Coding Framework Research Articles

Oscillatory traveling waves provide evidence for predictive coding abnormalities in schizophrenia

BackgroundThe computational mechanisms underlying psychiatric disorders are hotly debated. One hypothesis, grounded in the Bayesian predictive coding framework, proposes that schizophrenia patients have abnormalities in encoding prior beliefs about the environment, resulting in abnormal sensory inference, which can explain core aspects of the psychopathology, such as some of its symptoms. MethodsHere, we tested this hypothesis by identifying oscillatory traveling waves as neural signatures of predictive coding. By analyzing an EEG dataset comprising 146 schizophrenia patients and 96 age-matched healthy controls, during resting states and a visual backward masking task. ResultsWe found that schizophrenia patients have stronger top-down alpha-band traveling waves compared to healthy controls during resting state, supposedly reflecting overly precise priors at higher levels of the predictive processing hierarchy. We also found stronger bottom-up alpha-band waves in schizophrenia patients during a visual task, in line with the notion of enhanced signaling of sensory precision errors. ConclusionsOur results yield a novel spatial-based characterization of oscillatory dynamics in schizophrenia, considering brain rhythms as traveling waves and providing a unique framework to study the different components involved in a predictive coding scheme. Altogether, our findings significantly advance our understanding of the mechanisms involved in fundamental pathophysiological aspects of schizophrenia, promoting a more comprehensive and hypothesis-driven approach to psychiatric disorders.

Challenges in the Implementation of EU Risk Minimisation Measures for Medicinal Products in Clinical Practice Guidelines: Mixed Methods Multi-Case Study.

Risk minimisation measures (RMMs) aim to ensure safe use of medicines, but their implementation in clinical practice is complicated by the diversity of stakeholders whose clinical decision making they seek to inform. Clinical practice guidelines (CPGs) are considered integral in clinical decision making. To determine the extent to which RMMs are included in the relevant CPGs and to describe factors that determine RMM inclusion. A multi-case study design using quantitative document analysis of CPGs combined with qualitative interviews with informants from organisations that issue CPGs. Cases from five therapeutic areas (TAs) with a regulatory requirement for further RMMs were studied individually in six EU member states (Denmark, Greece, Latvia, Netherlands, Portugal and Slovenia). Clinical practice guidelines were analysed using pre-defined coding frameworks. Interviewees were sampled purposively for experience and knowledge about CPG development and RMM inclusion. Verbatim interview transcripts were analysed inductively. In total, 136 CPGs were analysed, and RMM information about TAs was included in 25% of CPGs. Based on 71 interviews we found that factors that determine RMM inclusion in CPGs include clinicians' low awareness of RMMs despite awareness of RMMs' safety concern, low expectation of RMMs' clinical utility, and unfamiliarity with pharmacovigilance data supporting RMMs and perceived incompatibility of CPGs' scope and purpose and RMM information. The inclusion of RMM information in relevant CPGs is remarkably limited. It may be explained by characteristics of CPGs and of RMMs as well as lack of connection between national regulators and organisations and authors developing CPGs. More collaboration between stakeholders, national regulators and the EMA may advance implementation.

Stakeholder mapping of precision weeding commercialization ecosystem in California

