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  1. Teachable object recognizers provide a solution for a very practical need for blind people – instance level object recognition. They assume one can visually inspect the photos they provide for training, a critical and inaccessible step for those who are blind. In this work, we engineer data descriptors that address this challenge. They indicate in real time whether the object in the photo is cropped or too small, a hand is included, the photos is blurred, and how much photos vary from each other. Our descriptors are built into open source testbed iOS app, called MYCam. In a remote user study in (N = 12) blind participants’ homes, we show how descriptors, even when error-prone, support experimentation and have a positive impact in the quality of training set that can translate to model performance though this gain is not uniform. Participants found the app simple to use indicating that they could effectively train it and that the descriptors were useful. However, many found the training being tedious, opening discussions around the need for balance between information, time, and cognitive load.
    Free, publicly-accessible full text available October 1, 2023
  2. Free, publicly-accessible full text available January 1, 2023
  3. Free, publicly-accessible full text available January 1, 2023
  4. Distributed model training suffers from communication bottlenecks due to frequent model updates transmitted across compute nodes. To alleviate these bottlenecks, practitioners use gradient compression techniques like sparsification, quantization, or low-rank updates. The techniques usually require choosing a static compression ratio, often requiring users to balance the trade-off between model accuracy and per-iteration speedup. In this work, we show that such performance degradation due to choosing a high compression ratio is not fundamental. An adaptive compression strategy can reduce communication while maintaining final test accuracy. Inspired by recent findings on critical learning regimes, in which small gradient errors can have irrecoverable impact on model performance, we propose Accordion a simple yet effective adaptive compression algorithm. While Accordion maintains a high enough compression rate on average, it avoids over-compressing gradients whenever in critical learning regimes, detected by a simple gradient-norm based criterion. Our extensive experimental study over a number of machine learning tasks in distributed environments indicates that Accordion, maintains similar model accuracy to uncompressed training, yet achieves up to 5.5x better compression and up to 4.1x end-to-end speedup over static approaches. We show that Accordion also works for adjusting the batch size, another popular strategy for alleviating communication bottlenecks.
  5. Antimicrobial resistance is a well-documented public health concern. The role that drinking water distribution pipes have as sources of antibiotic resistance genes (ARGs) is not well known. Metals are a known stressor for antibiotic resistance development, implying that aging metal-pipe infrastructure could be a source of ARGs. The objective of this study was to determine if ARGs, metal resistance genes (MRGs), and intI 1 were pervasive across various pipe biofilm sample types (biomass surfaces, pipe surfaces, corrosion tubercles, and under corrosion tubercles) and if the resistance genes associated with particular microbial taxa. Eight sample types in triplicate ( n = 24) were taken from inside a >100 year-old, six ft. section of a full-scale chloraminated cast iron drinking water main. Droplet digital PCR (ddPCR) was employed as a novel approach to quantify ARGs in pipes from full-scale drinking water distribution systems (DWDS) because it yielded higher detection frequencies than quantitative PCR (qPCR). Illumina sequencing was employed to characterize the microbial community based on 16S rRNA genes. ARGs and MRGs were detected in all 24 pipe samples. Every sample contained targeted genes. Interestingly, the mean absolute abundances of ARGs and MRGs only varied by approximately one log value across sample types,more »but the mean relative abundances (copy numbers normalized to 16S rRNA genes) varied by over two log values. The ARG and MRGs concentrations were not significantly different between sample types, despite significant changes in dominant microbial taxa. The most abundant genera observed in the biofilm communities were Mycobacterium (0.2–70%), and β-lactam resistance genes bla TEM , bla SHV , and the integrase gene of class 1 integrons ( intI 1) were positively correlated with Mycobacterium . The detection of ARGs, MRGs, and class 1 integrons across all sample types within the pipe indicates that pipes themselves can serve as sources for ARGs in DWDS. Consequently, future work should investigate the role of pipe materials as well as corrosion inhibitors to determine how engineering decisions can mitigate ARGs in drinking water that stem from pipe materials.« less