Naive mean field approximation for image restoration

We attempt image restoration in the framework of the Baysian inference. Recently, it has been shown that under a certain criterion the MAP (Maximum A Posterior) estimate, which corresponds to the minimization of energy, can be outperformed by the MPM (Maximizer of the Posterior Marginals) estimate, which is equivalent to a finite-temperature decoding method. Since a lot of computational time is needed for the MPM estimate to calculate the thermal averages, the mean field method, which is a deterministic algorithm, is often utilized to avoid this difficulty. We present a statistical-mechanical analysis of naive mean field approximation in the framework of image restoration. We compare our theoretical results with those of computer simulation, and investigate the potential of naive mean field approximation.Comment: 9 pages, 11 figure

A Replica Inference Approach to Unsupervised Multi-Scale Image Segmentation

We apply a replica inference based Potts model method to unsupervised image segmentation on multiple scales. This approach was inspired by the statistical mechanics problem of "community detection" and its phase diagram. Specifically, the problem is cast as identifying tightly bound clusters ("communities" or "solutes") against a background or "solvent". Within our multiresolution approach, we compute information theory based correlations among multiple solutions ("replicas") of the same graph over a range of resolutions. Significant multiresolution structures are identified by replica correlations as manifest in information theory overlaps. With the aid of these correlations as well as thermodynamic measures, the phase diagram of the corresponding Potts model is analyzed both at zero and finite temperatures. Optimal parameters corresponding to a sensible unsupervised segmentation correspond to the "easy phase" of the Potts model. Our algorithm is fast and shown to be at least as accurate as the best algorithms to date and to be especially suited to the detection of camouflaged images.Comment: 26 pages, 22 figure

Mineral and biological ice-nucleating particles above the South East of the British Isles

A small fraction of aerosol particles known as Ice-Nucleating Particles (INPs) have the potential to trigger ice formation in cloud droplets at higher temperatures than homogeneous freezing. INPs can strongly reduce the water content and albedo of shallow mixed-phase clouds and also influences the development of convective clouds. Therefore, it is important to understand which aerosol types serve as INP and how effectively they nucleate ice. Using a combination of INP measurements and Scanning Electron Microscopy with Energy Dispersive Spectroscopy (SEM-EDS), we both quantify the INP concentrations over a range of activation temperatures and the size-resolved composition. We show that the INP population of aerosol samples collected from an aircraft over the UK during July of 2017 is consistent with ice-nucleation on mineral dust below about –20 oC, but some other INP type must account for ice-nucleation at higher temperatures. Biological aerosol particles above ~2 µm were detected based on visual detection of their morphological features in all the analysed samples in concentrations of at least 10 to 100 L-1 in the boundary layer. We suggest that given the presence of biological material, it could substantially contribute to the enhanced ice-nucleation ability of the samples at above –20 oC. Organic material attached to mineral dust could be responsible for at least part of this enhancement. These results are consistent with a growing body of data which suggests mineral dust alone cannot explain the INP population in the mid-latitude terrestrial atmosphere and that biological ice nucleating particles are most likely important for cloud glaciation

Application of the quantum spin glass theory to image restoration

Quantum fluctuation is introduced into the Markov random fields (MRF's) model for image restoration in the context of Bayesian approach. We investigate the dependence of the quantum fluctuation on the quality of BW image restoration by making use of statistical mechanics. We find that the maximum posterior marginal (MPM) estimate based on the quantum fluctuation gives a fine restoration in comparison with the maximum a posterior (MAP) estimate or the thermal fluctuation based MPM estimate.Comment: 19 pages, 9 figures, 1 table, RevTe

Multi-State Image Restoration by Transmission of Bit-Decomposed Data

We report on the restoration of gray-scale image when it is decomposed into a binary form before transmission. We assume that a gray-scale image expressed by a set of Q-Ising spins is first decomposed into an expression using Ising (binary) spins by means of the threshold division, namely, we produce (Q-1) binary Ising spins from a Q-Ising spin by the function F(\sigma_i - m) = 1 if the input data \sigma_i \in {0,.....,Q-1} is \sigma_i \geq m and 0 otherwise, where m \in {1,....,Q-1} is the threshold value. The effects of noise are different from the case where the raw Q-Ising values are sent. We investigate which is more effective to use the binary data for transmission or to send the raw Q-Ising values. By using the mean-field model, we first analyze the performance of our method quantitatively. Then we obtain the static and dynamical properties of restoration using the bit-decomposed data. In order to investigate what kind of original picture is efficiently restored by our method, the standard image in two dimensions is simulated by the mean-field annealing, and we compare the performance of our method with that using the Q-Ising form. We show that our method is more efficient than the one using the Q-Ising form when the original picture has large parts in which the nearest neighboring pixels take close values.Comment: latex 24 pages using REVTEX, 10 figures, 4 table

