A Networked Dataflow Simulation Environment for Signal Processing and Data Mining Applications

In networked signal processing systems, dataflow graphs can be used to describe the processing on individual network nodes. However, to analyze the correctness and performance of these systems, designers must understand the interactions across these individual "node-level'' dataflow graphs --- as they communicate across the network --- in addition to the characteristics of the individual graphs. In this thesis, we present a novel simulation environment, called the NS-2 -- TDIF SIMulation environment (NT-SIM). NT-SIM provides integrated co-simulation of networked systems and combines the network analysis capabilities provided by the Network Simulator (ns) with the scheduling capabilities of a dataflow-based framework, thereby providing novel features for more comprehensive simulation of networked signal processing systems. Through a novel integration of advanced tools for network and dataflow graph simulation, our NT-SIM environment allows comprehensive simulation and analysis of networked systems. We present two case studies that concretely demonstrate the utility of NT-SIM in the contexts of a heterogeneous signal processing and data mining system design

Three dimensional tracking with misalignment between display and control axes

Human operators confronted with misaligned display and control frames of reference performed three dimensional, pursuit tracking in virtual environment and virtual space simulations. Analysis of the components of the tracking errors in the perspective displays presenting virtual space showed that components of the error due to visual motor misalignment may be linearly separated from those associated with the mismatch between display and control coordinate systems. Tracking performance improved with several hours practice despite previous reports that such improvement did not take place

αCP binding to a cytosine-rich subset of polypyrimidine tracts drives a novel pathway of cassette exon splicing in the mammalian transcriptome.

Alternative splicing (AS) is a robust generator of mammalian transcriptome complexity. Splice site specification is controlled by interactions of cis-acting determinants on a transcript with specific RNA binding proteins. These interactions are frequently localized to the intronic U-rich polypyrimidine tracts (PPT) located 5' to the majority of splice acceptor junctions. αCPs (also referred to as polyC-binding proteins (PCBPs) and hnRNPEs) comprise a subset of KH-domain proteins with high affinity and specificity for C-rich polypyrimidine motifs. Here, we demonstrate that αCPs promote the splicing of a defined subset of cassette exons via binding to a C-rich subset of polypyrimidine tracts located 5' to the αCP-enhanced exonic segments. This enhancement of splice acceptor activity is linked to interactions of αCPs with the U2 snRNP complex and may be mediated by cooperative interactions with the canonical polypyrimidine tract binding protein, U2AF65. Analysis of αCP-targeted exons predicts a substantial impact on fundamental cell functions. These findings lead us to conclude that the αCPs play a direct and global role in modulating the splicing activity and inclusion of an array of cassette exons, thus driving a novel pathway of splice site regulation within the mammalian transcriptome

Correspondence between neuroevolution and gradient descent

We show analytically that training a neural network by stochastic mutation or "neuroevolution" of its weights is equivalent, in the limit of small mutations, to gradient descent on the loss function in the presence of Gaussian white noise. Averaged over independent realizations of the learning process, neuroevolution is equivalent to gradient descent on the loss function. We use numerical simulation to show that this correspondence can be observed for finite mutations. Our results provide a connection between two distinct types of neural-network training, and provide justification for the empirical success of neuroevolution

BOtied: Multi-objective Bayesian optimization with tied multivariate ranks

Many scientific and industrial applications require joint optimization of multiple, potentially competing objectives. Multi-objective Bayesian optimization (MOBO) is a sample-efficient framework for identifying Pareto-optimal solutions. We show a natural connection between non-dominated solutions and the highest multivariate rank, which coincides with the outermost level line of the joint cumulative distribution function (CDF). We propose the CDF indicator, a Pareto-compliant metric for evaluating the quality of approximate Pareto sets that complements the popular hypervolume indicator. At the heart of MOBO is the acquisition function, which determines the next candidate to evaluate by navigating the best compromises among the objectives. Multi-objective acquisition functions that rely on box decomposition of the objective space, such as the expected hypervolume improvement (EHVI) and entropy search, scale poorly to a large number of objectives. We propose an acquisition function, called BOtied, based on the CDF indicator. BOtied can be implemented efficiently with copulas, a statistical tool for modeling complex, high-dimensional distributions. We benchmark BOtied against common acquisition functions, including EHVI and random scalarization (ParEGO), in a series of synthetic and real-data experiments. BOtied performs on par with the baselines across datasets and metrics while being computationally efficient.Comment: 10 pages (+5 appendix), 9 figures. Submitted to NeurIP

New York Aquaculture Industry: Status, Updates and Opportunities

This 79-page report details the status of the aquaculture industry in New York State and provides recommendations for further opportunities

Nets with collisions (unstable nets) and crystal chemistry

Nets in which different vertices have identical barycentric coordinates (i.e. have collisions) are called unstable. Some such nets have automorphisms that do not correspond to crystallographic symmetries and are called non-crystallographic. Examples are

Development of a Bamlanivimab Infusion Process in the Emergency Department for Outpatient COVID-19 Patients

The coronavirus disease 2019 (COVID-19) pandemic has prompted the creation of new therapies to help fight against the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Bamlanivimab is a SARS-CoV-2 monoclonal antibody that is administered as an intravenous infusion to ambulatory patients with mild or moderate COVID-19, but a concern that arose was deciding the optimal location for patients to receive the medication. This report describes the development and implementation of a bamlanivimab infusion center in the emergency department of three hospitals in Orange County, California, shortly after bamlanivimab received emergency use authorization. As a result, a total of 601 patients received bamlanivimab in one of these three emergency departments between December 2020 to April 2021. The emergency department was shown to be an optimal setting for administration of bamlanivimab due to its convenience, accessibility, and capabilities for monitoring patients