Random Numbers Generated from Audio and Video Sources

Random numbers are very useful in simulation, chaos theory, game theory, information theory, pattern recognition, probability theory, quantum mechanics, statistics, and statistical mechanics. The random numbers are especially helpful in cryptography. In this work, the proposed random number generators come from white noise of audio and video (A/V) sources which are extracted from high-resolution IPCAM, WEBCAM, and MPEG-1 video files. The proposed generator applied on video sources from IPCAM and WEBCAM with microphone would be the true random number generator and the pseudorandom number generator when applied on video sources from MPEG-1 video file. In addition, when applying NIST SP 800-22 Rev.1a 15 statistics tests on the random numbers generated from the proposed generator, around 98% random numbers can pass 15 statistical tests. Furthermore, the audio and video sources can be found easily; hence, the proposed generator is a qualified, convenient, and efficient random number generator

Treatment Learning Causal Transformer for Noisy Image Classification

Current top-notch deep learning (DL) based vision models are primarily based on exploring and exploiting the inherent correlations between training data samples and their associated labels. However, a known practical challenge is their degraded performance against "noisy" data, induced by different circumstances such as spurious correlations, irrelevant contexts, domain shift, and adversarial attacks. In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy by jointly estimating their treatment effects. Motivated from causal variational inference, we propose a transformer-based architecture, Treatment Learning Causal Transformer (TLT), that uses a latent generative model to estimate robust feature representations from current observational input for noise image classification. Depending on the estimated noise level (modeled as a binary treatment factor), TLT assigns the corresponding inference network trained by the designed causal loss for prediction. We also create new noisy image datasets incorporating a wide range of noise factors (e.g., object masking, style transfer, and adversarial perturbation) for performance benchmarking. The superior performance of TLT in noisy image classification is further validated by several refutation evaluation metrics. As a by-product, TLT also improves visual salience methods for perceiving noisy images.Comment: Accepted to IEEE WACV 2023. The first version was finished in May 201

Long-Term Prediction of Emergency Department Revenue and Visitor Volume Using Autoregressive Integrated Moving Average Model

This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA) model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume

Re-Encryption Method Designed by Row Complete Matrix

With the prevalence of Internet access, document storage has become a fundamental web service in recent years. One important topic is how to design a secure channel for efficiently sharing documents with another receiver. In this paper, we demonstrate a re-encryption method that is designed with row complete matrices. With this new method, the document owner can share a ciphertext in the cloud with another receiver by sending a serial number to the server and giving the receiver a corresponding key at the same time. This method ensures that the server cannot obtain the information about key and plaintext and that the receiver cannot obtain the original key of the owner either. Only the owner has the knowledge of all the information. Using this re-encryption system, the cloud server can provide a secure file-sharing service without worrying about the shared key management problem. Moreover, the cost of re-encryption will not increase even when the encryption is strengthened with longer encryption keys

Electroacupuncture at the Zusanli (ST-36) Acupoint Induces a Hypoglycemic Effect by Stimulating the Cholinergic Nerve in a Rat Model of Streptozotocine-Induced Insulin-Dependent Diabetes Mellitus

Animal studies have shown that electroacupuncture (EA) at Zusanli (ST-36) and Zhongwan (CV-12) acupoints reduces plasma glucose concentrations in rats with type II diabetes. However, whether EA reduces plasma glucose levels in type I diabetes is still unknown. In this study, we explore the various non-insulin-dependent pathways involved in EA-induced lowering of plasma glucose. Streptozotocin (STZ) (60 mg kg−1, i.v.) was administered via the femoral vein to induce insulin-dependent diabetes in non-adrenalectomized and in adrenalectomomized rats. EA (15 Hz) was applied for 30 min to bilateral ST-36 acupoints after administration of Atropine (0.1 mg kg−1 i.p.), Eserine (0.01 mg kg−1 i.p.), or Hemicholinium-3 (5 μg kg−1 i.p.) in non-adrenalectomized rats. Rats administered acetylcholine (0.01 mg kg−1 i.v.) did not undergo EA. Adrenalectomized rats underwent EA at bilateral ST-36 acupoints without further treatment. Blood samples were drawn from all rats before and after EA to measure changes in plasma glucose levels. Expression of insulin signaling proteins (IRS1, AKT2) in atropine-exposed rats before and after EA was measured by western blot. Atropine and hemicholinium-3 completely blocked the plasma glucose lowering effects of EA, whereas eserine led to a significant hypoglycemic response. In addition, plasma glucose levels after administration of acetylcholine were significantly lower than the fasting glucose levels. In STZ-adrenalectomized rats, EA did not induce a hypoglycemic response. EA stimulated the expression of IRS1 and AKT2 and atropine treatment blocked the EA-induced expression of those insulin signaling proteins. Taken together, EA at the ST-36 acupoint reduces plasma glucose concentrations by stimulating the cholinergic nerves

