Lina Pu

Yu Luo, Lina Pu, Aijun Song

This paper presents an underwater acoustic reconfigurable intelligent surface (UA-RIS) designed for long-range, high-speed, and environmentally friendly communication in oceanic environments. The proposed UA-RIS comprises multiple pairs of acoustic reflectors that utilize a synthetic reflection scheme to flexibly control the amplitude and phase of reflected waves. This capability enables precise beam steering to enhance or attenuate sound levels in specific directions. A prototype UA-RIS with 4*6 acoustic reflection units is constructed and tested in both tank and lake environments to evaluate performance. Experimental results using a continuous wave (CW) as the source signal demonstrate that the prototype is capable of effectively pointing reflected waves to targeted directions while minimizing side lobes through synthetic reflection. Field tests reveal that deploying the UA-RIS on the sender side considerably extends communication ranges by 28% in deep water and 46% in shallow waters. Furthermore, with a fixed communication distance, positioning the UA-RIS at the transmitter side substantially boosts the receiving signal-to-noise ratio (SNR), with an average increase of 2.13 dB and peaks up to 2.92 dB. When positioned on the receiver side, the UA-RIS can expand the communication range in shallow and deep water environments by 40.6% and 66%, respectively. Moreover, placing the UA-RIS close to the receiver enhances SNR by an average of 2.56 dB, reaching up to 4.2 dB under certain circumstances.

Lina Pu, Yu Luo, Aijun Song

This article introduces a multilayered acoustic reconfigurable intelligent surface (ML-ARIS) architecture designed for the next generation of underwater communications. ML-ARIS incorporates multiple layers of piezoelectric material in each acoustic reflector, with the load impedance of each layer independently adjustable via a control circuit. This design increases the flexibility in generating reflected signals with desired amplitudes and orthogonal phases, enabling passive synthetic reflection using a single acoustic reflector. Such a feature enables precise beam steering, enhancing sound levels in targeted directions while minimizing interference in surrounding environments. Extensive simulations and tank experiments were conducted to verify the feasibility of ML-ARIS. The experimental results indicate that implementing synthetic reflection with a multilayer structure is indeed practical in real-world scenarios, making it possible to use a single reflection unit to generate reflected waves with high-resolution amplitudes and phases.

MiniCPM Team, Chaojun Xiao, Yuxuan Li et al. · tencent-ai, tsinghua

This paper introduces MiniCPM4, a highly efficient large language model (LLM) designed explicitly for end-side devices. We achieve this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems. Specifically, in terms of model architecture, we propose InfLLM v2, a trainable sparse attention mechanism that accelerates both prefilling and decoding phases for long-context processing. Regarding training data, we propose UltraClean, an efficient and accurate pre-training data filtering and generation strategy, and UltraChat v2, a comprehensive supervised fine-tuning dataset. These datasets enable satisfactory model performance to be achieved using just 8 trillion training tokens. Regarding training algorithms, we propose ModelTunnel v2 for efficient pre-training strategy search, and improve existing post-training methods by introducing chunk-wise rollout for load-balanced reinforcement learning and data-efficient tenary LLM, BitCPM. Regarding inference systems, we propose CPM.cu that integrates sparse attention, model quantization, and speculative sampling to achieve efficient prefilling and decoding. To meet diverse on-device requirements, MiniCPM4 is available in two versions, with 0.5B and 8B parameters, respectively. Furthermore, we construct a hybrid reasoning model, MiniCPM4.1, which can be used in both deep reasoning mode and non-reasoning mode. Evaluation results demonstrate that MiniCPM4 and MiniCPM4.1 outperform similar-sized open-source models across benchmarks, with the 8B variants showing significant speed improvements on long sequence understanding and generation.

Long Li, Jiajia Li, Dong Chen et al.

Accurate classification plays a pivotal role in smart agriculture, enabling applications such as crop monitoring, fruit recognition, and pest detection. However, conventional centralized training often requires large-scale data collection, which raises privacy concerns, while standard federated learning struggles with non-independent and identically distributed (non-IID) data and incurs high communication costs. To address these challenges, we propose a federated learning framework that integrates a frozen Contrastive Language-Image Pre-training (CLIP) vision transformer (ViT) with a lightweight transformer classifier. By leveraging the strong feature extraction capability of the pre-trained CLIP ViT, the framework avoids training large-scale models from scratch and restricts federated updates to a compact classifier, thereby reducing transmission overhead significantly. Furthermore, to mitigate performance degradation caused by non-IID data distribution, a small subset (1%) of CLIP-extracted feature representations from all classes is shared across clients. These shared features are non-reversible to raw images, ensuring privacy preservation while aligning class representation across participants. Experimental results on agricultural classification tasks show that the proposed method achieve 86.6% accuracy, which is more than 4 times higher compared to baseline federated learning approaches. This demonstrates the effectiveness and efficiency of combining vision-language model features with federated learning for privacy-preserving and scalable agricultural intelligence.