Systems biologists often distance themselves from reductionist approaches and formulate their aim as understanding living systems “as a whole.” Yet, it is often unclear what kind of reductionism they have in mind, and in what sense their methodologies would offer a superior approach. To address these questions, we distinguish between two types of reductionism which we call “modular reductionism” and “bottom-up reductionism.” Much knowledge in molecular biology has been gained by decomposing living systems into functional modules or through detailed studies (...) of molecular processes. We ask whether systems biology provides novel ways to recompose these findings in the context of the system as a whole via computational simulations. As an example of computational integration of modules, we analyze the first whole-cell model of the bacterium M. genitalium. Secondly, we examine the attempt to recompose processes across different spatial scales via multi-scale cardiac models. Although these models rely on a number of idealizations and simplifying assumptions as well, we argue that they provide insight into the limitations of reductionist approaches. Whole-cell models can be used to discover properties arising at the interfaces of dynamically coupled processes within a biological system, thereby making more apparent what is lost through decomposition. Similarly, multi-scale modeling highlights the relevance of macroscale parameters and models and challenges the view that living systems can be understood “bottom-up.” Specifically, we point out that system-level properties constrain lower-scale processes. Thus, large-scale modeling reveals how living systems at the same time are more and less than the sum of the parts. (shrink)

We review some of the main implications of the free-energy principle (FEP) for the study of the self-organization of living systems – and how the FEP can help us to understand (and model) biotic self-organization across the many temporal and spatial scales over which life exists. In order to maintain its integrity as a bounded system, any biological system - from single cells to complex organisms and societies - has to limit the disorder or dispersion (i.e., the long-run entropy) of (...) its constituent states. We review how this can be achieved by living systems that minimize their variational free energy. Variational free energy is an information theoretic construct, originally introduced into theoretical neuroscience and biology to explain perception, action, and learning. It has since been extended to explain the evolution, development, form, and function of entire organisms, providing a principled model of biotic self-organization and autopoiesis. It has provided insights into biological systems across spatiotemporal scales, ranging from microscales (e.g., sub- and multicellular dynamics), to intermediate scales (e.g., groups of interacting animals and culture), through to macroscale phenomena (the evolution of entire species). A crucial corollary of the FEP is that an organism just is (i.e., embodies or entails) an implicit model of its environment. As such, organisms come to embody causal relationships of their ecological niche, which, in turn, is influenced by their resulting behaviors. Crucially, free-energy minimization can be shown to be equivalent to the maximization of Bayesian model evidence. This allows us to cast natural selection in terms of Bayesian model selection, providing a robust theoretical account of how organisms come to match or accommodate the spatiotemporal complexity of their surrounding niche. In line with the theme of this volume; namely, biological complexity and self-organization, this chapter will examine a variational approach to self-organization across multiple dynamical scales. (shrink)

The present paper describes a joint action paradigm in which individuals or pairs utilized two computer keys to keep a dot stimulus moving inside a larger rectangle. Members of a pair could neither see nor hear each other. This paradigm allowed us to combine the discrete-trial type dependent variables commonly utilized by representational theorists, with the continuous, temporal dependence variables utilized by dynamical theorists. Analysis revealed that individuals kept the dot in the rectangle longer than dyads and did so by (...) moving it back and forth within the rectangle. Dyads, however, pressed their individual buttons as quickly as possible in order to keep the dot near the middle of the rectangle. These findings indicate that joint action constitutes a multi-scale phenomena that is best investigated via multiple, complementary methodologies versus single-measure, competing theories. (shrink)

This chapter takes inspiration from Wittgenstein’s thinking to formulate a non-reductive toolbox for the study of religion associated with generative modelling, specifically as applied in complex adaptive systems theory. It converges on a communal perspective on religion as multiscale active inference that contrasts starkly with common ‘straw person’ perspectives on religion that reduce it to ‘erroneous’ theorising generated by the brain. In contrast, we argue, religious practices at the enculturated level of description involve implicit and explicit meanings, experienced both individually (...) and collectively. Firstly, we characterise these in terms of Wittgenstein’s notions of rule-following and language games. Secondly, we discuss how the collective and perspectival aspects of religion in enculturation with morals, doctrines, rituals, and expressions can be formalised in terms of deep active inference in multi-agent systems. This approach describes how religions are incommensurable with scientific methods due to the epistemic separation between the different kinds of language games of religious and scientific practices. Their touchpoints tend to give rise to perpetual confusion and unproductive discussions, requiring perspicuous presentations that can be formulated with our toolbox. In this sense, our account has implications for tackling complex societal challenges such as ethical policy-making. (shrink)

