Browsing Araştırma Çıktıları / WOS by Publisher "Amer Physical Soc"
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Complete density calculations of q-state Potts and clock models: Reentrance of interface densities under symmetry breaking
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Publisher and Date:(Amer Physical Soc, 2020)All local bond-state densities are calculated for q-state Potts and clock models in three spatial dimensions, d = 3. The calculations are done by an exact renormalization group on a hierarchical lattice, including the density recursion relations, and simultaneously are the Migdal-Kadanoff approximation for the cubic lattice. Reentrant behavior is found in the interface densities under symmetry breaking, in the sense that upon lowering the temperature, the value of the density first increases and ...
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Extrapolating continuous color emotions through deep learning
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Publisher and Date:(Amer Physical Soc, 2020)By means of an experimental dataset, we use deep learning to implement an RGB (red, green, and blue) extrapolation of emotions associated to color, and do a mathematical study of the results obtained through this neural network. In particular, we see that males (type-m individuals) typically associate a given emotion with darker colors, while females (typef individuals) associate it with brighter colors. A similar trend was observed with older people and associations to lighter colors. Moreover, ...
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Frustrated Potts model: Multiplicity eliminates chaos via reentrance
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Publisher and Date:(Amer Physical Soc, 2020)The frustrated q-state Potts model is solved exactly on a hierarchical lattice, yielding chaos under rescaling, namely, the signature of a spin-glass phase, as previously seen for the Ising (q = 2) model. However, the ground-state entropy introduced by the (q > 2)-state antiferromagnetic Potts bond induces an escape from chaos as multiplicity q increases. The frustration versus multiplicity phase diagram has a reentrant (as a function of frustration) chaotic phase.
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Revealing Dynamics, Communities, and Criticality from Data
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Publisher and Date:(Amer Physical Soc, 2020)Complex systems such as ecological communities and neuron networks are essential parts of our everyday lives. These systems are composed of units which interact through intricate networks. The ability to predict sudden changes in the dynamics of these networks, known as critical transitions, from data is important to avert disastrous consequences of major disruptions. Predicting such changes is a major challenge as it requires forecasting the behavior for parameter ranges for which no data on the ...
