Combined local search strategy for learning in networks of binary synapses

Learning in networks of binary synapses is known to be an NP-complete problem. A combined stochastic local search strategy in the synaptic weight space is constructed to further improve the learning performance of a single random walker. We apply two correlated random walkers guided by their Hamming distance and associated energy costs (the number of unlearned patterns) to learn a same large set of patterns. Each walker first learns a small part of the whole pattern set (partially different for both walkers but with the same amount of patterns) and then both walkers explore their respective weight spaces cooperatively to find a solution to classify the whole pattern set correctly. The desired solutions locate at the common parts of weight spaces explored by these two walkers. The efficiency of this combined strategy is supported by our extensive numerical simulations and the typical Hamming distance as well as energy cost is estimated by an annealed computation.Comment: 7 pages, 4 figures, figures and references adde

Learning by random walks in the weight space of the Ising perceptron

Several variants of a stochastic local search process for constructing the synaptic weights of an Ising perceptron are studied. In this process, binary patterns are sequentially presented to the Ising perceptron and are then learned as the synaptic weight configuration is modified through a chain of single- or double-weight flips within the compatible weight configuration space of the earlier learned patterns. This process is able to reach a storage capacity of $\alpha \approx 0.63$ for pattern length N = 101 and $\alpha \approx 0.41$ for N = 1001. If in addition a relearning process is exploited, the learning performance is further improved to a storage capacity of $\alpha \approx 0.80$ for N = 101 and $\alpha \approx 0.42$ for N=1001. We found that, for a given learning task, the solutions constructed by the random walk learning process are separated by a typical Hamming distance, which decreases with the constraint density $\alpha$ of the learning task; at a fixed value of $\alpha$, the width of the Hamming distance distributions decreases with $N$.Comment: 12 pages, 4 figures, An extensively revised versio

THE PO RIVER DELTA (ITALY) AND THE YELLOW RIVER DELTA (CHINA): THE STUDY AREA OF THE PROJECT

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Counting solutions from finite samplings

We formulate the solution counting problem within the framework of inverse Ising problem and use fast belief propagation equations to estimate the entropy whose value provides an estimate on the true one. We test this idea on both diluted models (random 2-SAT and 3-SAT problems) and fully-connected model (binary perceptron), and show that when the constraint density is small, this estimate can be very close to the true value. The information stored by the salamander retina under the natural movie stimuli can also be estimated and our result is consistent with that obtained by Monte Carlo method. Of particular significance is sizes of other metastable states for this real neuronal network are predicted.Comment: 9 pages, 4 figures and 1 table, further discussions adde

Coevolution of synchronous activity and connectivity in coupled chaotic oscillators

We investigate the coevolution dynamics of node activities and coupling strengths in coupled chaotic oscillators via a simple threshold adaptive scheme. The coupling strength is synchronous activity regulated, which in turn is able to boost the synchronization remarkably. In the case of weak coupling, the globally coupled oscillators present a highly clustered functional connectivity with a power-law distribution in the tail with γ≃3.1, while for strong coupling, they self-organize into a network with a heterogeneously rich connectivity at the onset of synchronization but exhibit rather sparse structure to maintain the synchronization in noisy environment. The relevance of the results is briefly discussed

Core-sheath structured electrospun nanofibrous membranes for oil-water separation

In recent years, both the increasing frequency of oil spill accidents and the urgency to deal seriously with industrial oil-polluted water, encouraged material scientists to design highly efficient, cost effective oil-water separation technologies. We report on electrospun nanofibrous membranes which are composed of core-sheath structured cellulose-acetate (CA)-polyimide (PI) nanofibers. On the surface of the CA-PI fibers a fluorinated polybenzoxazine (F-PBZ) functional layer, in which silica nanoparticles (SNPs) were incorporated, has been applied. Compared with F-PBZ/SNP modified CA fibers reported before for the separation of oil from water, the PI-core of the core-shell F-PBZ/SNP/CA-PI fibers makes the membranes much stronger, being a significant asset in their use. Nanofibrous membranes with a tensile strength higher than 200 MPa, a high water contact angle of 160 degrees and an extremely low oil contact angle of 0 degrees were obtained. F-PBZ/SNP/CA-PI membranes seemed very suitable for gravity-driven oil-water separation as fast and efficient separation (>99%) of oil from water was achieved for various oil-water mixtures. The designed core-sheath structured electrospun nanofibrous membranes may become interesting materials for the treatment of industrial oil-polluted water

Depression Involved in the Chemotherapy Induced Event-based Prospective Memory Impairment in Breast Cancer Survivors

The aim of this study was to investigate the relationships between depression and occurrence of chemotherapy induced prospective memory impairment in patients with breast cancer (BC).The 63 BC patients before and after chemotherapy were administered with the self-rating depression scale (SDS) and a battery of cognitive neuropsychological tests including event-based and time-based prospective memory (EBPM and TBPM, respectively) tasks. The changes in their prospective memory and cognitive neuropsychological characteristics before and after chemotherapy were compared. Compared with the scores before chemotherapy, the EBPM score exhibited a statistically significant difference after chemotherapy (t = 6.069, P 0.05). Further, compared with the patients without depression, the patients with depression exhibited a statistically significant difference in the EBPM score (t = -4.348, P 0.05). Post-chemotherapy, EBPM and overall cognitive functions in BC patients merged with depression were found to decline, while TBPM did not show a significant change, suggesting that the combination of chemotherapy and depression might be related with the occurrence of post-chemotherapy EBPM impairment

Pressure-induced structural modulations in coesite

Silica phases, SiO2, have attracted significant attention as important phases in the fields of condensed-matter physics, materials science, and (in view of their abundance in the Earth's crust) geoscience. Here, we experimentally and theoretically demonstrate that coesite undergoes structural modulations under high pressure. Coesite transforms to a distorted modulated structure, coesite-II, at 22–25 GPa with modulation wave vector q=0.5b∗. Coesite-II displays further commensurate modulation along the y axis at 36–40 GPa and the long-range ordered crystalline structure collapses beyond ∼40GPa and starts amorphizing. First-principles calculations illuminate the nature of the modulated phase transitions of coesite and elucidate the modulated structures of coesite caused by modulations along the y-axis direction. The structural modulations are demonstrated to result from phonon instability, preceding pressured-induced amorphization. The recovered sample after decompression develops a rim of crystalline coesite structure, but its interior remains low crystalline or partially amorphous. Our results not only clarify that the pressure-induced reversible phase transitions and amorphization in coesite originate from structural modulations along the y-axis direction, but also shed light on the densification mechanism of silica under high pressure