1,145 research outputs found
Self-organized Boolean game on networks
A model of Boolean game with only one free parameter 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 .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
Explicit Interaction for Fusion-Based Place Recognition
Fusion-based place recognition is an emerging technique jointly utilizing
multi-modal perception data, to recognize previously visited places in
GPS-denied scenarios for robots and autonomous vehicles. Recent fusion-based
place recognition methods combine multi-modal features in implicit manners.
While achieving remarkable results, they do not explicitly consider what the
individual modality affords in the fusion system. Therefore, the benefit of
multi-modal feature fusion may not be fully explored. In this paper, we propose
a novel fusion-based network, dubbed EINet, to achieve explicit interaction of
the two modalities. EINet uses LiDAR ranges to supervise more robust vision
features for long time spans, and simultaneously uses camera RGB data to
improve the discrimination of LiDAR point clouds. In addition, we develop a new
benchmark for the place recognition task based on the nuScenes dataset. To
establish this benchmark for future research with comprehensive comparisons, we
introduce both supervised and self-supervised training schemes alongside
evaluation protocols. We conduct extensive experiments on the proposed
benchmark, and the experimental results show that our EINet exhibits better
recognition performance as well as solid generalization ability compared to the
state-of-the-art fusion-based place recognition approaches. Our open-source
code and benchmark are released at: https://github.com/BIT-XJY/EINet
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 . 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
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