Cold Start Streaming Learning for Deep Networks

The ability to dynamically adapt neural networks to newly-available data without performance deterioration would revolutionize deep learning applications. Streaming learning (i.e., learning from one data example at a time) has the potential to enable such real-time adaptation, but current approaches i) freeze a majority of network parameters during streaming and ii) are dependent upon offline, base initialization procedures over large subsets of data, which damages performance and limits applicability. To mitigate these shortcomings, we propose Cold Start Streaming Learning (CSSL), a simple, end-to-end approach for streaming learning with deep networks that uses a combination of replay and data augmentation to avoid catastrophic forgetting. Because CSSL updates all model parameters during streaming, the algorithm is capable of beginning streaming from a random initialization, making base initialization optional. Going further, the algorithm's simplicity allows theoretical convergence guarantees to be derived using analysis of the Neural Tangent Random Feature (NTRF). In experiments, we find that CSSL outperforms existing baselines for streaming learning in experiments on CIFAR100, ImageNet, and Core50 datasets. Additionally, we propose a novel multi-task streaming learning setting and show that CSSL performs favorably in this domain. Put simply, CSSL performs well and demonstrates that the complicated, multi-step training pipelines adopted by most streaming methodologies can be replaced with a simple, end-to-end learning approach without sacrificing performance.Comment: 52 pages, 7 figures, pre-prin

Geographic Variability in Access to Primary Kidney Transplantation in the United States, 1996–2005

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75057/1/j.1600-6143.2007.01785.x.pd

DAS181 treatment of severe lower respiratory tract parainfluenza virus infection in immunocompromised patients: A phase 2 randomized, placebo-controlled study

BACKGROUND: There are no antiviral therapies for parainfluenza virus (PIV) infections. DAS181, a sialidase fusion protein, has demonstrated activity in in vitro and in animal models of PIV. METHODS: Adult immunocompromised patients diagnosed with PIV lower respiratory tract infection (LRTI) who required oxygen supplementation were randomized 2:1 to nebulized DAS181 (4.5 mg/day) or matching placebo for up to 10 days. Randomization was stratified by need for mechanical ventilation (MV) or supplemental oxygen (SO). The primary endpoint was the proportion of patients reaching clinical stability survival (CSS) defined as returning to room air (RTRA), normalization of vital signs for at least 24 hours, and survival up to day 45 from enrollment. RESULTS: A total of 111 patients were randomized to DAS181 (n = 74) or placebo (n = 37). CSS was achieved by 45.0% DAS181-treated patients in the SO stratum compared with 31.0% for placebo (P = .15), whereas patients on MV had no benefit from DAS181. The proportion of patients achieving RTRA was numerically higher for SO stratum DAS181 patients (51.7%) compared with placebo (34.5%) at day 28 (P = .17). In a post hoc analysis of solid organ transplant, hematopoietic cell transplantation within 1 year, or chemotherapy within 1 year, more SO stratum patients achieved RTRA on DAS181 (51.8%) compared with placebo (15.8%) by day 28 (P = .012). CONCLUSIONS: The primary endpoint was not met, but post hoc analysis of the RTRA component suggests DAS181 may have clinical activity in improving oxygenation in select severely immunocompromised patients with PIV LRTI who are not on mechanical ventilation. Clinical Trials Registration. NCT01644877

The GALEX Arecibo SDSS Survey II: The Star Formation Efficiency of Massive Galaxies

We use measurements of the HI content, stellar mass and star formation rates in ~190 massive galaxies with stellar masses greater than 10^10 Msun, obtained from the Galex Arecibo SDSS Survey (GASS) described in Paper I (Catinella et al. 2010) to explore the global scaling relations associated with the bin-averaged ratio of the star formation rate over the HI mass, which we call the HI-based star formation efficiency (SFE). Unlike the mean specific star formation rate, which decreases with stellar mass and stellar mass surface density, the star formation efficiency remains relatively constant across the sample with a value close to SFE = 10^-9.5 yr^-1 (or an equivalent gas consumption timescale of ~3 Gyr). Specifically, we find little variation in SFE with stellar mass, stellar mass surface density, NUV-r color and concentration. We interpret these results as an indication that external processes or feedback mechanisms that control the gas supply are important for regulating star formation in massive galaxies. An investigation into the detailed distribution of SFEs reveals that approximately 5% of the sample shows high efficiencies with SFE > 10^-9 yr^-1, and we suggest that this is very likely due to a deficiency of cold gas rather than an excess star formation rate. Conversely, we also find a similar fraction of galaxies that appear to be gas-rich for their given specific star-formation rate, although these galaxies show both a higher than average gas fraction and lower than average specific star formation rate. Both of these populations are plausible candidates for "transition" galaxies, showing potential for a change (either decrease or increase) in their specific star formation rate in the near future. We also find that 36+/-5% of the total HI mass density and 47+/-5% of the total SFR density is found in galaxies with stellar mass greater than 10^10 Msun. [abridged]Comment: 18 pages, 11 figures. Accepted for publication in MNRAS. GASS publications and released data can be found at http://www.mpa-garching.mpg.de/GASS/index.ph

A poisson regression approach for modelling spatial autocorrelation between geographically referenced observations

Abstract Background Analytic methods commonly used in epidemiology do not account for spatial correlation between observations. In regression analyses, omission of that autocorrelation can bias parameter estimates and yield incorrect standard error estimates. Methods We used age standardised incidence ratios (SIRs) of esophageal cancer (EC) from the Babol cancer registry from 2001 to 2005, and extracted socioeconomic indices from the Statistical Centre of Iran. The following models for SIR were used: (1) Poisson regression with agglomeration-specific nonspatial random effects; (2) Poisson regression with agglomeration-specific spatial random effects. Distance-based and neighbourhood-based autocorrelation structures were used for defining the spatial random effects and a pseudolikelihood approach was applied to estimate model parameters. The Bayesian information criterion (BIC), Akaike's information criterion (AIC) and adjusted pseudo R2, were used for model comparison. Results A Gaussian semivariogram with an effective range of 225 km best fit spatial autocorrelation in agglomeration-level EC incidence. The Moran's I index was greater than its expected value indicating systematic geographical clustering of EC. The distance-based and neighbourhood-based Poisson regression estimates were generally similar. When residual spatial dependence was modelled, point and interval estimates of covariate effects were different to those obtained from the nonspatial Poisson model. Conclusions The spatial pattern evident in the EC SIR and the observation that point estimates and standard errors differed depending on the modelling approach indicate the importance of accounting for residual spatial correlation in analyses of EC incidence in the Caspian region of Iran. Our results also illustrate that spatial smoothing must be applied with care.</p