From cognitive maps to spatial schemas

A schema refers to a structured body of prior knowledge that captures common patterns across related experiences. Schemas have been studied separately in the realms of episodic memory and spatial navigation across different species and have been grounded in theories of memory consolidation, but there has been little attempt to integrate our understanding across domains, particularly in humans. We propose that experiences during navigation with many similarly structured environments give rise to the formation of spatial schemas (for example, the expected layout of modern cities) that share properties with but are distinct from cognitive maps (for example, the memory of a modern city) and event schemas (such as expected events in a modern city) at both cognitive and neural levels. We describe earlier theoretical frameworks and empirical findings relevant to spatial schemas, along with more targeted investigations of spatial schemas in human and non-human animals. Consideration of architecture and urban analytics, including the influence of scale and regionalization, on different properties of spatial schemas may provide a powerful approach to advance our understanding of spatial schemas

Estimation of heart rate from foot worn photoplethysmography sensors during fast bike exercise

This paper presents a new method for estimating the average heart rate from a foot/ankle worn photoplethysmography (PPG) sensor during fast bike activity. Placing the PPG sensor on the lower half of the body allows more energy to be collected from energy harvesting in order to give a power autonomous sensor node, but comes at the cost of introducing significant motion interference into the PPG trace. We present a normalised least mean square adaptive filter and short-time Fourier transform based algorithm for estimating heart rate in the presence of this motion contamination. Results from 8 subjects show the new algorithm has an average error of 9 beats-per-minute when compared to an ECG gold standard

Willingness to Engage in Collective Action After the Killing of an Unarmed Black Man: Differential Pathways for Black and White Individuals

This cross-sectional survey study examined the underlying psychosocial constructs of Black (n = 163) and White (n = 246) university students\u27 willingness to endorse racially motivated collective action. Consistent with the defensive motivation system model, we expected the police shooting of an unarmed Black American to activate concerns about personal safety, thereby eliciting negative affect, lack of forgiveness of the perpetrator, and motivation to engage in collective action. This path model was expected for both Black and White participants, with stronger associations among Black participants. In the full model, Black participants identified more with the victim and indicated greater personal threat, which led to (1) more negative affect and greater endorsement of collective action and (2) greater avoidance of the shooter and greater endorsement of collective action. In the Black participants model, collective action was explained by identifying with the victim and feeling personally threatened. In the White participants model, collective action was explained by three pathways stemming from identifying with the victim and personal threat, including negative affect, seeking avoidance, and seeking revenge. The results indicate different mechanisms to explain Black and White individuals\u27 motivation to endorse collective action to prevent police-involved shootings of unarmed Black Americans

Regional coherence evaluation in mild cognitive impairment and Alzheimer's disease based on adaptively extracted magnetoencephalogram rhythms

This study assesses the connectivity alterations caused by Alzheimer's disease (AD) and mild cognitive impairment (MCI) in magnetoencephalogram (MEG) background activity. Moreover, a novel methodology to adaptively extract brain rhythms from the MEG is introduced. This methodology relies on the ability of empirical mode decomposition to isolate local signal oscillations and constrained blind source separation to extract the activity that jointly represents a subset of channels. Inter-regional MEG connectivity was analysed for 36 AD, 18 MCI and 26 control subjects in δ, θ, α and β bands over left and right central, anterior, lateral and posterior regions with magnitude squared coherence—c(f). For the sake of comparison, c(f) was calculated from the original MEG channels and from the adaptively extracted rhythms. The results indicated that AD and MCI cause slight alterations in the MEG connectivity. Computed from the extracted rhythms, c(f) distinguished AD and MCI subjects from controls with 69.4% and 77.3% accuracies, respectively, in a full leave-one-out cross-validation evaluation. These values were higher than those obtained without the proposed extraction methodology

On the sensitivity of local flexibility markets to forecast error : A bi-level optimization approach

The large-scale integration of intermittent distributed energy resources has led to increased uncertainty in the planning and operation of distribution networks. The optimal flexibility dispatch is a recently introduced, power flow-based method that a distribution system operator can use to effectively determine the amount of flexibility it needs to procure from the controllable resources available on the demand side. However, the drawback of this method is that the optimal flexibility dispatch is inexact due to the relaxation error inherent in the second-order cone formulation. In this paper we propose a novel bi-level optimization problem, where the upper level problem seeks to minimize the relaxation error and the lower level solves the earlier introduced convex second-order cone optimal flexibility dispatch (SOC-OFD) problem. To make the problem tractable, we introduce an innovative reformulation to recast the bi-level problem as a non-linear, single level optimization problem which results in no loss of accuracy. We subsequently investigate the sensitivity of the optimal flexibility schedules and the locational flexibility prices with respect to uncertainty in load forecast and flexibility ranges of the demand response providers which are input parameters to the problem. The sensitivity analysis is performed based on the perturbed Karush-Kuhn-Tucker (KKT) conditions. We investigate the feasibility and scalability of the proposed method in three case studies of standardized 9-bus, 30-bus, and 300-bus test systems. Simulation results in terms of local flexibility prices are interpreted in economic terms and show the effectiveness of the proposed approach.</p

