In a real-time transmission scenario, messages are transmitted through a channel that is subject to packet loss. The destination must recover the messages within the required deadline. In this paper, we consider a setup where two different types of messages with distinct decoding deadlines are transmitted through a channel that can introduce burst erasures of a length at most $B$, or $N$ random erasures. The message with a short decoding deadline $T_u$ is referred to as an urgent message, while the other one with a decoding deadline $T_v$ ($T_v > T_u$) is referred to as a less urgent message. We propose a merging method to encode two message streams of different urgency levels into a single flow. We consider the scenario where $T_v > T_u + B$. We establish that any coding strategy based on this merging approach has a closed-form upper limit on its achievable sum rate. Moreover, we present explicit constructions within a finite field that scales quadratically with the imposed delay, ensuring adherence to the upper bound. In a given parameter configuration, we rigorously demonstrate that the sum rate of our proposed streaming codes consistently surpasses that of separate encoding, which serves as a baseline for comparison.
相關內容
The openness and influence of video-sharing platforms (VSPs) such as YouTube and TikTok attracted creators to share videos on various social issues. Although social issue videos (SIVs) affect public opinions and breed misinformation, how VSP users obtain information and interact with SIVs is under-explored. This work surveyed 659 YouTube and 127 TikTok users to understand the motives for consuming SIVs on VSPs. We found that VSP users are primarily motivated by the information and entertainment gratifications to use the platform. VSP users use SIVs for information-seeking purposes and find YouTube and TikTok convenient to interact with SIVs. VSP users moderately watch SIVs for entertainment and inactively engage in social interactions. SIV consumption is associated with information and socialization gratifications of the platform. VSP users appreciate the diversity of information and opinions but would also do their own research and are concerned about the misinformation and echo chamber problems.
In NLP, incremental processors produce output in instalments, based on incoming prefixes of the linguistic input. Some tokens trigger revisions, causing edits to the output hypothesis, but little is known about why models revise when they revise. A policy that detects the time steps where revisions should happen can improve efficiency. Still, retrieving a suitable signal to train a revision policy is an open problem, since it is not naturally available in datasets. In this work, we investigate the appropriateness of regressions and skips in human reading eye-tracking data as signals to inform revision policies in incremental sequence labelling. Using generalised mixed-effects models, we find that the probability of regressions and skips by humans can potentially serve as useful predictors for revisions in BiLSTMs and Transformer models, with consistent results for various languages.
Simultaneous sequence generation is a pivotal task for real-time scenarios, such as streaming speech recognition, simultaneous machine translation and simultaneous speech translation, where the target sequence is generated while receiving the source sequence. The crux of achieving high-quality generation with low latency lies in identifying the optimal moments for generating, accomplished by learning a mapping between the source and target sequences. However, existing methods often rely on task-specific heuristics for different sequence types, limiting the model's capacity to adaptively learn the source-target mapping and hindering the exploration of multi-task learning for various simultaneous tasks. In this paper, we propose a unified segment-to-segment framework (Seg2Seg) for simultaneous sequence generation, which learns the mapping in an adaptive and unified manner. During the process of simultaneous generation, the model alternates between waiting for a source segment and generating a target segment, making the segment serve as the natural bridge between the source and target. To accomplish this, Seg2Seg introduces a latent segment as the pivot between source to target and explores all potential source-target mappings via the proposed expectation training, thereby learning the optimal moments for generating. Experiments on multiple simultaneous generation tasks demonstrate that Seg2Seg achieves state-of-the-art performance and exhibits better generality across various tasks.
Leveraging non-terrestrial platforms in 6G networks holds immense significance as it opens up opportunities to expand network coverage, enhance connectivity, and support a wide range of innovative applications, including global-scale Internet of Things and ultra-high-definition content delivery. To accomplish the seamless integration between terrestrial and non-terrestrial networks, substantial changes in radio access network (RAN) architecture are required. These changes involve the development of new RAN solutions that can efficiently manage the diverse characteristics of both terrestrial and non-terrestrial components, ensuring smooth handovers, resource allocation, and quality of service across the integrated network ecosystem. Additionally, the establishment of robust interconnection and communication protocols between terrestrial and non-terrestrial elements will be pivotal to utilize the full potential of 6G technology. Additionally, innovative approaches have been introduced to split the functionalities within the RAN into centralized and distributed domains. These novel paradigms are designed to enhance RAN's flexibility while simultaneously lowering the costs associated with infrastructure deployment, all while ensuring that the quality of service for end-users remains unaffected. In this work, we provide an extensive examination of various Non-Terrestrial Networks (NTN) architectures and the necessary adaptations required on the existing 5G RAN architecture to align with the distinct attributes of NTN. Of particular significance, we emphasize the crucial RAN functional split choices essential for the seamless integration of terrestrial and non-terrestrial components within advanced 6G networks.
