Exploring the evolution of color-luminosity parameter β\beta and its effects on parameter estimation

It has been found in previous studies that, for the Supernova Legacy Survey three-year (SNLS3) data, there is strong evidence for the redshift-evolution of color-luminosity parameter $\beta$. In this paper, using three simplest dark energy models ($\Lambda$CDM, $w$CDM, and CPL), we further explore the evolution of $\beta$ and its effects on parameter estimation. In addition to the SNLS3 data, we also take into account the Planck distance priors data, as well as the latest galaxy clustering (GC) data extracted from SDSS DR7 and BOSS. We find that, for all the models, adding a parameter of $\beta$ can reduce $\chi^2_{min}$ by $\sim$ 36, indicating that $\beta_1 = 0$ is ruled out at 6$\sigma$ confidence levels. In other words, $\beta$ deviates from a constant at 6$\sigma$ confidence levels. This conclusion is insensitive to the dark energy models considered, showing the importance of considering the evolution of $\beta$ in the cosmology-fits. Furthermore, it is found that varying $\beta$ can significantly change the fitting results of various cosmological parameters: using the SNLS3 data alone, varying $\beta$ yields a larger $\Omega_m$ for the $\Lambda$CDM model; using the SNLS3+CMB+GC data, varying $\beta$ yields a larger $\Omega_m$ and a smaller $h$ for all the models. Moreover, we find that these results are much closer to those given by the CMB+GC data, compared to the cases of treating $\beta$ as a constant. This indicates that considering the evolution of $\beta$ is very helpful for reducing the tension between supernova and other cosmological observations.Comment: 11 pages, 9 figures, 2 tables; accepted for publication in Physical Review D. arXiv admin note: text overlap with arXiv:1306.6423; and with arXiv:1109.3172 by other author

Survival and Continuation: An Analysis of the Women Characters of the American Indian Community in Louise Erdrich’s The Night Watchman

The Night Watchman is a novel published by Native American woman writer Louise Erdrich in 2020. The book tells the story of an Indian tribe located in the Turtle Mountain Reservation in the 1950s which makes arduous efforts to prevent the US government from enacting Termination Bill and relocation plan. The author vividly displays the unity of the tribal people in the Turtle Mountain Reservation. At the same time, the images of American Indian women are portrayed in details. In the mainstream white society, Indian images, especially Indian women’s images, always seem to be shrouded in mystery due to the long-term neglect and discrimination. At the time, Indian women were facing two crises: firstly, as women, they failed to avoid the fate of being persecuted; Secondly, as the members of the Indian community, their tribal survival and development were under threat. Therefore, analyzing the images of American Indian women in Erdrich’s The Night Watchman not only enables the public to pay attention to the identity and awareness of Native American women, but also helps readers better understand how the female characters in the book shape their unique gender and cultural identity through persistence and resistance

Using Context and Interactions to Verify User-Intended Network Requests

Client-side malware can attack users by tampering with applications or user interfaces to generate requests that users did not intend. We propose Verified Intention (VInt), which ensures a network request, as received by a service, is user-intended. VInt is based on "seeing what the user sees" (context). VInt screenshots the user interface as the user interacts with a security-sensitive form. There are two main components. First, VInt ensures output integrity and authenticity by validating the context, ensuring the user sees correctly rendered information. Second, VInt extracts user-intended inputs from the on-screen user-provided inputs, with the assumption that a human user checks what they entered. Using the user-intended inputs, VInt deems a request to be user-intended if the request is generated properly from the user-intended inputs while the user is shown the correct information. VInt is implemented using image analysis and Optical Character Recognition (OCR). Our evaluation shows that VInt is accurate and efficient