I discuss arguments about the relationship between different “levels” of explanation in the light of examples involving multi-scale analysis. I focus on arguments about causal competition between properties at different levels, such as Jaegwon Kim’s “supervenience argument.” A central feature of Kim’s argument is that higher-level properties can in general be identified with “micro-based” properties. I argue that explanations from multi-scale analysis give examples of explanations that are problematic for accounts such as Kim’s. I argue that (...) these difficulties suggest that some standard assumptions about causal competition need to be revised. (shrink)

There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't look at what makes those fake news spread. The threat grows, and at some point, it gets out of hand. That's why this paper is trying to figure out how to predict the features that make Arabic online fake news spread. It's using Naive Bayes, Logistic Regression, and Random forest of Machine Learning to do this. Online news stories that were (...) made up were used. They are found by using Term Frequency-Inverse Document Frequency. The best partition for testing and validating the prediction was chosen at random and used in the analysis. So, all three machine learning classifications for predicting fake news in Arabic online were done. The results of the experiment show that Random Forest Classifier outperformed the other two algorithms. It had the best TF-IDF with an accuracy of 86 percent. Naive Bayes had an accuracy rate of 84%, and Logistic Regression had an accuracy rate of 85%, so they all did well. As such, the model shows that the features in TF-IDF are the most essential point about the content of an online Arabic fake news. (shrink)

The paper begins with a generic discussion of modelling, focusing on some of its practices and problems. I then move on to a philosophical discussion about emergence and multi-scale modelling; more specifically, the reasons why what looks like a promising strategy for dealing with emergence is sometimes incapable of delivering interesting results. This becomes especially evident when we look more closely at turbulence and what I take to be the main ontological feature of emergent behavior—universality. Finally, I conclude (...) by showing why, despite displaying multi-scale behaviour and some of the characteristics we identify with emergence, turbulence fails to fit neatly into the latter category and is not successfully captured using multi-scale modelling. The complex nature of turbulence illustrates the difficulties in characterizing emergence and why specific criteria are needed in order to prevent every complex behaviour we don’t understand being classified as emergent. (shrink)

Many researchers have documented how AI and data driven technologies have the potential to have profound effects on our lives—in ways that make these technologies stand out from those that went before. Around the world, we are seeing a significant growth in interest and investment in AI in healthcare. This has been coupled with rising concerns about the ethical implications of these technologies and an array of ethical guidelines for the use of AI and data in healthcare has arisen. Nevertheless, (...) the question of if and how AI and data technologies can be ethical remains open to debate. This paper aims to contribute to this debate by considering the wide range of implications that have been attributed to these technologies and asking whether current ethical guidelines take these factors into account. In particular, the paper argues that while current ethics guidelines for AI in healthcare effectively account for the four key issues identified in the ethics literature (transparency; fairness; responsibility and privacy), they have largely neglected wider issues relating to the way in which these technologies shape institutional and social arrangements. This, I argue, has given current ethics guidelines a strong focus on evaluating the impact of these technologies on the individual, while not accounting for the powerful social shaping effects of these technologies. To address this, the paper proposes a Multiscale Ethics Framework, which aims to help technology developers and ethical evaluations to consider the wider implications of these technologies. (shrink)

The purpose of crowd counting is to estimate the number of pedestrians in crowd images. Crowd counting or density estimation is an extremely challenging task in computer vision, due to large scale variations and dense scene. Current methods solve these issues by compounding multi-scale Convolutional Neural Network with different receptive fields. In this paper, a novel end-to-end architecture based on Multi-Scale Adversarial Convolutional Neural Network (MSA-CNN) is proposed to generate crowd density and estimate the amount (...) of crowd. Firstly, a multi-scale network is used to extract the globally relevant features in the crowd image, and then fractionally-strided convolutional layers are designed for up-sampling the output to recover the loss of crucial details caused by the earlier max pooling layers. An adversarial loss is directly employed to shrink the estimated value into the realistic subspace to reduce the blurring effect of density estimation. Joint training is performed in an end-to-end fashion using a combination of Adversarial loss and Euclidean loss. The two losses are integrated via a joint training scheme to improve density estimation performance.We conduct some extensive experiments on available datasets to show the significant improvements and supremacy of the proposed approach over the available state-of-the-art approaches. (shrink)

