Mental fatigue (MF) jeopardizes performance and safety through a variety of cognitive impairments and according to the complexity loss theory, should represent “complexity loss” in electroencephalogram (EEG). However, the studies are few and inconsistent concerning the relationship between MF and loss of complexity, probably because of the susceptibility of brain waves to noise. In this study, MF was induced in thirteen male college students by a simulated flight task. Before and at the end of the task, spontaneous EEG and auditory (...) steady-state response (ASSR) were recorded and instantaneous frequency variation (IFV) in alpha rhythm was extracted and analyzed by multiscale entropy (MSE) analysis. The results show that there were significant differences in IFV in alpha rhythm either from spontaneous EEG or from ASSR for all subjects. Therefore, the proposed method can be effective in revealing the complexity loss caused by MF in spontaneous EEG and ASSR, which may serve as a promising analyzing method to mark mild mental impairments. (shrink)

Autism spectrum disorders (ASD) are heterogeneous neurodevelopmental disorders that are reportedly characterized by aberrant neural networks. Recently developed multiscale entropy analysis (MSE) can characterize the complexity inherent in EEG dynamics over multiple temporal scales in the dynamics of neural networks. We encountered an 18-year-old man with ASD whose refractory catatonic obsessive–compulsive symptoms were improved dramatically after electroconvulsive therapy (ECT). In this clinical case study, we strove to clarify the neurophysiological mechanism of ECT in ASD by assessing EEG complexity (...) using MSE. Along with ECT, the frontocentral region showed decreased EEG complexity at higher temporal scales, whereas the occipital region expressed an increase at lower temporal scales. Furthermore, these changes were associated with clinical improvement associated with the elevation of brain-derived neurotrophic factor, which is a molecular hypothesis of ECT, playing key roles in ASD pathogenesis. Changes in EEG complexity in a region-specific and temporal scale-specific manner we found might reflect atypical EEG dynamics in ASD. Although MSE is not a direct approach to measuring neural connectivity and the results are from only a single case, they might reflect specific aberrant neural network activity and the therapeutic neurophysiological mechanism of ECT in ASD. (shrink)

Nonlinear time series denoising is the prerequisite for extracting effective information from observation sequence. An effective chaotic signal denoising method not only has a good signal-to-noise ratio enhancement performance, but also can remain as a good unpredictable denoised signal. However, the inherent characteristics of chaos, such as extreme sensitivity to initial values and broadband spectrum, pose challenges for noise reduction of polluted chaotic signals. To address these issues, an adaptive smoothing multiscale morphological filtering is proposed to reconstruct chaotic signals. (...) In the process of noise reduction for contaminated chaotic signals, firstly, a multiscale morphological filter is constructed adaptively according to the multiscale permutation entropy of morphological filter residuals, and the contaminated signals are filtered. Secondly, the weight coefficients of scale structural element are calculated by the residual sum of squares operation, and the chaotic signals are reconstructed. Finally, the resampled filter signals are smoothed by cubic B-spline interpolation operation. In the experiment, the Lorenz signal with white Gaussian noise, the measured sunspot, and the chaotic vibration signal are reconstructed by four comparison methods. The test results show that the proposed ASMMF method has obvious advantages in noise suppression and topological trajectory restoration. (shrink)

Most binaural speech source localization models perform poorly in unprecedentedly noisy and reverberant situations. Here, this issue is approached by modelling a multiscale dilated convolutional neural network. The time-related crosscorrelation function and energy-related interaural level differences are preprocessed in separate branches of dilated convolutional network. The multiscale dilated CNN can encode discriminative representations for CCF and ILD, respectively. After encoding, the individual interaural representations are fused to map source direction. Furthermore, in order to improve the parameter adaptation, a (...) novel semiadaptive entropy is proposed to train the network under directional constraints. Experimental results show the proposed method can adaptively locate speech sources in simulated noisy and reverberant environments. (shrink)