CONTEXTPrecision weeding, a sector of agricultural technology in which drones and/or automated weeders use chemical, mechanical, or thermal means to eradicate weeds, has moved from academic research settings to commercialization. Because of labor shortage pressures, the push to gain competitive advantages, and the environmental impacts of excessive chemical inputs, many California growers have been interested in adopting precision weeding technologies in their operations. OBJECTIVEUsing semi-structured qualitative interviews, this study investigated the viewpoints of three key stakeholder groups involved in the diffusion and adoption of precision weeding technologies: California growers, precision weeding startups, and agricultural technology venture capital firms. With the supplemental viewpoints of large agricultural firms and their corporate venture capital arms and government agencies, this study seeks to understand the compatible motivations between stakeholders, current collaborative models between stakeholders and their limitations, and the user journey for growers adopting precision weeding technology. METHODSWe conducted 17 semi-structured qualitative interviews with diverse stakeholders in the precision weeding sector to gather textual data and gain high data saturation. Data collection balanced rigorous criteria for participant selection with adaptive interview questions to ensure depth and relevance. The analysis procedure involved coding and thematic framework development, complemented by grounded theory for iterative data examination and stakeholder map creation for distilling cross-organizational insights and stakeholder dynamics. RESULTS AND CONCLUSIONSThe results indicated that compatible motivations include addressing current labor issues, reducing costs, and the potential of precision weeding to transform agriculture. Less cited but still popular motivations included having more weeding options, meeting specific field needs, and environmental sustainability. The individual and stakeholder group cognitive maps demonstrated that concerns such as startup longevity, the high expenses of precision weeding machinery, and some startups lacking a direct connection to growers commonly limit the growers' adoption of precision weeding technologies. SIGNIFICANCEThe procedure and cognitive mapping presented in this study can be applied to other emerging agtech technologies and ecosystems.

Enhanced stochastic coding framework for discriminating faults and cyber incidents in hybrid systems

AbstractThis article presents an innovative approach for distinguishing replay attacks from faults in hybrid systems. To develop the idea at first, a novel technique for replay attack detection, which differentiates it from typical system faults, is introduced. This is achieved through a tactical combination of stochastic coding and a window‐based comparison mechanism, setting a new standard in hybrid system security. In the realm of fault detection, robust L2‐L∞ adaptive observers are employed for precise mode detection, allowing for an accurate assessment of the system's operational state. Additionally, robust H∞ Sliding Mode Observers are utilized to identify abnormal behaviours in the system's output, further solidifying the fault detection capabilities. A significant enhancement in the proposed approach is the integration of a Luenberger observer with the Butterworth low‐pass filter. This novel addition not only refines the filtering process but also contributes to the overall reliability and accuracy of the system. The efficiency and versatility of these methods are demonstrated through their application to a four‐tank hybrid system. This practical simulation showcases the adaptability of the approach to real‐world scenarios, highlighting its potential in diverse applications within the field of hybrid systems.

The impact of functional correlations on task information coding

ABSTRACT State-dependent neural correlations can be understood from a neural coding framework. Noise correlations—trial-to-trial or moment-to-moment covariability—can be interpreted only if the underlying signal correlation—similarity of task selectivity between pairs of neural units—is known. Despite many investigations in local spiking circuits, it remains unclear how this coding framework applies to large-scale brain networks. Here, we investigated relationships between large-scale noise correlations and signal correlations in a multitask human fMRI dataset. We found that task-state noise correlation changes (e.g., functional connectivity) did not typically change in the same direction as their underlying signal correlation (e.g., tuning similarity of two regions). Crucially, noise correlations that changed in the opposite direction as their signal correlation (i.e., anti-aligned correlations) improved information coding of these brain regions. In contrast, noise correlations that changed in the same direction (aligned noise correlations) as their signal correlation did not. Interestingly, these aligned noise correlations were primarily correlation increases, suggesting that most functional correlation increases across fMRI networks actually degrade information coding. These findings illustrate that state-dependent noise correlations shape information coding of functional brain networks, with interpretation of correlation changes requiring knowledge of underlying signal correlations.

