A Genuine Random Sequential Multi-signature Scheme

The usual sequential multi-signature scheme allows the multi-signers to sign the document with their own information and sequence, and the signature is not real random and secure. The paper analyzes the reasons for the insecurity of the previous multi-signature scheme, and puts forward a Genuine Random Sequential Multi-signature Scheme based on The Waters signature scheme, and the experiment proves that this scheme is a good scheme suitable for the practical application with high computing efficiency

A Genuine Random Sequential Multi-signature Scheme

The usual sequential multi-signature scheme allows the multi-signers to sign the document with their own information and sequence, and the signature is not real random and secure. The paper analyzes the reasons for the insecurity of the previous multi-signature scheme, and puts forward a Genuine Random Sequential Multi-signature Scheme based on The Waters signature scheme, and the experiment proves that this scheme is a good scheme suitable for the practical application with high computing efficiency

Pedestrian Detection aided by Deep Learning Semantic Tasks

Deep learning methods have achieved great success in pedestrian detection, owing to its ability to learn features from raw pixels. However, they mainly capture middle-level representations, such as pose of pedestrian, but confuse positive with hard negative samples, which have large ambiguity, e.g. the shape and appearance of `tree trunk' or `wire pole' are similar to pedestrian in certain viewpoint. This ambiguity can be distinguished by high-level representation. To this end, this work jointly optimizes pedestrian detection with semantic tasks, including pedestrian attributes (e.g. `carrying backpack') and scene attributes (e.g. `road', `tree', and `horizontal'). Rather than expensively annotating scene attributes, we transfer attributes information from existing scene segmentation datasets to the pedestrian dataset, by proposing a novel deep model to learn high-level features from multiple tasks and multiple data sources. Since distinct tasks have distinct convergence rates and data from different datasets have different distributions, a multi-task objective function is carefully designed to coordinate tasks and reduce discrepancies among datasets. The importance coefficients of tasks and network parameters in this objective function can be iteratively estimated. Extensive evaluations show that the proposed approach outperforms the state-of-the-art on the challenging Caltech and ETH datasets, where it reduces the miss rates of previous deep models by 17 and 5.5 percent, respectively

Fuzzy knowledge searching on the basis of the traditional and-or graph search algorithm

Abstract Based on the fuzzy propositional logic FLCOM and fuzzy set FSCOM, we research the formal denotation, inference and computation of fuzzy knowledge. We extend the fuzzy and-or graph, turn the propositional formulas as state nodes, express the logical rules as the search space, construct and-or graph of the fuzzy propositional formula. We modify heuristic function on the basis of the traditional and-or graph search algorithm, and give out a method to process negation information in the process of reasoning, transforming the fuzzy knowledge reasoning into the state space searching problem, and using the state space searching to solve the problem of fuzzy knowledge reasoning

Glutaredoxin Desensitizes Lens to Oxidative Stress by Connecting and Integrating Specific Signaling and Transcriptional Regulation for Antioxidant Response

Background/Aims: Oxidative stress plays a critical role in the development of cataracts, and glutaredoxins (Grxs) play a major protective role against oxidative stress in the lens. This study aimed to reveal the global regulatory network of Grx1. Methods: Stable isotope labeling by amino acids in cell culture (SILAC) was used in a proteome-wide quantitative approach to identify the Grx1 regulatory signaling cascades at a subcellular resolution in response to oxidative stress. Results: A total of 1,291 proteins were identified to be differentially expressed, which were further categorized into a variety of signaling cascades including redox regulation, apoptosis, cell cycle control, glucose metabolism, protein synthesis, DNA damage response, protein folding, proteasome and others. Thirteen key signaling node molecules representing each pathway were verified. Notably, the subunits of proteasome complexes, which play a pivotal role in preventing cytotoxicity via the degradation of oxidized proteins, were highly enriched by Grx1. By data-dependent network analysis, we found global functional links among these signaling pathways which elucidate how Grx1 integrates the operation of these regulatory networks in an interconnected way for H2O2-induced response. Conclusion: Our data provide a system-wide insight into the function of Grx1 and provide a basis for further mechanistic investigation of Grx1 in antioxidant responses in the lens

Geometry-Modulated Magnetoplasmonic Optical Activity of Au Nanorod-Based Nanostructures

Comprehension and modulation of optical activity at nanoscale have attracted tremendous interest in the past decades due to its potential application in many fields including chemical/biological sensing, artificial metamaterials, asymmetric catalysis, and so forth. As for the conventional molecular materials, magnetic field is among the most effective routes in inducing and manipulating their optical activity; whereas the magnetic optical activity at nanoscale calls for deeper understanding, especially for anisotropic noble metal nanoparticles. In this work, distinctly different magnetic circular dichroism (MCD) responses are demonstrated in gold nanorods (GNRs) with a derivative-shaped MCD signal corresponding to the transverse surface plasmon resonance (TSPR) band and a Gaussian-shaped signal at the position of the longitudinal surface plasmon resonance (LSPR) band. Furthermore, changing the aspect ratio of GNRs easily regulates such magnetoplasmonic CD response. More interestingly, GNR assemblies with different geometric configuration (end-to-end and side-by-side) show structure-sensitive magnetoplasmonic CD response. Armed with theoretical calculation, we clearly elucidate the intrinsic relationship of the resultant magnetoplasmonic CD response with the optical symmetry and geometry factor inside one-dimensional GNRs. This work not only greatly benefits our understanding toward the nature of SPR mode in anisotropic plasmonic nanostructures but also opens the way to achieve tunable magnetoplasmonic response, which will significantly advance the design and application of optical nanodevices