Both glycemic control and handgrip strength affect microvascular function. Multiscale entropy of photoplethysmographic pulse amplitudes may differ by diabetes status and hand activity. Of a middle-to-old aged and right-handed cohort without clinical cardiovascular disease, we controlled age, sex, and weight to select the unaffected,the well-controlled diabetes, and the poorly controlled diabetes groups. MSEs were calculated from consecutive 1,500 PPG pulse amplitudes of bilateral index fingertips. Thesmall-, medium-,and large-scale MSEs were defined as the average of scale 1, scales 2–4, (...) and scales 5–10, respectively. Intra- and intergroups were compared by one- and two-samplet-tests, respectively. The dominant handMSE5–10was lower in the poorly controlled diabetes group than the well-controlled diabetes and the unaffected groups, whereas the nondominant handMSE5–10was lower in the well- and poorly controlled diabetes groups than the unaffected group. TheMSE1of dominant hand was higher than that of nondominant hand in the well-controlled diabetes. In conclusion, diabetes status and hand dominance may affect the MSE of PPG pulse amplitudes. (shrink)

Various walking speeds may induce different responses on the plantar pressure patterns. Current methods used to analyze plantar pressure patterns are linear and ignore nonlinear features. The purpose of this study was to analyze the complexity of plantar pressure images after walking at various speeds using nonlinear bidimensional multiscale entropy. Twelve participants were recruited for walking at three speeds for 20 minutes. A plantar pressure measurement system was used to measure plantar pressure patterns. Complexity index, a summation of (...) MSE2D from all time scales, was used to quantify the changes of complexity of plantar pressure images. The analysis of variance with repeated measures and Fisher’s least significant difference correction were used to examine the results of this study. The results showed that CI of plantar pressure images of 1.8 mph was significantly lower compared with 3.6 and 5.4 mph. The results also showed that CI significantly increased from the 1st min to the 10th min and 20th min with slow walking. Our results indicate that slow walking at 1.8 mph may not be good for postural control compared with moderate walking and fast walking. This study demonstrates that bidimensional multiscale entropy is able to quantify complexity changes of plantar pressure images after different walking speeds. (shrink)

In the human-computer interaction, electroencephalogram access for automatic emotion recognition is an effective way for robot brains to perceive human behavior. In order to improve the accuracy of the emotion recognition, a method of EEG access for emotion recognition based on a deep hybrid network was proposed in this paper. Firstly, the collected EEG was decomposed into four frequency band signals, and the multiscale sample entropy features of each frequency band were extracted. Secondly, the constructed 3D MSE feature (...) matrices were fed into a deep hybrid network for autonomous learning. The deep hybrid network was composed of a continuous convolutional neural network and hidden Markov models. Lastly, HMMs trained with multiple observation sequences were used to replace the artificial neural network classifier in the CNN, and the emotion recognition task was completed by HMM classifiers. The proposed method was applied to the DEAP dataset for emotion recognition experiments, and the average accuracy could achieve 79.77% on arousal, 83.09% on valence, and 81.83% on dominance. Compared with the latest related methods, the accuracy was improved by 0.99% on valence and 14.58% on dominance, which verified the effectiveness of the proposed method. (shrink)

Inspired by the application of CycleGAN networks to the image style conversion problem Zhu et al., this paper proposes an end-to-end network, DefogNet, for solving the single-image dehazing problem, treating the image dehazing problem as a style conversion problem from a fogged image to a nonfogged image, without the need to estimate a priori information from an atmospheric scattering model. DefogNet improves on CycleGAN by adding a cross-layer connection structure in the generator to enhance the network’s multiscale feature extraction (...) capability. The loss function was redesigned to add detail perception loss and color perception loss to improve the quality of texture information recovery and produce better fog-free images. In this paper, the novel Defog-SN algorithm is presented. This algorithm adds a spectral normalization layer to the discriminator’s convolution layer to make the discriminant network conform to a 1-Lipschitz continuum and further improve the model’s stability. In this study, the experimental process is completed based on the O-HAZE, I-HAZE, and RESIDE datasets. The dehazing results show that the method outperforms traditional methods in terms of PSNR and SSIM on synthetic datasets and Avegrad and Entropy on naturalistic images. (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)

