous subsequences of a fixed spatial extent. The spatial extent is constant over time and the temporal extent of a subsequence is maximized subject to the condition that the subsequence has occurred previously. The principle is illustrated on image sequences. It is further demonstrated on images which are structured as pseudo-temporal sequences of their rows. The demonstrations use diverse complex and simple examples to illustrate the versatility of the method. The demonstrations show that to a large degree it is not necessary for the user to supply explicit models for different pattern recognition tasks.