Improving Scene Graph Generation with Superpixel-Based Interaction Learning

Recent advances in Scene Graph Generation (SGG) typically model the relationships among entities utilizing box-level features from pre-defined detectors. We argue that an overlooked problem in SGG is the coarse-grained interactions between boxes, which inadequately capture contextual semantics for relationship modeling, practically limiting the development of the field. In this paper, we take the initiative to explore and propose a generic paradigm termed Superpixel-based Interaction Learning (SIL) to remedy coarse-grained interactions at the box level. It allows us to model fine-grained interactions at the superpixel level in SGG. Specifically, (i) we treat a scene as a set of points and cluster them into superpixels representing sub-regions of the scene. (ii) We explore intra-entity and cross-entity interactions among the superpixels to enrich fine-grained interactions between entities at an earlier stage. Extensive experiments on two challenging benchmarks (Visual Genome and Open Image V6) prove that our SIL enables fine-grained interaction at the superpixel level above previous box-level methods, and significantly outperforms previous state-of-the-art methods across all metrics. More encouragingly, the proposed method can be applied to boost the performance of existing box-level approaches in a plug-and-play fashion. In particular, SIL brings an average improvement of 2.0% mR (even up to 3.4%) of baselines for the PredCls task on Visual Genome, which facilitates its integration into any existing box-level method

Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations

Most video-and-language representation learning approaches employ contrastive learning, e.g., CLIP, to project the video and text features into a common latent space according to the semantic similarities of text-video pairs. However, such learned shared latent spaces are not often optimal, and the modality gap between visual and textual representation can not be fully eliminated. In this paper, we propose Expectation-Maximization Contrastive Learning (EMCL) to learn compact video-and-language representations. Specifically, we use the Expectation-Maximization algorithm to find a compact set of bases for the latent space, where the features could be concisely represented as the linear combinations of these bases. Such feature decomposition of video-and-language representations reduces the rank of the latent space, resulting in increased representing power for the semantics. Extensive experiments on three benchmark text-video retrieval datasets prove that our EMCL can learn more discriminative video-and-language representations than previous methods, and significantly outperform previous state-of-the-art methods across all metrics. More encouragingly, the proposed method can be applied to boost the performance of existing approaches either as a jointly training layer or an out-of-the-box inference module with no extra training, making it easy to be incorporated into any existing methods.Comment: Accepted to NeurIPS 202

GPT-4V(ision) as A Social Media Analysis Engine

Recent research has offered insights into the extraordinary capabilities of Large Multimodal Models (LMMs) in various general vision and language tasks. There is growing interest in how LMMs perform in more specialized domains. Social media content, inherently multimodal, blends text, images, videos, and sometimes audio. Understanding social multimedia content remains a challenging problem for contemporary machine learning frameworks. In this paper, we explore GPT-4V(ision)'s capabilities for social multimedia analysis. We select five representative tasks, including sentiment analysis, hate speech detection, fake news identification, demographic inference, and political ideology detection, to evaluate GPT-4V. Our investigation begins with a preliminary quantitative analysis for each task using existing benchmark datasets, followed by a careful review of the results and a selection of qualitative samples that illustrate GPT-4V's potential in understanding multimodal social media content. GPT-4V demonstrates remarkable efficacy in these tasks, showcasing strengths such as joint understanding of image-text pairs, contextual and cultural awareness, and extensive commonsense knowledge. Despite the overall impressive capacity of GPT-4V in the social media domain, there remain notable challenges. GPT-4V struggles with tasks involving multilingual social multimedia comprehension and has difficulties in generalizing to the latest trends in social media. Additionally, it exhibits a tendency to generate erroneous information in the context of evolving celebrity and politician knowledge, reflecting the known hallucination problem. The insights gleaned from our findings underscore a promising future for LMMs in enhancing our comprehension of social media content and its users through the analysis of multimodal information

Heterotic Trait Locus (HTL) Mapping Identifies Intra-Locus Interactions That Underlie Reproductive Hybrid Vigor in Sorghum bicolor

Identifying intra-locus interactions underlying heterotic variation among whole-genome hybrids is a key to understanding mechanisms of heterosis and exploiting it for crop and livestock improvement. In this study, we present the development and first use of the heterotic trait locus (HTL) mapping approach to associate specific intra-locus interactions with an overdominant heterotic mode of inheritance in a diallel population using Sorghum bicolor as the model. This method combines the advantages of ample genetic diversity and the possibility of studying non-additive inheritance. Furthermore, this design enables dissecting the latter to identify specific intra-locus interactions. We identified three HTLs (3.5% of loci tested) with synergistic intra-locus effects on overdominant grain yield heterosis in 2 years of field trials. These loci account for 19.0% of the heterotic variation, including a significant interaction found between two of them. Moreover, analysis of one of these loci (hDPW4.1) in a consecutive F2 population confirmed a significant 21% increase in grain yield of heterozygous vs. homozygous plants in this locus. Notably, two of the three HTLs for grain yield are in synteny with previously reported overdominant quantitative trait loci for grain yield in maize. A mechanism for the reproductive heterosis found in this study is suggested, in which grain yield increase is achieved by releasing the compensatory tradeoffs between biomass and reproductive output, and between seed number and weight. These results highlight the power of analyzing a diverse set of inbreds and their hybrids for unraveling hitherto unknown allelic interactions mediating heterosis