Rapid diagnosis of the geospatial distribution of intertidal macroalgae using large-scale UAVs

Macroalgae have been used as indicators of the health of coastal ecosystems, they function as sinks of CO2 and are essential contributors to primary production. With the increase in anthropogenic activities, it is crucial to assess the impact of such activities on these ecosystems. As traditional surveying techniques, although accurate, are time-consuming and their area coverage is limited, novel techniques are required to monitor the coverage and diversity of intertidal macroalgae. We propose a methodology using the free-source Semi-Automatic Classification Plugin from QGIS to use UAV and multispectral cameras for the spatiotemporal monitoring of intertidal macroalgae. We also compared the performance of six classifiers: Minimum Distance (MD), Maximum Likelihood (ML), Spectral Angle Mapping (SAM), Multi-Layer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM), for three types of macroalgae classification: general, taxonomical groups and species. As proof of concept, an intertidal rocky shore in a marine protected area (NW Spain) was studied for four months. RF and SVM achieved similar results, with both being recommended for the general (OASVM = 97.4±1.7 and OARF = 98.3±1.7) and taxonomical groups (OASVM = 91.6±1.9 and OARF = 89.2±4.5). SVM and ML were found to be more suitable for species classification (OASVM = 77.4±11.4 and OAML = 74.2±9.7). SAM and MLP provided the least performant species classifiers because of the overlap in the macroalgae spectral signatures. The plugin showed limitations when tuning the input parameters of the MLP classifier and did not let to add a validation dataset. Additionally, we present an open-access GIS web application, Alganat 2000 GIS web, to facilitate the monitoring and management of coastal areas. We conclude that the proposed methodology using the SVM or ML classifiers is an effective tool for assessing intertidal macroalgal assemblages. Its easy and rapid implementation is beneficial for researchers who are not very familiar with coding and machine learning frameworks and reduces the time and cost of fieldwork. As future work, we propose the combination of the multispectral bands with topographic and spectral indices and to research the application of deep learning models to the classification of intertidal macroalgae.

A gradual transition toward categorical representations along the visual hierarchy during working memory, but not perception.

The ability to stably maintain visual information over brief delays is central to healthy cognitive functioning, as is the ability to differentiate such internal representations from external inputs. One possible way to achieve both is via multiple concurrent mnemonic representations along the visual hierarchy that differ systematically from the representations of perceptual inputs. To test this possibility, we examine orientation representations along the visual hierarchy during perception and working memory. Human participants directly viewed, or held in mind, oriented grating patterns, and the similarity between fMRI activation patterns for different orientations was calculated throughout retinotopic cortex. During direct viewing of grating stimuli, similarity was relatively evenly distributed amongst all orientations, while during working memory the similarity was higher around oblique orientations. We modeled these differences in representational geometry based on the known distribution of orientation information in the natural world: The "veridical" model uses an efficient coding framework to capture hypothesized representations during visual perception. The "categorical" model assumes that different "psychological distances" between orientations result in orientation categorization relative to cardinal axes. During direct perception, the veridical model explained the data well. During working memory, the categorical model gradually gained explanatory power over the veridical model for increasingly anterior retinotopic regions. Thus, directly viewed images are represented veridically, but once visual information is no longer tethered to the sensory world there is a gradual progression to more categorical mnemonic formats along the visual hierarchy.

In silico modeling of reservoir-based predictive coding in biological neuronal networks on microelectrode arrays

Reservoir computing and predictive coding together yield a computational model for exploring how neuronal dynamics in the mammalian cortex underpin temporal signal processing. Here, we construct an in-silico model of biological neuronal networks grown on microelectrode arrays and explore their computing capabilities through a sine wave prediction task in a reservoir-based predictive coding framework. Our results show that the time interval between stimulation pulses is a critical determinant of task performance. Additionally, under a fixed feedback latency, pulse amplitude modulation is a favorable encoding scheme for input signals. These findings provide practical guidelines for future implementation of the model in biological experiments.

