Unsupervised Out-of-Distribution Detection with Diffusion Inpainting

Unsupervised out-of-distribution detection (OOD) seeks to identify out-of-domain data by learning only from unlabeled in-domain data. We present a novel approach for this task - Lift, Map, Detect (LMD) - that leverages recent advancement in diffusion models. Diffusion models are one type of generative models. At their core, they learn an iterative denoising process that gradually maps a noisy image closer to their training manifolds. LMD leverages this intuition for OOD detection. Specifically, LMD lifts an image off its original manifold by corrupting it, and maps it towards the in-domain manifold with a diffusion model. For an out-of-domain image, the mapped image would have a large distance away from its original manifold, and LMD would identify it as OOD accordingly. We show through extensive experiments that LMD achieves competitive performance across a broad variety of datasets. Code can be found at https://github.com/zhenzhel/lift_map_detect.Comment: ICML 202

Simulation of Particle Mixing and Separation in Multi-Component Fluidized Bed Using Eulerian-Eulerian Method: A Review

In practical engineering applications, the mixing and separation behavior of multi-component particles is importance to the fluidized bed operation. The development of many practical processes is inseparable from the knowledge of particle mixing and separation, such as material processing of ash-soluble coal gasification, multi-phase flow in boilers, and petrochemical catalytic processes. In recent years, due to the obvious advantages of the Eulerian–Eulerian model, many researchers at home and abroad have used it to study the mixing and separation behavior of particles. The paper reviews the use of Eulerian–Eulerian model to study the mixing and separation of multi-component particles in fluidized beds. The Eulerian–Eulerian model describes the gas-phase and each of the individual particles as continuums. The mechanism of particle mixing and separation, the influence of different factors on the particle mixing and separation including differences in particle size and density, the differences in apparent air velocity, the differences in model factors are discussed. Finally, an outlook for the use of Eulerian–Eulerian model to study the mixing and separation behavior of three component particles and related research on the drag model between particles

UCF-Crime Annotation: A Benchmark for Surveillance Video-and-Language Understanding

Surveillance videos are an essential component of daily life with various critical applications, particularly in public security. However, current surveillance video tasks mainly focus on classifying and localizing anomalous events. Existing methods are limited to detecting and classifying the predefined events with unsatisfactory generalization ability and semantic understanding, although they have obtained considerable performance. To address this issue, we propose constructing the first multimodal surveillance video dataset by manually annotating the real-world surveillance dataset UCF-Crime with fine-grained event content and timing. Our newly annotated dataset, UCA (UCF-Crime Annotation), provides a novel benchmark for multimodal surveillance video analysis. It not only describes events in detailed descriptions but also provides precise temporal grounding of the events in 0.1-second intervals. UCA contains 20,822 sentences, with an average length of 23 words, and its annotated videos are as long as 102 hours. Furthermore, we benchmark the state-of-the-art models of multiple multimodal tasks on this newly created dataset, including temporal sentence grounding in videos, video captioning, and dense video captioning. Through our experiments, we found that mainstream models used in previously publicly available datasets perform poorly on multimodal surveillance video scenarios, which highlights the necessity of constructing this dataset. The link to our dataset and code is provided at: https://github.com/Xuange923/UCA-dataset

IL-17A Upregulates Keratin 17 Expression in Keratinocytes through STAT1- and STAT3-Dependent Mechanisms