It is argued that multiscale approaches are necessary for an explanatory modeling of biological systems. A first step, besides common to the multiscale modeling of physical and living systems, is a bottom-up integration based on the notions of effective parameters and minimal models. Top-down effects can be accounted for in terms of effective constraints and inputs. Biological systems are essentially characterized by an entanglement of bottom-up and top-down influences following from their evolutionary history. A self-consistent multiscale scheme (...) is proposed to capture the ensuing circular causality. Its differences with standard mean-field self-consistent equations and slow-fast decompositions are discussed. As such, this scheme offers a way to unravel the multilevel architecture of living systems and their regulation. Two examples, genome functions and biofilms, are detailed. (shrink)

We present a multiscale integrationist interpretation of the boundaries of cognitive systems, using the Markov blanket formalism of the variational free energy principle. This interpretation is intended as a corrective for the philosophical debate over internalist and externalist interpretations of cognitive boundaries; we stake out a compromise position. We first survey key principles of new radical views of cognition. We then describe an internalist interpretation premised on the Markov blanket formalism. Having reviewed these accounts, we develop our positive (...) class='Hi'>multiscale account. We argue that the statistical seclusion of internal from external states of the system—entailed by the existence of a Markov boundary—can coexist happily with the multiscale integration of the system through its dynamics. Our approach does not privilege any given boundary, nor does it argue that all boundaries are equally prescient. We argue that the relevant boundaries of cognition depend on the level being characterised and the explanatory interests that guide investigation. We approach the issue of how and where to draw the boundaries of cognitive systems through a multiscale ontology of cognitive systems, which offers a multidisciplinary research heuristic for cognitive science. (shrink)

Multiscale modeling techniques have attracted increasing attention by philosophers of science, but the resulting discussions have almost exclusively focused on issues surrounding explanation (e.g., reduction and emergence). In this paper, I argue that besides explanation, multiscale techniques can serve important exploratory functions when scientists model systems whose organization at different scales is ill-understood. My account distinguishes explanatory and descriptive multiscale modeling based on which epistemic goal scientists aim to achieve when using multiscale techniques. In explanatory (...) class='Hi'>multiscale modeling, scientists use multiscale techniques to select information that is relevant to explain a particular type of behavior of the target system. In descriptive multiscale modeling scientists use multiscale techniques to explore lower-scale features which could be explanatorily relevant to many different types of behavior, and to determine which features of a target system an upper-scale data pattern could refer to. Using multiscale models from data-driven neuroscience as a case study, I argue that descriptive multiscale models have an exploratory function because they are a sources of potential explanations and serve as tools to reassess our conception of the target system. (shrink)

Multiscale dynamics, linear approximations, global boundary conditions, experimental verification, and global influences on local cell assemblies are considered in the context of Wright & Liley's work. W&L provide a nice introduction to these issues and a reasonable simulation of intermediate scale dynamics, but the model does not adequately simulate combined local and global processes.

In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given (...) that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description—four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity, to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene–environment interactions. We argue that gene–behavior associations can inform cognitive theory with respect to effect size, specificity, and timing. The model demonstrates a means by which researchers can undertake multiscale modeling with respect to cognition and develop highly specific and complex hypotheses across multiple levels of description. (shrink)

For the first time Entropy has been completely revised and updated to include a new subtitle which reflects the expanded focus on the greenhouse effect--the largest crisis ever to face mankind.

A new axiomatic characterization with a minimum of conditions for entropy as a function on the set of states in quantum mechanics is presented. Traditionally unspoken assumptions are unveiled and replaced by proven consequences of the axioms. First the Boltzmann–Planck formula is derived. Building on this formula, using the Law of Large Numbers—a basic theorem of probability theory—the von Neumann formula is deduced. Axioms used in older theories on the foundations are now derived facts.

We give a “direction for use” of the scale relativity theory and apply it to an example of spontaneous multiscale integration including four embedded levels of organization (intracellular, cell, tissue and organism-like levels). We conclude by an update of our analysis of the arctic sea ice melting.