In this paper, I draw a parallel between the stability of physical systems and that of economic ones, such as the US financial system. I argue that the use of equilibrium assumptions is central to the analysis of dynamic behavior for both kinds of systems, and that we ought to interpret such idealizing strategies as footholds for causal exploration and explanation. Our considerations suggest multi-scale modeling as a natural home for such reasoning strategies, which can provide a backdrop (...) for the assessment and prevention of financial crises. Equilibrium assumptions are critical elements of the epistemic scaffolding that make up multi-scale models, which we should understand as a means of constructing explanations of dynamic behavior. (shrink)

A common reductionist assumption is that macro-scale behaviors can be described "bottom-up" if only sufficient details about lower-scale processes are available. The view that an "ideal" or "fundamental" physics would be sufficient to explain all macro-scale phenomena has been met with criticism from philosophers of biology. Specifically, scholars have pointed to the impossibility of deducing biological explanations from physical ones, and to the irreducible nature of distinctively biological processes such as gene regulation and evolution. This paper takes (...) a step back in asking whether bottom-up modeling is feasible even when modeling simple physical systems across scales. By comparing examples of multi-scale modeling in physics and biology, we argue that the “tyranny of scales” problem presents a challenge to reductive explanations in both physics and biology. The problem refers to the scale-dependency of physical and biological behaviors that forces researchers to combine different models relying on different scale-specific mathematical strategies and boundary conditions. Analyzing the ways in which different models are combined in multi-scale modeling also has implications for the relation between physics and biology. Contrary to the assumption that physical science approaches provide reductive explanations in biology, we exemplify how inputs from physics often reveal the importance of macro-scale models and explanations. We illustrate this through an examination of the role of biomechanics modeling in developmental biology. In such contexts, the relation between models at different scales and from different disciplines is neither reductive nor completely autonomous, but interdependent. (shrink)

Convolutional neural networks, which automatically learn features from raw data to approximate functions, are being increasingly applied to the end-to-end analysis of electroencephalographic signals, especially for decoding brain states in brain-computer interfaces. Nevertheless, CNNs introduce a large number of trainable parameters, may require long training times, and lack in interpretability of learned features. The aim of this study is to propose a CNN design for P300 decoding with emphasis on its lightweight design while guaranteeing high performance, on the effects of (...) different training strategies, and on the use of post-hoc techniques to explain network decisions. The proposed design, named MS-EEGNet, learned temporal features in two different timescales in an efficient and optimized way, and was validated on three P300 datasets. The CNN was trained using different strategies and was compared with several state-of-the-art algorithms. Furthermore, variants of the baseline MS-EEGNet were analyzed to evaluate the impact of different hyper-parameters on performance. Lastly, saliency maps were used to derive representations of the relevant spatio-temporal features that drove CNN decisions. MS-EEGNet was the lightest CNN compared with the tested SOA CNNs, despite its multiple timescales, and significantly outperformed the SOA algorithms. Post-hoc hyper-parameter analysis confirmed the benefits of the innovative aspects of MS-EEGNet. Furthermore, MS-EEGNet did benefit from transfer learning, especially using a low number of training examples, suggesting that the proposed approach could be used in BCIs to accurately decode the P300 event while reducing calibration times. Representations derived from the saliency maps matched the P300 spatio-temporal distribution, further validating the proposed decoding approach. This study, by specifically addressing the aspects of lightweight design, transfer learning, and interpretability, can contribute to advance the development of deep learning algorithms for P300-based BCIs. (shrink)

