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Permanent URI for this communityhttps://hdl.handle.net/20.500.12469/28
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Browsing Fakülteler by browse.metadata.publisher "Amer Physical Soc"
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Article Citation - WoS: 9Citation - Scopus: 9Complete Density Calculations of Q-State Potts and Clock Models: Reentrance of Interface Densities Under Symmetry Breaking(Amer Physical Soc, 2020) Artun, E. Can; Berker, A. NihatAll 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 then decreases to its zero value at zero temperature. For this behavior, a physical mechanism is proposed. A contrast between the phase transition of the two models is found and explained by alignment and entropy, as the number of states q goes to infinity. For the clock models, the renormalization-group flows of up to 20 energies are used.Article Citation - WoS: 12Citation - Scopus: 13Extrapolating Continuous Color Emotions Through Deep Learning(Amer Physical Soc, 2020) Ram, Vishaal; Schaposnik, Laura P.; Konstantinou, Nikos; Volkan, Eliz; Papadatou-Pastou, Marietta; Manav, Banu; Jonauskaite, Domicele; Mohr, ChristineBy 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, through our classification matrix, we identify which colors have weak associations to emotions and which colors are typically confused with other colors.Article Citation - WoS: 3Citation - Scopus: 3Frustrated Potts Model: Multiplicity Eliminates Chaos Via Reentrance(Amer Physical Soc, 2020) Türkoğlu, Alpar; Berker, A. NihatThe 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.Article Citation - WoS: 21Citation - Scopus: 22Revealing Dynamics, Communities, and Criticality From Data(Amer Physical Soc, 2020) Eroğlu, Deniz; Tanzi, Matteo; van Strien, Sebastian; Pereira, TiagoComplex 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 system are available. We address this issue for networks with weak individual interactions and chaotic local dynamics. We do this by building a model network, termed an effective network, consisting of the underlying local dynamics and a statistical description of their interactions. We show that behavior of such networks can be decomposed in terms of an emergent deterministic component and a fluctuation term. Traditionally, such fluctuations are filtered out. However, as we show, they are key to accessing the interaction structure. We illustrate this approach on synthetic time series of realistic neuronal interaction networks of the cat cerebral cortex and on experimental multivariate data of optoelectronic oscillators. We reconstruct the community structure by analyzing the stochastic fluctuations generated by the network and predict critical transitions for coupling parameters outside the observed range.
