Crime incidents embedding using restricted Boltzmann machines

We present a new approach for detecting related crime series, by unsupervised learning of the latent feature embeddings from narratives of crime record via the Gaussian-Bernoulli Restricted Boltzmann Machines (RBM). This is a drastically different approach from prior work on crime analysis, which typically considers only time and location and at most category information. After the embedding, related cases are closer to each other in the Euclidean feature space, and the unrelated cases are far apart, which is a good property can enable subsequent analysis such as detection and clustering of related cases. Experiments over several series of related crime incidents hand labeled by the Atlanta Police Department reveal the promise of our embedding methods.Comment: 5 pages, 3 figure

Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data

Most COVID-19 studies commonly report figures of the overall infection at a state- or county-level. This aggregation tends to miss out on fine details of virus propagation. In this paper, we analyze a high-resolution COVID-19 dataset in Cali, Colombia, that records the precise time and location of every confirmed case. We develop a non-stationary spatio-temporal point process equipped with a neural network-based kernel to capture the heterogeneous correlations among COVID-19 cases. The kernel is carefully crafted to enhance expressiveness while maintaining model interpretability. We also incorporate some exogenous influences imposed by city landmarks. Our approach outperforms the state-of-the-art in forecasting new COVID-19 cases with the capability to offer vital insights into the spatio-temporal interaction between individuals concerning the disease spread in a metropolis

Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data

Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to local health departments to limit the spread of the disease. Understanding the spatio-temporal dynamics of hotspot events is of great importance to support policy decisions and prevent large-scale outbreaks. This paper presents a spatio-temporal Bayesian framework for early detection of COVID-19 hotspots (at the county level) in the United States. We assume both the observed number of cases and hotspots depend on a class of latent random variables, which encode the underlying spatio-temporal dynamics of the transmission of COVID-19. Such latent variables follow a zero-mean Gaussian process, whose covariance is specified by a non-stationary kernel function. The most salient feature of our kernel function is that deep neural networks are introduced to enhance the model's representative power while still enjoying the interpretability of the kernel. We derive a sparse model and fit the model using a variational learning strategy to circumvent the computational intractability for large data sets. Our model demonstrates better interpretability and superior hotspot-detection performance compared to other baseline methods

Control of Citrus Post-harvest Green Molds, Blue Molds, and Sour Rot by the Cecropin A-Melittin Hybrid Peptide BP21

In this study, the activity of the cecropin A-melittin hybrid peptide BP21 (Ac-FKLFKKILKVL-NH2) in controlling of citrus post-harvest green and blue molds and sour rot and its involved mechanism was studied. The minimum inhibitory concentrations of BP21 against Penicillium digitatum, Penicillium italicum, and Geotrichum candidum were 8, 8, and 4 μmol L-1, respectively. BP21 could inhibit the growth of mycelia, the scanning electron microscopy results clearly showed that the mycelia treated with BP21 shrank, formed a rough surface, became distorted and collapsed. Fluorescent staining with SYTOX Green (SG) indicated that BP21 could disintegrate membranes. Membrane permeability parameters, including extracellular conductivity, the leakage of potassium ions, and the release of cellular constituents, visibly increased as the BP21 concentration increased. Gross and irreversible damage to the cytoplasm and membranes was observed. There was a positive correlation between hemolytic activity and the concentration of BP21. These results suggest peptide BP21 could be used to control citrus post-harvest diseases