The r-th nonlinearity of Boolean functions is an important cryptographic criterion associated with higher order linearity attacks on stream and block ciphers. In this paper, we tighten the lower bound of the second-order nonlinearity of a class of Boolean function over finite field F2n, fλ(x)=Tr(λxd), where λ∈F*2r, d=22r+2r+1 and n=7r. This bound is much better than the lower bound of Iwata-Kurosawa.

In this letter, we present a construction of bent functions which generalizes a work of Zhang et al. in 2016. Based on that, we obtain a cubic bent function in 10 variables and prove that, it has no affine derivative and does not belong to the completed Maiorana-McFarland class, which is opposite to all 6/8-variable cubic bent functions as they are inside the completed Maiorana-McFarland class. This is the first time a theoretical proof is given to show that the cubic bent functions in 10 variables can be outside the completed Maiorana-McFarland class. Before that, only a sporadic example with such properties was known by computer search. We also show that our function is EA-inequivalent to that sporadic one.

For amateur creators, it has been becoming popular to create new content based on existing original work: such new content is called derivative work. We know that derivative creation is popular, but why are individual derivative works created? Although there are several factors that inspire the creation of derivative works, such factors cannot usually be observed on the Web. In this paper, we propose a model for inferring latent factors from sequences of derivative work posting events. We assume a sequence to be a stochastic process incorporating the following three factors: (1) the original work's attractiveness, (2) the original work's popularity, and (3) the derivative work's popularity. To characterize content popularity, we use content ranking data and incorporate rank-biased popularity based on the creators' browsing behaviors. Our main contributions are three-fold. First, to the best of our knowledge, this is the first study modeling derivative creation activity. Second, by using real-world datasets of music-related derivative work creation, we conducted quantitative experiments and showed the effectiveness of adopting all three factors to model derivative creation activity and considering creators' browsing behaviors in terms of the negative logarithm of the likelihood for test data. Third, we carried out qualitative experiments and showed that our model is useful in analyzing following aspects: (1) derivative creation activity in terms of category characteristics, (2) temporal development of factors that trigger derivative work posting events, (3) creator characteristics, (4) N-th order derivative creation process, and (5) original work ranking.

Super resolution (SR) image reconstruction has attracted increasing attention these years and many SR image reconstruction algorithms have been proposed for restoring a high-resolution image from one or multiple low-resolution images. However, how to objectively evaluate the quality of SR reconstructed images remains an open problem. Although a great number of image quality metrics have been proposed, they are quite limited to evaluate the quality of SR reconstructed images. Inspired by this, this paper presents a blind quality index for SR reconstructed images using first- and second-order structural degradation. First, the SR reconstructed image is decomposed into multi-order derivative magnitude maps, which are effective for first- and second-order structural representation. Then, log-energy based features are extracted on these multi-order derivative magnitude maps in the frequency domain. Finally, support vector regression is used to learn the quality model for SR reconstructed images. The results of extensive experiments that were conducted on one public database demonstrate the superior performance of the proposed method over the existing quality metrics. Moreover, the proposed method is less dependent on the number of training images and has low computational cost.

We describe the preparation of an α-phenyl-4'-(diphenylamino)stilbene (TPA) single crystal and the evaluation of its hole transport property. Based on the characterization using optical microscopy, polarizing microscopy, and X-ray diffraction, a large-scale TPA single crystal of dimensions 7.0×0.9×0.8mm is successfully synthesized using a solution method based on the solubility and supersolubility curves of the TPA. Notably, the current in the long-axis direction is larger than those in the short-axis and thickness directions (i(long) > i(short) > i(thickness)), which reveals the anisotropic charge transfer of the TPA single crystal. The observed anisotropic conductivity is well explained by the orientation of the triphenylamine unit in the TPA single crystal. Furthermore, the activation energy of the long-axis direction in the TPA single crystal is lower than that of the short-axis in TPA and all the axes in the α-phenyl-4'-[bis(4-methylphenyl)amino]stilbene single crystal reported in our previous study.