Psoriasis, an immunological skin disease, is characterized by epidermal hyperproliferation, chronic inflammation, and an accumulation of infiltrating T cells. IL-17A is a key cytokine that has a critical role in the pathogenesis of psoriasis. Keratin 17 (K17) is strongly expressed in psoriatic lesions but not in normal skin. Thus, K17 expression is regarded as a hallmark of psoriasis. We previously reported that the K17/T cells/cytokine autoimmune loop was involved in psoriasis. However, the relationship between IL-17A and K17 has yet to be determined. In the present study, IL-17A-induced K17 expression was confirmed in cultured keratinocytes in both mRNA and protein levels. In addition, increased K17 expression was found in the epidermis of IL-17A-injected mouse skin. The regulatory mechanism of K17 expression was further investigated. We found that both the signal transducer and activator of transcription (STAT) 1 and STAT3 pathways were involved in the upregulation of K17 expression induced by IL-17A, and that such regulation could be partially suppressed by STAT1 or STAT3 small interfering RNA and inhibitor. Our data suggest that IL-17A can upregulate K17 expression in keratinocytes in a dose-dependent manner through STAT1- and STAT3-dependent mechanisms. The results indicate that IL-17A might be an important cytokine in the K17/T cells/cytokine autoimmune loop associated with psoriasis

Correction with Backtracking Reduces Hallucination in Summarization

Abstractive summarization aims at generating natural language summaries of a source document that are succinct while preserving the important elements. Despite recent advances, neural text summarization models are known to be susceptible to hallucinating (or more correctly confabulating), that is to produce summaries with details that are not grounded in the source document. In this paper, we introduce a simple yet efficient technique, CoBa, to reduce hallucination in abstractive summarization. The approach is based on two steps: hallucination detection and mitigation. We show that the former can be achieved through measuring simple statistics about conditional word probabilities and distance to context words. Further, we demonstrate that straight-forward backtracking is surprisingly effective at mitigation. We thoroughly evaluate the proposed method with prior art on three benchmark datasets for text summarization. The results show that CoBa is effective and efficient in reducing hallucination, and offers great adaptability and flexibility

Time trends and heterogeneity in the disease burden of visual impairment due to cataract, 1990–2019: A global analysis

ObjectivesThis study aimed to estimate the disease burden of cataract and evaluate the contributions of risk factors to cataract-associated disability-adjusted life years (DALYs).Materials and methodsPrevalence and DALYs of visual impairment due to cataract were extracted from the Global Burden of Disease (GBD) study 2019 to explore time trends and annual changes. Regional and country-level socioeconomic indexes were obtained from open databases. The time trend of prevalence and DALYs was demonstrated. Stepwise multiple linear regression was used to evaluate associations between the age-standardized rate of DALYs of cataract and potential predictors.ResultsGlobal Prevalence rate of visual impairment due to cataract rose by 58.45% to 1,253.9 per 100,000 population (95% CI: 1,103.3 to 1,417.7 per 100,000 population) in 2019 and the DALYs rate rose by 32.18% from 65.3 per 100,000 population (95% CI: 46.4 to 88.2 per 100,000 population) in 1990 to 86.3 per 100,000 population (95% CI: 61.5 to 116.4 per 100,000 population) in 2019. Stepwise multiple linear regression model showed that higher refractive error prevalence (β = 0.036, 95% CI: 0.022, 0.050, P < 0.001), lower number of physicians per 10,000 population (β = −0.959, 95% CI: −1.685, −0.233, P = 0.010), and lower level of HDI (β = −134.93, 95% CI: −209.84, −60.02, P = 0.001) were associated with a higher disease burden of cataract.ConclusionSubstantial increases in the prevalence of visual impairment and DALYs of cataract were observed from 1990 to 2019. Successful global initiatives targeting improving cataract surgical rate and quality, especially in regions with lower socioeconomic status, is a prerequisite to combating this growing burden of cataract in the aging society

Defining and Testing a Safety Cognition Framework Incorporating Safety Hazard Perception

