Multi-task learning (MTL) enables joint learning over multiple tasks based on shared representations, but suffers from task interference issue during optimization.Existing works mainly focus on task balancing or probabilistic modeling but fail to address the issue since they struggle to learn sufficient representations for all target tasks.To address this, we propose a multi-task representation alignment (MTRA) framework to achieve task-specific alignment and self-alignment on the shared representations from a mutual information perspective.MTRA ensures that the learned representations contain task-relevant features while mitigating the negative effects of task-irrelevant features.First, we design a task-specific alignment objective to align the shared representations and task-specific representations with the expected targets of all tasks via information maximization.Besides, we design a self-alignment objective to eliminate task-irrelevant features via conditional information minimization.Experiments on two multi-task language benchmarks show that MTRA outperforms 13 representative MTL methods under the same settings, particularly under label-noisy and dataconstrained conditions.Further analysis shows that the learned shared representations exhibit sufficient task informativeness and superior alignment properties.

Exemplar-based image translation, which aims to transfer the style of an exemplar image to an input semantic image, is challenging and important in many applications. Most current methods build coarse correspondences and overlook extracting faithful style information from the exemplar image, leading to unsatisfactory results with style inconsistent with the exemplar image. In this paper, we propose a novel and efficient style mixture block to extract faithful style information and build reliable correspondences progressively. Specifically, instead of modeling explicit correspondences, we extract faithful style descriptors by considering global information about the exemplar features. Then, we generate coefficients for these style descriptors by modeling the interaction between the exemplar image and the input image, and efficiently compose these descriptors using the coefficients. The efficiency of the style mixture block allows a multi-scale architecture to extract and transform style descriptors at different resolutions, deforming the features of the exemplar image and refining the correspondences progressively. Experimental results on several datasets show that our SMixNet outperforms the current state-of-the-art, and is faster. Code is available for research purposes at https://github.com/Zhangjinso/SMixNet.

Abstract Existing LLM-based role-playing methods often rely on superficial textual descriptions or simplistic metrics, inadequately modeling both intrinsic and extrinsic character dimensions. Additionally, they typically simulate character memory with implicit model knowledge or basic retrieval augment generation without explicit memory alignment, compromising memory consistency. The two issues weaken reliability of role-playing LLMs in several applications, such as trustworthy social simulation. To address these limitations, we propose PsyMem, a novel framework integrating fine-grained psychological attributes and explicit memory control for role-playing. PsyMem supplements textual descriptions with 26 psychological indicators to detailed model character. Additionally, PsyMem implements memory alignment training, explicitly trains the model to align character’s response with memory, thereby enabling dynamic memory-controlled responding during inference. By training Qwen2.5-7B-Instruct on our specially designed dataset (including 5,414 characters and 38,962 dialogues extracted from novels), the resulting model, termed as PsyMem-Qwen, outperforms baseline models in role-playing, achieving the best performance in human-likeness and character fidelity.

For resource-constrained wireless scenarios, this paper proposes a cooperative cross-layer scheme to improve the performance of scalable video transmission. At the application layer, the extraction parameters of scalable video coding are first optimized to maximize the visual quality and fulfill the resource constraints of decoding capacity and bandwidth. At the link layer, a priority-based cooperative scheduling strategy is further proposed to transmit the video packets of extracted substream. In this strategy, the relay link may substitute for the unreliable direct link if it has a relatively smaller packet error rate (PER). The packet priorities are determined by both the link PERs and the video layers properties. Simulations demonstrate the effectiveness of our proposed scheme.

The multiple-input-multiple-output (MIMO) and cooperative communication are two state-of-the-art techniques to provide high-rate high-quality video communication services. By taking advantage of both techniques, this paper presents a novel joint source-channel coding (JSCC) framework for scalable video transmission over cooperative MIMO. In this framework, we first propose a cooperative MIMO architecture, which employs macro-micro power control strategy as a relaying protocol to determine the on/off mode of relays and the specific power allocation among them either by equal power amplification or by cooperative beamforming. Then, an unequal error protection structure is proposed to protect the video layers with different importance levels by concatenating the rate-variable low-density parity-check codes and diversity-embedded space-time block codes. Moreover, for the purpose of channel adaptation, the switch of space-time codes is designed to achieve different diversity and multiplexing tradeoff points. Finally, the JSCC algorithm integrated with diversity-multiplexing-coding gain tradeoff is proposed to optimize the resources of cooperative system to improve the transmission quality of scalable video. Experimental results demonstrate the effectiveness of our proposed schemes.

This paper proposes a cooperative multiple-input multiple-output (MIMO) architecture to transmit the video reliably. By exploring the configurable resources of the scalable video and the coded MIMO system, we further propose a novel joint source-channel coding (JSCC) framework with optimal tradeoff among diversity, multiplexing and coding gains. In this framework, the concatenated low-density parity-check codes (LDPC) and diversity-embedded space-time block codes (DE-STBC) provide double unequal error protection for the video layers, and the STBCs switching enables the adaptability to the varying channel. Experiments demonstrate the superiority of cooperative architecture and the effectiveness of our JSCC algorithm.