Exploiting Pseudo Image Captions for Multimodal Summarization

Cross-modal contrastive learning in vision language pretraining (VLP) faces the challenge of (partial) false negatives. In this paper, we study this problem from the perspective of Mutual Information (MI) optimization. It is common sense that InfoNCE loss used in contrastive learning will maximize the lower bound of MI between anchors and their positives, while we theoretically prove that MI involving negatives also matters when noises commonly exist. Guided by a more general lower bound form for optimization, we propose a contrastive learning strategy regulated by progressively refined cross-modal similarity, to more accurately optimize MI between an image/text anchor and its negative texts/images instead of improperly minimizing it. Our method performs competitively on four downstream cross-modal tasks and systematically balances the beneficial and harmful effects of (partial) false negative samples under theoretical guidance.Comment: Accepted at ACL2023 Finding

Nonlinear interaction of head−-on solitary waves in integrable and nonintegrable systems

This study numerically investigates the nonlinear interaction of head-on solitary waves in a granular chain (a nonintegrable system) and compares the simulation results with the theoretical results in fluid (an integrable system). Three stages (i.e., pre-in-phase traveling stage, central-collision stage, and post-in-phase traveling stage) are identified to describe the nonlinear interaction processes in the granular chain. The nonlinear scattering effect occurs in the central-collision stage, which decreases the amplitude of incident solitary waves. Compared with the leading-time phase in the incident and separation collision processes, the lagging-time phase in the separation collision process is smaller. This asymmetrical nonlinear collision results in an occurrence of leading phase shifts of time and space in the post-in-phase traveling stage. We next find that solitary wave amplitude does not influence the immediate space-phase shift in the granular chain. The space$-$phase shift of the post-in-phase traveling stage is only determined by measurement position rather than wave amplitude. The results are reversed in the fluid. An increase in solitary wave amplitude leads to decreased attachment, detachment and residence times for granular chain and fluid. For the immediate time-phase shift, leading and lagging phenomena appear in the granular chain and the fluid, respectively. These results offer new knowledge for designing mechanical metamaterials and energy-mitigating systems

When Parameter-efficient Tuning Meets General-purpose Vision-language Models

Instruction tuning has shown promising potential for developing general-purpose AI capabilities by using large-scale pre-trained models and boosts growing research to integrate multimodal information for creative applications. However, existing works still face two main limitations: the high training costs and heavy computing resource dependence of full model fine-tuning, and the lack of semantic information in instructions, which hinders multimodal alignment. Addressing these challenges, this paper proposes a novel approach to utilize Parameter-Efficient Tuning for generAl-purpose vision-Language models, namely PETAL. PETAL revolutionizes the training process by requiring only 0.5% of the total parameters, achieved through a unique mode approximation technique, which significantly reduces the training costs and reliance on heavy computing resources. Furthermore, PETAL enhances the semantic depth of instructions in two innovative ways: 1) by introducing adaptive instruction mixture-of-experts(MOEs), and 2) by fortifying the score-based linkage between parameter-efficient tuning and mutual information. Our extensive experiments across five multimodal downstream benchmarks reveal that PETAL not only outperforms current state-of-the-art methods in most scenarios but also surpasses full fine-tuning models in effectiveness. Additionally, our approach demonstrates remarkable advantages in few-shot settings, backed by comprehensive visualization analyses. Our source code is available at: https://github. com/melonking32/PETAL

Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning

Relational graph neural networks have garnered particular attention to encode graph context in knowledge graphs (KGs). Although they achieved competitive performance on small KGs, how to efficiently and effectively utilize graph context for large KGs remains an open problem. To this end, we propose the Relation-based Embedding Propagation (REP) method. It is a post-processing technique to adapt pre-trained KG embeddings with graph context. As relations in KGs are directional, we model the incoming head context and the outgoing tail context separately. Accordingly, we design relational context functions with no external parameters. Besides, we use averaging to aggregate context information, making REP more computation-efficient. We theoretically prove that such designs can avoid information distortion during propagation. Extensive experiments also demonstrate that REP has significant scalability while improving or maintaining prediction quality. Notably, it averagely brings about 10% relative improvement to triplet-based embedding methods on OGBL-WikiKG2 and takes 5%-83% time to achieve comparable results as the state-of-the-art GC-OTE.Comment: Accepted by IJCAI 202

Dual effects of gonadotropin-inhibitory hormone on testicular development in prepubertal Minxinan Black rabbits (Oryctolagus cuniculus)

Gonadotropin-inhibitory hormone (GnIH) is a neurohormone that not only suppresses reproduction at the brain level but also regulates steroidogenesis and gametogenesis at the gonad level. However, its function in gonadal physiology has received little attention in rabbits. The main objective of this study was to evaluate the effects of GnIH on testicular development and function in prepubertal Minxinan Black rabbits (Oryctolagus cuniculus). In the present study, we investigated the serum reproductive hormone concentration, testicular parameters, morphology of seminiferous tubules, apoptosis of testicular cells, and expression of reproductive-related genes in male prepubertal Minxinan Black rabbits intraperitoneally administered with 0, 0.5, 5, or 50 μg quail GnIH-related peptides (qGnIH) for 10 days. Compared with the vehicle, administration with 5 μg of qGnIH downregulated the serum testosterone concentration and mRNA levels of spermatogenic genes (PCNA, FSHR, INHβA, HSF1, and AR) and upregulated the apoptosis rate of testicular cells; administration with 50 μg of qGnIH decreased the serum testosterone concentration and hypothalamic GnIH gene mRNA level and increased the serum LH concentration, pituitary LHβ gene mRNA level, testicular weight, gonadosomatic index (GSI), and spermatogenic cell layer thickness. It is concluded that GnIH could exert dual actions on testicular development depending on the male prepubertal rabbits receiving different intraperitoneal doses

Characterisation of Staphylococcus aureus strain causing severe respiratory disease in rabbits

[EN] Staphylococcus aureus is acknowledged as one of the important pathogens isolated from humans and animals. However, the S. aureus causing severe respiratory diseases in rabbits have not been well characterised. A S. aureus named FZHW001, isolated from the lungs of dead rabbits with severe respiratory disease, was characterised by artificial infection of rabbits, detection of virulence factors, multi-locus sequencing typing and antimicrobial susceptibility test. The FZHW001 infected rabbits showed identical respiratory symptoms to those of naturally infected ones, and the isolate could spread through directed contact among rabbits. The isolate was typed into clonal complex 121 and carried 7 of 13 tested virulence factors. Furthermore, the isolate was identified to be methicillin-susceptible S. aureus and was susceptible to 7 of 12 tested antibiotics. 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