Umwelt theory is an expression of von Uexküll’s subjective biology and as such usually applied in analysis of individual animals, yet it is fundamentally relational and therefore also suitable for analysis of more complex wholes. Since the birth of the modern environmental movement in the 1960s, there has been growing scientific and political acknowledgement of there being a global environmental crisis, which today manifests itself as a climate change and biodiversity crisis. This calls for a multi-scale ecosemiotic approach (...) to analysis of human ecology at various levels and scales. In this article I explore to what extent ecosemiotic methodology, drawing on Umwelt theory and its consistently subjective perspective, can be applied in analysis of human ecology at different geographical and ecological scales ranging from the global to the local. The article incorporates a case study of human–animal relations in Mamirauá Sustainable Development Reserve in the Central Amazon. This is a seasonal floodplain forest area surrounded by rivers. I investigate aspects of the living conditions and ecology of the reserve, with a main focus on indigenous communities and the circumstances of two primate species, namely the red howler monkey and the black-headed squirrel monkey. I outline matrixes of levels of study in ecosemiotics, and scales in human ecology, and apply two scales to the Mamirauá case. These take an individual animal’s and an individual human being’s subjective experience as their respectively starting points. This allows for multi-scale studies of human ecology from complementary angles. (shrink)

Objective: Tangent Space Mapping using the geometric structure of the covariance matrices is an effective method to recognize multiclass motor imagery. Compared with the traditional CSP method, the Riemann geometric method based on TSM takes into account the nonlinear information contained in the covariance matrix, and can extract more abundant and effective features. Moreover, the method is an unsupervised operation, which can reduce the time of feature extraction. However, EEG features induced by MI mental activities of different subjects are not (...) the same, so selection of subject-specific discriminative EEG frequency components play a vital role in the recognition of multiclass MI. In order to solve the problem, a discriminative and multi-scale filter bank tangent space mapping algorithm is proposed in this article to design the subject-specific Filter Bank so as to effectively recognize multiclass MI tasks. Methods: On the 4-class BCI competition IV-2a dataset, first, a non-parametric method of multivariate analysis of variance based on the sum of squared distances is used to select discriminative frequency bands for a subject; next, a multi-scale FB is generated according to the range of these frequency bands, and then decompose multi-channel EEG of the subject into multiple sub-bands combined with several time windows. Then TSM algorithm is used to estimate Riemannian tangent space features in each sub-band and finally a liner Support Vector Machines is used for classification. Main Results: The analysis results show that the proposed discriminative FB enhances the multi-scale TSM algorithm, improves the classification accuracy and reduces the execution time during training and testing. On the 4-class BCI competition IV-2a dataset, the average session to session classification accuracy of nine subjects reached 77.33 ± 12.3%. When the training time and the test time are similar, the average classification accuracy is 2.56% higher than the latest TSM method based on multi-scale filter bank analysis technology. When the classification accuracy is similar, the training speed is increased by more than three times, and the test speed is increased two times more. Compared with Supervised Fisher Geodesic Minimum Distance to the Mean, another new variant based on Riemann geometry classifier, the average accuracy is 3.36% higher, we also compared with the latest Deep Learning method, and the average accuracy of 10-fold cross validation improved by 2.58%. Conclusion: Research shows that the proposed DMFBTSM algorithm can improve the classification accuracy of MI tasks. Significance: Compared with the MFBTSM algorithm, the algorithm proposed in this article is expected to select frequency bands with good separability for specific subject to improve the classification accuracy of multiclass MI tasks and reduce the feature dimension to reduce training time and testing time. (shrink)

Cardiovascular modelling has been a major research subject for the last decade. Different cardiac models have been developed at a cellular level as well as at the whole organ level. Most of these models are defined by a comprehensive cellular modelling using continuous formalisms or by a tissue-level modelling often based on discrete formalisms. Nevertheless, both views still suffer from difficulties that reduce their clinical applications: the first approach requires heavy computational resources while the second one is not able (...) to reproduce certain pathologies.This paper presents an original methodology trying to gather advantages from both approaches, by means of a hybrid model mixing discrete and continuous formalisms. This method has been applied to define a hybrid model of cardiac action potential propagation on a 2D grid of endocardial cells, combining cellular automata and a set of cells defined by the Beeler-Reuter model. For simulations under physiological and ischemic conditions, results show that the action potential propagation as well as electrogram reconstructions are consistent with clinical diagnosis. Finally, the advantage of the proposed approach is discussed within the frame of cardiac modelling and simulation. (shrink)