Creating new content based on existing original work is becoming popular especially among amateur creators. Such new content is called derivative work and can be transformed into the next new derivative work. Such derivative work creation is called “N-th order derivative creation.” Although derivative creation is popular, the reason an individual derivative work was created is not observable. To infer the factors that trigger derivative work creation, we have proposed a model that incorporates three factors: (1) original work's attractiveness, (2) original work's popularity, and (3) derivative work's popularity. Based on this model, in this paper, we describe a public web service for browsing derivation factors called Songrium Derivation Factor Analysis. Our service is implemented by applying our model to original works and derivative works uploaded to a video sharing service. Songrium Derivation Factor Analysis provides various visualization functions: Original Works Map, Derivation Tree, Popularity Influence Transition Graph, Creator Distribution Map, and Creator Profile. By displaying such information when users browse and watch videos, we aim to enable them to find new content and understand the N-th order derivative creation activity at a deeper level.

In this article, we investigate the depth distribution and the depth spectra of linear codes over the ring R=F2+uF2+u2F2, where u3=1. By using homomorphism of abelian groups from R to F2 and the generator matrices of linear codes over R, the depth spectra of linear codes of type 8k14k22k3 are obtained. We also give the depth distribution of a linear code C over R. Finally, some examples are presented to illustrate our obtained results.

The numerical dispersibility measurement system was fabricated based on optical transparency to objectively evaluate undetectable dispersibility by naked eyes. The small deference of dispersibility and the dispersibility behaviors were simultaneously elucidated by the system. The abundance of octadecyl groups was also discussed from the result of dispersibility behaviors. The objective numerical evaluation is needed for precise analysis of dispersibility with respect to graphene, graphene derivatives and graphene related materials.

This paper presents a novel filter to keep from over-smoothing the edges and corners and rectify the outliers in the flow field after each incremental computation step, which plays a key role during the process of estimating flow field. This filter works according to the spatial-temporal derivatives distance of the input image and velocity field distance, whose principle is more reasonable in filtering mechanism for optical flow than other existing nonlinear filters. Moreover, we regard the spatial-temporal derivatives as new powerful descriptions of different motion layers or regions and give a detailed explanation. Experimental results show that our proposed method achieves better performance.

Detailed high capacity vector maps must be compressed effectively for transmission or storage in Web GIS (geographic information system) and mobile GIS applications. In this paper, we present a polyline compression method that consists of polyline feature-based hybrid simplification and second derivative-based data compression. The polyline hybrid simplification function detects the feature points from a polyline using DP, SF, and TF algorithms, and divides the polyline into sectors using these feature points. It then simplifies the sectors using an algorithm to determine the minimum area difference among the DP, SF, and TF results. The polyline data compression method segments the second derivatives of the simplified polylines into integer and fractional parts. The integer parts are compressed using the minimum bounding box of the layer to determine the broad position of the object. The fractional parts are compressed using hierarchical precision levels. Experimental results verify that our method has higher simplification and compression efficiency than conventional methods and produces good quality compressed maps.

This paper firstly presents a processor design with Derivative ASIP approach. The architecture of processor is designed by making use of a well-known embedded processor's instruction-set as a base architecture. To improve its performance, the architecture is enhanced with more hardware resources such as registers, interfaces and instruction extensions which might achieve target specifications. Secondly, a new approach for retargeting compiler by means of assembly converter tool is proposed. Our retargeting approach is practical because it is performed by the assembly converter tool with a simple configuration file and independent from a base compiler. With our proposed approach, both architecture flexibility and a good quality of assembly code can be obtained at once. Compared to other compilers, experiments show that our approach capable of generating code as high efficiency as its base compiler and the developed ASIP results in better performance than its base processor.

We characterized bulk-heterojunction (BHJ) solar cells using a new phenylene-thiophene oligomer, 3,7-bis[5-(4-n-hexylphenyl)-2-thienyl]dibenzothiophene-5,5-dioxide (37HPTDBTSO), and phenyl-C61-butyric-acid methyl ester (PCBM). Their photovoltaic properties including current-voltage characteristics and spectrum response were investigated. It was found that 37HPTDBTSO is appraised to be valuable electron donor. The characteristics of BHJ solar cells using mixed two donors of 37HPTDBTSO and a polymer of poly(3-hexylthiophene) (P3HT) were further investigated. OSC using the blend film of mixed donars and PCBM achieved a power conversion efficiency of 0.89%.

A new scheme based on multi-order visual comparison is proposed for full-reference image quality assessment. Inspired by the observation that various image derivatives have great but different effects on visual perception, we perform respective comparison on different orders of image derivatives. To obtain an overall image quality score, we adaptively integrate the results of different comparisons via a perception-inspired strategy. Experimental results on public databases demonstrate that the proposed method is more competitive than some state-of-the-art methods, benchmarked against subjective assessment given by human beings.