A popular approach to streaming speech translation is to employ a single offline model with a wait-k policy to support different latency requirements, which is simpler than training multiple online models with different latency constraints. However, there is a mismatch problem in using a model trained with complete utterances for streaming inference with partial input. We demonstrate that speech representations extracted at the end of a streaming input are significantly different from those extracted from a complete utterance. To address this issue, we propose a new approach called Future-Aware Streaming Translation (FAST) that adapts an offline ST model for streaming input. FAST includes a Future-Aware Inference (FAI) strategy that incorporates future context through a trainable masked embedding, and a Future-Aware Distillation (FAD) framework that transfers future context from an approximation of full speech to streaming input. Our experiments on the MuST-C EnDe, EnEs, and EnFr benchmarks show that FAST achieves better trade-offs between translation quality and latency than strong baselines. Extensive analyses suggest that our methods effectively alleviate the aforementioned mismatch problem between offline training and online inference.
Entity and Relation Extraction (ERE) is an important task in information extraction. Recent marker-based pipeline models achieve state-of-the-art performance, but still suffer from the error propagation issue. Also, most of current ERE models do not take into account higher-order interactions between multiple entities and relations, while higher-order modeling could be beneficial.In this work, we propose HyperGraph neural network for ERE ($\hgnn{}$), which is built upon the PL-marker (a state-of-the-art marker-based pipleline model). To alleviate error propagation,we use a high-recall pruner mechanism to transfer the burden of entity identification and labeling from the NER module to the joint module of our model. For higher-order modeling, we build a hypergraph, where nodes are entities (provided by the span pruner) and relations thereof, and hyperedges encode interactions between two different relations or between a relation and its associated subject and object entities. We then run a hypergraph neural network for higher-order inference by applying message passing over the built hypergraph. Experiments on three widely used benchmarks (\acef{}, \ace{} and \scierc{}) for ERE task show significant improvements over the previous state-of-the-art PL-marker.
Video grounding aims to localize the target moment in an untrimmed video corresponding to a given sentence query. Existing methods typically select the best prediction from a set of predefined proposals or directly regress the target span in a single-shot manner, resulting in the absence of a systematical prediction refinement process. In this paper, we propose DiffusionVG, a novel framework with diffusion models that formulates video grounding as a conditional generation task, where the target span is generated from Gaussian noise inputs and interatively refined in the reverse diffusion process. During training, DiffusionVG progressively adds noise to the target span with a fixed forward diffusion process and learns to recover the target span in the reverse diffusion process. In inference, DiffusionVG can generate the target span from Gaussian noise inputs by the learned reverse diffusion process conditioned on the video-sentence representations. Our DiffusionVG follows the encoder-decoder architecture, which firstly encodes the video-sentence features and iteratively denoises the predicted spans in its specialized span refining decoder. Without bells and whistles, our DiffusionVG demonstrates competitive or even superior performance compared to existing well-crafted models on mainstream Charades-STA and ActivityNet Captions benchmarks.
Answering questions that require reading texts in an image is challenging for current models. One key difficulty of this task is that rare, polysemous, and ambiguous words frequently appear in images, e.g., names of places, products, and sports teams. To overcome this difficulty, only resorting to pre-trained word embedding models is far from enough. A desired model should utilize the rich information in multiple modalities of the image to help understand the meaning of scene texts, e.g., the prominent text on a bottle is most likely to be the brand. Following this idea, we propose a novel VQA approach, Multi-Modal Graph Neural Network (MM-GNN). It first represents an image as a graph consisting of three sub-graphs, depicting visual, semantic, and numeric modalities respectively. Then, we introduce three aggregators which guide the message passing from one graph to another to utilize the contexts in various modalities, so as to refine the features of nodes. The updated nodes have better features for the downstream question answering module. Experimental evaluations show that our MM-GNN represents the scene texts better and obviously facilitates the performances on two VQA tasks that require reading scene texts.
Dense video captioning aims to generate text descriptions for all events in an untrimmed video. This involves both detecting and describing events. Therefore, all previous methods on dense video captioning tackle this problem by building two models, i.e. an event proposal and a captioning model, for these two sub-problems. The models are either trained separately or in alternation. This prevents direct influence of the language description to the event proposal, which is important for generating accurate descriptions. To address this problem, we propose an end-to-end transformer model for dense video captioning. The encoder encodes the video into appropriate representations. The proposal decoder decodes from the encoding with different anchors to form video event proposals. The captioning decoder employs a masking network to restrict its attention to the proposal event over the encoding feature. This masking network converts the event proposal to a differentiable mask, which ensures the consistency between the proposal and captioning during training. In addition, our model employs a self-attention mechanism, which enables the use of efficient non-recurrent structure during encoding and leads to performance improvements. We demonstrate the effectiveness of this end-to-end model on ActivityNet Captions and YouCookII datasets, where we achieved 10.12 and 6.58 METEOR score, respectively.
Visual Question Answering (VQA) models have struggled with counting objects in natural images so far. We identify a fundamental problem due to soft attention in these models as a cause. To circumvent this problem, we propose a neural network component that allows robust counting from object proposals. Experiments on a toy task show the effectiveness of this component and we obtain state-of-the-art accuracy on the number category of the VQA v2 dataset without negatively affecting other categories, even outperforming ensemble models with our single model. On a difficult balanced pair metric, the component gives a substantial improvement in counting over a strong baseline by 6.6%.
注冊地址： 北京市海淀區羊坊店路18號2幢3層301-191