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Spectrum map construction, which is crucial in cognitive radio (CR) system, visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation. Traditional reconstruction methods are generally for two-dimensional (2D) spectrum map and driven by abundant sampling data. In this paper, we propose a data-model-knowledge-driven reconstruction scheme to construct the three-dimensional (3D) spectrum map under multi-radiation source scenarios. We firstly design a maximum and minimum path loss difference (MMPLD) clustering algorithm to detect the number of radiation sources in a 3D space. Then, we develop a joint location-power estimation method based on the heuristic population evolutionary optimization algorithm. Considering the variation of electromagnetic environment, we self-learn the path loss (PL) model based on the sampling data. Finally, the 3D spectrum is reconstructed according to the self-learned PL model and the extracted knowledge of radiation sources. Simulations show that the proposed 3D spectrum map reconstruction scheme not only has splendid adaptability to the environment, but also achieves high spectrum construction accuracy even when the sampling rate is very low.

In this paper, we investigate the inverse scattering transform(IST) for the focusing and defocusing mKdV equation with fully asymmetric nonzero boundary conditions. Our analysis focuses on the properties of the Jost function, allowing us to establish the associated Riemann-Hilbert (RH) problem for both the focusing and defocusing cases. Furthermore, we recover the potential from the RH problem, which plays a crucial role in calculating the long-time asymptotic behavior of the solution. In the symmetric case, the eigenvalues are given by $\lambda^2 = k^2 \pm q_0^2$. However, in the asymmetric case, the eigenvalues are represented as $\lambda_\pm^2 = k^2 \pm q_\pm^2$. As a result, we directly handle the branch cut instead of utilizing the four-sheeted Riemann surface. Naturally, this approach leads to the discontinuity of the functions across their respective branch cuts, which significantly impacts the entire development of the IST.

A calibration method aimed for the differential nonlinearity (DNL) error of the Low Energy X-ray Instrument (LE) onboard the Hard X-ray Modulation Telescope (HXMT) is presented, which is independent with electronic systems used as testing platform and is only determined by the analog-to-digital converter (ADC) itself. Exploring this method, ADCs that are used within the flight model phase of HXMT-LE can be calibrated individually and independently by a non-destructive and low-cost way, greatly alleviating the complexity of the problem. As a result, the performance of the energy spectrum can be significantly improved, further more, noise reduced and resolution enhanced.

How ATP binding initiates the docking process of kinesin's neck linker is a key question in understanding kinesin mechanism. It is believed that the formation of an extra turn structure by the first three amino acids of neck linker (LYS325, THR326, ILE327 in 2KIN) is crucial for initiating the docking process. But the initial conformation of neck linker (specially the three amino acids of the extra turn) and the neck linker docking initiation mechanism remain unclear. By using molecular dynamics method, we investigate the initial conformation of kinesin's neck linker in the docking process. We find that, in the initial state of NL docking process, NL still has interactions with {\beta}0 and forms a conformation similar to the "cover-neck bundle" structure proposed by Hwang et al. [Structure 2008, 16(1): 62-71]. From this initial structure, the docking of the "cover-neck bundle" structure can be achieved. The motor head provides a forward force on the initial cover-neck bundle structure through ATP-induced rotation. This force, together with the hydrophobic interaction of ILE327 with the hydrophobic pocket on the motor head, drives the formation of the extra turn and initiates the neck linker docking process.

We describe Information Forests, an approach to classification that generalizes Random Forests by replacing the splitting criterion of non-leaf nodes from a discriminative one -- based on the entropy of the label distribution -- to a generative one -- based on maximizing the information divergence between the class-conditional distributions in the resulting partitions. The basic idea consists of deferring classification until a measure of "classification confidence" is sufficiently high, and instead breaking down the data so as to maximize this measure. In an alternative interpretation, Information Forests attempt to partition the data into subsets that are "as informative as possible" for the purpose of the task, which is to classify the data. Classification confidence, or informative content of the subsets, is quantified by the Information Divergence. Our approach relates to active learning, semi-supervised learning, mixed generative/discriminative learning., Comment: Proceedings of the Information Theory and Applications (ITA) Workshop, 2/7/2012

Current design guidelines for determining torque ranges for composites are based on tests and analysis from isotropic materials. Properties of composites are not taken into account. No design criteria based upon a systematic analytical and test analyses is available. This paper is to study the maximum torque load a composite component could carry prior to any failure. Specifically, the torque-tension tests are conducted. NDT techniques including acoustic emission, thermography and photomicroscopy are also utilized to characterize the damage modes.