Long-run effects of Catholic schooling on wages

Using panel data from the Household, Income and Labour Dynamics Australia Survey and fixed effects estimation, this report examines the effect of Catholic schooling on long-term wage rates in Australia, independent of effects on academic achievement. Abstract: Previous studies have linked Catholic schooling to higher academic achievement. We add to the literature on Catholic schooling by examining its effect on long-term wage rates in Australia, independent of effects on academic achievement. Using panel data from the Household, Income and Labour Dynamics Australia (HILDA) Survey and fixed effects estimation, we find that during the prime-time of a career, wage rates for Catholic school graduates progress with labor market experience at a greater rate, on average, than wage rates for public school graduates. Importantly, we find no evidence to suggest that these benefits are peculiar to Catholic schooling, with similar benefits estimated for graduates of independent private schools. These findings suggest that private schooling may be important in not only fostering higher academic achievement, but also in better preparing students for a working life

SMS Text Compression through IDBE (Intelligent Dictionary based Encoding) for Effective Mobile Storage Utilization

Effective storage utilization is the key concept for better working of any operating system. Even operating systems used for mobile phones are not an exception for this fact. This paper proposes a technique for maximizing the utilization of the storage space present in mobile phones. Thus it is important to utilize the space occupied by SMS files in phone’s memory, which take maximum space. The objective involved is designing a semantic dictionary based on Intelligent Dictionary Based Encoding (IDBE) which provides a high text compression ratio to utilize the space in phone’s memory. When SMS file will be received, English words present in the text will be replaced by the respective short words in the designed semantic dictionary. Thus replacing English words by the respective short forms reduces the space occupied by the SMS file. The paper describes the IDBE Compression Techniques for SMS Text Compression

Positive Geometries for all Scalar Theories from Twisted Intersection Theory

We show that accordiohedra furnish polytopes which encode amplitudes for all massive scalar field theories with generic interactions. This is done by deriving integral formulae for the Feynman diagrams at tree level and integrands at one loop level in the planar limit using the twisted intersection theory of convex realizations of the accordiohedron polytopes.Comment: v2: Corrected typos and added references - published versio

Educational Achievement and the Allocation of School Resources

The school resources educational outcomes debate has focused almost exclusively on spending levels. We extend this by analysing the relationship between student achievement and schools' budget allocation decisions using panel data. Per-pupil expenditure has only a modest relationship with improvement in students' standardised test scores. However, budget allocation across spending categories matters for student achievement, particularly in grade 7. Ancillary teaching staff seems especially important in primary- and middle-school years. Spending on school leadership primarily principals is also linked to faster growth in literacy levels in these grades. On the whole, schools' spending patterns are broadly efficient

RUPNet: Residual upsampling network for real-time polyp segmentation

Colorectal cancer is among the most prevalent cause of cancer-related mortality worldwide. Detection and removal of polyps at an early stage can help reduce mortality and even help in spreading over adjacent organs. Early polyp detection could save the lives of millions of patients over the world as well as reduce the clinical burden. However, the detection polyp rate varies significantly among endoscopists. There is numerous deep learning-based method proposed, however, most of the studies improve accuracy. Here, we propose a novel architecture, Residual Upsampling Network (RUPNet) for colon polyp segmentation that can process in real-time and show high recall and precision. The proposed architecture, RUPNet, is an encoder-decoder network that consists of three encoders, three decoder blocks, and some additional upsampling blocks at the end of the network. With an image size of $512 \times 512$, the proposed method achieves an excellent real-time operation speed of 152.60 frames per second with an average dice coefficient of 0.7658, mean intersection of union of 0.6553, sensitivity of 0.8049, precision of 0.7995, and F2-score of 0.9361. The results suggest that RUPNet can give real-time feedback while retaining high accuracy indicating a good benchmark for early polyp detection.Comment: Accepted SPIE Medical Imaging 202

TransRUPNet for Improved Out-of-Distribution Generalization in Polyp Segmentation

Out-of-distribution (OOD) generalization is a critical challenge in deep learning. It is specifically important when the test samples are drawn from a different distribution than the training data. We develop a novel real-time deep learning based architecture, TransRUPNet that is based on a Transformer and residual upsampling network for colorectal polyp segmentation to improve OOD generalization. The proposed architecture, TransRUPNet, is an encoder-decoder network that consists of three encoder blocks, three decoder blocks, and some additional upsampling blocks at the end of the network. With the image size of $256\times256$, the proposed method achieves an excellent real-time operation speed of \textbf{47.07} frames per second with an average mean dice coefficient score of 0.7786 and mean Intersection over Union of 0.7210 on the out-of-distribution polyp datasets. The results on the publicly available PolypGen dataset (OOD dataset in our case) suggest that TransRUPNet can give real-time feedback while retaining high accuracy for in-distribution dataset. Furthermore, we demonstrate the generalizability of the proposed method by showing that it significantly improves performance on OOD datasets compared to the existing methods

On the Robustness of Topics API to a Re-Identification Attack

Web tracking through third-party cookies is considered a threat to users' privacy and is supposed to be abandoned in the near future. Recently, Google proposed the Topics API framework as a privacy-friendly alternative for behavioural advertising. Using this approach, the browser builds a user profile based on navigation history, which advertisers can access. The Topics API has the possibility of becoming the new standard for behavioural advertising, thus it is necessary to fully understand its operation and find possible limitations. This paper evaluates the robustness of the Topics API to a re-identification attack where an attacker reconstructs the user profile by accumulating user's exposed topics over time to later re-identify the same user on a different website. Using real traffic traces and realistic population models, we find that the Topics API mitigates but cannot prevent re-identification to take place, as there is a sizeable chance that a user's profile is unique within a website's audience. Consequently, the probability of correct re-identification can reach 15-17%, considering a pool of 1,000 users. We offer the code and data we use in this work to stimulate further studies and the tuning of the Topic API parameters.Comment: Privacy Enhancing Technologies Symposium (PETS) 202

The Internet with Privacy Policies: Measuring The Web Upon Consent

To protect user privacy, legislators have regulated the use of tracking technologies, mandating the acquisition of users' consent before collecting data. As a result, websites started showing more and more consent management modules -- i.e., Consent Banners -- the visitors have to interact with to access the website content. Since these banners change the content the browser loads, they challenge web measurement collection, primarily to monitor the extent of tracking technologies, but also to measure web performance. If not correctly handled, Consent Banners prevent crawlers from observing the actual content of the websites. In this paper, we present a comprehensive measurement campaign focusing on popular websites in Europe and the US, visiting both landing and internal pages from different countries around the world. We engineer \TOOL, a Web crawler able to accept the Consent Banners, as most users would do in practice. It lets us compare how webpages change before and after accepting such policies, if present. Our results show that all measurements performed ignoring the Consent Banners offer a biased and partial view of the Web. After accepting the privacy policies, web tracking is far more pervasive, webpages are larger and slower to load

Unawareness and selective disclosure: The effect of school quality information on property prices

The Australian Government launched the "My School" website in 2010 to provide standardised information about the quality of schools to the Australian public. This paper combines data from this website with home sales data for the state of Victoria to estimate the effect of the publication of school quality information on property prices. We use a difference-in-difference approach to estimate the causal effect of the release of information about high-quality and low-quality schools relative to medium-quality schools in the neighborhood and find that the release of information about high-quality schools increases property prices by 3.6 percent, whereas the release of information about low-quality schools has no significant effect. The findings indicate that many buyers are unaware of the relevance of school quality information and that real estate agents pursue a strategy of disclosing information about high-quality schools to increase the sales price. Results from a survey of Victorian real estate agents provide evidence in favor of this strategy