Smooth and Strong PCPs

Probabilistically checkable proofs (PCPs) can be verified based only on a constant amount of random queries, such that any correct claim has a proof that is always accepted, and incorrect claims are rejected with high probability (regardless of the given alleged proof). We consider two possible features of PCPs: - A PCP is strong if it rejects an alleged proof of a correct claim with probability proportional to its distance from some correct proof of that claim. - A PCP is smooth if each location in a proof is queried with equal probability. We prove that all sets in NP have PCPs that are both smooth and strong, are of polynomial length, and can be verified based on a constant number of queries. This is achieved by following the proof of the PCP theorem of Arora, Lund, Motwani, Sudan and Szegedy (JACM, 1998), providing a stronger analysis of the Hadamard and Reed - Muller based PCPs and a refined PCP composition theorem. In fact, we show that any set in NP has a smooth strong canonical PCP of Proximity (PCPP), meaning that there is an efficiently computable bijection of NP witnesses to correct proofs. This improves on the recent construction of Dinur, Gur and Goldreich (ITCS, 2019) of PCPPs that are strong canonical but inherently non-smooth. Our result implies the hardness of approximating the satisfiability of "stable" 3CNF formulae with bounded variable occurrence, where stable means that the number of clauses violated by an assignment is proportional to its distance from a satisfying assignment (in the relative Hamming metric). This proves a hypothesis used in the work of Friggstad, Khodamoradi and Salavatipour (SODA, 2019), suggesting a connection between the hardness of these instances and other stable optimization problems

High-reliability release mechanism

Release mechanism employing simple clevis fitting in combination with two pin-pullers achieves high reliability degree through active mechanical redundancy. Mechanism releases solar arrays. It is simple and inexpensive and performs effectively. It adapts to other release-system applications with variety of pin-puller devices

The Impact of Medicaid and SCHIP on Low-Income Children's Health

Reviews the literature on the impact of Medicaid and State Children's Health Insurance Programs on the coverage, access to care, and health outcomes for low-income children, as well as remaining challenges in preventive, primary, and dental care

A Profile of Medicaid Managed Care Programs in 2010: Findings From a 50-State Survey

Examines types of Medicaid managed care organizations and contracts by state, including managed care organizations and primary care case management programs; issues for measuring, monitoring, and improving quality; and implications of health reform

Turning to Medicaid and SCHIP in an Economic Recession: Conversations With Recent Applicants and Enrollees

Based on focus group discussions, examines how the loss of jobs and employer-sponsored health insurance affects families. Explores the limitations of COBRA and private insurance and the role of Medicaid and State Children's Health Insurance Programs

CLASSIFICATION OF BATIK MOTIF USING TRANSFER LEARNING ON CONVOLUTIONAL NEURAL NETWORK (CNN)

The number of batik motifs in Indonesia is not comparable to the knowledge possessed by the Indonesian people about batik motifs. The diversity of batik motifs can be a problem because classifying them can only be done by those who are familiar with batik in depth, both the pattern and the philosophy behind the motif, most of which are elderly people. To classify batik accurately and quickly is to use image classification technology. In this study, data were obtained from the previous researchers' GitHub repository, google images, and camera shots with a total dataset of 3,534 images. The data only focused on five batik motifs, namely Ceplok, Kawung, Parang, Megamendung, and Sidomukti. Before the batik motif is processed, preprocessing is carried out to obtain various quality data. Then the dataset was trained using the CNN model then the results were retrained using the VGG-16 and Xception Transfer Learning models. The researcher made several model scenarios, namely the CNN model without Transfer Learning and the model with Transfer Learning which took into account the effect of the learning rate values ​​of 0.0004 and 0.0001. Therefore, the results of the CNN model without Transfer Learning (M0) obtained training accuracy results of 89.64%. While the results of the model with the best Transfer Learning is the M2 model (CNN + VGG-16, learning rate = 0.0001) with an accuracy of 91.23%, a loss of 24.48%, and the test results obtained an accuracy of 89%. Based on the results of the classification method, it can be concluded that the CNN model with Transfer Learning performs classification better in terms of accuracy and computation time than the CNN model