Permutation Methods in Relative Risk Regression Models

In this paper, we develop a weighted permutation (WP) method to construct confidence intervals for regression parameters in relative risk regression models. The WP method is a generalized permutation approach. It constructs a resampled history which mimics the observed history for individuals under study. Inference procedures are based on studentized score statistics that are insensitive to the forms of the relative risk function. This makes the WP method appealing in the general framework of the relative risk regression model. First order accuracy of the WP method is established using the counting process approach with a partial likelihood filtration. A simulation study indicates that the method typically improves accuracy over asymptotic confidence intervals

Resampling methods for estimating functions with U-statistic structure

Suppose that inference about parameters of interest is to be based on an unbiased estimating function that is U-statistic of degree 1 or 2. We define suitable studentized versions of such estimating functions and consider asymptotic approximations as well as an estimating function bootstrap (EFB) method based on resampling the estimated terms in the estimating functions. These methods are justified asymptotically and lead to confidence intervals produced directly from the studentized estimating functions. Particular examples in this class of estimating functions arise in La estimation as well as Wilcoxon rank regression and other related estimation problems. The proposed methods are evaluated in examples and simulations and compared with a recent suggestion for inference in such problems which relies on resampling an underlying objective functions with U-statistic structure

Perceived Quality of Packet Audio under Bursty Losses

We examine the impact of bursty losses on the perceived quality of packet audio, and investigate the effectiveness various approaches to improve the quality. Because the degree of burstiness depends on the packet interval, we first derive a formula to re-compute the conditional loss probability of a Gilbert loss model when the packet interval changes. We find that FEC works better at a larger packet interval under bursty losses. In our MOS-based (Mean Opinion Score) listening tests, we did not find a consistent trend in MOS when burstiness increases if FEC is not used. That is, In some occasions MOS can be higher with a higher burstiness. With FEC, our results confirms the analytical results that quality is better with a larger packet interval, but T should not be too large to avoid severe penalty on a single packet loss. We also find that low bit-rate redundancy generally produces lower perceived quality than FEC, if the main codec is already a low bit-rate codec. Finally, we compare our MOS results with objective quality estimation algorithms (PESQ, PSQM/PSQM+, MNB and EMBSD). We find PESQ has the best linear correlation with MOS, but the value is still less than commonly cited, implying they cannot be used in isolation to predict MOS

DOS: Diverse Outlier Sampling for Out-of-Distribution Detection

Modern neural networks are known to give overconfident prediction for out-of-distribution inputs when deployed in the open world. It is common practice to leverage a surrogate outlier dataset to regularize the model during training, and recent studies emphasize the role of uncertainty in designing the sampling strategy for outlier dataset. However, the OOD samples selected solely based on predictive uncertainty can be biased towards certain types, which may fail to capture the full outlier distribution. In this work, we empirically show that diversity is critical in sampling outliers for OOD detection performance. Motivated by the observation, we propose a straightforward and novel sampling strategy named DOS (Diverse Outlier Sampling) to select diverse and informative outliers. Specifically, we cluster the normalized features at each iteration, and the most informative outlier from each cluster is selected for model training with absent category loss. With DOS, the sampled outliers efficiently shape a globally compact decision boundary between ID and OOD data. Extensive experiments demonstrate the superiority of DOS, reducing the average FPR95 by up to 25.79% on CIFAR-100 with TI-300K

Integrating Transaction Services into Web-based Software Development Environments

Software Development Environments (SDE) require sophisticated database transaction models due to the long-duration,interactive, and cooperative nature of the software engineering activities. Such Extended Transaction Models (ETM) have been proposed and implemented by building application-specific databases for the SDEs. With the development of World Wide Web (WWW), there have been a number of efforts to build SDEs on top of the WWW. Using web servers as the databases to store the software artifacts provided us with a new challenge: how to implement the ETMs in such web-based SDEs without requiring the web servers to be customized specifically according to the application domains of the SDEs. This paper presents our experiences of integrating transaction services into web based SDEs. We evolved from the traditional approach of building a transaction management component that operated on top of a dedicated database to the external transaction server approach. A transaction server, called JPernLite, was built to operate independently of the web servers and provide the necessary extensibility for SDEs to implement their ETMs. The transaction server can be integrated into the SDE via a number of interfaces, and we discuss the pros and cons of each alternative in detail

Modifier loci condition autoimmunity provoked by Aire deficiency

Loss of function mutations in the autoimmune regulator (Aire) gene in autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy patients and mutant mice lead to autoimmune manifestations that segregate as a monogenic trait, but with wide variation in the spectrum of organs targeted. To investigate the cause of this variability, the Aire knockout mutation was backcrossed to mice of diverse genetic backgrounds. The background loci strongly influenced the pattern of organs that were targeted (stomach, eye, pancreas, liver, ovary, thyroid, and salivary gland) and the severity of the targeting (particularly strong on the nonobese diabetic background, but very mild on the C57BL/6 background). Autoantibodies mimicked the disease pattern, with oligoclonal reactivity to a few antigens that varied between Aire-deficient strains. Congenic analysis and a whole genome scan showed that autoimmunity to each organ had a distinctive pattern of genetic control and identified several regions that controlled the pattern of targeting, including the major histocompatibility complex and regions of Chr1 and Chr3 previously identified in controlling type 1 diabetes

Thymic negative selection is functional in NOD mice

Based on analyses of multiple TCR transgenic (tg) models, the emergence of pathogenic T cells in diabetes-prone NOD mice has been ascribed to a failure to censure autoreactive clones in the thymus. In contrast, using isolated and preselected thymocytes, we show that nonobese diabetic (NOD) genetic variation impairs neither clonal deletion nor downstream transcriptional programs. However, we find that NOD genetic variation influences αβ/γδ-lineage decisions promoted by early expression of tg αβ-TCRs at the double-negative (DN) stage. In B6 and other genetic backgrounds, tg αβ-TCRs behave like γδ-TCRs and commit a large fraction of DNs toward the γδ-lineage, thereby decreasing the size of the double-positive (DP) pool, which is efficiently positively and negatively selected. In NOD DNs, αβ-TCR signalosomes instead behave like pre-TCRs, resulting in high numbers of DPs competing for limited selection niches, and poor positive and negative selection. Once niche effects are neutralized in mixed bone marrow chimeras, positive and negative selection are equally efficient on B6 and NOD backgrounds. Biochemical analysis revealed a selective defect in the activation of Erk1/2 downstream of NOD αβ-TCR signalosomes. Therefore, NOD genetic variation influences αβ/γδ-lineage decisions when the αβ-TCR heterodimer is prematurely expressed, but not the process of negative selection