Measurement of peripheral blood Treg, Tr1, and Th17 cells in patients with chronic hepatitis B and its clinical significance

ObjectiveTo investigate the expression characteristics of Treg, Tr1, and Th17 cells in the peripheral blood of patients with chronic hepatitis B (CHB) and their correlations with the state of CHB. MethodsA total of 40 patients with mild-to-moderate CHB and 40 patients with severe CHB who were admitted to The 180th Hospital of PLA from January 2013 to June 2014 were included into this study, and 40 healthy people with normal liver function were recruited as control group. Peripheral blood was collected from CHB patients and healthy controls, and then the percentages of Treg, Tr1, and Th17 cells in peripheral blood were determined by flow cytometry. The levels of Foxp3 mRNA and RORγt mRNA in serum were measured by real-time PCR, and the expression levels of TGF-β1, IL-10, and IL-17 were measured by ELISA. Comparison of continuous data between the groups was made by one-way ANOVA analysis and LSD-t test. Results(1)Compared with the control group, the two groups of CHB patients had significantly higher percentages of Tr1, Treg, and Th17 cells (P＜0.05), and had significantly higher levels of Foxp3 mRNA and RORγt mRNA (P＜0.05) and levels of TGF-β1, IL-10, and IL-17 (P＜0.05), while the two groups of CHB patients had significantly lower Treg/Th17 ratio (P＜0.05) and Foxp3/RORγt ratio (P＜0.05) than the healthy controls. (2)The patients with severe CHB had significantly higher percentages of Treg, Tr1, and Th17 cells (P＜0.05), and had significantly higher levels of Foxp3 mRNA and RORγt mRNA (P＜0.05) and levels of TGF-β1, IL-10, and IL-17 (P＜0.05), as compared with the patients with mild-to-moderate CHB, while the patients with severe CHB had significantly lower Treg/Th17 ratio (P＜0.05) and Foxp3/RORγt ratio (P＜0.05) than those with mild-to-moderate CHB. ConclusionThe expression imbalance of Treg cells (especially Tr1 cells) and Th17 cells in CHB patients is positively correlated with the progression of the disease; therefore, it may be important to correct the expression imbalance of Treg cells and Th17 cells in the treatment of CHB. Meanwhile, monitoring the percentages of Treg cells (especially Tr1 cells) and Th17 cells is helpful for assessing the clinical outcomes of CHB patients and guiding the clinical treatment

Formulation of Organic Fertilizer Standard in Jiaxing City Based on NY525—2012

On the basis of research, analysis and argumentation, we conduct analysis and research on the current situation of quality of commercial organic fertilizer in Jiaxing City, and carry out the inspection and testing of organic matter indicators using the laboratory. Based on the standard of NY525-2012 Organic Fertilizer by the Ministry of Agriculture, this paper formulates the quality level indicators of local commercial organic fertilizer and adding specifications of raw materials

UCP1 modulates immune infiltration level and survival outcome in ovarian cancer patients

Abstract Background The uncoupling proteins (UCPs) are critical genes associated with tumorigenesis and chemoresistance. However, little is known about the molecular mechanism of the UCPs in ovarian cancer (OV). Material and methods UCPs expression analysis was conducted using Gene Expression Profiling Interactive Analysis (GEPIA), and its potential in clinical prognosis was analyzed using Kaplan- Meier analyses. The influence of UCPs on immune infiltration was analyzed by TIMER. In addition, the correlation between UCPs expression and molecular mechanisms was investigated by TIMER and Cancer Single-cell State Atlas (CancerSEA). Results UCP1, UCP2, UCP3 and UCP5 expression levels correlated with a favorable prognosis and tumor progression. Moreover, UCP1 expression correlated to several immune cell markers and regulated tumorigenesis, such as tumor invasion, EMT, metastasis and DNA repair. In addition, UCP1 potentially involved in genes expression of SNAI2, MMP2, BRCA1 and PARP1. Conclusions These results implied a critical role of UCP1 in the prognosis and immune infiltration of ovarian cancer. In addition, UCP1 expression participated in regulating multiple oncogenes and tumorigenesis

Age affects the association between socioeconomic status and infertility: a cross-sectional study

Abstract Background Previous studies have shown the interaction between age and socioeconomic status (SES) on the risk of infertility in the UK, but the association is still unclear in the United States. Therefore, the present study investigated the effect of age on the relationship between SES and the risk of infertility in American women. Methods The study included adults who participated in the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2018. The poverty income ratio (PIR) was used to represent the SES of the population. With participants stratified according to age category (< 35 years; ≥ 35 years), we further assessed differences in the relationship between PIR and infertility risk among participants of different age groups using multivariate logistic regression and interaction tests. Results Approximately 3,273 participants were enrolled in the study. There were 399 cases of infertility and 2,874 cases without infertility. In women ≥ 35 years of age, PIR levels were significantly higher in infertile participants than in non-infertile participants, but no such difference was found in those < 35 years of age. The association of PIR with the risk of infertility appeared to differ between age < 35 years and age ≥ 35 years (OR: 0.99, 95%Cl: 0.86–1.13 vs. OR: 1.24, 95%Cl: 1.12–1.39) in a fully adjusted model. Furthermore, an interaction between age and PIR increased the risk of infertility (p-value for interaction < 0.001). Conclusion Our study found that age may influence the association between PIR and infertility. It is imperative to perform further studies to provide more evidence