Traditional video compression continues to advance, but the gains in efficiency are diminishing and come at the cost of higher computational complexity. Despite achieving competitive rate-distortion results, current neural video codecs (NVCs) generally lack support for a wide range of quality levels, often requiring multiple models to achieve flexible rate control, which increases both training cost and deployment complexity. To address the limitations of both traditional codecs and current NVCs, we propose a hybrid video compression framework that integrates traditional codecs with hint-guided video frame interpolation (VFI), a learning-based technique for synthesizing intermediate frames. By using decoded reference frames and leveraging compressed-domain hints to guide interpolation, our method improves both motion compensation and reconstruction quality. This design combines the efficiency of traditional codecs with the adaptability of neural interpolation, achieving consistent rate-distortion performance and supporting a wide range of quality levels on standard benchmarks.

Protein language models (PLMs) have shown remarkable capabilities in various protein function prediction tasks. However, while protein function is intricately tied to structure, most existing PLMs do not incorporate protein structure information. To address this issue, we introduce ProSST, a Transformer-based protein language model that seamlessly integrates both protein sequences and structures. ProSST incorporates a structure quantization module and a Transformer architecture with disentangled attention. The structure quantization module translates a 3D protein structure into a sequence of discrete tokens by first serializing the protein structure into residue-level local structures and then embeds them into dense vector space. These vectors are then quantized into discrete structure tokens by a pre-trained clustering model. These tokens serve as an effective protein structure representation. Furthermore, ProSST explicitly learns the relationship between protein residue token sequences and structure token sequences through the sequence-structure disentangled attention. We pre-train ProSST on millions of protein structures using a masked language model objective, enabling it to learn comprehensive contextual representations of proteins. To evaluate the proposed ProSST, we conduct extensive experiments on the zero-shot mutation effect prediction and several supervised downstream tasks, where ProSST achieves the state-of-the-art performance among all baselines. Our code and pre-trained models are publicly available.

Codebooks with small inner-product correlation are preferred in many practical applications such as direct spread code division multiple access communications, coding theory, and compressed sensing. The well-known Welch bound and Levenstein bound are useful benchmarks for the correlation of codebooks. In general, it is very hard to obtain codebooks achieving the Welch bound or the Levenstein bound. The objective of this letter is to present a construction of codebooks based on additive and multiplicative characters of finite fields. It generates codebooks nearly achieving the Levenstein bound.