Self-organized Boolean game on networks

A model of Boolean game with only one free parameter $p$ that denotes the strength of herd behavior is proposed where each agent acts according to the information obtained from his neighbors in network and those in the minority are rewarded. The simulation results indicate that the dynamic of system is sensitive to network topology, where the network of larger degree variance, i.e. the system of greater information heterogeneity, leads to less system profit. The system can self-organize to a stable state and perform better than random choice game, although only the local information is available to the agents. In addition, in heterogeneity networks, the agents with more information gain more than those with less information for a wide extent of herd strength $p$.Comment: 5 pages, 5 eps figure

Scaling behaviour and memory in heart rate of healthy human

We investigate a set of complex heart rate time series from healthy human in different behaviour states with the detrended fluctuation analysis and diffusion entropy (DE) method. It is proposed that the scaling properties are influenced by behaviour states. The memory detected by DE exhibits an approximately same pattern after a detrending procedure. Both of them demonstrate the long-range strong correlations in heart rate. These findings may be helpful to understand the underlying dynamical evolution process in the heart rate control system, as well as to model the cardiac dynamic process

Scale-invariance of human EEG signals in sleep

We investigate the dynamical properties of electroencephalogram (EEG) signals of human in sleep. By using a modified random walk method, We demonstrate that the scale-invariance is embedded in EEG signals after a detrending procedure. Further more, we study the dynamical evolution of probability density function (PDF) of the detrended EEG signals by nonextensive statistical modeling. It displays scale-independent property, which is markedly different from the turbulent-like scale-dependent PDF evolution.Comment: 4 pages and 6 figure

Context-Aware Block Net for Small Object Detection.

State-of-the-art object detectors usually progressively downsample the input image until it is represented by small feature maps, which loses the spatial information and compromises the representation of small objects. In this article, we propose a context-aware block net (CAB Net) to improve small object detection by building high-resolution and strong semantic feature maps. To internally enhance the representation capacity of feature maps with high spatial resolution, we delicately design the context-aware block (CAB). CAB exploits pyramidal dilated convolutions to incorporate multilevel contextual information without losing the original resolution of feature maps. Then, we assemble CAB to the end of the truncated backbone network (e.g., VGG16) with a relatively small downsampling factor (e.g., 8) and cast off all following layers. CAB Net can capture both basic visual patterns as well as semantical information of small objects, thus improving the performance of small object detection. Experiments conducted on the benchmark Tsinghua-Tencent 100K and the Airport dataset show that CAB Net outperforms other top-performing detectors by a large margin while keeping real-time speed, which demonstrates the effectiveness of CAB Net for small object detection

Preparation of N, N, N-trimethyl chitosan-functionalized retinoic acid-loaded lipid nanoparticles for enhanced drug delivery to glioblastoma

Purpose: To formulate trimethyl chitosan-functionalized retinoic acid-encapsulated solid lipid nanoparticles for the effective treatment of glioma.Methods: Retinoic acid-loaded solid lipid nanoparticles (R-SLNs) were prepared using homogenization followed by sonication. R-SLN surfaces were functionalized electrostatically with trimethyl chitosan as a nanocarrier (TR-SLNs) with enhanced anti-cancer activity. They were evaluated by dynamic light scattering (DLS), scanning electron microscopy, in vitro drug release, and cell cytotoxicity and apoptosis studies.Results: Morphological images showed spherical and uniformly dispersed nanoparticles. A sustained monophasic release pattern was observed throughout the study period. Furthermore, the anti-cancer effect of TR-SLNs was demonstrated by increased cell killing activity compared with the free drug (p < 0.01); negligible cytotoxicity was observed with blank carriers. Apoptosis assay showed increased cell populations in early/late apoptotic and necrotic phases.Conclusion: This study showed the potential application of surface-modified solid lipid nanoparticles for the effective treatment of brain cancer.Keywords: Lipid nanoparticles, Trimethyl chitosan, Retinoic acid, Glioma, Anti-cancer, Cytotoxicity, Apoptosi

Scaling and memory in recurrence intervals of Internet traffic

By studying the statistics of recurrence intervals, τ, between volatilities of Internet traffic rate changes exceeding a certain threshold q, we find that the probability distribution functions, Pq(τ), for both byte and packet flows, show scaling property as $P_{q}(\tau)=\frac{1}{\overline{\tau}}f(\frac{\tau}{\overline{\tau}})$. The scaling functions for both byte and packet flows obey the same stretching exponential form, f(x)=Aexp (-Bxβ), with β≈0.45. In addition, we detect a strong memory effect that a short (or long) recurrence interval tends to be followed by another short (or long) one. The detrended fluctuation analysis further demonstrates the presence of long-term correlation in recurrence intervals

Diffusion entropy analysis on the scaling behavior of financial markets

In this paper the diffusion entropy technique is applied to investigate the scaling behavior of financial markets. The scaling behaviors of four representative stock markets, Dow Jones Industrial Average, Standard&Poor 500, Heng Seng Index, and Shang Hai Stock Synthetic Index, are almost the same; with the scale-invariance exponents all in the interval $[0.92, 0.95]$. These results provide a strong evidence of the existence of long-rang correlation in financial time series, thus several variance-based methods are restricted for detecting the scale-invariance properties of financial markets. In addition, a parsimonious percolation model for stock markets is proposed, of which the scaling behavior agrees with the real-life markets well.Comment: 5 pages, 3 figure