The SSIS SEL Brief Scales are multi-informant measures that were developed to efficiently assess the SEL competencies of school-age youth in the United States. Recently, the SSIS SELb was translated into multiple languages for use in a multi-site study across six European countries. The purpose of the current study was to examine concurrent and predictive evidence for the SEL Composite scores from the translated versions of the SSIS SELb Scales. Results indicated that SSIS SELb Composite scores demonstrated expected (...) positive concurrent and predictive relationships with scores from the Connor-Davidson Resilience Scale and negative relationships with scores from the problem behavior scales of the Strengths and Difficulties Questionnaire. Although there were a few exceptions, these patterns generally were consistent across informants and samples providing initial validity evidence for the Composite score from the translated versions of the SSIS SELb Scales. Limitations and future research directions are discussed. (shrink)

Prior research has shown the importance of institutional pressures in investigating corporate environmental behaviour. To date, the literature has been lacking in survey-based reflective measures of institutional pressures. This paper focuses on the development of reflective measures of coercive, mimetic, and normative isomorphism.

Finding meaning in our lives is a central tenet to the human experience and a core contributor to mental health. Individuals tend to actively seek the sources of meaning in their lives or consciously enact efforts to create or “craft” meaning in different life domains. These overall “Life Crafting” behaviors refer to the conscious efforts individuals exert to create meaning in their lives through cognitively framing how they view life, seeking social support systems to manage life challenges, and actively seeking (...) challenges to facilitate personal growth. Specifically, these behaviors are actioned to better align life goals, personal needs, values, and capabilities. However, no psychological assessment instrument currently exists to measure overall life crafting. As such, the purpose of this paper was twofold: to conceptualize life crafting and to develop, validate and evaluate a robust measure of overall life crafting. A mixed-method, multi-study research design was employed. First, nine participants were interviewed to determine the methods or techniques used to craft meaningful life experiences. These methods/techniques were used as indicators to create an initial item pool which was then reviewed by a panel of experts to ensure face validity. Second, in Study 1, the factorial structure of the instrument was explored by gathering data from a convenience sample, with the results showing support for a three-factor structure of life crafting, consisting of cognitive crafting, seeking social support, and seeking challenges. Finally, in Study 2, the aim was to confirm the factorial structure of the Life Crafting scale and to determine its level of internal consistency, partial measurement invariance across genders, and criterion validity [meaning in life, mental health, work engagement, and job burnout ]. The results supported a second-order factorial model of Life Crafting, which comprised of three first-order factors. Therefore, the Life Crafting Scale can be used as a valid and reliable instrument to measure- and track the effectiveness of life crafting interventions. (shrink)

Interactions in service ecosystems, as opposed to the service dyad, have recently gained much attention from research. However, it is still unclear how they influence a customer’s experiential value and trigger desired prosocial behavior. The purpose of this study is to identify which elements of the multi-actor service ecosystem contribute to a customer’s experiential value and to investigate its relation to a customer’s interaction attitude and inter-customer helping behavior. The authors adopted a scale development procedure from the existing (...) literature. Service, brand, retail and tourism management research as well as expert feedback is used to generate a pool of 33 items. Exploratory and confirmatory factor analysis were conducted. The scale was validated based on more than 468 responses to a CASI at one of the world’s largest trade shows. Scale-development procedure was followed by structural equation modeling. CFA supports that experiential value in multi-actor ecosystems comprises five dimensions. The functional value of personnel, the perception of other customers’ appearance, the perception of other customers’ behavior, multisensory stimuli, and a customer’s enjoyment. Experiential value positively and directly relates to a customer’s interaction attitude and inter-customer helping behavior. Furthermore, the effect of experiential value on inter-customer helping behavior is partially mediated by interaction attitude. Managers interested in getting more out of interactions with customers will develop an understanding for the interplay between the physical environment and individuals within a multi-actor ecosystem. Social scientists and managers can use the scale to assess experiential value, encourage a customer’s interaction attitude and utilize the customers’ influence on their peers. This paper synthesizes insights from existing research on experiential value, from various fields, in one scale. This holistic approach is the first to simultaneously account for a customer’s interactions with the multisensory physical environment, personal interactions with employees and interactions between customers in a multi-actor service ecosystem. (shrink)

