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This paper presents Integrated Information Theory (IIT) 4.0. IIT aims to account for the properties of experience in physical (operational) terms. It identifies the essential properties of experience (axioms), infers the necessary and sufficient properties that its substrate must satisfy (postulates), and expresses them in mathematical terms. In principle, the postulates can be applied to any system of units in a state to determine whether it is conscious, to what degree, and in what way. IIT offers a parsimonious explanation of empirical evidence, makes testable predictions, and permits inferences and extrapolations. IIT 4.0 incorporates several developments of the past ten years, including a more accurate translation of axioms into postulates and mathematical expressions, the introduction of a unique measure of intrinsic information that is consistent with the postulates, and an explicit assessment of causal relations. By fully unfolding a system's irreducible cause-effect power, the distinctions and relations specified by a substrate can account for the quality of experience., Comment: Correspondence to: gtononi@wisc.edu. 53 pages, 8 figures

Integrated information theory (IIT) starts from consciousness itself and identifies a set of properties (axioms) that are true of every conceivable experience. The axioms are translated into a set of postulates about the substrate of consciousness (called a complex), which are then used to formulate a mathematical framework for assessing both the quality and quantity of experience. The explanatory identity proposed by IIT is that an experience is identical to the cause-effect structure unfolded from a maximally irreducible substrate (a $\Phi$-structure). In this work we introduce a definition for the integrated information of a system ($\varphi_s$) that is based on the existence, intrinsicality, information, and integration postulates of IIT. We explore how notions of determinism, degeneracy, and fault lines in the connectivity impact system integrated information. We then demonstrate how the proposed measure identifies complexes as systems whose $\varphi_s$ is greater than the $\varphi_s$ of any overlapping candidate systems., Comment: 16 pages, 4 figures

We examined multiple deep neural network (DNN) architectures for suitability in predicting neurotransmitter concentrations from labeled in vitro fast scan cyclic voltammetry (FSCV) data collected on carbon fiber electrodes. Suitability is determined by the predictive performance in the "out-of-probe" case, the response to artificially induced electrical noise, and the ability to predict when the model will be errant for a given probe. This work extends prior comparisons of time series classification models by focusing on this specific task. It extends previous applications of machine learning to FSCV task by using a much larger data set and by incorporating recent advancements in deep neural networks. The InceptionTime architecture, a deep convolutional neural network, has the best absolute predictive performance of the models tested but was more susceptible to noise. A naive multilayer perceptron architecture had the second lowest prediction error and was less affected by the artificial noise, suggesting that convolutions may not be as important for this task as one might suspect., Comment: 13 pages, 7 figures

Cognitive-Driven Development (CDD) is a coding design technique that aims to reduce the cognitive effort that developers place in understanding a given code unit (e.g., a class). By following CDD design practices, it is expected that the coding units to be smaller, and, thus, easier to maintain and evolve. However, it is so far unknown whether these smaller code units coded using CDD standards are, indeed, easier to understand. In this work we aim to assess to what CDD improves code readability. To achieve this goal, we conducted a two-phase study. We start by inviting professional software developers to vote (and justify their rationale) on the most readable pair of code snippets (from a set of 10 pairs); one of the pairs was coded using CDD practices. We received 133 answers. In the second phase, we applied the state-of-the art readability model on the 10-pairs of CDD-guided refactorings. We observed some conflicting results. On the one hand, developers perceived that seven (out of 10) CDD-guided refactorings were more readable than their counterparts; for two other CDD-guided refactorings, developers were undecided, while only in one of the CDD-guided refactorings, developers preferred the original code snippet. On the other hand, we noticed that only one CDD-guided refactorings have better performance readability, assessed by state-of-the-art readability models. Our results provide initial evidence that CDD could be an interesting approach for software design., Comment: 10 pages, 7 figures

In this paper, we report a new boron nitride molecular structure called BN-nanobelt, an inorganic analog of (12) cyclophenacene synthesized in 2017. An extensive investigation using Density Functional Theory (DFT) and Quantum Molecular Dynamics (QMD) calculations showed that BN-nanobelt is a structurally and thermally stable molecule with all positive vibrational frequencies. BN-nanobelt behaves as an insulator, and it absorbs in the ultraviolet region, suggesting a potential application as a UV detector. All results presented in this paper indicate structural stability and the possibility of its synthesis. We hope that the proposed BN-nanobelt structure could stimulate further experimental investigations on its synthesis and bring potential novel technological applications., Comment: Paper accepted for publication in the Computational and Theoretical Chemistry journal

Data obtained at RHIC can be reproduced by relativistic viscous hydrodynamic simulations by adjusting the viscosity and initial conditions but it is difficult to disentangle these quantities. It is therefore important to find orthogonal observables to constrain the initial conditions separately from the viscosity. New observables have been measured at the LHC and shown to be sensitive to initial conditions and less to medium properties, specifically factorization breaking ratios appears to be promising. Here we consider two models (NeXus and TRENTO) that reproduce these quantities reasonably well at the LHC and then we make predictions for RHIC. While both models lead to similar results for the scaled flow harmonic distributions, they predict very different patterns for the factorization breaking ratios at RHIC.

We study source localization from high dimensional M/EEG data by extending a multiscale method based on Entropic inference devised to increase the spatial resolution of inverse problems. This method is used to construct informative prior distributions in a manner inspired in the context of fMRI (Amaral et al 2004). We construct a set of renormalized lattices that approximate the cortex region where the source activity is located and address the related problem of defining the relevant variables in a coarser scale representation of the cortex. The priors can be used in conjunction with other Bayesian methods such as the Variational Bayes method (VB, Sato et al 2004). The central point of the algorithm is that it uses a posterior obtained at a coarse scale to induce a prior at the next finer scale stage of the problem. We present results which suggest, on simulated data, that this way of including prior information is a useful aid for the source location problem. This is judged by the rate and magnitude of errors in source localization. Better convergence times are also achieved. We also present results on public data collected during a face recognition task.