Angle-Aware and Tone-Aware Luminosity Analysis for Paper Model Surface

Luminosity contributes to the paper model surface perception. It has a significant impact on the perception of colour and details. The main purpose of this paper is to study the reflection luminosity of paper model surface which can be of complex or difficult shape surface. The final perception quality of a product, whether it is plain or 3D or other different shape, depends on the surface luminosity perceived by the receptor, such as eyes or measurement instruments. However, the number of parameters and limits of the paper model surface are enormous. It is a time-consuming work to select every parameter by a trial-and-error procedure. For a paper surface under the fixed lighting environment, the most important factors to decide the performance of perception are commonly viewing angles and surface tone. Therefore, the two related terms, perception angle and surface tone, were chosen to work in the analysis process. The final analysis, based on the initial conditions, enabled to predict the perception of paper model surface and to set the optimal perceived angels and tones. It still proposed the next step to model the perception of paper model surface of different shapes in a relatively short period

Counterfactual Fairness with Partially Known Causal Graph

Fair machine learning aims to avoid treating individuals or sub-populations unfavourably based on \textit{sensitive attributes}, such as gender and race. Those methods in fair machine learning that are built on causal inference ascertain discrimination and bias through causal effects. Though causality-based fair learning is attracting increasing attention, current methods assume the true causal graph is fully known. This paper proposes a general method to achieve the notion of counterfactual fairness when the true causal graph is unknown. To be able to select features that lead to counterfactual fairness, we derive the conditions and algorithms to identify ancestral relations between variables on a \textit{Partially Directed Acyclic Graph (PDAG)}, specifically, a class of causal DAGs that can be learned from observational data combined with domain knowledge. Interestingly, we find that counterfactual fairness can be achieved as if the true causal graph were fully known, when specific background knowledge is provided: the sensitive attributes do not have ancestors in the causal graph. Results on both simulated and real-world datasets demonstrate the effectiveness of our method

Luminance Prediction of Paper Model Surface Based on Non-Contact Measurement

The overall appearance perception is affected by luminance perception accuracy and efficiency mostly. The surface luminance prediction correlated with surface angle and surface tone value was performed by measuring and modeling the paper model surface luminance. First, we used a rotating bracket designed to facilitate to set the paper surface angle. Then, we set the surface angle from 5° to 85° at the interval of 5° using the designed rotating bracket. Additionally, the four primary color scales, cyan, magenta, yellow, and black, were printed and set at the designed angle. The angle-ware and tone-ware luminance was measured using spectroradiometer, CS-2000. Finally, we proposed and evaluated a mathematical model to reveal the relationship between luminance and surface angle and surface tone using the least squares method. The results indicated that the surface luminance of paper model could be predicted and obtained quickly and accurately for any surface angles and surface tone values by the proposed prediction model

Design, Analysis and Testing of Piezoelectric Tool Actuator for Elliptical Vibration Cutting

In the field of ultraprecision machining, the structured surfaces with various micro/nano characteristics may have different advanced functions, such as wettability modifications, tribological control and hybrid micro-optics. However, the machining of micro/nano structured surfaces is becoming a challenge for present cutting method. Especially for the difficult-to-cut materials, it is impossible to manufacture complex micro/nano features by using traditional cutting methods. The complex features require a cutting tool no longer confined to the traditional motion guide. The cutting tool should have more quick response velocity and flexible modulated ability. This chapter aims to make an introduction for piezoelectric tool actuator used in elliptical vibration cutting, which can be offering tertiary cutting operations with quick response and flexible modulated ability. The content covers the working principle of piezoelectric tool actuator, compliant mechanism design, static modeling, kinematic and dynamic modeling, structure optimization and offline testing

Anti-tumor activity of polysaccharides extracted from Senecio scandens Buch, -Ham root on hepatocellular carcinoma

Purpose: To optimize the extraction conditions of polysaccharides from the root of Senecio scandens Buch,-Ham. (PRS) and evaluate its anti-tumor effect on hepatocellular carcinoma.Methods: Response surface methodology (RSM) applied with a Box-Behnken design (BBD, three levels and three factors) was employed to determine the effect of extraction time, number of extraction and ratio of water to raw material on the yield of PRS. The anti-tumor effect of PRS on A549, HL60, S180 and H22 cell lines was evaluated in vitro by 3-(4,5-dimethylthiazol-2-yl) -2,5-diphenyltetrazolium bromide (MTT) assay, while in vivo anti-tumor effect was evaluated in H22 tumor transplanted mice. Furthermore, expressions of proteins including caspase-3, caspase-9, Bcl-2 and Bax were determined by western blotting assay.Results: The established BBD model was highly significant and the optimal conditions were: extraction time, 3.06 h; number of extractions, 2; and ratio of water to raw material, 16.17 mL/g. PRS showed significant inhibitory effect on H22 cells (IC50 = 42.4 μg/mL), and significantly inhibited the growth of transplanted H22 tumors in mice at the doses of 20, 40 and 80 mg/kg (p < 0.05, p < 0.05 and p < 0.01, respectively). Treatment with PRS (20, 40 and 80 μg/mL) significantly up-regulated the expressions of Bax, caspase-3 and caspase-9 in H22 cells, whereas Bcl-2 protein was significantly down-regulated.Conclusion: The results suggest that PRS possesses significant anti-tumor activity on H22 cell line in vitro and in vivo, and the mechanism may be closely related to the induction of mitochondria-mediated apoptosis.Keywords: Senecio scandens, Polysaccharides, Hepatocellular carcinoma, Response surface methodology, Anti-tumor activity, Apoptosi

Towards Mixture Proportion Estimation without Irreducibility

\textit{Mixture proportion estimation} (MPE) is a fundamental problem of practical significance, where we are given data from only a \textit{mixture} and one of its two \textit{components} to identify the proportion of each component. All existing MPE methods that are distribution-independent explicitly or implicitly rely on the \textit{irreducible} assumption---the unobserved component is not a mixture containing the observable component. If this is not satisfied, those methods will lead to a critical estimation bias. In this paper, we propose \textit{Regrouping-MPE} that works without irreducible assumption: it builds a new irreducible MPE problem and solves the new problem. It is worthwhile to change the problem: we prove that if the assumption holds, our method will not affect anything; if the assumption does not hold, the bias from problem changing is less than the bias from violation of the irreducible assumption in the original problem. Experiments show that our method outperforms all state-of-the-art MPE methods on various real-world datasets

Adversarial Robustness through the Lens of Causality

The adversarial vulnerability of deep neural networks has attracted significant attention in machine learning. From a causal viewpoint, adversarial attacks can be considered as a specific type of distribution change on natural data. As causal reasoning has an instinct for modeling distribution change, we propose to incorporate causality into mitigating adversarial vulnerability. However, causal formulations of the intuition of adversarial attack and the development of robust DNNs are still lacking in the literature. To bridge this gap, we construct a causal graph to model the generation process of adversarial examples and define the adversarial distribution to formalize the intuition of adversarial attacks. From a causal perspective, we find that the label is spuriously correlated with the style (content-independent) information when an instance is given. The spurious correlation implies that the adversarial distribution is constructed via making the statistical conditional association between style information and labels drastically different from that in natural distribution. Thus, DNNs that fit the spurious correlation are vulnerable to the adversarial distribution. Inspired by the observation, we propose the adversarial distribution alignment method to eliminate the difference between the natural distribution and the adversarial distribution. Extensive experiments demonstrate the efficacy of the proposed method. Our method can be seen as the first attempt to leverage causality for mitigating adversarial vulnerability