Integrated Water Quality Monitoring of Skaneateles Lake Tributaries

Skaneateles Lake is the drinking water source for the City of Syracuse and surrounding areas. Harmful algal blooms (HABs) have been occurring in Skaneateles Lake every year since 2017 and posing a great threat to water quality and drinking water safety. Although the exact cause of the HABs is still unclear, sunlight, quiescent conditions, warm temperature, and elevated concentrations of nitrogen and phosphorus seem to favor the development of such blooms. In this study, a custom-built, low-cost, multi-parameter sensor unit was designed, built, and deployed on a third-order tributary. The unit collected high-frequency data for stage, temperature, pH, and dissolved oxygen. The unit successfully captured the variability in stream discharge calculated from stage measurements and temperature. The pH and dissolved oxygen sensors performed well during low-flow periods but deviated during high-flow events. Overheating of electronics also contributed to instability in sensor measurements.Grab water samples were collected from nine tributaries and analyzed for total and dissolved organic carbon, phosphorus species, and nitrogen species. Nutrient analysis suggested export of phosphorus and nitrogen during high flow events. Analysis of physical watershed characteristics such as stream order, watershed area, and basin slope, implied that hydrologic flow paths were controlling the concentrations of some carbon, nitrogen, and phosphorus species. This study could provide a blueprint for building low-cost water monitoring systems in non-navigational rivers and contribute to the understanding of how hydrological and nutrient dynamics influences HABs in lakes

An Online Parallel and Distributed Algorithm for Recursive Estimation of Sparse Signals

In this paper, we consider a recursive estimation problem for linear regression where the signal to be estimated admits a sparse representation and measurement samples are only sequentially available. We propose a convergent parallel estimation scheme that consists in solving a sequence of $\ell_{1}$-regularized least-square problems approximately. The proposed scheme is novel in three aspects: i) all elements of the unknown vector variable are updated in parallel at each time instance, and convergence speed is much faster than state-of-the-art schemes which update the elements sequentially; ii) both the update direction and stepsize of each element have simple closed-form expressions, so the algorithm is suitable for online (real-time) implementation; and iii) the stepsize is designed to accelerate the convergence but it does not suffer from the common trouble of parameter tuning in literature. Both centralized and distributed implementation schemes are discussed. The attractive features of the proposed algorithm are also numerically consolidated.Comment: Part of this work has been presented at The Asilomar Conference on Signals, Systems, and Computers, Nov. 201

Stretched Exponential Relaxation of Glasses at Low Temperature

The question of whether glass continues to relax at low temperature is of fundamental and practical interest. Here, we report a novel atomistic simulation method allowing us to directly access the long-term dynamics of glass relaxation at room temperature. We find that the potential energy relaxation follows a stretched exponential decay, with a stretching exponent $\beta = 3/5$, as predicted by Phillips' diffusion-trap model. Interestingly, volume relaxation is also found. However, it is not correlated to the energy relaxation, but is rather a manifestation of the mixed alkali effect

Does Immigration Cause Crime? Evidence from the United States

Donald Trump announced that immigration should be responsible for higher crime incidents in the United States, and he in turn aimed to strengthen his anti-immigrant policies. Recently, his entire anti-immigrant agenda has triggered debates all over the United States. There are not too many previous studies focusing on empirical evidence, and they have never reached a consensus. This paper investigates the relationship between three kinds of immigration and crime in different regional groups to provide an updated assessment, including unauthorized immigrants, foreign population and Mexican unauthorized immigrants. State level cross-sectional data in 2014 is analyzed using multivariate regression. The results of the regression analysis reveal that immigration has significantly positive effects on violent crime. Compared with foreign population, the influence of unauthorized immigration appears to be stronger. Compared with foreign population and Mexican unauthorized immigrants, the influence of unauthorized immigration appears to be stronger. Contrary to inland state group, the study reveals that immigration accounts for crime. The evidence in fact shows that poverty rate increases the amount of violent crime and crime rate significantly. In the end, the findings provide important implications for the concerned authorities and policymakers

Region based Ensemble Learning Network for Fine-grained Classification

As an important research topic in computer vision, fine-grained classification which aims to recognition subordinate-level categories has attracted significant attention. We propose a novel region based ensemble learning network for fine-grained classification. Our approach contains a detection module and a module for classification. The detection module is based on the faster R-CNN framework to locate the semantic regions of the object. The classification module using an ensemble learning method, which trains a set of sub-classifiers for different semantic regions and combines them together to get a stronger classifier. In the evaluation, we implement experiments on the CUB-2011 dataset and the result of experiments proves our method s efficient for fine-grained classification. We also extend our approach to remote scene recognition and evaluate it on the NWPU-RESISC45 dataset.Comment: 6 pages, 3 figures, 2018 Chinese Automation Congress (CAC

FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing

As a typical entity of MEC (Mobile Edge Computing), 5G CPE (Customer Premise Equipment)/HGU (Home Gateway Unit) has proven to be a promising alternative to traditional Smart Home Gateway. Network TC (Traffic Classification) is a vital service quality assurance and security management method for communication networks, which has become a crucial functional entity in 5G CPE/HGU. In recent years, many researchers have applied Machine Learning or Deep Learning (DL) to TC, namely AI-TC, to improve its performance. However, AI-TC faces challenges, including data dependency, resource-intensive traffic labeling, and user privacy concerns. The limited computing resources of 5G CPE further complicate efficient classification. Moreover, the "black box" nature of AI-TC models raises transparency and credibility issues. The paper proposes the FedEdge AI-TC framework, leveraging Federated Learning (FL) for reliable Network TC in 5G CPE. FL ensures privacy by employing local training, model parameter iteration, and centralized training. A semi-supervised TC algorithm based on Variational Auto-Encoder (VAE) and convolutional neural network (CNN) reduces data dependency while maintaining accuracy. To optimize model light-weight deployment, the paper introduces XAI-Pruning, an AI model compression method combined with DL model interpretability. Experimental evaluation demonstrates FedEdge AI-TC's superiority over benchmarks in terms of accuracy and efficient TC performance. The framework enhances user privacy and model credibility, offering a comprehensive solution for dependable and transparent Network TC in 5G CPE, thus enhancing service quality and security.Comment: 13 pages, 13 figure