Development of the Guide to Disseminating Research (GuiDiR): A consolidated framework

BackgroundLess than one third of research evidence is translated into policy or practice. Knowledge translation requires effective dissemination, adoption and finally implementation. These three stages are equally important, however, existing knowledge translation models and frameworks provide little and disparate information about the steps and activities required for effective dissemination. ObjectiveThis study aimed to empirically develop a consolidated framework of evidence-based steps and activities for disseminating research evidence. MethodsWe identified models and frameworks from a scoping review and dissemination and implementation webtool. We synthesised them into a prototype dissemination framework. Models and frameworks were eligible to inform steps in our framework if they fulfilled at least one of three elements of dissemination: intending to generate awareness of a message, incorporates targeting an audience: tailoring communication. An initial coding framework was created to organise data into dissemination steps.Drawing on ‘co-approach’ principles, authors of the included models and frameworks (dissemination experts) and health service researchers (end users) were invited to test and refine the prototype framework at a workshop. ResultsFrom 48 models and frameworks reviewed, only 32 fulfilled one or more of the three dissemination elements. The initial coding framework, upon refinement, yielded the Guide to Disseminating Research (GuiDiR) comprising five steps.1) Identify target audiences and dissemination partners.2) Engage with dissemination partners.3) Identify barriers and enablers to dissemination.4) Create dissemination messages.5) Disseminate and evaluate.Multiple activities were identified for each step and no single model or framework represents all steps and activities in GuiDiR. ConclusionsGuiDiR unifies dissemination components from knowledge translation models and frameworks and harmonises language into a format accessible to non-experts. It outlines for researchers, funders and practitioners the expected structure of dissemination and details the activities for executing an evidence-based dissemination strategy.

A Coding Framework and Benchmark Towards Low-Bitrate Video Understanding.

Video compression is indispensable to most video analysis systems. Despite saving the transportation bandwidth, it also deteriorates downstream video understanding tasks, especially at low-bitrate settings. To systematically investigate this problem, we first thoroughly review the previous methods, revealing that three principles, i.e., task-decoupled, label-free, and data-emerged semantic prior, are critical to a machine-friendly coding framework but are not fully satisfied so far. In this paper, we propose a traditional-neural mixed coding framework that simultaneously fulfills all these principles, by taking advantage of both traditional codecs and neural networks (NNs). On one hand, the traditional codecs can efficiently encode the pixel signal of videos but may distort the semantic information. On the other hand, highly non-linear NNs are proficient in condensing video semantics into a compact representation. The framework is optimized by ensuring that a transportation-efficient semantic representation of the video is preserved w.r.t. the coding procedure, which is spontaneously learned from unlabeled data in a self-supervised manner. The videos collaboratively decoded from two streams (codec and NN) are of rich semantics, as well as visually photo-realistic, empirically boosting several mainstream downstream video analysis task performances without any post-adaptation procedure. Furthermore, by introducing the attention mechanism and adaptive modeling scheme, the video semantic modeling ability of our approach is further enhanced. Fianlly, we build a low-bitrate video understanding benchmark with three downstream tasks on eight datasets, demonstrating the notable superiority of our approach. All codes, data, and models will be open-sourced for facilitating future research.

Developing and testing a framework for coding general practitioners’ free-text diagnoses in electronic medical records - a reliability study for generating training data in natural language processing

BackgroundDiagnoses entered by general practitioners into electronic medical records have great potential for research and practice, but unfortunately, diagnoses are often in uncoded format, making them of little use. Natural language processing (NLP) could assist in coding free-text diagnoses, but NLP models require local training data to unlock their potential. The aim of this study was to develop a framework of research-relevant diagnostic codes, to test the framework using free-text diagnoses from a Swiss primary care database and to generate training data for NLP modelling.MethodsThe framework of diagnostic codes was developed based on input from local stakeholders and consideration of epidemiological data. After pre-testing, the framework contained 105 diagnostic codes, which were then applied by two raters who independently coded randomly drawn lines of free text (LoFT) from diagnosis lists extracted from the electronic medical records of 3000 patients of 27 general practitioners. Coding frequency and mean occurrence rates (n and %) and inter-rater reliability (IRR) of coding were calculated using Cohen’s kappa (Κ).ResultsThe sample consisted of 26,980 LoFT and in 56.3% no code could be assigned because it was not a specific diagnosis. The most common diagnostic codes were, ‘dorsopathies’ (3.9%, a code covering all types of back problems, including non-specific lower back pain, scoliosis, and others) and ‘other diseases of the circulatory system’ (3.1%). Raters were in almost perfect agreement (Κ ≥ 0.81) for 69 of the 105 diagnostic codes, and 28 codes showed a substantial agreement (K between 0.61 and 0.80). Both high coding frequency and almost perfect agreement were found in 37 codes, including codes that are particularly difficult to identify from components of the electronic medical record, such as musculoskeletal conditions, cancer or tobacco use.ConclusionThe coding framework was characterised by a subset of very frequent and highly reliable diagnostic codes, which will be the most valuable targets for training NLP models for automated disease classification based on free-text diagnoses from Swiss general practice.

VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision.

Almost all digital videos are coded into compact representations before being transmitted. Such compact representations need to be decoded back to pixels before being displayed to humans and - as usual - before being enhanced/analyzed by machine vision algorithms. Intuitively, it is more efficient to enhance/analyze the coded representations directly without decoding them into pixels. Therefore, we propose a versatile neural video coding (VNVC) framework, which targets learning compact representations to support both reconstruction and direct enhancement/analysis, thereby being versatile for both human and machine vision. Our VNVC framework has a feature-based compression loop. In the loop, one frame is encoded into compact representations and decoded to an intermediate feature that is obtained before performing reconstruction. The intermediate feature can be used as reference in motion compensation and motion estimation through feature-based temporal context mining and cross-domain motion encoder-decoder to compress the following frames. The intermediate feature is directly fed into video reconstruction, video enhancement, and video analysis networks to evaluate its effectiveness. The evaluation shows that our framework with the intermediate feature achieves high compression efficiency for video reconstruction and satisfactory task performances with lower complexities.

The fear-avoidance model as an embodied prediction of threat.

The fear-avoidance model is a well-established framework in the understanding of persistent pain. It proposes a dichotomous path: either the context is interpreted as safe; there is no fear reaction and, therefore, the individual engages in active (positive) coping; or the context is interpreted as threatening, leading to a self-reinforcing vicious circle of fear and (negative) avoidance. We propose an embodied interpretation of this phenomenon employing the joint framework of predictive coding and active inference. The key idea is that multisensory integration of exteroceptive, proprioceptive, and interoceptive sensory inputs can lead to dysfunctional experiences of threat in nonthreatening situations. Threat inference can promote fear responses, maladaptive strategies (i.e., avoidance) and self-provides evidence for threat in associated or future contexts, or both. Under this treatment, the prediction of nonrealized threat becomes self-evidencing and context-invariant, and hence self-perpetuating. Safety cues are unable to attenuate the interpretation of the negative context as the dominant inference of the context is threatful and gains more precision and becomes resistant over time. Our model provides an explanation for the emergence of a dysfunctional fear response in the clinical setting despite apparent safety based on modern concepts from theoretical (computational) neuroscience.

A comparative study of Dutch and Chinese policy visions for agricultural transitions by applying the transition management model

ABSTRACT Government policies play a crucial role in guiding sustainability transitions by providing directionality and pathways. However, their effectiveness in steering transformative changes remains uncertain in various settings. In this study, a comparative analysis of transition policies in two distinct sociopolitical contexts – the Netherlands and China – is conducted by focusing on their respective policy visions for agricultural transitions, namely, Dutch Circular Agriculture (DCA) and Chinese Agricultural Green Development (CAGD). The transition management (TM) model is used as a benchmark to examine transition policies and to explain the nuanced dynamics of transition governance in the two contexts. Qualitative coding frameworks are developed to facilitate policy analysis. The findings show major deviations in the governance activities of DCA and CAGD from the TM model’s prescriptions. In both contexts, governance activities reproduce the dominant approaches to decision-making and resource organization – the Polder Model in the Netherlands and the Project System in China; however, these approaches are unfavorable for sustainability transitions. This study shows that reassessing path dependence inherent in established governance approaches, which tend to perpetuate incumbent practices, is pertinent for the success of transition policies. The theoretical and policy implications of applying the TM model to transition policy studies are discussed.