Game Solving with Online Fine-Tuning

Game solving is a similar, yet more difficult task than mastering a game. Solving a game typically means to find the game-theoretic value (outcome given optimal play), and optionally a full strategy to follow in order to achieve that outcome. The AlphaZero algorithm has demonstrated super-human level play, and its powerful policy and value predictions have also served as heuristics in game solving. However, to solve a game and obtain a full strategy, a winning response must be found for all possible moves by the losing player. This includes very poor lines of play from the losing side, for which the AlphaZero self-play process will not encounter. AlphaZero-based heuristics can be highly inaccurate when evaluating these out-of-distribution positions, which occur throughout the entire search. To address this issue, this paper investigates applying online fine-tuning while searching and proposes two methods to learn tailor-designed heuristics for game solving. Our experiments show that using online fine-tuning can solve a series of challenging 7x7 Killall-Go problems, using only 23.54% of computation time compared to the baseline without online fine-tuning. Results suggest that the savings scale with problem size. Our method can further be extended to any tree search algorithm for problem solving. Our code is available at https://rlg.iis.sinica.edu.tw/papers/neurips2023-online-fine-tuning-solver.Comment: Accepted by the 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Electroacupuncture-Induced Cholinergic Nerve Activation Enhances the Hypoglycemic Effect of Exogenous Insulin in a Rat Model of Streptozotocin-Induced Diabetes

The aim of this study is to explore the mechanisms by which electroacupuncture (EA) enhances the hypoglycemic effect of exogenous insulin in a streptozotocin- (STZ-) diabetic rats. Animals in the EA group were anesthetized and subjected to the insulin challenge test (ICT) and EA for 60 minutes. In the control group, rats were subjected to the same treatment with the exception of EA stimulation. Blood samples were drawn to measure changes in plasma glucose, free fatty acids (FFA), and insulin levels. Western blot was used to assay proteins involved in insulin signaling. Furthermore, atropine, hemicholinium-3 (HC-3), and Eserine were used to explore the relationship between EA and cholinergic nerve activation during ICT. EA augmented the blood glucose-lowering effects of EA by activating the cholinergic nerves in STZ rats that had been exposed to exogenous insulin. This phenomenon may be related to enhancement of insulin signaling rather than to changes in FFA concentration

Improving Success Rates of Percutaneous Coronary Intervention for Chronic Total Occlusion at a Rural Hospital in East Taiwan

SummaryBackgroundWe aimed to report the results of percutaneous coronary intervention for chronic total occlusion (CTO) in a remote hospital of southeast Taiwan that does not have on-site coronary artery bypass graft support and has insufficient medical resources.MethodsFrom 2006 to 2009, we identified 96 patients who underwent percutaneous coronary intervention and whose coronary angiogram showed CTO lesions. On-site cardiovascular surgeons were unavailable from 2006 to 2009.ResultsThe success rate (test for trend, p = 0.02) and numbers of guidewires used (test for trend, p = 0.59) significantly increased from 2006 to 2009, and the procedural time reduced significantly (test for trend, p = 0.001). The volume of contrast media injected decreased, although this result was not statistically significant (p = 0.70).ConclusionOur experience in managing CTO lesions substantially improved and the procedural time reduced over 4 years, even when constrained by a relative shortage of medical resources

Rural–urban disparities in the incidence and treatment intensity of periodontal disease among patients with diabetes

BackgroundDiabetes threatens population health, especially in rural areas. Diabetes and periodontal diseases have a bidirectional relationship. A persistence of rural–urban disparities in diabetes may indicate a rural–urban difference in periodontal disease among patients with diabetes; however, the evidence is lacking. This retrospective study aimed to investigate rural–urban discrepancies in the incidence and treatment intensity of periodontal disease among patients who were newly diagnosed with type 2 diabetes in the year 2010.MethodsThe present study was a retrospective cohort design, with two study samples: patients with type 2 diabetes and those who were further diagnosed with periodontal disease. The data sources included the 2010 Diabetes Mellitus Health Database at the patient level, the National Geographic Information Standardization Platform and the Department of Statistics, Ministry of Health and Welfare in Taiwan at the township level. Two dependent variables were a time-to-event outcome for periodontal disease among patients with type 2 diabetes and the treatment intensity measured for patients who were further diagnosed with periodontal disease. The key independent variables are two dummy variables, representing rural and suburban areas, with urban areas as the reference group. The Cox and Poisson regression models were applied for analyses.ResultsOf 68,365 qualified patients, 49% of them had periodontal disease within 10 years after patients were diagnosed with diabetes. Compared to urban patients with diabetes, rural (HR = 0.83, 95% CI: 0.75–0.91) and suburban patients (HR = 0.86, 95% CI: 0.83–0.89) had a lower incidence of periodontal disease. Among 33,612 patients with periodontal disease, rural patients received less treatment intensity of dental care (Rural: RR = 0.87, 95% CI: 0.83, 0.92; suburban: RR = 0.93, 95% CI: 0.92, 0.95) than urban patients.ConclusionGiven the underutilization of dental care among rural patients with diabetes, a low incidence of periodontal disease indicates potentially undiagnosed periodontal disease, and low treatment intensity signals potentially unmet dental needs. Our findings provide a potential explanation for the persistence of rural–urban disparities in poor diabetes outcomes. Policy interventions to enhance the likelihood of identifying periodontal disease at the early stage for proper treatment would ease the burden of diabetes care and narrow rural–urban discrepancies in diabetes outcomes