Entropy is ubiquitous in physics, and it plays important roles in numerous other disciplines ranging from logic and statistics to biology and economics. However, a closer look reveals a complicated picture: entropy is defined differently in different contexts, and even within the same domain different notions of entropy are at work. Some of these are defined in terms of probabilities, others are not. The aim of this chapter is to arrive at an understanding of some of the (...) most important notions of entropy and to clarify the relations between them, After setting the stage by introducing the thermodynamic entropy, we discuss notions of entropy in information theory, statistical mechanics, dynamical systems theory and fractal geometry. (shrink)

The present paper is a survey of the economics of survival, a branch of ecological economics that stresses the preservation of the opportunities of future generations over an extended time horizon. It outlines the main analytical foundation of the branch - in which the concept of entropy is a major building block -, and its analysis of the interaction between the economic system and the environment. Regarding its outlook of the future, we see that the founders of the branch (...) were mainly concerned with the consequences of a serious depletion of natural resources - particularly the energetic capital of the earth. More recently, however, emphasis is being placed on problems that stem from the fragility of the global ecosystem in face of the disturbances caused by the entropic acceleration imposed by mankind. It is feared that the ongoing expansion of the scale of the economy may bring about irreversible damages to vital environmental functions, such as protection against undesirable consequences of solar radiation, maintenance of temperature within a range that will support life, and preservation of ecosystem resiliency. (shrink)

The standard assumptions that underlie many conceptual and quantitative frameworks do not hold for many complex physical, biological, and social systems. Complex systems science clarifies when and why such assumptions fail and provides alternative frameworks for understanding the properties of complex systems. This review introduces some of the basic principles of complex systems science, including complexity profiles, the tradeoff between efficiency and adaptability, the necessity of matching the complexity of systems to that of their environments, multiscale analysis, and evolutionary (...) processes. Our focus is on the general properties of systems as opposed to the modeling of specific dynamics; rather than provide a comprehensive review, we pedagogically describe a conceptual and analytic approach for understanding and interacting with the complex systems of our world. This paper assumes only a high school mathematical and scientific background so that it may be accessible to academics in all fields, decision-makers in industry, government, and philanthropy, and anyone who is interested in systems and society. (shrink)

Both ecologists and statistical physicists use a variety of highly idealized models to study active matter and self-organizing critical phenomena. In this paper, I show how universality classes play a crucial role in justifying the application of highly idealized ‘minimal’ models to explain and understand the critical behaviors of active matter systems across a wide range of scales and scientific fields. Appealing to universality enables us to see why the same minimal models can be used to explain and understand behaviors (...) across these different systems despite drastic differences in the causes and mechanisms responsible for the behaviors of interest. After analyzing these cases in detail, I argue that accounts that focus on identifying common causes or mechanisms in order to explain patterns are unable to accommodate these cases. In contrast, I argue that the justification for using these minimal models is that they are within the same universality class as real systems whose causes and mechanisms are known to be different. I also use these cases to identify several different kinds of explanatory autonomy that have important implications for how scientists ought to approach the modeling of multiscale phenomena. (shrink)

The Anthropocene crisis is frequently described as the rarefaction of resources or resources per capita. However, both energy and minerals correspond to fundamentally conserved quantities from the perspective of physics. A specific concept is required to understand the rarefaction of available resources. This concept, entropy, pertains to energy and matter configurations and not just to their sheer amount. However, the physics concept of entropy is insufficient to understand biological and social organizations. Biological phenomena display both historicity and systemic (...) properties. A biological organization, the ability of a specific living being to last over time, results from history, expresses itself by systemic properties, and may require generating novelties The concept of anti-entropy stems from the combination of these features. We propose that Anthropocene changes disrupt biological organizations by randomizing them, that is, decreasing anti-entropy. Moreover, second-order disruptions correspond to the decline of the ability to produce functional novelties, that is, to produce anti-entropy. (shrink)