To be high-risk, or not to be - semantic specifications and implications of the AI act’s high-risk AI applications and harmonised standards

The EU’s proposed AI Act sets out a risk-based regulatory framework to govern the potential harms emanating from use of AI systems. Within the AI Act’s hierarchy of risks, the AI systems that are likely to incur “high-risk” to health, safety, and fundamental rights are subject to the majority of the Act’s provisions. To include uses of AI where fundamental rights are at stake, Annex III of the Act provides a list of applications wherein the conditions that shape high-risk AI are described. For high-risk AI systems, the AI Act places obligations on providers and users regarding use of AI systems and keeping appropriate documentation through the use of harmonised standards. In this paper, we analyse the clauses defining the criteria for high-risk AI in Annex III to simplify identification of potential high-risk uses of AI by making explicit the “core concepts” whose combination makes them high-risk. We use these core concepts to develop an open vocabulary for AI risks (VAIR) to represent and assist with AI risk assessments in a form that supports automation and integration. VAIR is intended to assist with identification and documentation of risks by providing a common vocabulary that facilitates knowledge sharing and interoperability between actors in the AI value chain. Given that the AI Act relies on harmonised standards for much of its compliance and enforcement regarding high-risk AI systems, we explore the implications of current international standardisation activities undertaken by ISO and emphasise the necessity of better risk and impact knowledge bases such as VAIR that can be integrated with audits and investigations to simplify the AI Act’s application

Comparison and analysis of 3 Key AI documents: EU’s proposed AI Act, assessment list for trustworthy AI (ALTAI), and ISO/IEC 42001 AI management system

Conforming to multiple and sometimes conflicting guidelines, standards, and legislations regarding development, deployment, and governance of AI is a serious challenge for organisations. While the AI standards and regulations are both in early stages of development, it is prudent to avoid a highly-fragmented landscape and market confusion by finding out the gaps and resolving the potential conflicts. This paper provides an initial comparison of ISO/IEC 42001 AI management sys- tem standard with the EU trustworthy AI assessment list (ALTAI) and the proposed AI Act using an upper-level ontology for semantic interop- erability between trustworthy AI documents with a focus on activities. The comparison is provided as an RDF resource graph to enable further enhancement and reuse in an extensible and interoperable manner

Gyroscope vs. accelerometer measurements of motion from wrist PPG during physical exercise

Many wearable devices include PPG (photoplethysmography) sensors for non-invasive heart rate monitoring. However, PPG signals are heavily corrupted by motion interference, and rely on simultaneous motion measurements to remove the interference. Accelerometers are used commonly, but cannot differentiate between acceleration due to movement and acceleration due to gravity. This paper compares measurements of motion using accelerometers and gyroscopes to give a more complete estimate of wrist motion. Results show the two sensor signals are very different, with low correlations present. When used in a wrist PPG heart rate algorithm gyroscope motion estimates obtain better performance in half of the cases

An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed the temporal and anterior lobes of hippocampus regions of brain get affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to obtain accurate diagnosis of SZ. This paper presents a comprehensive overview of studies conducted on automated diagnosis of SZ using MRI modalities. Main findings, various challenges, and future works in developing the automated SZ detection are described in this paper

Accelerometry-Based Estimation of Respiratory Rate for Post-Intensive Care Patient Monitoring

This paper evaluates the use of accelerometers for continuous monitoring of respiratory rate (RR), which is an important vital sign in post-intensive care patients or those inside the intensive care unit (ICU). The respiratory rate can be estimated from accelerometer and photoplethysmography (PPG) signals for patients following ICU discharge. Due to sensor faults, sensor detachment, and various artifacts arising from motion, RR estimates derived from accelerometry and PPG may not be sufficiently reliable for use with existing algorithms. This paper described a case study of 10 selected patients, for which fewer RR estimates have been obtained from PPG signals in comparison to those from accelerometry. We describe an algorithm for which we show a maximum mean absolute error between estimates derived from PPG and accelerometer of 2.56 breaths/min. Our results obtained using the 10 selected patients are highly promising for estimation of RR from accelerometers, where significant agreements have been observed with the PPG-based RR estimates in many segments and across various patients. We present this research as a step towards producing reliable RR monitoring systems using low-cost mobile accelerometers for monitoring patients inside the ICU or on the ward (post-ICU)