There has been insufficient research focusing on checking the reliability of construction employees’ hazard perceptions by comparing them with empirical safety data. There have also been limited studies focusing on how site employees’ perceptions could be affected by multiple external and internal factors such as workers’ experience levels. This study firstly developed a theoretical safety cognition framework addressing site employees’ perceptions toward hazards. Empirical data from construction safety reports in China were collected to quantify and define eight common safety hazard or accident scenarios. Following the empirical data analysis, these eight hazards were ranked in terms of their occurrence, severity, and controllability. Based on the results of questionnaires received in China’s construction industry, site employees’ perceptions toward these eight hazards were analyzed, ranked, and compared with the empirical ranking. Major findings included but were not limited to: (1) hazards with higher occurrences tended to be perceived with less deviation; (2) more experienced employees were more likely to apply prior scenarios and safety knowledge to perceiving given hazards and to have more reliable perceptions; and (3) prior scenarios might also create biased perceptions in the case of electrocution. The current study contributed to the knowledge in safety climate by proposing and testing the framework incorporating safety perceptions. Further research could be performed to explore more subgroup factors’ effects on workers’ perceptions, as well as how to design an effective safety training program to correct their biased perceptions

Molecular Pathways Mediating Immunosuppression in Response to Prolonged Intensive Physical Training, Low-Energy Availability, and Intensive Weight Loss

Exercise and exercise-induced weight loss have a beneficial effect on overall health, including positive effects on molecular pathways associated with immune function, especially in overweight individuals. The main aim of our study was to assess how energy deprivation (i.e., "semi-starvation") leading to substantial fat mass loss affects the immune system and immunosuppression in previously normal weight individuals. Thus, to address this hypothesis, we applied a high-throughput systems biology approach to better characterize potential key pathways associated with immune system modulation during intensive weight loss and subsequent weight regain. We examined 42 healthy female physique athletes (age 27.5 +/- 4.0 years, body mass index 23.4 +/- 1.7 kg/m(2)) volunteered into either a diet group (n = 25) or a control group (n = 17). For the diet group, the energy intake was reduced and exercise levels were increased to induce loss of fat mass that was subsequently regained during a recovery period. The control group was instructed to maintain their typical lifestyle, exercise levels, and energy intake at a constant level. For quantification of systems biology markers, fasting blood samples were drawn at three time points: baseline (PRE), at the end of the weight loss period (MID 21.1 +/- 3.1 weeks after PRE), and at the end of the weight regain period (POST 18.4 +/- 2.9 weeks after MID). In contrast to the control group, the diet group showed significant (false discovery ratePeer reviewe

Evolutionary history of two evergreen Rhododendron species as revealed by chromosome-level genome assembly

BackgroundThe genus Rhododendron (Ericaceae), a species-rich and widely distributed genus of woody plants, is distinguished for the beautiful and diverse flowers. Rhododendron delavayi Franch. and Rhododendron irroratum Franch., are highly attractive species widely distributed in south-west China and abundant new varieties have been selected from their genetic resources.MethodsWe constructed chromosome-scale genome assemblies for Rhododendron delavayi and Rhododendron irroratum. Phylogenetic and whole-genome duplication analyses were performed to elucidate the evolutionary history of Rhododendron. Further, different types of gene duplications were identified and their contributions to gene family expansion were investigated. Finally, comprehensive characterization and evolutionary analysis of R2R3-MYB and NBS-encoding genes were conducted to explore their evolutionary patterns.ResultsThe phylogenetic analysis classified Rhododendron species into two sister clades, ‘rhododendrons’ and ‘azaleas’. Whole-genome duplication (WGD) analysis unveiled only one WGD event that occurred in Rhododendron after the ancestral γ triplication. Gene duplication and gene family expansion analyses suggested that the younger tandem and proximal duplications contributed greatly to the expansion of gene families involved in secondary metabolite biosynthesis and stress response. The candidate R2R3-MYB genes likely regulating anthocyanin biosynthesis and stress tolerance in Rhododendron will facilitate the breeding for ornamental use. NBS-encoding genes had undergone significant expansion and experienced species-specific gain and loss events in Rhododendron plants.ConclusionsThe reference genomes presented here will provide important genetic resources for molecular breeding and genetic improvement of plants in this economically important Rhododendron genus