Construction of RDF(S) from UML Class Diagrams

RDF (Resource Description Framework) and RDF Schema (collectively called RDF(S)) are the normative language to describe the Web resource information. How to construct RDF(S) from the existing data sources is becoming an important research issue. In particular, UML (Unified Modeling Language) is being widely applied to data modeling in many application domains, and how to construct RDF(S) from the existing UML models becomes an important issue to be solved in the context of Semantic Web. By comparing and analyzing the characteristics of UML and RDF(S), this paper proposes an approach for constructing RDF(S) from UML and implements a prototype construction tool. First, we give the formal definitions of UML and RDF(S). After that, a construction approach from UML to RDF(S) is proposed, a construction example is provided, and the analyses and discussions about the approach are done. Further, based on the proposed approach, a prototype construction tool is implemented, and the experiment shows that the approach and the tool are feasible

Fighting against fast speckle decorrelation for light focusing inside live tissue by photon frequency shifting

Light focusing inside live tissue by digital optical phase conjugation (DOPC) has drawn increasing interest due to its potential biomedical applications in optogenetics, microsurgery, phototherapy, and deep-tissue imaging. However, fast physiological motions in a live animal, including blood flow and respiratory motions, produce undesired photon perturbation and thus inevitably deteriorate the performance of light focusing. Here, we develop a photon-frequency-shifting DOPC method to fight against fast physiological motions by switching the states of a guide star at a distinctive frequency. Therefore, the photons tagged by the guide star are well detected at the specific frequency, separating them from the photons perturbed by fast motions. Light focusing was demonstrated in both phantoms in vitro and mice in vivo with substantially improved focusing contrast. This work puts a new perspective on light focusing inside live tissue and promises wide biomedical applications

Image Denoising via Style Disentanglement

Image denoising is a fundamental task in low-level computer vision. While recent deep learning-based image denoising methods have achieved impressive performance, they are black-box models and the underlying denoising principle remains unclear. In this paper, we propose a novel approach to image denoising that offers both clear denoising mechanism and good performance. We view noise as a type of image style and remove it by incorporating noise-free styles derived from clean images. To achieve this, we design novel losses and network modules to extract noisy styles from noisy images and noise-free styles from clean images. The noise-free style induces low-response activations for noise features and high-response activations for content features in the feature space. This leads to the separation of clean contents from noise, effectively denoising the image. Unlike disentanglement-based image editing tasks that edit semantic-level attributes using styles, our main contribution lies in editing pixel-level attributes through global noise-free styles. We conduct extensive experiments on synthetic noise removal and real-world image denoising datasets (SIDD and DND), demonstrating the effectiveness of our method in terms of both PSNR and SSIM metrics. Moreover, we experimentally validate that our method offers good interpretability

FFTPL: An Analytic Placement Algorithm Using Fast Fourier Transform for Density Equalization

We propose a flat nonlinear placement algorithm FFTPL using fast Fourier transform for density equalization. The placement instance is modeled as an electrostatic system with the analogy of density cost to the potential energy. A well-defined Poisson's equation is proposed for gradient and cost computation. Our placer outperforms state-of-the-art placers with better solution quality and efficiency

The Effect of Altruistic Tendency on Fairness in Third-Party Punishment

Third-party punishment, as an altruistic behavior, was found to relate to inequity aversion in previous research. Previous researchers have found that altruistic tendencies, as an individual difference, can affect resource division. Here, using the event-related potential (ERP) technique and a third-party punishment of dictator game paradigm, we explored third-party punishments in high and low altruists and recorded their EEG data. Behavioral results showed high altruists (vs. low altruists) were more likely to punish the dictators in unfair offers. ERP results revealed that patterns of medial frontal negativity (MFN) were modulated by unfairness. For high altruists, high unfair offers (90:10) elicited a larger MFN than medium unfair offers (70:30) and fair offers (50:50). By contrast, for low altruists, fair offers elicited larger MFN while high unfair offers caused the minimal MFN. It is suggested that the altruistic tendency effect influences fairness consideration in the early stage of evaluation. Moreover, the results provide further neuroscience evidence for inequity aversion