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Most current models for analyzing multimodal sequences often disregard the imbalanced contributions of individual modal representations caused by varying information densities, as well as the inherent multi-relational interactions across distinct modalities. Consequently, a biased understanding of the intricate interplay among modalities may be fostered, limiting prediction accuracy and effectiveness.

Conventional fine-tuning encounters increasing difficulties given the size of current Pre-trained Language Models, which makes parameter-efficient tuning become the focal point of frontier research. Recent advances in this field is the unified tuning methods that aim to tune the representations of both multi-head attention (MHA) and fully connected feed-forward network (FFN) simultaneously, but they rely on existing tuning methods and do not explicitly model domain knowledge for downstream tasks.

Image set compression (ISC) refers to compressing the sets of semantically similar images. Traditional ISC methods typically aim to eliminate redundancy among images at either signal or frequency domain, but often struggle to handle complex geometric deformations across different images effectively. 

Existing JPEG encryption approaches pose a security risk due to the difficulty in changing all block-feature values while considering format compatibility and file size expansion. To address these concerns, this paper introduces a novel JPEG image encryption scheme. First, the security of sketch information against chosen-plaintext attacks is improved by increasing the change rate of block-feature values.

Deep neural networks have demonstrated considerable effectiveness in recognizing complex communications signals through their applications in the tasks of automatic modulation recognition. However, the resilience of these networks is undermined by the introduction of carefully designed adversarial examples that compromise the reliability of the decision processes.