Rule-driven News Captioning

News captioning task aims to generate sentences by describing named entities or concrete events for an image with its news article. Existing methods have achieved remarkable results by relying on the large-scale pre-trained models, which primarily focus on the correlations between the input news content and the output predictions. However, the news captioning requires adhering to some fundamental rules of news reporting, such as accurately describing the individuals and actions associated with the event. In this paper, we propose the rule-driven news captioning method, which can generate image descriptions following designated rule signal. Specifically, we first design the news-aware semantic rule for the descriptions. This rule incorporates the primary action depicted in the image (e.g., "performing") and the roles played by named entities involved in the action (e.g., "Agent" and "Place"). Second, we inject this semantic rule into the large-scale pre-trained model, BART, with the prefix-tuning strategy, where multiple encoder layers are embedded with news-aware semantic rule. Finally, we can effectively guide BART to generate news sentences that comply with the designated rule. Extensive experiments on two widely used datasets (i.e., GoodNews and NYTimes800k) demonstrate the effectiveness of our method

The spectrum of low-pTp_{T} J/ψJ/\psi in heavy ion collisions in a fractal description

Transverse momentum spectrum of particles in hadron gas are affected by flow, quantum and strong interaction effects. Previously, most models focus on only one of the three effects but ignore others. The unconsidered effects are taken into the fitted parameters. In this paper, we study the three effects together from a new fractal angle by physical calculation instead of data fitting. Near the critical temperature, the three effects induce $J/\psi$ and neighboring meson to form a two-meson structure. We set up a two-particle fractal (TPF) model to describe this structure. We propose that under the three effects, $J/\psi$-$\pi$ two-meson state, $J/\psi$ and $\pi$ two-quark states form a self-similarity structure. With evolution, the two-meson structure disintegrate. We introduce an influencing factor $q_{fqs}$ to describe the flow, quantum and strong interaction effects and an escort factor $q_2$ to describe the binding force and the three effects. By solving the probability and entropy equations, we obtain the values of $q_{fqs}$ and $q_2$ at different collision energies and centrality classes. By substituting the value of $q_{fqs}$ into distribution function, we obtain the transverse momentum spectrum of low-$p_T$ $J/\psi$ and find it in good agreement with experimental data. We also analyze the evolution of $q_{fqs}$ with the temperature. It is found that $q_{fqs}$ is larger than 1. This is because the three effects decrease the number of microstates. We also find $q_{fqs}$ decreases with decreasing the temperature. This is consistent with the fact that with the system expansion, the influence of the three effects decrease.Comment: 9 pages, 3 figure

How to Understand Named Entities: Using Common Sense for News Captioning

News captioning aims to describe an image with its news article body as input. It greatly relies on a set of detected named entities, including real-world people, organizations, and places. This paper exploits commonsense knowledge to understand named entities for news captioning. By ``understand'', we mean correlating the news content with common sense in the wild, which helps an agent to 1) distinguish semantically similar named entities and 2) describe named entities using words outside of training corpora. Our approach consists of three modules: (a) Filter Module aims to clarify the common sense concerning a named entity from two aspects: what does it mean? and what is it related to?, which divide the common sense into explanatory knowledge and relevant knowledge, respectively. (b) Distinguish Module aggregates explanatory knowledge from node-degree, dependency, and distinguish three aspects to distinguish semantically similar named entities. (c) Enrich Module attaches relevant knowledge to named entities to enrich the entity description by commonsense information (e.g., identity and social position). Finally, the probability distributions from both modules are integrated to generate the news captions. Extensive experiments on two challenging datasets (i.e., GoodNews and NYTimes) demonstrate the superiority of our method. Ablation studies and visualization further validate its effectiveness in understanding named entities

Effects of Brain-Derived Neurotrophic Factor on Local Inflammation in Experimental Stroke of Rat

This study was aimed to investigate whether brain-derived neurotrophic factor (BDNF) can modulate local cerebral inflammation in ischemic stroke. Rats were subjected to ischemia by occluding the right middle cerebral artery (MCAO) for 2 hours. Rats were randomized as control, BDNF, and antibody groups. The local inflammation was evaluated on cellular, cytokine, and transcription factor levels with immunofluorescence, enzyme-linked immunosorbent assay, real-time qPCR, and electrophoretic mobility shift assay, respectively. Exogenous BDNF significantly improved motor-sensory, sensorimotor function, and vestibulomotor function, while BDNF did not decrease the infarct volume. Exogenous BDNF increased the number of both activated and phagocytotic microglia in brain. BDNF upregulated interleukin10 and its mRNA expression, while downregulated tumor necrosis factor α and its mRNA expression. BDNF also increased DNA-binding activity of nuclear factor-kappa B. BDNF antibody, which blocked the activity of endogenous BDNF, showed the opposite effect of exogenous BDNF. Our data indicated that BDNF may modulate local inflammation in ischemic brain tissues on the cellular, cytokine, and transcription factor levels

Treatment of Nutrient-rich Municipal Wastewater Using Mixotrophic Strain Chlorella kessleri GXLB-9

Growing algae on wastewaters offers a promising way for effective N and P recycling as well as low-cost algal biofuel feedstock accumulation. In this study, a locally isolated microalgae strain Chlorella kessleri GXLB-9 (C. kessleri GXLB-9), was evaluated for growth and nutrient removal efficiency grown in nutrient-rich wastewater centrifuged from activated sludge (NWCAS). And 3-(3, 4-dichlorophenyl)-1, 1-dimethyl urea (DCMU), one chemical that could block microalgae-based photosynthetic pathway, was used to evaluate the growth mode (autotrophy, heterotrophy or mixotrophy) of C. kessleri GXLB-9. The results showed that C. kessleri GXLB-9 was a facultative heterotrophic strain and 7-day batch cultivation idicated that the maximal removal efficiencies for total nitrogen, total phosphorus, and chemical oxygen demand (COD) were over 59%, 81%, and 88%, respectively, with high growth rate (0.490 d-1) and high biomass productivity (269 mg L-1 d-1). In addition, the impact of light-dark cycle on algae growth and nutrient removal was minimal while pH has significant impact on both algae growth and nutrient removal efficiency

Nuclear Respiratory Factor 1 Negatively Regulates the P1 Promoter of the Peroxisome Proliferator-Activated Receptor-γ Gene and Inhibits Chicken Adipogenesis

Peroxisome proliferator-activated receptor-γ (PPARγ) is a master regulator of adipogenesis, and alterations in its function are associated with various pathological processes related to metabolic syndrome. Recently, we found that the chicken PPARγ gene is regulated by three alternative promoters (P1, P2 and P3), producing five different transcript isoforms and two protein isoforms. In this study, the P1 promoter structure was characterized. Bioinformatics identified six putative nuclear respiratory factor 1 (NRF1) binding sites in the P1 promoter, and a reporter assay showed that NRF1 inhibited the activity of the P1 promoter. Of the six putative NRF1 binding sites, individual mutations of three of them abolished the inhibitory effect of NRF1 on P1 promoter activity. Furthermore, a ChIP assay indicated that NRF1 directly bound to the P1 promoter, and real-time quantitative RT-PCR analysis showed that NRF1 mRNA expression was negatively correlated with PPARγ1 expression (Pearson’s r = -0.148, p = 0.033). Further study showed that NRF1 overexpression inhibited the differentiation of the immortalized chicken preadipocyte cell line (ICP1), which was accompanied by reduced PPARγ1 mRNA expression. Taken together, our findings indicated that NRF1 directly negatively regulates the P1 promoter of the chicken PPARγ gene and inhibits adipogenesis