In this paper, we propose an algorithm for contrast enhancement based on Adaptive Histogram Equalization (AHE) to improve image quality. Most histogram-based contrast enhancement methods have problems with excessive or low image contrast enhancement. This results in unnatural output images and the loss of visual information. The proposed method manipulates the slope of the input of the Probability Density Function (PDF) histogram. We also propose a pixel redistribution method using convolution to compensate for excess pixels after the slope modification procedure. Our method adaptively enhances the contrast of the input image and shows good simulation results compared with conventional methods.

In this paper, a simple yet efficient texture representation is proposed for texture classification by exploring the joint statistics of local quantized patterns (jsLQP). In order to combine information of different domains, the Gaussian derivative filters are first employed to obtain the multi-scale gradient responses. Then, three feature maps are generated by encoding the local quantized binary and ternary patterns in the image space and the gradient space. Finally, these feature maps are hybridly encoded, and their joint histogram is used as the final texture representation. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art LBP based and even learning based methods for texture classification.

We propose a maximum likelihood estimation approach for the recovery of continuously-defined sparse signals from noisy measurements, in particular periodic sequences of Diracs, derivatives of Diracs and piecewise polynomials. The conventional approach for this problem is based on least-squares (a.k.a. annihilating filter method) and Cadzow denoising. It requires more measurements than the number of unknown parameters and mistakenly splits the derivatives of Diracs into several Diracs at different positions. Moreover, Cadzow denoising does not guarantee any optimality. The proposed approach based on maximum likelihood estimation solves all of these problems. Since the corresponding log-likelihood function is non-convex, we exploit the stochastic method called particle swarm optimization (PSO) to find the global solution. Simulation results confirm the effectiveness of the proposed approach, for a reasonable computational cost.

Pulse coupled neural network (PCNN) is a new type of artificial neural network specific for image processing applications. It is a single layer, two dimensional network with neurons which have 1:1 correspondence to the pixels of an input image. It is convenient to process the intensities and spatial locations of image pixels simultaneously by applying a PCNN. Therefore, we propose a modified PCNN with anisotropic synaptic weight matrix for image edge detection from the aspect of intensity similarities of pixels to their neighborhoods. By applying the anisotropic synaptic weight matrix, the interconnections are only established between the central neuron and the neighboring neurons corresponding to pixels with similar intensity values in a 3 by 3 neighborhood. Neurons corresponding to edge pixels and non-edge pixels will receive different input signal from the neighboring neurons. By setting appropriate threshold conditions, image step edges can be detected effectively. Comparing with conventional PCNN based edge detection methods, the proposed modified PCNN is much easier to control, and the optimal result can be achieved instantly after all neurons pulsed. Furthermore, the proposed method is shown to be able to distinguish the isolated pixels from step edge pixels better than derivative edge detectors.

This letter presents a robust receiver using the generalized sidelobe canceller aided with the high-order derivative constraint technique for multicarrier code-division multiple-access (MC-CDMA) uplink against carrier frequency offset (CFO). Numerical results demonstrate the efficacy of the proposed receiver.

We fabricated solution-processed organic complementary inverters based on α,ω-bis(2-hexyldecyl)sexithiophene (BHD6T) for p-channel and C60-fused N-methylpyrrolidine-meta-dodecyl phenyl (C60MC12) for n-channel. The BHD6T and C60MC12 thin-film transistors showed high field-effect mobilities of 0.035 and 0.057 cm2/Vs, respectively. The complementary inverter with a supply voltage of 50 V exhibited inverting voltages of 26.8 V for forward and 27.0 V for backward sweeps and a high gain of 76.

Boosting over weak classifiers is widely used in pedestrian detection. As the number of weak classifiers is large, researchers always use a sampling method over weak classifiers before training. The sampling makes the boosting process harder to reach the fixed target. In this paper, we propose a partial derivative guidance for weak classifier mining method which can be used in conjunction with a boosting algorithm. Using weak classifier mining method makes the sampling less degraded in the performance. It has the same effect as testing more weak classifiers while using acceptable time. Experiments demonstrate that our algorithm can process quicker than [1] algorithm in both training and testing, without any performance decrease. The proposed algorithms is easily extending to any other boosting algorithms using a window-scanning style and HOG-like features.