PubMed İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/4466

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Now showing 1 - 10 of 10

Citation Count: 1
Across dimensions: Two- and three-dimensional phase transitions from the iterative renormalization-group theory of chains
(2020) Keçoğlu, İbrahim; Berker, A. Nihat
Sharp two- and three-dimensional phase transitional magnetization curves are obtained by an iterative renormalization-group coupling of Ising chains, which are solved exactly. The chains by themselves do not have a phase transition or nonzero magnetization, but the method reflects crossover from temperaturelike to fieldlike renormalization-group flows as the mechanism for the higher-dimensional phase transitions. The magnetization of each chain acts, via the interaction constant, as a magnetic field on its neighboring chains, thus entering its renormalization-group calculation. The method is highly flexible for wide application.
Citation Count: 9
Complete 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. Nihat
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 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.
Citation Count: 9
Devil's staircase continuum in the chiral clock spin glass with competing ferromagnetic-antiferromagnetic and left-right chiral interactions
(Amer Physical Soc., 2017) Caglar, Tolga; Berker, A. Nihat
The chiral clock spin-glass model with q = 5 states with both competing ferromagnetic-antiferromagnetic and left-right chiral frustrations is studied in d = 3 spatial dimensions by renormalization-group theory. The global phase diagram is calculated in temperature antiferromagnetic bond concentration p random chirality strength and right-chirality concentration c. The system has a ferromagnetic phase a multitude of different chiral phases a chiral spin-glass phase and a critical (algebraically) ordered phase. The ferromagnetic and chiral phases accumulate at the disordered phase boundary and form a spectrum of devil's staircases where different ordered phases characteristically intercede at all scales of phase-diagram space. Shallow and deep reentrances of the disordered phase bordered by fragments of regular and temperature-inverted devil's staircases are seen. The extremely rich phase diagrams are presented as continuously and qualitatively changing videos.
Citation Count: 3
Frustrated Potts model: Multiplicity eliminates chaos via reentrance
(Amer Physical Soc, 2020) Türkoğlu, Alpar; Berker, A. Nihat
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.
Citation Count: 6
Growth and shape stability of Cu-Ni core-shell nanoparticles: an atomistic perspective
(Royal Soc Chemistry, 2018) İlker, Efe; Madran, Melihat; Konuk, Mine; Durukanoğlu, Sondan
The growth and shape stability of bi-metallic cubic Cu-Ni nanoparticles are studied using atomic-level simulations. Cubic nano-crystals coated with an ultra-thin Cu layer can be readily obtained when Ni cubic nanoparticles are used as the seeds. At elevated temperatures the Cu seed with extending Ni branches preserves its shape compared to the Ni seed with extending Cu branches.
Citation Count: 2
Maximally random discrete-spin systems with symmetric and asymmetric interactions and maximally degenerate ordering
(Amer Physical Soc., 2018) Atalay, Bora; Berker, A. Nihat
Discrete-spin systems with maximally random nearest-neighbor interactions that can be symmetric or asymmetric ferromagnetic or antiferromagnetic including off-diagonal disorder are studied for the number of states q = 34 in d dimensions. We use renormalization-group theory that is exact for hierarchical lattices and approximate (Migdal-Kadanoff) for hypercubic lattices. For all d > 1 and all nonmfimte temperatures the system eventually renormalizes to a random single state thus signaling q x q degenerate ordering. Note that this is the maximally degenerate ordering. For high-temperature initial conditions the system crosses over to this highly degenerate ordering only after spending many renormalization-group iterations near the disordered (infinite-temperature) fixed point. Thus a temperature range of short-range disorder in the presence of long-range order is identified as previously seen in underfrustrated Ising spin-glass systems. The entropy is calculated for all temperatures behaves similarly for ferromagnetic and antiferromagnetic interactions and shows a derivative maximum at the short-range disordering temperature. With a sharp immediate contrast of infinitesimally higher dimension 1 + epsilon the system is as expected disordered at all temperatures for d = 1.
Citation Count: 1
Metastable reverse-phase droplets within ordered phases: Renormalization-group calculation of field and temperature dependence of limiting size
(AMER PHYSICAL SOC, 2020) Eren, Ege; Berker, A. Nihat
Metastable reverse-phase droplets are calculated by renormalization-group theory by evaluating the magnetization of a droplet under magnetic field, matching the boundary condition with the reverse phase and noting whether the reverse-phase magnetization sustains. The maximal metastable droplet size and the discontinuity across the droplet boundary are thus calculated as a function of temperature and magnetic field for the Ising model in three dimensions. The method also yields hysteresis loops for finite systems, as function of temperature and system size.
Citation Count: 11
Optical spatial modulation design
(Royal Soc, 2020) Tezcan, Çoğalan; Harald, Haas; Panayırcı, Erdal
Visible light communication (VLC) systems are inherently signal-to-noise ratio (SNR) limited due to link budget constraints. One favourable method to overcome this limitation is to focus on the pre-log factors of the channel capacity. Multiple-input multiple-output (MIMO) techniques are therefore a promising avenue of research. However, inter-channel interference in MIMO limits the achievable capacity. Spatial modulation (SM) avoids this limitation. Furthermore, the performance of MIMO systems in VLC is limited by the similarities among spatial channels. This limitation becomes particularly severe in intensity modulation/direct detection (IM/DD) systems because of the lack of phase information. The motivation of this paper is to propose a system that results in a multi-channel transmission system that enables reliable multi-user optical MIMO SM transmission without the need for a precoder, power allocation algorithm or additional optics at the receiver. A general bit error performance model for the SM system is developed for an arbitrary number of light-emitting diodes (LEDs) in conjunction with pulse amplitude modulation. Based on this model, an LED array structure is designed to result in spatially separated multiple channels by manipulating the transmitter geometry. This article is part of the theme issue 'Optical wireless communication'.
Citation Count: 11
Phase transitions between different spin-glass phases and between different chaoses in quenched random chiral systems
(Amer Physical Soc., 2017) Çağlar, Tolga; Berker, A. Nihat
The left-right chiral and ferromagnetic-antiferromagnetic double-spin-glass clock model with the crucially even number of states q = 4 and in three dimensions d = 3 has been studied by renormalization-group theory. We find for the first time to our knowledge four spin-glass phases including conventional chiral and quadrupolar spin-glass phases and phase transitions between spin-glass phases. The chaoses in the different spin-glass phases and in the phase transitions of the spin-glass phases with the other spin-glass phases with the non-spin-glass ordered phases and with the disordered phase are determined and quantified by Lyapunov exponents. It is seen that the chiral spin-glass phase is the most chaotic spin-glass phase. The calculated phase diagram is also otherwise very rich including regular and temperature-inverted devil's staircases and reentrances.
Citation Count: 18
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy
(Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020) Aydın, Mehmet Nafiz; Aydın, Mehmet Nafiz; Öğrenci, Arif Selçuk
Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions' COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.

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