An automatic classifier of emotions built from entropy of noise

The electrocardiogram (ECG) signal has been widely used to study the physiological substrates of emotion. However, searching for better filtering techniques in order to obtain a signal with better quality and with the maximum relevant information remains an important issue for researchers in this field. Signal processing is largely performed for ECG analysis and interpretation, but this process can be susceptible to error in the delineation phase. In addition, it can lead to the loss of important information that is usually considered as noise and, consequently, discarded from the analysis. The goal of this study was to evaluate if the ECG noise allows for the classification of emotions, while using its entropy as an input in a decision tree classifier. We collected the ECG signal from 25 healthy participants while they were presented with videos eliciting negative (fear and disgust) and neutral emotions. The results indicated that the neutral condition showed a perfect identification (100%), whereas the classification of negative emotions indicated good identification performances (60% of sensitivity and 80% of specificity). These results suggest that the entropy of noise contains relevant information that can be useful to improve the analysis of the physiological correlates of emotion.

Unsupervised reinforcement learning via state entropy maximization

Reinforcement Learning (RL) provides a powerful framework to address sequential decision-making problems in which the transition dynamics is unknown or too complex to be represented. The RL approach is based on speculating what is the best decision to make given sample estimates obtained from previous interactions, a recipe that led to several breakthroughs in various domains, ranging from game playing to robotics. Despite their success, current RL methods hardly generalize from one task to another, and achieving the kind of generalization obtained through unsupervised pre-training in non-sequential problems seems unthinkable. Unsupervised RL has recently emerged as a way to improve generalization of RL methods. Just as its non-sequential counterpart, the unsupervised RL framework comprises two phases: An unsupervised pre-training phase, in which the agent interacts with the environment without external feedback, and a supervised fine-tuning phase, in which the agent aims to efficiently solve a task in the same environment by exploiting the knowledge acquired during pre-training. In this thesis, we study unsupervised RL via state entropy maximization, in which the agent makes use of the unsupervised interactions to pre-train a policy that maximizes the entropy of its induced state distribution. First, we provide a theoretical characterization of the learning problem by considering a convex RL formulation that subsumes state entropy maximization. Our analysis shows that maximizing the state entropy in finite trials is inherently harder than RL. Then, we study the state entropy maximization problem from an optimization perspective. Especially, we show that the primal formulation of the corresponding optimization problem can be (approximately) addressed through tractable linear programs. Finally, we provide the first practical methodologies for state entropy maximization in complex domains, both when the pre-training takes place in a single environment as well as multiple environments.

A maximum entropy approach to disturbed ecosystems

The current climate crisis requires a comprehensive understanding of biodiversity to acknowledge how ecosystems’ responses to anthropogenic disturbances may result in feedback that can either mitigate or exacerbate global warming. Although ecosystems are dynamic and macroecological patterns change drastically in response to disturbance, dynamic macroecology has received insufficient attention and theoretical formalisation. In this context, the maximum entropy principle (MaxEnt) could provide an effective inference procedure to study ecosystems. Since the improper usage of entropy outside its scope often leads to misconceptions, the opening chapter will clarify its meaning by following its evolution from classical thermodynamics to information theory. The second chapter introduces the study of ecosystems from a physicist’s viewpoint. In particular, the MaxEnt Theory of Ecology (METE) will be the cornerstone of the discussion. METE predicts the shapes of macroecological metrics in relatively static ecosystems using constraints imposed by static state variables. However, in disturbed ecosystems with macroscale state variables that change rapidly over time, its predictions tend to fail. In the final chapter, DynaMETE is therefore presented as an extension of METE from static to dynamic. By predicting how macroecological patterns are likely to change in response to perturbations, DynaMETE can contribute to a better understanding of disturbed ecosystems’ fate and the improvement of conservation and management of carbon sinks, like forests. Targeted strategies in ecosystem management are now indispensable to enhance the interdependence of human well-being and the health of ecosystems, thus avoiding climate change tipping points.