Understanding the implication of point cloud is still challenging in the aim of classification or segmentation for point cloud due to its irregular and sparse structure. As we have known, PointNet architecture as a ground-breaking work for point cloud process can learn shape features directly on unordered 3D point cloud and has achieved favorable performance, such as 86% mean accuracy and 89.2% overall accuracy for classification task, respectively. However, this model fails to consider the fine-grained semantic information of local structure (...) for point cloud. Then, a multiscale receptive fields graph attention network by means of semantic features of local patch for point cloud is proposed in this paper, and the learned feature map for our network can well capture the abundant features information of point cloud. The proposed MRFGAT architecture is tested on ModelNet datasets, and results show it achieves state-of-the-art performance in shape classification tasks, such as it outperforms GAPNet model by 0.1% in terms of OA and compete with DGCNN model in terms of MA. (shrink)

In speaker recognition systems, feature extraction is a challenging task under environment noise conditions. To improve the robustness of the feature, we proposed a multiscale chaotic feature for speaker recognition. We use a multiresolution analysis technique to capture more finer information on different speakers in the frequency domain. Then, we extracted the speech chaotic characteristics based on the nonlinear dynamic model, which helps to improve the discrimination of features. Finally, we use a GMM-UBM model to develop a speaker recognition (...) system. Our experimental results verified its good performance. Under clean speech and noise speech conditions, the ERR value of our method is reduced by 13.94% and 26.5% compared with the state-of-the-art method, respectively. (shrink)

For unmanned aerial vehicle, object detection at different scales is an important component for the visual recognition. Recent advances in convolutional neural networks have demonstrated that attention mechanism remarkably enhances multiscale representation of CNNs. However, most existing multiscale feature representation methods simply employ several attention blocks in the attention mechanism to adaptively recalibrate the feature response, which overlooks the context information at a multiscale level. To solve this problem, a multiscale feature filtering network is proposed in (...) this paper for image recognition system in the UAV. A novel building block, namely, multiscale feature filtering module, is proposed for ResNet-like backbones and it allows feature-selective learning for multiscale context information across multiparallel branches. These branches employ multiple atrous convolutions at different scales, respectively, and further adaptively generate channel-wise feature responses by emphasizing channel-wise dependencies. Experimental results on CIFAR100 and Tiny ImageNet datasets reflect that the MFFNet achieves very competitive results in comparison with previous baseline models. Further ablation experiments verify that the MFFNet can achieve consistent performance gains in image classification and object detection tasks. (shrink)

A probability distribution can be given to the set of isomorphism classes of models with universe {1, ..., n} of a sentence in first-order logic. We study the entropy of this distribution and derive a result from the 0–1 law for first-order sentences.

Two open questions of inductive reasoning are solved: (1) does the principle of maximum entropy (pme) give a solution to the obverse Majerník problem; and (2) is Wagner correct when he claims that Jeffrey’s updating principle (jup) contradicts pme? Majerník shows that pme provides unique and plausible marginal probabilities, given conditional probabilities. The obverse problem posed here is whether pme also provides such conditional probabilities, given certain marginal probabilities. The theorem developed to solve the obverse Majerník problem demonstrates that (...) in the special case introduced by Wagner pme does not contradict jup, but elegantly generalizes it and offers a more integrated approach to probability updating. (shrink)

The notion of conditional entropy is extended to noncomposite systems. The \-deformed entropic inequalities, which usually are associated with correlations of the subsystem degrees of freedom in bipartite systems, are found for the noncomposite systems. New entropic inequalities for quantum tomograms of qudit states including the single qudit states are obtained. The Araki–Lieb inequality is found for systems without subsystems.

We review some notions for general quantum entropies. The entropy of the compound systems is discussed and a numerical computation of the quantum dynamical systems is carried for the noisy optical channel.

