Dong Wang

Pingrui Zhang, Yifei Su, Pengyuan Wu et al.

Vision-and-Language Navigation (VLN) requires the agent to navigate by following natural instructions under partial observability, making it difficult to align perception with language. Recent methods mitigate this by imagining future scenes, yet they rely on vision-based synthesis, leading to high computational cost and redundant details. To this end, we propose to adaptively imagine key environmental semantics via \textit{language} form, enabling a more reliable and efficient strategy. Specifically, we introduce a novel Adaptive Text Dreamer (ATD), a dual-branch self-guided imagination policy built upon a large language model (LLM). ATD is designed with a human-like left-right brain architecture, where the left brain focuses on logical integration, and the right brain is responsible for imaginative prediction of future scenes. To achieve this, we fine-tune only the Q-former within both brains to efficiently activate domain-specific knowledge in the LLM, enabling dynamic updates of logical reasoning and imagination during navigation. Furthermore, we introduce a cross-interaction mechanism to regularize the imagined outputs and inject them into a navigation expert module, allowing ATD to jointly exploit both the reasoning capacity of the LLM and the expertise of the navigation model. We conduct extensive experiments on the R2R benchmark, where ATD achieves state-of-the-art performance with fewer parameters. The code is \href{https://github.com/zhangpingrui/Adaptive-Text-Dreamer}{here}.

Yifan Liu, Yike Li, Dong Wang

Media bias significantly shapes public perception by reinforcing stereotypes and exacerbating societal divisions. Prior research has often focused on isolated media bias dimensions such as \textit{political bias} or \textit{racial bias}, neglecting the complex interrelationships among various bias dimensions across different topic domains. Moreover, we observe that models trained on existing media bias benchmarks fail to generalize effectively on recent social media posts, particularly in certain bias identification tasks. This shortfall primarily arises because these benchmarks do not adequately reflect the rapidly evolving nature of social media content, which is characterized by shifting user behaviors and emerging trends. In response to these limitations, our research introduces a novel dataset collected from YouTube and Reddit over the past five years. Our dataset includes automated annotations for YouTube content across a broad spectrum of bias dimensions, such as gender, racial, and political biases, as well as hate speech, among others. It spans diverse domains including politics, sports, healthcare, education, and entertainment, reflecting the complex interplay of biases across different societal sectors. Through comprehensive statistical analysis, we identify significant differences in bias expression patterns and intra-domain bias correlations across these domains. By utilizing our understanding of the correlations among various bias dimensions, we lay the groundwork for creating advanced systems capable of detecting multiple biases simultaneously. Overall, our dataset advances the field of media bias identification, contributing to the development of tools that promote fairer media consumption. The comprehensive awareness of existing media bias fosters more ethical journalism, promotes cultural sensitivity, and supports a more informed and equitable public discourse.

Ruixin Lia, Guoxu Zhaoa, Dylan Richard Muir et al.

Analyzing electroencephalogram (EEG) signals to detect the epileptic seizure status of a subject presents a challenge to existing technologies aimed at providing timely and efficient diagnosis. In this study, we aimed to detect interictal and ictal periods of epileptic seizures using a spiking neural network (SNN). Our proposed approach provides an online and real-time preliminary diagnosis of epileptic seizures and helps to detect possible pathological conditions.To validate our approach, we conducted experiments using multiple datasets. We utilized a trained SNN to identify the presence of epileptic seizures and compared our results with those of related studies. The SNN model was deployed on Xylo, a digital SNN neuromorphic processor designed to process temporal signals. Xylo efficiently simulates spiking leaky integrate-and-fire neurons with exponential input synapses. Xylo has much lower energy requirments than traditional approaches to signal processing, making it an ideal platform for developing low-power seizure detection systems.Our proposed method has a high test accuracy of 93.3% and 92.9% when classifying ictal and interictal periods. At the same time, the application has an average power consumption of 87.4 uW(IO power) + 287.9 uW(computational power) when deployed to Xylo. Our method demonstrates excellent low-latency performance when tested on multiple datasets. Our work provides a new solution for seizure detection, and it is expected to be widely used in portable and wearable devices in the future.