Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting

For person re-identification, existing deep networks often focus on representation learning. However, without transfer learning, the learned model is fixed as is, which is not adaptable for handling various unseen scenarios. In this paper, beyond representation learning, we consider how to formulate person image matching directly in deep feature maps. We treat image matching as finding local correspondences in feature maps, and construct query-adaptive convolution kernels on the fly to achieve local matching. In this way, the matching process and results are interpretable, and this explicit matching is more generalizable than representation features to unseen scenarios, such as unknown misalignments, pose or viewpoint changes. To facilitate end-to-end training of this architecture, we further build a class memory module to cache feature maps of the most recent samples of each class, so as to compute image matching losses for metric learning. Through direct cross-dataset evaluation, the proposed Query-Adaptive Convolution (QAConv) method gains large improvements over popular learning methods (about 10%+ mAP), and achieves comparable results to many transfer learning methods. Besides, a model-free temporal cooccurrence based score weighting method called TLift is proposed, which improves the performance to a further extent, achieving state-of-the-art results in cross-dataset person re-identification. Code is available at https://github.com/ShengcaiLiao/QAConv.Comment: This is the ECCV 2020 version, including the appendi

DHX33 transcriptionally controls genes involved in the cell cycle

The RNA helicase DHX33 has been shown to be a critical regulator of cell proliferation and growth. However, the underlying mechanisms behind DHX33 function remain incompletely understood. We present original evidence in multiple cell lines that DHX33 transcriptionally controls the expression of genes involved in the cell cycle, notably cyclin, E2F1, cell division cycle (CDC), and minichromosome maintenance (MCM) genes. DHX33 physically associates with the promoters of these genes and controls the loading of active RNA polymerase II onto these promoters. DHX33 deficiency abrogates cell cycle progression and DNA replication and leads to cell apoptosis. In zebrafish, CRISPR-mediated knockout of DHX33 results in downregulation of cyclin A2, cyclin B2, cyclin D1, cyclin E2, cdc6, cdc20, E2F1, and MCM complexes in DHX33 knockout embryos. Additionally, we found the overexpression of DHX33 in a subset of non-small-cell lung cancers and in Ras-mutated human lung cancer cell lines. Forced reduction of DHX33 in these cancer cells abolished tumor formation in vivo. Our study demonstrates for the first time that DHX33 acts as a direct transcriptional regulator to promote cell cycle progression and plays an important role in driving cell proliferation during both embryo development and tumorigenesis

Residual Denoising Diffusion Models

We propose residual denoising diffusion models (RDDM), a novel dual diffusion process that decouples the traditional single denoising diffusion process into residual diffusion and noise diffusion. This dual diffusion framework expands the denoising-based diffusion models, initially uninterpretable for image restoration, into a unified and interpretable model for both image generation and restoration by introducing residuals. Specifically, our residual diffusion represents directional diffusion from the target image to the degraded input image and explicitly guides the reverse generation process for image restoration, while noise diffusion represents random perturbations in the diffusion process. The residual prioritizes certainty, while the noise emphasizes diversity, enabling RDDM to effectively unify tasks with varying certainty or diversity requirements, such as image generation and restoration. We demonstrate that our sampling process is consistent with that of DDPM and DDIM through coefficient transformation, and propose a partially path-independent generation process to better understand the reverse process. Notably, our RDDM enables a generic UNet, trained with only an $\ell _1$ loss and a batch size of 1, to compete with state-of-the-art image restoration methods. We provide code and pre-trained models to encourage further exploration, application, and development of our innovative framework (https://github.com/nachifur/RDDM)

Analysis of risk factors and short-term prognostic factors of arrhythmia in patients infected with mild/moderate SARS-CoV-2 Omicron variant

BackgroundComplications, including arrhythmia, following severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection continue to be of concern. Omicron is the mainstream SARS-CoV-2 mutant circulating in mainland China. At present, there are few epidemiological studies concerning the relationship between arrhythmia and Omicron variant infection in mainland China.ObjectivesTo investigate the risk factors of arrhythmia in patients infected with the SARS-CoV-2 Omicron variant and the factors influencing prognosis.MethodsData from 192 Omicron infected patients with symptoms of arrhythmia (AH group) and 100 Omicron infected patients without arrhythmia (Control group) were collected. Patients in the AH group were divided into the good and poor prognosis groups, according to the follow-up results 4–6 weeks after infection. The general and clinical data between the AH and Control groups, and between the good and poor prognosis groups were compared. The variables with differences between the groups were included in the multivariate logistic regression analysis, and the quantitative variables were analyzed by receiver operating characteristic curve to obtain their cut-off values.ResultsCompared with the control group, the body mass index (BMI), proportion of patients with a history of arrhythmia, proportion of antibiotics taken, heart rate, moderate disease severity, white blood cell (WBC) count, and the aspartate aminotransferase, creatine kinase (CK), CK isoenzyme (CK-MB), myoglobin (Mb), high-sensitive troponin I (hs-cTnI), lymphocyte ratio and high sensitivity C-reactive protein (hs-CRP) levels in the AH group were significantly higher (p < 0.05). In addition, obesity (BMI ≥24 kg/m2), fast heart rate (≥100 times/min), moderate disease severity, and WBC, CK-MB and hs-cTnI levels were independent risk factors of arrhythmia for patients with Omicron infection (p < 0.05), and hs-CRP was a protective factor (p < 0.05). Compared with the good prognosis group, the age, proportion of patients with a history of arrhythmia, heart rate, proportion of moderate disease severity, and hs-CRP, CK, Mb and hs-cTnI levels were significantly higher in the poor prognosis group, while the proportion of vaccination was lower in the poor prognosis group (p < 0.05). Advanced age (≥65 years old), proportion of history of arrhythmia, moderate disease severity, vaccination, and hs-CRP, Mb and cTnI levels were independent factors for poor prognosis of patients with arrhythmia (p < 0.05).ConclusionThe factors that affect arrhythmia and the prognosis of patients infected with Omicron include obesity, high heart rate, severity of the disease, age. history of arrhythmia, WBC, hs-CRP, and myocardial injury indexes, which could be used to evaluate and prevent arrhythmia complications in patients in the future