Interoception in the broader sense refers to the perception of internal states, including the perception of the actual state of the internal organs and the motor system. Dimensions of interoception include interoceptive accuracy, i.e., the ability to sense internal changes assessed with behavioral tests, confidence rating with respect to perceived performance in an actual behavioral test, and interoceptive sensibility, i.e., the self-reported generalized ability to perceive body changes. The relationship between dimension of cardioceptive and proprioceptive modalities and their association with (...) affect are scarcely studied. In the present study, undergraduate students filled out questionnaires assessing positive and negative affect, interoceptive sensibility, and body competence. Following this, they completed a behavioral task assessing cardioceptive accuracy and two tasks assessing proprioceptive accuracy with respect to the tension of arm flexor muscles and the angular position of the elbow joint. Confidence ratings were measured with visual analog scales after the tasks. With the exception of a weak association between cardioceptive accuracy and the respective confidence rating, no associations between and within modalities were found with respect to various dimensions of interoception. Further, the interoceptive dimensions were not associated with state and trait positive and negative affect and perceived body competence. In summary, interoceptive accuracy scores do not substantially contribute to conscious representations of cardioceptive and proprioceptive ability. Within our data, non-pathological affective states are not associated with the major dimensions of interoception for the cardiac and proprioceptive modalities. (shrink)

(1978). Development, Norms and Factorial Validity of Scales for Measuring Racial Attitudes in Adolescents in Multi‐Ethnic Settings. Educational Studies: Vol. 4, No. 3, pp. 189-200.

Natural forms, often characterized by irregularity and roughness, have a unique complexity that exhibit self-similarity across different spatial scales or levels of magnification. Our visual system is remarkably efficient in the processing of natural scenes and tuned to the multi-scale, fractal-like properties they possess. The fractal-like scaling characteristics are ubiquitous in many physical and biological domains, with recent research also highlighting their importance in aesthetic perception, particularly in the visual and, to some extent, auditory modalities. Given the multitude (...) of fractal-like scaling manifestations, we explore potential commonalities in the way they might affect aesthetic preference within and across different physical and sensory domains. We use a range of visual and tactile stimuli to explore the equivalence of fractal-scaling effects on aesthetic preferences within and across visual and tactile modalities. Our results suggest that, despite some superficial differences, the underlying dimensional structure mediating the preference across the two domains is remarkably similar. The qualitative nature of this dimensional structure as well as suggestions for future research are discussed. (shrink)

BackgroundUncertainty intolerance (IU), the tendency to think or react negatively toward uncertain events may have implication on individuals’ mental health and psychological wellbeing. The Intolerance of Uncertainty Scale-12 (IU-12) is commonly used across the globe to measure IU, however, its’ psychometric properties are yet to be evaluated in Iran with a Persian-speaking population. Therefore, the purpose of this research was to translate and validate the IU-12 among Iranian undergraduate students.Materials and MethodsThe multi-stage cluster random sampling was employed to (...) recruit 410 Iranian undergraduate students (260 females) from the Azad University to complete the IU-12, the Depression Anxiety Stress Scale-2, and the Penn State Worry Questionnaire in a cross-sectional design. In this study, face validity, content validity, construct validity, and concurrent validity were measured and Construct Reliability (CR) and Cronbach’s alpha were used to measure reliability.ResultsThe impact score of the translated IU-12 indicated acceptable face validity (value of impact score was greater than 1.5). The value of Content Validity Index (CVI) and the value of Content Validity Ratio (CVR) were above 0.7 and 0.78, respectively. The values of CVI and CVR indicated the items had acceptable content validity and were deemed essential to the measure. The measurement model analysis showed the measure with two subscales had good fit indices (CMIN/df = 2.75, p < 0.01, RMSEA = 0.07, TLI = 0.94, CFI = 0.95, GFI = 0.94). A Confirmatory Factor Analysis (CFA) indicated the scale was composed of the two subscales found in the English-version of the scale (prospective anxiety and inhibitory anxiety), and no items were removed from the scale. The values of CR (0.86) and Cronbach’s alphas (0.89) showed the measure had appropriate internal consistency.ConclusionThe findings support the psychometric properties of the Persian version of the IU-12. This scale could be used to reliably and accurately measure uncertainty intolerance among undergraduate students in Iran. (shrink)

