A Covariate-Adjusted Homogeneity Test with Application to Facial Recognition Accuracy Assessment

Ordinal scores occur commonly in medical imaging studies and in black-box forensic studies \citep{Phillips:2018}. To assess the accuracy of raters in the studies, one needs to estimate the receiver operating characteristic (ROC) curve while accounting for covariates of raters. In this paper, we propose a covariate-adjusted homogeneity test to determine differences in accuracy among multiple rater groups. We derived the theoretical results of the proposed test and conducted extensive simulation studies to evaluate the finite sample performance of the proposed test. Our proposed test is applied to a face recognition study to identify statistically significant differences among five participant groups

Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification

Person re-identification has seen significant advancement in recent years. However, the ability of learned models to generalize to unknown target domains still remains limited. One possible reason for this is the lack of large-scale and diverse source training data, since manually labeling such a dataset is very expensive and privacy sensitive. To address this, we propose to automatically synthesize a large-scale person re-identification dataset following a set-up similar to real surveillance but with virtual environments, and then use the synthesized person images to train a generalizable person re-identification model. Specifically, we design a method to generate a large number of random UV texture maps and use them to create different 3D clothing models. Then, an automatic code is developed to randomly generate various different 3D characters with diverse clothes, races and attributes. Next, we simulate a number of different virtual environments using Unity3D, with customized camera networks similar to real surveillance systems, and import multiple 3D characters at the same time, with various movements and interactions along different paths through the camera networks. As a result, we obtain a virtual dataset, called RandPerson, with 1,801,816 person images of 8,000 identities. By training person re-identification models on these synthesized person images, we demonstrate, for the first time, that models trained on virtual data can generalize well to unseen target images, surpassing the models trained on various real-world datasets, including CUHK03, Market-1501, DukeMTMC-reID, and almost MSMT17. The RandPerson dataset is available at this https URL

Recognizing people from dynamic and static faces and bodies: Dissecting identity with a fusion approach

AbstractThe goal of this study was to evaluate human accuracy at identifying people from static and dynamic presentations of faces and bodies. Participants matched identity in pairs of videos depicting people in motion (walking or conversing) and in “best” static images extracted from the videos. The type of information presented to observers was varied to include the face and body, the face-only, and the body-only. Identification performance was best when people viewed the face and body in motion. There was an advantage for dynamic over static stimuli, but only for conditions that included the body. Control experiments with multiple-static images indicated that some of the motion advantages we obtained were due to seeing multiple images of the person, rather than to the motion, per se. To computationally assess the contribution of different types of information for identification, we fused the identity judgments from observers in different conditions using a statistical learning algorithm trained to optimize identification accuracy. This fusion achieved perfect performance. The condition weights that resulted suggest that static displays encourage reliance on the face for recognition, whereas dynamic displays seem to direct attention more equitably across the body and face

The challenge of face recognition from digital point-and-shoot cameras

Inexpensive “point-and-shoot ” camera technology has combined with social network technology to give the gen-eral population a motivation to use face recognition tech-nology. Users expect a lot; they want to snap pictures, shoot videos, upload, and have their friends, family and acquain-tances more-or-less automatically recognized. Despite the apparent simplicity of the problem, face recognition in this context is hard. Roughly speaking, failure rates in the 4 to 8 out of 10 range are common. In contrast, error rates drop to roughly 1 in 1,000 for well controlled imagery. To spur advancement in face and person recognition this pa-per introduces the Point-and-Shoot Face Recognition Chal-lenge (PaSC). The challenge includes 9,376 still images of 293 people balanced with respect to distance to the cam-era, alternative sensors, frontal versus not-frontal views, and varying location. There are also 2,802 videos for 265 people: a subset of the 293. Verification results are pre-sented for public baseline algorithms and a commercial al-gorithm for three cases: comparing still images to still im-ages, videos to videos, and still images to videos. 1

American Society of Transplantation and Cellular Therapy, Center of International Blood and Marrow Transplant Research, and European Society for Blood and Marrow Transplantation Clinical Practice Recommendations for Transplantation and Cellular Therapies in Mantle Cell Lymphoma

Autologous (auto-) and allogeneic (allo-) hematopoietic cell transplantation (HCT) are accepted treatment modalities in contemporary treatment algorithms for mantle cell lymphoma (MCL). Chimeric antigen receptor (CAR) T cell therapy recently received approval for MCL; however, its exact place and sequence in relation to HCT remain unclear. The American Society of Transplantation and Cellular Therapy, Center of International Blood and Marrow Transplant Research, and the European Society for Blood and Marrow Transplantation jointly convened an expert panel to formulate consensus recommendations for role, timing, and sequencing of auto-HCT, allo-HCT, and CAR T cell therapy for patients with newly diagnosed and relapsed/refractory (R/R) MCL. The RAND-modified Delphi method was used to generate consensus statements. Seventeen consensus statements were generated, with a few key statements as follows: in the first line setting, auto-HCT consolidation represents standard of care in eligible patients, whereas there is no clear role of allo-HCT or CAR T cell therapy outside of clinical trials. In the R/R setting, the preferential option is CAR T cell therapy, especially in patients with MCL failing or intolerant to at least one Bruton's tyrosine kinase inhibitor, while allo-HCT is recommended if CAR T cell therapy fails or is infeasible. Several recommendations were based on expert opinion, where the panel developed consensus statements for important real-world clinical scenarios to guide clinical practice. In the absence of contemporary evidence-based data, the panel found RAND-modified Delphi methodology effective in providing a formal framework for developing consensus recommendations for the timing and sequence of cellular therapies for MCL

HLA-DQA1*05 carriage associated with development of anti-drug antibodies to infliximab and adalimumab in patients with Crohn's Disease

Anti-tumor necrosis factor (anti-TNF) therapies are the most widely used biologic drugs for treating immune-mediated diseases, but repeated administration can induce the formation of anti-drug antibodies. The ability to identify patients at increased risk for development of anti-drug antibodies would facilitate selection of therapy and use of preventative strategies.This article is freely available via Open Access. Click on Publisher URL to access the full-text

Crowdsourcing hypothesis tests: Making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer fiveoriginal research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams renderedstatistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.</div