Solar Storm Type Classification Using Probabilistic Neural Network compared with the Self-Organizing Map

One of the task of the LAPAN is making observation and forecasting of solar storms disturbance. This disturbances can affect the earths electromagnetic field that disrupt the electronic and navigational equipment on earth. LAPAN wanted a computer application that can automatically classify the type of solar storms, which became part of early warning systems to be created. The classification of the digital images of solar storm / sunspot is based on Modified - Zurich Sunspot Classification System. Classification method that we use here is the Probabilistic Neural Networks. The result of testing is promising because it has an accuracy of 94 for testing data. The accuracy is better than the accuracy of similar applications weve built with a combination of methods Self-Organizing Map and K-Nearest Neighbor

The Fractal Dimension of SAT Formulas

Modern SAT solvers have experienced a remarkable progress on solving industrial instances. Most of the techniques have been developed after an intensive experimental testing process. Recently, there have been some attempts to analyze the structure of these formulas in terms of complex networks, with the long-term aim of explaining the success of these SAT solving techniques, and possibly improving them. We study the fractal dimension of SAT formulas, and show that most industrial families of formulas are self-similar, with a small fractal dimension. We also show that this dimension is not affected by the addition of learnt clauses. We explore how the dimension of a formula, together with other graph properties can be used to characterize SAT instances. Finally, we give empirical evidence that these graph properties can be used in state-of-the-art portfolios.Comment: 20 pages, 11 Postscript figure

Self-Healing Polyphosphonium Ionic Networks

Self healing, ionically crosslinked networks were prepared from poly(acrylic acid) (PAA) and poly(triethyl(4-vinylbenzyl)phosphonium chloride (P-Et-P) and their properties were studied. Three different ratios of PAA/P-Et-P were incorporated into the networks by varying the addition orders of the components. Swelling of the networks increased with increasing NaCl concentration when they were immersed in aqueous solution. All networks retained their structural integrity in 0.1 M NaCl. Studies of the rheological and tensile properties of the networks swelled in 0.1 M NaCl showed that PAA\u3eP-Et-Pexhibited high elongation and viscoelastic properties suitable for self-healing with a relaxation time of ~30 s, whereas the other networks exhibited predominantly elastic behavior. The moduli were similar to those of soft tissues. Self-healing of PAA\u3eP-Et-Pin 0.1 M NaCl was demonstrated through repair of a 0.5 mm diameter puncture in the material whereas healing was incomplete for the other networks and also for PAA\u3eP-Et-Pin the absence of NaCl. Healing after completely severing a tensile testing sample showed significant recovery of the modulus, strength, and elongation. The properties of these materials and their ability to self-heal in low and physiologically relevant salt concentrations make them promising candidates for a variety of applications, particularly in the biomedical area

COVID-19 Testing in a Weekly Cohort Study of Gay and Bisexual Men: The Impact of Health-Seeking Behaviors and Social Connection

Gay, bisexual, and other men who have sex with men (GBM) have developed community norms for regular HIV/STI testing. We investigated factors associated with self-reported COVID-19 testing in response to reported COVID-19 cases and public health restrictions. Participants responded to weekly cohort surveys between 10th May 2021 and 27th September 2021. We used the Andersen-Gill extensions to the Cox proportional hazards model for multivariable survival data to predict factors influencing COVID-19 testing. Mean age of the 942 study participants was 45.6 years (SD: 13.9). In multivariable analysis, GBM were more likely to report testing during periods of high COVID-19 caseload in their state of residence; if they were younger; university educated; close contact of someone with COVID-19; or reported coping with COVID-19 poorly. COVID-19 testing was higher among men who: were more socially engaged with other GBM; had a higher proportion of friends willing to vaccinate against COVID-19; and were willing to contact sexual partners for contact tracing. Social connection with other gay men was associated with COVID-19 testing, similar to what has been observed throughout the HIV epidemic, making community networks a potential focus for the promotion of COVID-19 safe practices

Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary

Single Image Super-Resolution is a classic computer vision problem that involves estimating high-resolution (HR) images from low-resolution (LR) ones. Although deep neural networks (DNNs), especially Transformers for super-resolution, have seen significant advancements in recent years, challenges still remain, particularly in limited receptive field caused by window-based self-attention. To address these issues, we introduce a group of auxiliary Adaptive Token Dictionary to SR Transformer and establish an ATD-SR method. The introduced token dictionary could learn prior information from training data and adapt the learned prior to specific testing image through an adaptive refinement step. The refinement strategy could not only provide global information to all input tokens but also group image tokens into categories. Based on category partitions, we further propose a category-based self-attention mechanism designed to leverage distant but similar tokens for enhancing input features. The experimental results show that our method achieves the best performance on various single image super-resolution benchmarks.Comment: 15 pages, 9 figure

Towards Robust Neural Networks via Random Self-ensemble

Recent studies have revealed the vulnerability of deep neural networks: A small adversarial perturbation that is imperceptible to human can easily make a well-trained deep neural network misclassify. This makes it unsafe to apply neural networks in security-critical applications. In this paper, we propose a new defense algorithm called Random Self-Ensemble (RSE) by combining two important concepts: {\bf randomness} and {\bf ensemble}. To protect a targeted model, RSE adds random noise layers to the neural network to prevent the strong gradient-based attacks, and ensembles the prediction over random noises to stabilize the performance. We show that our algorithm is equivalent to ensemble an infinite number of noisy models $f_\epsilon$ without any additional memory overhead, and the proposed training procedure based on noisy stochastic gradient descent can ensure the ensemble model has a good predictive capability. Our algorithm significantly outperforms previous defense techniques on real data sets. For instance, on CIFAR-10 with VGG network (which has 92\% accuracy without any attack), under the strong C\&W attack within a certain distortion tolerance, the accuracy of unprotected model drops to less than 10\%, the best previous defense technique has $48\%$ accuracy, while our method still has $86\%$ prediction accuracy under the same level of attack. Finally, our method is simple and easy to integrate into any neural network.Comment: ECCV 2018 camera read