The gap between the decision to engage in physical activity and subsequent behavioral enactment is considerable for many. Action control theories focus on this discordance in an attempt to improve the translation of intention into behavior. The purpose of this mini-review was to overview one of these approaches, the multi-process action control framework, which has evolved from a collection of previous works. The main concepts and operational structure of M-PAC was overviewed followed by applications of the framework in physical (...) activity, and concluded with unanswered questions, limitations, and possibilities for future research. In M-PAC, it is suggested that three layered processes build upon each other from the formation of an intention to a sustained profile of physical activity action control. Intention-behavior discordance is because of strategic challenges in goal pursuit and automatic tendencies. Regulatory processes are employed to hold the relationship between reflective processes and behavior concordant by countering these strategic challenges and automatic tendencies until the development of reflexive processes begin to co-determine action control. Results from 29 observational and preliminary experimental studies generally support the proposed M-PAC framework. Future research is needed to explore the temporal dynamic between reflexive and regulatory constructs, and implement M-PAC interventions in different forms, and at different levels of scale. (shrink)

The global environmental and social-economic crises of industrialized agriculture have led to the emergence of agroecology as an alternative approach aiming to increase the ecological, social and economic sustainability of agri–food systems. The ‘multi-level perspective’ is now a widely used framework to understand and promote the upscaling of local innovation niches, such as agroecology, to broader scales (e.g., regional, national, international), thus reconfiguring the dominant socio-technical regimes. Additionally, emergent ‘hybrid forums’ can provide a space between niche and regime where (...) niche innovators can become important actors in scaling up and out emergent innovations. In this paper, we examine a university training program (Postgraduate Diploma in Local Agroecological Dynamization at the Universitat Autònoma de Barcelona), to better understand its role as a ‘hybrid forum’. Our analysis focuses especially on how the program, as an example of a hybrid forum, worked to reconfigure practices, concepts, and tools of local development practitioners. We also assess to what extent the program contributed to transitioning local development institutions toward agroecology. An online survey (n = 46) and in-depth interviews (n = 16) were carried out to determine how the training program has impacted the student’s opinions and their respective institutions. The results show that most of the students consider that they have acquired new theoretical frameworks and useful methods to re-framing their local development projects, that new alliances with multi-actor networks have been perceived, and that some internal changes of the local development practices have taken place. We conclude that the training program, as a hybrid forum, is capable of outscaling niche innovations through linkages with different kind of actors both from the niche and the regime. Political changes in the socio-technical landscape level offer an opportunity to amplify the impact of the innovations which are being generated by those multi-actor networks, but with a limited multi-level impact as far as institutional regime-actors not aligned with agroecological transition keep the most of the competencies on agri–food systems. (shrink)

The global environmental and social-economic crises of industrialized agriculture have led to the emergence of agroecology as an alternative approach aiming to increase the ecological, social and economic sustainability of agri–food systems. The ‘multi-level perspective’ is now a widely used framework to understand and promote the upscaling of local innovation niches, such as agroecology, to broader scales, thus reconfiguring the dominant socio-technical regimes. Additionally, emergent ‘hybrid forums’ can provide a space between niche and regime where niche innovators can become (...) important actors in scaling up and out emergent innovations. In this paper, we examine a university training program, to better understand its role as a ‘hybrid forum’. Our analysis focuses especially on how the program, as an example of a hybrid forum, worked to reconfigure practices, concepts, and tools of local development practitioners. We also assess to what extent the program contributed to transitioning local development institutions toward agroecology. An online survey and in-depth interviews were carried out to determine how the training program has impacted the student’s opinions and their respective institutions. The results show that most of the students consider that they have acquired new theoretical frameworks and useful methods to re-framing their local development projects, that new alliances with multi-actor networks have been perceived, and that some internal changes of the local development practices have taken place. We conclude that the training program, as a hybrid forum, is capable of outscaling niche innovations through linkages with different kind of actors both from the niche and the regime. Political changes in the socio-technical landscape level offer an opportunity to amplify the impact of the innovations which are being generated by those multi-actor networks, but with a limited multi-level impact as far as institutional regime-actors not aligned with agroecological transition keep the most of the competencies on agri–food systems. (shrink)

