Variations in visual factors such as viewpoint, pose, illumination and background, are usually viewed as important challenges in person re-identification (re-ID). In spite of acknowledging these factors to be influential, quantitative studies on how they affect a re-ID system are still lacking. To derive insights in this scientific campaign, this paper makes an early attempt in studying a particular factor, viewpoint. We narrow the viewpoint problem down to the pedestrian rotation angle to obtain focused conclusions. In this regard, this paper makes two contributions to the community. First, we introduce a large-scale synthetic data engine, PersonX. Composed of hand-crafted 3D person models, the salient characteristic of this engine is "controllable". That is, we are able to synthesize pedestrians by setting the visual variables to arbitrary values. Second, on the 3D data engine, we quantitatively analyze the influence of pedestrian rotation angle on re-ID accuracy. Comprehensively, the person rotation angles are precisely customized from 0 • to 360 • , allowing us to investigate its effect on the training, query, and gallery sets. Extensive experiment helps us have a deeper understanding of the fundamental problems in person re-ID. Our research also provides useful insights for dataset building and future practical usage, e.g., a person of a side view makes a better query.

Skin disease is one of the most common human illnesses. It pervades all cultures, occurs at all ages, and affects between 30 % and 70 % of individuals, with even higher rates in at-risk. However, diagnosis of skin diseases by observing is a very difficult job for both doctors and patients, where an intelligent system can be helpful. In this paper, we mainly introduce a benchmark dataset for clinical skin diseases to address this problem. To the best of our knowledge, this dataset is currently the largest for visual recognition of skin diseases. It contains 6,584 images from 198 classes, varying according to scale, color, shape and structure. We hope that this benchmark dataset will encourage further research on visual skin disease classification. Moreover, the recent successes of many computer vision related tasks are due to the adoption of Convolutional Neural Networks(CNNs), we also perform extensive analyses on this dataset using the state of the art methods including CNNs.

BACKGROUND: Information is limited on messenger RNA (mRNA) BNT162b2 (Pfizer–BioNTech) and mRNA–1273 (Moderna) COVID–19 vaccine effectiveness (VE) in preventing SARS–CoV–2 infection or attenuating disease when administered in real–world conditions. METHODS: Prospective cohorts of 3,975 healthcare personnel, first responders, and other essential and frontline workers completed weekly SARS–CoV–2 testing during December 14 2020—April 10 2021. Self–collected mid–turbinate nasal swabs were tested by qualitative and quantitative reverse–transcription—polymerase–chain–reaction (RT–PCR). VE was calculated as 100%× (1−hazard ratio); adjusted VE was calculated using vaccination propensity weights and adjustments for site, occupation, and local virus circulation . RESULTS: SARS–CoV–2 was detected in 204 (5.1%) participants; 16 were partially (≥14 days post–dose–1 to 13 days after dose–2) or fully (≥14 days post–dose–2) vaccinated, and 156 were unvaccinated; 32 with indeterminate status (<14 days after dose–1) were excluded. Adjusted mRNA VE of full vaccination was 91% (95% confidence interval [CI]=76%—97%) against symptomatic or asymptomatic SARS–CoV–2 infection; VE of partial vaccination was 81% (95% CI=64%–90%). Among partially or fully vaccinated participants with SARS–CoV–2 infection, mean viral RNA load (Log10 copies/mL) was 40% lower (95% CI=16%–57%), the risk of self–reported febrile COVID–19 was 58% lower (Risk Ratio=0.42, 95% CI=0.18–0.98), and 2.3 fewer days (95% CI=0.8–3.7) were spent sick in bed compared to unvaccinated infected participants. CONCLUSIONS: Authorized mRNA vaccines were highly effective among working–age adults in preventing SARS–CoV–2 infections when administered in real–world conditions and attenuated viral RNA load, febrile symptoms, and illness duration among those with breakthrough infection despite vaccination.

The development of whole-genome bisulfite sequencing (WGBS) has resulted in a number of exciting discoveries about the role of DNA methylation leading to a plethora of novel testable hypotheses. Methods for constructing sodium bisulfite-converted and amplified libraries have recently advanced to the point that the bottleneck for experiments that use WGBS has shifted to data analysis and interpretation. Here we present empirical evidence for an over-representation of reads from methylated DNA in WGBS. This enrichment for methylated DNA is exacerbated by higher cycles of PCR and is influenced by the type of uracil-insensitive DNA polymerase used for amplifying the sequencing library. Future efforts to computationally correct for this enrichment bias will be essential to increasing the accuracy of determining methylation levels for individual cytosines. It is especially critical for studies that seek to accurately quantify DNA methylation levels in populations that may segregate for allelic DNA methylation states.

Many scientific studies collect data where the response and predictor variables are both functions of time, location, or some other covariate. Understanding the relationship between these functional variables is a common goal in these studies. Motivated from two real-life examples, we present in this paper a function-on-function regression model that can be used to analyze such kind of functional data. Our estimator of the 2D coefficient function is the optimizer of a form of penalized least squares where the penalty enforces a certain level of smoothness on the estimator. Our first result is the Representer Theorem which states that the exact optimizer of the penalized least squares actually resides in a data-adaptive finite dimensional subspace although the optimization problem is defined on a function space of infinite dimensions. This theorem then allows us an easy incorporation of the Gaussian quadrature into the optimization of the penalized least squares, which can be carried out through standard numerical procedures. We also show that our estimator achieves the minimax convergence rate in mean prediction under the framework of function-on-function regression. Extensive simulation studies demonstrate the numerical advantages of our method over the existing ones, where a sparse functional data extension is also introduced. The proposed method is then applied to our motivating examples of the benchmark Canadian weather data and a histone regulation study.