The use of multimodal sensors for lane line segmentation has become a growing trend. To achieve robust multimodal fusion, we introduced a new multimodal fusion method and proved its effectiveness in an improved fusion network. Specifically, a multiscale fusion module is proposed to extract effective features from data of different modalities, and a channel attention module is used to adaptively calculate the contribution of the fused feature channels. We verified the effect of multimodal fusion on the KITTI benchmark dataset (...) and A2D2 dataset and proved the effectiveness of the proposed method on the enhanced KITTI dataset. Our method achieves robust lane line segmentation, which is 4.53% higher than the direct fusion on the precision index, and obtains the highest F2 score of 79.72%. We believe that our method introduces an optimization idea of modal data structure level for multimodal fusion. (shrink)

The biosemiotic complexity of acoustic communities in the primary forests of Ulu Temburong and Yasunì was investigated with continuous 24-h recordings, using the acoustic signature and multiscale approach of ecoacoustic events and their emergent fractal dimensions. The 3D recordings used for the analysis were collected in undisturbed primary equatorial forests under the scope of the project, Fragments of Extinction, which produces 3D sound portraits with the highest definition possible using current technologies – a perfect dataset on which to perform (...) a multiscale qualitative analysis. The ecoacoustic events detected by a combination of the Acoustic Complexity Indices, ACIft, ACIft evenness, and ACItf evenness, and its fractal dimension were developed according to a biosemiotics approach in which the ecofield theory states that EE functions like a species-specific carrier of meaning. EEs, extracted according to 10 levels of temporal resolution, from 1 to 360 s, confirm the hypothesis that these acoustic communities have an internal complexity that responds to a fractal structure. Yasunì was richer in EEs, with a higher coefficient of variation of hourly fractal dimension. This methodology opens up promising new perspectives in the acoustic assessment of habitat quality and monitoring landscape modification. It also confirms the great potential of the biosemiotics approach in converting acoustic frequencies into ecoacoustic events through encoding procedures that mimic potential species-specific interpretations of the sonic environment. (shrink)

A new way of understanding entropy as a macroscopic property is presented. This is based on the fact that heat flows from a hot body to a cold one even when the hot one is smaller and has less energy. A quantity that determines the direction of flow is shown to be the increment of heat gained divided by the absolute temperature. The same quantity is shown to determine the direction of other processes taking place in isolated systems provided (...) that q is determined by the state of the system. Entropy emerges as the potent energy of a system [Σ], the potency being determined by 1/T. This is shown to tie in with the statistical mechanical interpretation of entropy. The treatment is shorter than the traditional one based on heat engines. (shrink)

Recent accounts of multiscale modeling investigate ontic and epistemic constraints imposed by relations between component models at varying relative scales (macro, meso, micro). These accounts often focus especially on the role of the meso, or intermediate, relative scale in a multiscale model. We aid this effort by highlighting a novel role for mesoscale models: they can function as a focal point, and rationale, for disagreement between researchers who otherwise share theoretical commitments. We illustrate with a case study in (...) multiscale modeling of insect behavior, arguing that the cognitive map debate in neuroethology research is best understood as a mesoscale disagreement. (shrink)

Both a method of therapy and an exploration of psychic reality, free association is a fundamental element of psychoanalytical practices that refers to the way a patient is asked to describe what comes spontaneously to mind in the therapeutic setting. This paper examines the role of free association from the point of view of psychoanalysis and neuroscience in order to improve our understanding of therapeutic effects induced by psychoanalytic therapies and psychoanalysis. In this regard, we first propose a global overview (...) of the historical origins of the concept of free association in psychoanalysis and examine how Freud established its principles. Then, from Freud’s distinction between primary and secondary processes, we proceed to compare the psychoanalytic model with research originating from cognitive psychology and neuroscience. The notions of entropy and free energy appear particularly relevant at the intersection of these different domains. Finally, we propose the notion of symbolizing transmodality to describe certain specificities of symbolization processes within free association and we summarize the main functions of free association in psychoanalytic practices. (shrink)

The paper tries to demonstrate that the process of the increase of entropy does not explain the asymmetry of time itself because it is unable to account for its fundamental asymmetries, that is, the asymmetry of traces (we have traces of the past and no traces of the future), the asymmetry of causation (we have an impact on future events with no possibility of having an impact on the past), and the asymmetry between the fixed past and the open (...) future, To this end, the approaches of Boltzmann, Reichenbach (and his followers), and Albert are analysed. It is argued that we should look for alternative approaches instead of this, namely we should consider a temporally asymmetrical physical theory or seek a source of the asymmetry of time in metaphysics. This second approach may even turn out to be complementary if only we accept that metaphysics can complement scientific research programmes. (shrink)