Conditional entropy and predictability for non-reversible Markov systems

The purpose of this thesis is to clarify the role of non-equilibrium stationary currents of Markov processes in the context of the predictability of future states of the system. Once the connection between the predictability and the conditional entropy is established, we provide a comprehensive approach to the definition of a multi-particle Markov system. In particular, starting from the well-known theory of random walk on network, we derive the non-linear master equation for an interacting multi-particle system under the one-step process hypothesis, highlighting the limits of its tractability and the prop- erties of its stationary solution. Lastly, in order to study the impact of the NESS on the predictability at short times, we analyze the conditional entropy by modulating the intensity of the stationary currents, both for a single-particle and a multi-particle Markov system. The results obtained analytically are numerically tested on a 5-node cycle network and put in correspondence with the stationary entropy production. Furthermore, because of the low dimensionality of the single-particle system, an analysis of its spectral properties as a function of the modulated stationary currents is performed.

Triboelectricity In Insulating Polymers: Evidence For A Mechanochemical Mechanism.

Transfer of reaction products formed on the surfaces of two mutually rubbed dielectric solids makes an important if not dominating contribution to triboelectricity. New evidence in support of this statement is presented in this report, based on analytical electron microscopy coupled to electrostatic potential mapping techniques. Mechanical action on contacting surface asperities transforms them into hot-spots for free-radical formation, followed by electron transfer producing cationic and anionic polymer fragments, according to their electronegativity. Polymer ions accumulate creating domains with excess charge because they are formed at fracture surfaces of pulled-out asperities. Another factor for charge segregation is the low polymer mixing entropy, following Flory and Huggins. The formation of fractal charge patterns that was previously described is thus the result of polymer fragment fractal scatter on both contacting surfaces. The present results contribute to the explanation of the centuries-old difficulties for understanding the triboelectric series and triboelectricity in general, as well as the dissipative nature of friction, and they may lead to better control of friction and its consequences.

Hyperhoneycomb Iridate β-li_{2}iro_{3} As A Platform For Kitaev Magnetism.

A complex iridium oxide β-Li_{2}IrO_{3} crystallizes in a hyperhoneycomb structure, a three-dimensional analogue of honeycomb lattice, and is found to be a spin-orbital Mott insulator with J_{eff}=1/2 moment. Ir ions are connected to the three neighboring Ir ions via Ir-O_{2}-Ir bonding planes, which very likely gives rise to bond-dependent ferromagnetic interactions between the J_{eff}=1/2 moments, an essential ingredient of Kitaev model with a spin liquid ground state. Dominant ferromagnetic interaction between J_{eff}=1/2 moments is indeed confirmed by the temperature dependence of magnetic susceptibility χ(T) which shows a positive Curie-Weiss temperature θ_{CW}∼+40  K. A magnetic ordering with a very small entropy change, likely associated with a noncollinear arrangement of J_{eff}=1/2 moments, is observed at T_{c}=38  K. With the application of magnetic field to the ordered state, a large moment of more than 0.35  μ_{B}/Ir is induced above 3 T, a substantially polarized J_{eff}=1/2 state. We argue that the close proximity to ferromagnetism and the presence of large fluctuations evidence that the ground state of hyperhoneycomb β-Li_{2}IrO_{3} is located in close proximity of a Kitaev spin liquid.

A segunda lei da termodinâmica

Considering intrinsic characteristics of the system exclusively, both statistical and information theory interpretations of the second law are used to provide more comprehensive meanings for the concepts of entropy, temperature, and Helmholtz and Gibbs energies. The coherence of Clausius inequality to these concepts is emphasized. The aim of this work is to re-discuss the second law of thermodynamics in accordance to homogeneous processes thermodynamics, a temporal science which is the very special oversimplification of continuum mechanics for spatially constant intensive properties.

