13 research outputs found

    Enhanced Multimodal Representation Learning with Cross-modal KD

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    This paper explores the tasks of leveraging auxiliary modalities which are only available at training to enhance multimodal representation learning through cross-modal Knowledge Distillation (KD). The widely adopted mutual information maximization-based objective leads to a short-cut solution of the weak teacher, i.e., achieving the maximum mutual information by simply making the teacher model as weak as the student model. To prevent such a weak solution, we introduce an additional objective term, i.e., the mutual information between the teacher and the auxiliary modality model. Besides, to narrow down the information gap between the student and teacher, we further propose to minimize the conditional entropy of the teacher given the student. Novel training schemes based on contrastive learning and adversarial learning are designed to optimize the mutual information and the conditional entropy, respectively. Experimental results on three popular multimodal benchmark datasets have shown that the proposed method outperforms a range of state-of-the-art approaches for video recognition, video retrieval and emotion classification.Comment: Accepted by CVPR202

    Redundancy-Adaptive Multimodal Learning for Imperfect Data

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    Multimodal models trained on complete modality data often exhibit a substantial decrease in performance when faced with imperfect data containing corruptions or missing modalities. To address this robustness challenge, prior methods have explored various approaches from aspects of augmentation, consistency or uncertainty, but these approaches come with associated drawbacks related to data complexity, representation, and learning, potentially diminishing their overall effectiveness. In response to these challenges, this study introduces a novel approach known as the Redundancy-Adaptive Multimodal Learning (RAML). RAML efficiently harnesses information redundancy across multiple modalities to combat the issues posed by imperfect data while remaining compatible with the complete modality. Specifically, RAML achieves redundancy-lossless information extraction through separate unimodal discriminative tasks and enforces a proper norm constraint on each unimodal feature representation. Furthermore, RAML explicitly enhances multimodal fusion by leveraging fine-grained redundancy among unimodal features to learn correspondences between corrupted and untainted information. Extensive experiments on various benchmark datasets under diverse conditions have consistently demonstrated that RAML outperforms state-of-the-art methods by a significant margin

    CRISPR/Cas9-mediated targeted chromosome elimination

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    Abstract Background The CRISPR/Cas9 system has become an efficient gene editing method for generating cells carrying precise gene mutations, including the rearrangement and deletion of chromosomal segments. However, whether an entire chromosome could be eliminated by this technology is still unknown. Results Here we demonstrate the use of the CRISPR/Cas9 system to eliminate targeted chromosomes. Using either multiple cleavages induced by a single-guide RNA (sgRNA) that targets multiple chromosome-specific sites or a cocktail of multiple sgRNAs, each targeting one specific site, we found that a sex chromosome could be selectively eliminated in cultured cells, embryos, and tissues in vivo. Furthermore, this approach was able to produce a targeted autosome loss in aneuploid mouse embryonic stem cells with an extra human chromosome and human induced pluripotent stem cells with trisomy 21, as well as cancer cells. Conclusions CRISPR/Cas9-mediated targeted chromosome elimination offers a new approach to develop animal models with chromosome deletions, and a potential therapeutic strategy for human aneuploidy diseases involving additional chromosomes

    Effect of atrial fibrillation on outcomes after mechanical thrombectomy and long-term ischemic recurrence in patients with acute basilar artery occlusion

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    IntroductionAccording to the literature on anterior circulation, comorbid atrial fibrillation (AF) is not associated with a worse functional outcome, lower reperfusion rates, or higher rates of intracranial hemorrhage after mechanical thrombectomy (MT) compared to intravenous thrombolysis (IVT) or treatment with supportive care. However, data are limited for the effect of comorbid AF on procedural and clinical outcomes of acute basilar artery occlusion (ABAO) after MT. This study aimed to investigate the effect of atrial fibrillation on outcomes after MT and long-term ischemic recurrence in patients with ABAO.MethodsWe performed a registered study of the Endovascular Treatment for Acute Basilar Artery Occlusion Study (BASILAR, which is registered in the Chinese Clinical Trial Registry, http://www.chictr.org.cn; ChiCTR1800014759) from January 2014 to May 2019, which included 647 patients who underwent MT for ABAO, 136 of whom had comorbid AF. Prospectively defined baseline characteristics, procedural outcomes, and clinical outcomes were reported and compared.ResultsOn multivariate analysis, AF predicted a shorter puncture-to-recanalization time, higher first-pass effect rate, and lower incidence of angioplasty and/or stenting (p < 0.01). AF had no effect on intracranial hemorrhage incidence [adjusted odds ratio (aOR), 1.093; 95% confidence interval (CI), 0.451–2.652], 90-day functional outcomes (adjusted common odds ratio, 0.915; 95% CI, 0.588–1.424), or mortality (aOR, 0.851; 95% CI, 0.491–1.475) after MT. The main findings were robust in the subgroup and 1-year follow-up analyses. Comorbid AF was the remaining predictor of ischemic recurrence (aOR, 4.076; 95% CI, 1.137–14.612).ConclusionsThe study revealed no significant difference in the safety and efficacy of MT for ABAO regardless of whether patients had comorbid AF. However, a higher proportion of patients with AF experienced ischemic recurrence within 1 year after MT
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