Maximum flag-rank distance codes

In this paper we extend the study of linear spaces of upper triangular matrices endowed with the flag-rank metric. Such metric spaces are isometric to certain spaces of degenerate flags and have been suggested as suitable framework for network coding. In this setting we provide a Singleton-like bound which relates the parameters of a flag-rank-metric code. This allows us to introduce the family of maximum flag-rank distance codes, that are flag-rank-metric codes meeting the Singleton-like bound with equality. Finally, we provide several constructions of maximum flag-rank distance codes.

Equity, diversity and inclusion in simulation-based education: constructing a developmental framework for medical educators

BackgroundThemes of equity, diversity and inclusion (EDI) arise commonly within healthcare simulation. Though faculty development guidance and standards include increasing reference to EDI, information on how faculty might develop in this area is lacking. With increasingly formal expectations being placed on simulation educators to adhere to EDI principles, we require a better understanding of the developmental needs of educators and clear guidance so that teams can work towards these expectations. Our study had two aims: Firstly, to explore the extent to which an existing competency framework for medical teachers to teach ethnic and cultural diversity is relevant for simulation educator competency in EDI, and secondly, informed by the data gathered, to construct a modified competency framework in EDI for simulation educators.MethodsWe engaged our participants (10 simulation faculty) in a 5-month period of enhanced consideration of EDI, using the SIM-EDI tool to support faculty debriefing conversations focussed on EDI within a pre-existing programme of simulation. We interviewed participants individually at two timepoints and analysed transcript data using template analysis. We employed an existing competency framework for medical teachers as the initial coding framework. Competencies were amended for the simulation context, modified based on the data, and new themes were added inductively, to develop a new developmental framework for simulation educators.ResultsInterview data supported the relevance of the existing competency framework to simulation. Modifications made to the framework included the incorporation of two inductively coded themes (‘team reflection on EDI’ and ‘collaboration’), as well as more minor amendments to better suit the healthcare simulation context. The resultant Developmental Framework for Simulation Educators in EDI outlines 10 developmental areas we feel are required to incorporate consideration of EDI into simulation programmes during the design, delivery and debriefing phases. We propose that the framework acts as a basis for simulation faculty development in EDI.ConclusionsSimulation faculty development in EDI is important and increasingly called for by advisory bodies. We present a Developmental Framework for Simulation Educators in EDI informed by qualitative data. We encourage simulation teams to incorporate this framework into faculty development programmes and report on their experiences.

Promoting pedestrian safety in Bangladesh: Identifying factors for drivers’ yielding behavior at designated crossings using behavior change theories

Objective In Bangladesh, drivers’ failure to yield to pedestrians at designated crossings poses a significant safety risk and discourages their use of such crossings. The use of behavior change theories could be more appropriate in such complex situations where the interdependent behaviors of drivers and pedestrians interact. While many studies have identified factors that affect drivers’ yielding behavior in the literature, fewer efforts have been made to apply behavior change theories in exploring and validating these factors, and to reach a consensus among competing road users. This study is among the first to utilize behavior change theories in Bangladesh to identify pedestrians’ safety factors that could promote drivers’ yielding behavior, upon which a consensus between drivers and pedestrians could be established. Methods A self-reported attitudinal survey was administered to 202 drivers on two highways in Bangladesh with a questionnaire using the capability, opportunity, motivation, and behavior (COM-B) model for the comprehensive coverage of behavior change theories. The focus group interviews were also conducted with 40 pedestrians and 19 drivers who have experience with four crossing sites on the selected highways. The collected data were analyzed using a regression model to identify significant factors influencing the drivers’ yielding behavior. These factors were then justified using a deductive thematic coding framework based on behavior change theories. Results The regression model explained the variance in drivers’ yielding by 45.1% with eight factors. The model found seven positive significant contributory factors in the drivers’ yielding that could promote pedestrian safety. Of them, the motivation factors were avoiding random crossing by pedestrians, vulnerable groups, assertiveness, and facial fear expressions; and the opportunity factors were traffic signs or advanced yield lines, crossing in groups at specific times, and enforcement. Conclusions The study’s findings have practical implications for policymakers, highway designers, and other stakeholders involved in promoting pedestrian safety by acknowledging their stake in making any decision that might impact them. Highway designers can use the thematic coding framework to recommend any contributory factors involved, where competing drivers’ unwillingness to yield is the primary threat to pedestrians’ safety.