Entropia, reversibilidade, irreversibilidade, equação de transporte e teorema H de Boltzmann e o teorema do retorno de Poincaré

Neste trabalho analisamos as conexões entre entropia, reversibilidade, irreversibilidade, teorema H e equação de transporte de Boltzmann e o teorema de retorno de Poincaré. Estes tópicos são estudados separadamente em muitos artigos e livros, mas não são em geral analisados em conjunto mostrando as relações entre eles como fizemos aqui. Procuramos redigir o artigo didaticamente seguindo um caminho que achamos ser o mais simples possível a fim de tornar o conteúdo acessível aos alunos de graduação de física.

Videodensitometric analysis of advanced carotid plaque: correlation with MMP-9 and TIMP-1 expression

Background: Matrix metalloproteinase-9 (MMP-9) and tissue inhibitor of MMP (TIMP) promote derangement of the extracellular matrix, which is ultimately reflected in plaque images seen on ultrasound. Videodensitometry can identify structural disturbances in plaques. Objectives: To establish the correlations between values determined using videodensitometry in B-mode ultrasound images of advanced carotid plaques and the total expression of MMP-9 and TIMP-1 in these removed plaques. Methods: Thirty patients underwent ultrasonic tissue characterization of carotid plaques before surgery, using mean gray level (MGL), energy, entropy and homogeneity. Each patient was assigned preoperatively to one of 2 groups: group I, symptomatic patients (n = 16; 12 males; mean age 66.7 +/- 6.8 years), and group II, asymptomatic patients (n = 14; 8 males; mean age 67.6 +/- 6.81 years). Tissue specimens were analyzed for MMP-9 and TIMP-1 expression. Nine carotid arteries were used as normal tissue controls. Results: MMP-9 expression levels were elevated in group II and in normal tissues compared to group I (p < 0.001). TIMP-1 levels were higher in group II than in group I, and significantly higher in normal tissues than in group I (p = 0.039). The MGL was higher in group II compared to group I (p = 0.038). Energy had greater values in group II compared to group I (p = 0.02). There were no differences between patient groups in homogeneity and entropy. Energy positively correlated with MMP-9 and TIMP-1 expression (p = 0.012 and p = 0.031 respectively). Homogeneity positively correlated with MMP-9 and TIMP-1 expression (p = 0.034 and p = 0.047 respectively). There were no correlations between protein expression and MGL or entropy. Conclusions: Videodensitometric computer analysis of ultrasound scanning images can be used to identify stable carotid plaques, which have higher total expression levels of MMP-9 and TIMP-1 than unstable plaques.

Chemical potential and the nature of dark energy: The case of a phantom field

The influence of a possible nonzero chemical potential mu on the nature of dark energy is investigated by assuming that the dark energy is a relativistic perfect simple fluid obeying the equation of state, p=omega rho (omega < 0, constant). The entropy condition, S >= 0, implies that the possible values of omega are heavily dependent on the magnitude, as well as on the sign of the chemical potential. For mu > 0, the omega parameter must be greater than -1 (vacuum is forbidden) while for mu < 0 not only the vacuum but even a phantomlike behavior (omega <-1) is allowed. In any case, the ratio between the chemical potential and temperature remains constant, that is, mu/T=mu(0)/T(0). Assuming that the dark energy constituents have either a bosonic or fermionic nature, the general form of the spectrum is also proposed. For bosons mu is always negative and the extended Wien's law allows only a dark component with omega <-1/2, which includes vacuum and the phantomlike cases. The same happens in the fermionic branch for mu < 0. However, fermionic particles with mu > 0 are permitted only if -1

Administration of Mycobacterium leprae rHsp65 Aggravates Experimental Autoimmune Uveitis in Mice

