Consensus of self-driven agents with avoidance of collisions

In recent years, many efforts have been addressed on collision avoidance of collectively moving agents. In this paper, we propose a modified version of the Vicsek model with adaptive speed, which can guarantee the absence of collisions. However, this strategy leads to an aggregated state with slowly moving agents. We therefore further introduce a certain repulsion, which results in both faster consensus and longer safe distance among agents, and thus provides a powerful mechanism for collective motions in biological and technological multi-agent systems.Comment: 8 figures, and 7 page

Amplification and adaptation of centromeric repeats in polyploid switchgrass species.

Centromeres in most higher eukaryotes are composed of long arrays of satellite repeats from a single satellite repeat family. Why centromeres are dominated by a single satellite repeat and how the satellite repeats originate and evolve are among the most intriguing and long-standing questions in centromere biology. We identified eight satellite repeats in the centromeres of tetraploid switchgrass (Panicum virgatum). Seven repeats showed characteristics associated with classical centromeric repeats with monomeric lengths ranging from 166 to 187 bp. Interestingly, these repeats share an 80-bp DNA motif. We demonstrate that this 80-bp motif may dictate translational and rotational phasing of the centromeric repeats with the cenH3 nucleosomes. The sequence of the last centromeric repeat, Pv156, is identical to the 5S ribosomal RNA genes. We demonstrate that a 5S ribosomal RNA gene array was recruited to be the functional centromere for one of the switchgrass chromosomes. Our findings reveal that certain types of satellite repeats, which are associated with unique sequence features and are composed of monomers in mono-nucleosomal length, are favorable for centromeres. Centromeric repeats may undergo dynamic amplification and adaptation before the centromeres in the same species become dominated by the best adapted satellite repeat

Two types of generalized integrable decompositions and new solitary-wave solutions for the modified Kadomtsev-Petviashvili equation with symbolic computation

The modified Kadomtsev-Petviashvili (mKP) equation is shown in this paper to be decomposable into the first two soliton equations of the 2N-coupled Chen-Lee-Liu and Kaup-Newell hierarchies by respectively nonlinearizing two sets of symmetry Lax pairs. In these two cases, the decomposed (1+1)-dimensional nonlinear systems both have a couple of different Lax representations, which means that there are two linear systems associated with the mKP equation under the same constraint between the potential and eigenfunctions. For each Lax representation of the decomposed (1+1)-dimensional nonlinear systems, the corresponding Darboux transformation is further constructed such that a series of explicit solutions of the mKP equation can be recursively generated with the assistance of symbolic computation. In illustration, four new families of solitary-wave solutions are presented and the relevant stability is analyzed.Comment: 23 page

Truncated Cauchy Non-Negative Matrix Factorization

© 1979-2012 IEEE. Non-negative matrix factorization (NMF) minimizes the euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers. In this paper, we propose a Truncated CauchyNMF loss that handle outliers by truncating large errors, and develop a Truncated CauchyNMF to robustly learn the subspace on noisy datasets contaminated by outliers. We theoretically analyze the robustness of Truncated CauchyNMF comparing with the competing models and theoretically prove that Truncated CauchyNMF has a generalization bound which converges at a rate of order where is the sample size. We evaluate Truncated CauchyNMF by image clustering on both simulated and real datasets. The experimental results on the datasets containing gross corruptions validate the effectiveness and robustness of Truncated CauchyNMF for learning robust subspaces

High Precision Synchronous Detection Method for Multi-gas detection using a Single Laser

There are two main drawbacks seen in the use of the multi-gas detection method based on the tuneable diode laser absorption spectroscopy (TDLAS). The first is that multiple lasers have traditionally been employed in the detection system, which means not only the system cost, but also the system response time is increased. The second major issue is the existence of a number of kinds of cross interference, and thus the sensitivity and accuracy of the existing multi-component gas detection approach has been greatly restricted, and this has become a technical bottleneck for practical multi component gas detection. To address this, a synchronous detection technology for a high precision multi-component gas detection scheme using a single light source is reported. By measuring the spectral absorption feature of each individual gas at different concentrations, the relationship of each gas sample present to the spectral absorption value can be established and the concentrations of each individual gas can be calculated. This novel method has been shown to improve the precision achieved in the detection of the multi-component gas samples by20%, compared with the previous precision measured, with the induction in the interference effects on the measurements due to the different gases present, while at the same time reducing the detection cost and response time

A spectral dissimilarity constrained nonnegative matrix factorization based cancer screening algorithm from hyperspectral fluorescence images

Bioluminescence from living body can help screen cancers without penetrating the inside of living body. Hyperspectral imaging technique is a novel way to obtain physical meaningful signatures, providing very fine spectral resolution, that can be very used in distinguishing different kinds of materials, and have been widely used in remote sensing field. Fluorescence imaging has proved effective in monitoring probable cancer cells. Recent work has made great progress on the hyperspectral fluorescence imaging techniques, which makes the elaborate spectral observation of cancer areas possible. So how to propose the proper hyperspectral image processing methods to handle the hyperspectral medical images is of practical importance. Cancer cells would be distinguishable with normal ones when the living body is injected with fluorescence, which helps organs inside the living body emit lights, and then the signals can be catched by the passive imaging sensor. Spectral unmixing technique in hyperspectral remote sensing has been introduced to detect the probable cancer areas. However, since the cancer areas are small and the normal areas and the cancer ares may not pure pixels so that the predefined endmembers would not available. In this case, the classic blind signals separation methods are applicable. Considering the spectral dissimilarity between cancer and normal cells, a novel spectral dissimilarity constrained based NMF method is proposed in this paper for cancer screening from fluorescence hyperspectral images. Experiments evaluate the performance of variable NMF based method and our proposed spectral dissimilarity based NMF methods: 1) The NMF methods do perform well in detect the cancer areas inside the living body; 2) The spectral dissimilarity constrained NMF present more accurate cancer areas; 3) The spectral dissimilarity constraint presents better performance in different SNR and different purities of the mixing endmembers. © 2012 IEEE