Logical information theory is the quantitative version of the logic of partitions just as logical probability theory is the quantitative version of the dual Boolean logic of subsets. The resulting notion of information is about distinctions, differences and distinguishability and is formalized using the distinctions of a partition. All the definitions of simple, joint, conditional and mutual entropy of Shannon information theory are derived by a uniform transformation from the corresponding definitions at the logical level. The purpose of this (...) paper is to give the direct generalization to quantum logical information theory that similarly focuses on the pairs of eigenstates distinguished by an observable, i.e., qudits of an observable. The fundamental theorem for quantum logical entropy and measurement establishes a direct quantitative connection between the increase in quantum logical entropy due to a projective measurement and the eigenstates that are distinguished by the measurement. Both the classical and quantum versions of logical entropy have simple interpretations as “two-draw” probabilities for distinctions. The conclusion is that quantum logical entropy is the simple and natural notion of information for quantum information theory focusing on the distinguishing of quantum states. (shrink)

Vacuum radiation causes a particle to make a random walk about its dynamical trajectory. In this random walk the root mean square change in spatial coordinate is proportional to t 1/2, and the fractional changes in momentum and energy are proportional to t −1/2, where t is time. Thus the exchange of energy and momentum between a particle and the vacuum tends to zero over time. At the end of a mean free path the fractional change in momentum of a (...) particle in a gas is very small. However, at the end of the mean free path each particle undergoes an interaction that magnifies the preceding change, and the net result is that the momentum distribution of the particles in a gas is randomized in a few collision times. In this way the random action of vacuum radiation and its subsequent magnification by molecular interaction produces entropy increase. This process justifies the assumption of molecular chaos used in the Boltzmann transport equation. (shrink)

Daniel R. Brooks and E. O. Wiley have proposed a theory of evolution in which fitness is merely a rate determining factor. Evolution is driven by non-equilibrium processes which increase the entropy and information content of species together. Evolution can occur without environmental selection, since increased complexity and organization result from the likely capture at the species level of random variations produced at the chemical level. Speciation can occur as the result of variation within the species which decreases the (...) probability of sharing genetic information. Critics of the Brooks-Wiley theory argue that they have abused terminology from information theory and t thermodynamics. In this paper I review the essentials of the theory, and give an account of hierarchical physical information systems within which the theory can be interpreted. I then show how the major conceptual objections can be answered. (shrink)

This essay is, primarily, a discussion of four results about the principle of maximizing entropy (MAXENT) and its connections with Bayesian theory. Result 1 provides a restricted equivalence between the two: where the Bayesian model for MAXENT inference uses an "a priori" probability that is uniform, and where all MAXENT constraints are limited to 0-1 expectations for simple indicator-variables. The other three results report on an inability to extend the equivalence beyond these specialized constraints. Result 2 established a sensitivity (...) of MAXENT inference to the choice of the algebra of possibilities even though all empirical constraints imposed on the MAXENT solution are satisfied in each measure space considered. The resulting MAXENT distribution is not invariant over the choice of measure space. Thus, old and familiar problems with the Laplacian principle of Insufficient Reason also plague MAXENT theory. Result 3 builds upon the findings of Friedman and Shimony (1971; 1973) and demonstrates the absence of an exchangeable, Bayesian model for predictive MAXENT distributions when the MAXENT constraints are interpreted according to Jaynes's (1978) prescription for his (1963) Brandeis Dice problem. Lastly, Result 4 generalizes the Friedman and Shimony objection to cross-entropy (Kullback-information) shifts subject to a constraint of a new odds-ratio for two disjoint events. (shrink)