Image restoration using the chiral Potts spin-glass

We report on the image reconstruction (IR) problem by making use of the random chiral q-state Potts model, whose Hamiltonian possesses the same gauge invariance as the usual Ising spin glass model. We show that the pixel representation by means of the Potts variables is suitable for the gray-scale level image which can not be represented by the Ising model. We find that the IR quality is highly improved by the presence of a glassy term, besides the usual ferromagnetic term under random external fields, as very recently pointed out by Nishimori and Wong. We give the exact solution of the infinite range model with q=3, the three gray-scale level case. In order to check our analytical result and the efficiency of our model, 2D Monte Carlo simulations have been carried out on real-world pictures with three and eight gray-scale levels.Comment: RevTex 13 pages, 10 figure

Smallholder Dairy Farmers in the Peruvian Andes Fulfilling the Role of Extension Agents

Dairy farming in the Peruvian Andes is mostly undertaken by smallholder farmers (4-6 cows/family) and of relatively recent development. In fact, over the last 2 decades dairy farming at high altitudes (3,500‒4200 masl) has grown rapidly, replacing the camelids and sheep farming that once predominated. Dairying growth has been catalysed by subsides from state and private organizations. It promotes high input systems based on feedlot technology. Compared to sheep and camelids farming, dairying at the Andes does not have yet an inherent local/indigenous knowledge associated to it. High altitude Andean ecosystems pose many constraints for dairy farming (hypoxia and high UV radiation, high variation between day and night temperatures, short rainy season, and hence shortage of feed and water; and not less importantly, accelerated climate change (CC)). Under these conditions, not only are productivity and profitability low, but there are high negative environmental impacts and poor animal welfare. In Peru, institutionalised research and extension (R&E) services are precarious. Research tackling current issues of high-altitude livestock farming is almost inexistent, whereas extension in support of farmers is dispersed, poorly funded, of short duration (a few months), focused on transfer of technology suitable to intensive farming systems, and has a high turnover of staff. A systems approach to address the complexity of Andean livestock farming development is lacking. The initiatives from the institutions promoting farming are directed to remediate recurrent problems (e.g., cold stress) or prioritise high cost, low impact activities (e.g., genetic improvement). Here, we present the successful experience of the New Zealand Peru Dairy Support Project (NZPDSP) to promote the adoption of improved low input pastoral dairying husbandry principles, where trained smallholder farmers play a key role as agents of change

Dairy Cattle Genetics by Environment Interaction Mismatch Contributes to Poor Mitigation and Adaptation of Grazing Systems to Climate Change Actions in the Peruvian High Andes: A Review

The high Andes of Peru includes fragile ecosystems. Nevertheless, it plays important ecosystem functions (e.g., biodiversity, water supply for the lowlands, CO2 sinks in soil, etc). More than 80% of the livestock population of Peru is farmed in this area, supporting the livelihood of approximately 1’400,000 poor families, who are vulnerable to climate change (CC). Climate change in the high Andes is occurring at accelerated rates, compared to lowlands regions. Prevalent factors in the high Andes, such as hypoxia, high UV radiation, climatic extremes, large variation between maximum and minimum temperatures, seasonality in rainfall (determining highly seasonal forage growth) and CC, not only increase the feed and water needs of animals, but also affect animal production, reproduction, rumen function and welfare, making them more vulnerable to CC. During the last three decades, livestock farming in the high Andes has undergone transformation. The farming of camelids and creole species has been almost replaced by smallholder dairying, which have a higher environmental footprint. Institutions promoting dairying neglect the fitness requirement for the animal genetics to perform in such environments. Recent work of the New Zealand Peru Dairy Support Project (NZPDSP; 2016‒2020) demonstrated that rapid and significant improvements in animal productivity and profitability of dairying can be achieved by promoting adoption of simple and low-cost husbandry practices. Nevertheless, further improvements are constrained by the unfitness of the current animal genetics. Here, based on a literature review and experience from the NZPDSP, we propose a search for dairy cattle genetics that contributes to mitigation and adaptation to CC, while enhancing the livelihoods of the poor

Image restoration using the Q-Ising spin glass

We investigate static and dynamic properties of gray-scale image restoration (GSIR) by making use of the Q-Ising spin glass model, whose ladder symmetry allows to take in account the distance between two spins. We thus give an explicit expression of the Hamming distance between the original and restored images as a function of the hyper-parameters in the mean field limit. Finally, numerical simulations for real-world pictures are carried out to prove the efficiency of our model.Comment: 27pages, 13figures, revte