The hierarchical predictive coding framework of post-traumatic stress disorder

Humans rely on internal models from sensory information to predict future action, and the default mode network (DMN) plays a pivotal role in this process. These models, however, can become distorted or disrupted in cases of post-traumatic stress disorder (PTSD) because traumatic stress can maladaptively alter how humans understand themselves and predict what happens in the world around them. Here, we examine the relationship between PTSD and this predictive phenomenon known as hierarchical predictive coding implicated in the DMN. We postulate that a hierarchical predictive coding framework will help researchers and clinicians more effectively explain the neural and psychological effects of PTSD among individuals who live with this condition, which may contribute to more targeted research and treatment outcomes.

Implementation characteristics that may promote sustainability of a rural physical activity initiative: examination of Play Streets through the lens of community implementers

BackgroundPlay Streets, which are community-based environmental initiatives where public spaces/streets are temporarily closed to create safe, low-cost physical activity opportunities, have demonstrated feasibility and physical activity benefit in rural US areas. Yet, information is needed to identify implementation characteristics that may promote sustainability. This study examined rural Play Streets implementation characteristics that could impact sustainability from local partners’ perspectives.MethodsSixteen Play Streets implementation team members in rural Maryland, North Carolina, Oklahoma, and Texas, USA, participated in interviews. Semi-structured in-person individual and group interviews were conducted in the fall of 2018 (after Play Streets implementation in 2017 and 2018), recorded, and transcribed verbatim. Transcripts were analyzed using iterative, content analyses. Coding frameworks were based on the Public Health Program Capacity for Sustainability Framework, and emergent themes were also identified.ResultsInterviewees’ perceived characteristics for facilitating Play Streets implementation aligned with the Public Health Program Capacity for Sustainability Framework: funding stability, political support, partnerships, organizational capacity, program adaption, and communication. Interviewees also noted the importance of cultural alignment/support and the reciprocal impact of community connectedness/engagement.ConclusionsFuture research should examine the reciprocal role of public health impacts, as both outcomes and factors which may influence sustainability.

A Hybrid Neural Coding Approach for Pattern Recognition With Spiking Neural Networks.

Recently, brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. However, these SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. Given that each neural coding scheme possesses its own merits and drawbacks, these SNNs encounter challenges in achieving optimal performance such as accuracy, response time, efficiency, and robustness, all of which are crucial for practical applications. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes. As an initial exploration in this direction, we propose a hybrid neural coding and learning framework, which encompasses a neural coding zoo with diverse neural coding schemes discovered in neuroscience. Additionally, it incorporates a flexible neural coding assignment strategy to accommodate task-specific requirements, along with novel layer-wise learning methods to effectively implement hybrid coding SNNs. We demonstrate the superiority of the proposed framework on image classification and sound localization tasks. Specifically, the proposed hybrid coding SNNs achieve comparable accuracy to state-of-the-art SNNs, while exhibiting significantly reduced inference latency and energy consumption, as well as high noise robustness. This study yields valuable insights into hybrid neural coding designs, paving the way for developing high-performance neuromorphic systems.