Aberrations introduced by the atmospheric turbulence in large telescopes are compensated using adaptive optics systems, where the use of deformable mirrors and multiple sensors relies on complex control systems. Recently, the development of larger scales of telescopes as the E-ELT or TMT has created a computational challenge due to the increasing complexity of the new adaptive optics systems. The Complex Atmospheric Reconstructor based on Machine Learning is an algorithm based on artificial neural networks, designed to compensate the atmospheric turbulence. During (...) recent years, the use of GPUs has been proved to be a great solution to speed up the learning process of neural networks, and different frameworks have been created to ease their development. The implementation of CARMEN in different Multi-GPU frameworks is presented in this paper, along with its development in a language originally developed for GPU, like CUDA. This implementation offers the best response for all the presented cases, although its advantage of using more than one GPU occurs only in large networks. (shrink)

Intelligence in current AI research is measured according to designer-assigned tasks that lack any relevance for an agent itself. As such, tasks and their evaluation reveal a lot more about our intelligence than the possible intelligence of agents that we design and evaluate. As a possible first step in remedying this, this article introduces the notion of “self-concern,” a property of a complex system that describes its tendency to bring about states that are compatible with its continued self-maintenance. Self-concern, as (...) argued, is the foundation of the kind of basic intelligence found across all biological systems, because it reflects any such system's existential task of continued viability. This article aims to cautiously progress a few steps closer to a better understanding of some necessary organisational conditions that are central to self-concern in biological systems. By emulating these conditions in embodied AI, perhaps something like genuine self-concern can be implemented in machines, bringing AI one step closer to its original goal of emulating human-like intelligence. (shrink)

Around the world, agricultural landscapes are increasingly seen as “multi-functional” spaces, expected to deliver food supplies while improving rural livelihoods and protecting and restoring healthy ecosystems. To support this array of functions and benefits, governments and civil society in many regions are now promoting integrated farm- and landscape-scale management strategies, in lieu of fragmented management strategies. While rural producers are fundamental to achieving multi-functional landscapes, they are frequently viewed as targets of, or barriers to, landscape-oriented initiatives, rather (...) than as leading agents of change. In reality, however, rural producers in many areas have embraced elements of multi-functional land management. In this paper, we explore the role and recent evolution of producer movements in influencing multi-functional farm and landscape management. We explore these roles through six case studies, including a land reform movement in Brazil, indigenous territorial development in Bolivia, conservation agriculture associations in Canada, environmental cooperatives in the Netherlands, indigenous and biocultural heritage associations in Peru, and Landcare groups in the Philippines. These experiences suggest that producer movements are playing pivotal roles in supporting landscape multi-functionality, not only through agroecological farming practices but also through off-farm efforts to conserve ecosystems and support multi-stakeholder landscape planning. On the other hand, interests of producer movements are not always fully aligned with multi-functional landscape management approaches. The contribution of producer movements to multi-functional landscapes depends on these movements including farm and landscape stewardship in their values and goals, and having the political support and capacity to engage meaningfully in multi-stakeholder processes. (shrink)

More and more research on left ventricle quantification skips segmentation due to its requirement of large amounts of pixel-by-pixel labels. In this study, a framework is developed to directly quantify left ventricle multiple indices without the process of segmentation. At first, DenseNet is utilized to extract spatial features for each cardiac frame. Then, in order to take advantage of the time sequence information, the temporal feature for consecutive frames is encoded using gated recurrent unit. After that, the attention mechanism (...) is integrated into the decoder to effectively establish the mappings between the input sequence and corresponding output sequence. Simultaneously, a regression layer with the same decoder output is used to predict multi-indices of the left ventricle. Different weights are set for different types of indices based on experience, and l2-norm is used to avoid model overfitting. Compared with the state-of-the-art, our method can not only produce more competitive results but also be more flexible. This is because the prediction results in our study can be obtained for each frame online while the SOTA only can output results after all frames are analyzed. (shrink)