The dominant paradigm in biomedicine focuses on genetically‐specified components of cells and their biochemical dynamics, emphasizing bottom‐up emergence of complexity. Here, I explore the biomedical implications of a complementary emerging field: diverse intelligence. Using tools from behavioral science and multiscale neuroscience, we can study development, regenerative repair, and cancer suppression as behaviors of a collective intelligence of cells navigating the spaces of possible morphologies and transcriptional and physiological states. A focus on the competencies of living material—from molecular to organismal (...) scales—reveals a new landscape for interventions. Such top‐down approaches take advantage of the memories and homeodynamic goal‐seeking behavior of cells and tissues, offering the same massive advantages in biomedicine and bioengineering that reprogrammable hardware has provided information technologies. The bioelectric networks that bind individual cells toward large‐scale anatomical goals are an especially tractable interface to organ‐level plasticity, and tools to modulate them already exist. This suggests a research program to understand and tame the software of life for therapeutic gain by understanding the many examples of basal cognition that operate throughout living bodies. (shrink)

This paper explores a possible relationship between entropy and evil in terms of metaphor. After presenting the various meanings of entropy in classical thermodynamics and statical mechanics, and the Augustinian and Irenaean theodicies, several similarities and dissimilarities between entropy and evil are described. Underlying the concepts of evil and entropy is the assumption that time has a direction. After examining the scientific basis for this assumption, it is hypothesized that, if evil is real in nature, (...) class='Hi'>entropy is what one would expect to find at the level of physical processes, and conversely that, if entropy is coupled to a physical arrow of time, one could expect to find dissipative yet catalytic processes in history and religious experience. (shrink)

I assess the thesis that counterfactual asymmetries are explained by an asymmetry of the global entropy at the temporal boundaries of the universe by developing a new semantic element for counterfactuals called objective assertibility and a method of evaluating counterfactuals that constrains consideration to possibilities where the early universe has low entropy. The resulting theory vindicates the common practice of holding the past mostly fixed under counterfactual supposition while at the same time allowing the counterfactual's antecedent to obtain (...) by a natural physical development. While excellent for evaluating a wide variety of ordinary counterfactuals, it does not fully succeed as an explanation of counterfactual asymmetry. (shrink)

I discuss the statistical mechanics of gravitating systems and in particular its cosmological implications, and argue that many conventional views on this subject in the foundations of statistical mechanics embody significant confusion; I attempt to provide a clearer and more accurate account. In particular, I observe that (i) the role of gravity in entropy calculations must be distinguished from the entropy of gravity, that (ii) although gravitational collapse is entropy-increasing, this is not usually because the collapsing matter (...) itself increases in entropy, and that (iii) the Second Law of thermodynamics does not owe its validity to the statistical mechanics of gravitational collapse. (shrink)

Due to the advent of deglobalization and regional integration, this article aims to adopt LASSO-based network connectedness to estimate the multiscale tail risk spillover effects of global stock markets. The results show that tail risk varies across frequencies and shocks. In static analysis, the risk is centered mostly on the developed European and North American markets at a low frequency, and regionalization is imposed on the moderate frequency. Moreover, emerging markets could be sources of risk spillover, especially at the (...) highest frequency where there is no absolute risk center. In dynamic analysis, we use rolling window estimation and find that different frequencies identify distinct episodes of shocks, which provides us with the reason for the diverse risk centers at different time scales in static analysis. Our findings provide heterogeneous financial practitioners, regulators, and investors with diverse characteristics of stock markets under multiple time horizons and help them operate their own trading strategies. (shrink)

With reference to two specific modalities of sensation, the taste of saltiness of chloride salts, and the loudness of steady tones, it is shown that the laws of sensation (logarithmic and power laws) are expressions of the entropy per mole of the stimulus. That is, the laws of sensation are linear functions of molar entropy. In partial verification of this hypothesis, we are able to derive an approximate value for the gas constant, a fundamental physical constant, directly from (...) psychophysical measurements. The significance of our observation lies in the linking of the phenomenon of “sensation” directly to a physical measure. It suggests that if the laws of physics are universal, the laws of sensation and perception are similarly universal. It also connects the sensation of a simple, steady physical signal with the molecular structure of the signal: the greater the number of microstates or complexions of the stimulus signal, the greater the magnitude of the sensation (saltiness or loudness). The hypothesis is currently tested on two sensory modalities. (shrink)