Unsupervised Adaptation for High-Dimensional with Limited-Sample Data Classification Using Variational Autoencoder

High-dimensional with limited-sample size (HDLSS) datasets exhibit two critical problems: (1) Due to the insufficiently small-sample size, there is a lack of enough samples to build classification models. Classification models with a limited-sample may lead to overfitting and produce erroneous or meaningless results. (2) The 'curse of dimensionality' phenomena is often an obstacle to the use of many methods for solving the high-dimensional with limited-sample size problem and reduces classification accuracy. This study proposes an unsupervised framework for high-dimensional limited-sample size data classification using dimension reduction based on variational autoencoder (VAE). First, the deep learning method variational autoencoder is applied to project high-dimensional data onto lower-dimensional space. Then, clustering is applied to the obtained latent-space of VAE to find the data groups and classify input data. The method is validated by comparing the clustering results with actual labels using purity, rand index, and normalized mutual information. Moreover, to evaluate the proposed model strength, we analyzed 14 datasets from the Arizona State University Digital Repository. Also, an empirical comparison of dimensionality reduction techniques shown to conclude their applicability in the high-dimensional with limited-sample size data settings. Experimental results demonstrate that variational autoencoder can achieve more accuracy than traditional dimensionality reduction techniques in high-dimensional with limited-sample-size data analysis

A new global river network database for macroscale hydrologic modeling

Coarse-resolution (upscaled) river networks are critical inputs for runoff routing in macroscale hydrologic models. Recently, Wu et al. (2011) developed a hierarchical dominant river tracing (DRT) algorithm for automated extraction and spatial upscaling of river networks using fine-scale hydrography inputs. We applied the DRT algorithms using combined HydroSHEDS and HYDRO1k global fine-scale hydrography inputs and produced a new series of upscaled global river network data at multiple (1/16° to 2°) spatial resolutions. The new upscaled results are internally consistent and congruent with the baseline fine-scale inputs and should facilitate improved regional to global scale hydrologic simulations

Modeling wind-induced waves in the Salish Sea

There have been on-going efforts for increasing coastal resilience to the risk of coastal inundation as a result of sea-level rise in Washington. Accurate coastal risk projection depends on detailed and accurate information of sea level rise, including waves and storm surge induced by windstorms. This paper presents a modeling study simulating wind-induced waves in the Salish Sea. A nested-grid modeling approach was used to provide accurate and robust model simulations at various scales. The NOAA NCEP’s WaveWatch III (WW3) model is configured at global and regional scales with wind forcing obtained from the Climate Forecast System Reanalysis (CFSR). For the Salish Sea and Washington outer coast, a high-resolution wave model is implemented with the Unstructured Simulating WAve Nearshore (UnSWAN) model. The Salish Sea wave model is driven by spectral open boundary conditions from the nested regional WW3 models. To further improve the model accuracy inside the Salish Sea, sea surface winds were obtained from a Weather Research and Forecasting (WRF) historical model simulation covering the entire west coast at a resolution of 6-km resolution. These were used to drive the Salish Sea UnSWAN model. Comparisons of model results with observed wave data at available buoy stations indicated that the model successfully reproduced the wave climates in the Salish Sea. Wave characteristics and exposure areas of large waves in the Salish Sea were analyzed based on model results simulated from 2011 to 2015

Performance analysis of Multi-Phase cooperative NOMA systems under passive eavesdropping

A key feature of the non-orthogonal multiple access (NOMA) technique is that users with better channel conditions have prior knowledge about the information of other weak users. Given this prior knowledge, the idea that a strong user can serve as a relay node for other weak users in order to improve their performance, is known as cooperative NOMA. In this paper, we study the physical layer security of such a cooperative NOMA system. In order to reduce the complexity of the analytical process, the considered system in this paper has three users, in which the performance of the weaker users are enhanced by the stronger users. Given that there is an eavesdropper in the system that can hear all the transmissions, we study the secrecy performance of all the users. More specifically, we make an attempt to derive the ergodic secrecy capacity (ESC) and secrecy outage probability (SOP) of all the users. Due to the intractable nature of the exact analysis for the weak users, we provide the closed form expressions of the ESC and SOP for these users at the high SNR regime, while providing the exact analysis for the strongest user. Targeting on the optimality, we further reveal that better secrecy performance of the system is achievable through an appropriate power control mechanism. Finally, based on the analytical methodology of the three-user cooperative system, we provide insightful observations on the performance (in terms of ESC and SOP) of a multi-phase cooperative NOMA system with N users at the high SNR regime. Through rigorous numerical simulations, we verify the correctness of our analytical derivations under different practical scenarios while providing evidence of achieving optimal secrecy performance with the proposed power control scheme.acceptedVersionPeer reviewe

New insights into control of arbovirus replication and spread by insect RNA interference pathways

Arthropod-borne (arbo) viruses are transmitted by vectors, such as mosquitoes, to susceptible vertebrates. Recent research has shown that arbovirus replication and spread in mosquitoes is not passively tolerated but induces host responses to control these pathogens. Small RNA-mediated host responses are key players among these antiviral immune strategies. Studies into one such small RNA-mediated antiviral response, the exogenous RNA interference (RNAi) pathway, have generated a wealth of information on the functions of this mechanism and the enzymes which mediate antiviral activities. However, other small RNA-mediated host responses may also be involved in modulating antiviral activity. The aim of this review is to summarize recent research into the nature of small RNA-mediated antiviral responses in mosquitoes and to discuss future directions for this relatively new area of research

Threat by marine heatwaves to adaptive large marine ecosystems in an eddy-resolving model.

Marine heatwaves (MHWs), episodic periods of abnormally high sea surface temperature (SST), severely affect marine ecosystems. Large Marine Ecosystems (LMEs) cover ~22% of the global ocean but account for 95% of global fisheries catches. Yet how climate change affects MHWs over LMEs remains unknown, because such LMEs are confined to the coast where low-resolution climate models are known to have biases. Here, using a high-resolution Earth system model and applying a "future threshold" that considers MHWs as anomalous warming above the long-term mean warming of SSTs, we find that future intensity and annual days of MHWs over majority of the LMEs remain higher than in the present-day climate. Better resolution of ocean mesoscale eddies enables simulation of more realistic MHWs than low-resolution models. These increases in MHWs under global warming poses a serious threat to LMEs, even if resident organisms could adapt fully to the long-term mean warming