The 60kDa heat shock protein family, Hsp60, constitutes an abundant and highly conserved class of molecules that are highly expressed in chronic-inflammatory and autoimmune processes. Experimental autoimmune uveitis [EAU] is a T cell mediated intraocular inflammatory disease that resembles human uveitis. Mycobacterial and homologous Hsp60 peptides induces uveitis in rats, however their participation in aggravating the disease is poorly known. We here evaluate the effects of the Mycobacterium leprae Hsp65 in the development/progression of EAU and the autoimmune response against the eye through the induction of the endogenous disequilibrium by enhancing the entropy of the immunobiological system with the addition of homologous Hsp. B10. RIII mice were immunized subcutaneously with interphotoreceptor retinoid-binding protein [IRBP], followed by intraperitoneally inoculation of M. leprae recombinant Hsp65 [rHsp65]. We evaluated the proliferative response, cytokine production and the percentage of CD4(+)IL-17(+), CD4(+)IFN-gamma(+) and CD4(+)Foxp3(+) cells ex vivo, by flow cytometry. Disease severity was determined by eye histological examination and serum levels of anti-IRBP and anti-Hsp60/65 measured by ELISA. EAU scores increased in the Hsp65 group and were associated with an expansion of CD4(+)IFN-gamma(+) and CD4(+)IL-17(+) T cells, corroborating with higher levels of IFN-gamma. Our data indicate that rHsp65 is one of the managers with a significant impact over the immune response during autoimmunity, skewing it to a pathogenic state, promoting both Th1 and Th17 commitment. It seems comprehensible that the specificity and primary function of Hsp60 molecules can be considered as a potential pathogenic factor acting as a whistleblower announcing chronic-inflammatory diseases progression.

Dynamical estimates of chaotic systems from Poincare recurrences

We show a function that fits well the probability density of return times between two consecutive visits of a chaotic trajectory to finite size regions in phase space. It deviates from the exponential statistics by a small power-law term, a term that represents the deterministic manifestation of the dynamics. We also show how one can quickly and easily estimate the Kolmogorov-Sinai entropy and the short-term correlation function by realizing observations of high probable returns. Our analyses are performed numerically in the Henon map and experimentally in a Chua's circuit. Finally, we discuss how our approach can be used to treat the data coming from experimental complex systems and for technological applications. (C) 2009 American Institute of Physics. [doi: 10.1063/1.3263943]

Scattering of sound waves by a canonical acoustic hole

This is a study of a monochromatic planar perturbation impinging upon a canonical acoustic hole. We show that acoustic hole scattering shares key features with black hole scattering. The interference of wave fronts passing in opposite senses around the hole creates regular oscillations in the scattered intensity. We examine this effect by applying a partial wave method to compute the differential scattering cross section for a range of incident wavelengths. We demonstrate the existence of a scattering peak in the backward direction, known as the glory. We show that the glory created by the canonical acoustic hole is approximately 170 times less intense than the glory created by the Schwarzschild black hole, for equivalent horizon-to-wavelength ratios. We hope that direct experimental observations of such effects may be possible in the near future.

Precise determination of quantum critical points by the violation of the entropic area law

Finite-size scaling analysis turns out to be a powerful tool to calculate the phase diagram as well as the critical properties of two-dimensional classical statistical mechanics models and quantum Hamiltonians in one dimension. The most used method to locate quantum critical points is the so-called crossing method, where the estimates are obtained by comparing the mass gaps of two distinct lattice sizes. The success of this method is due to its simplicity and the ability to provide accurate results even considering relatively small lattice sizes. In this paper, we introduce an estimator that locates quantum critical points by exploring the known distinct behavior of the entanglement entropy in critical and noncritical systems. As a benchmark test, we use this new estimator to locate the critical point of the quantum Ising chain and the critical line of the spin-1 Blume-Capel quantum chain. The tricritical point of this last model is also obtained. Comparison with the standard crossing method is also presented. The method we propose is simple to implement in practice, particularly in density matrix renormalization group calculations, and provides us, like the crossing method, amazingly accurate results for quite small lattice sizes. Our applications show that the proposed method has several advantages, as compared with the standard crossing method, and we believe it will become popular in future numerical studies.