Yiqing Zhou

Yiqing Zhou, Karsten Naert, Dirk Nuyens

Accurate knowledge of scrap composition can increase the usage of recycled material to produce steel, reducing the need for raw ore extraction and minimizing environmental impact by conserving natural resources and lowering carbon emissions. First, we introduce two state-space models for the elemental composition of scrap in Electric Arc Furnaces (EAF) and Basic Oxygen Furnaces (BOF): a linear model for elements that transfer entirely into steel, and a non-linear model for elements that partition between steel and slag. The models are fitted with the Kalman filter and the unscented Kalman filter, respectively, using only data already collected in the standard steel production process. Crucially, the resulting scrap composition estimates can in turn be used to predict the elemental composition of future steel production. Second, we analyze how key hyperparameters affect estimation accuracy and stability, and we provide practical guidelines for tuning them from expert knowledge and historical data. Third, we validate the models on real BOF data from ArcelorMittal, using Cu and Cr as representative elements. Both filters outperform windowed non-negative least squares regression, a strong baseline method for scrap composition estimation, yielding reliable real-time estimates of scrap composition.

Jihao Luo, Zesong Fei, Xinyi Wang et al.

The rapid growth of the low-altitude economy drives increasingly autonomous unmanned aerial vehicle (UAV) operations, giving rise to low-altitude embodied intelligence (LAEI), in which sensing, communication, computation, and control (SC$^3$) are tightly integrated to enable closed-loop interaction, ensuring timely, effective, and safe responses in complex or unknown environments. This article systematically explores the LAEI networks, from its fundamental architecture to the diverse scenarios that it can support. We examine key enabling techniques that sustain timely information exchange and effective decision feedback within the $\text{SC}^3$ closed loop. A representative low-altitude UAV mission in an unknown urban area is presented as a case study, where the UAV provides communication services and performs environmental sensing to inform closed-loop control, illustrating how coordinated $\text{SC}^3$ capabilities enable efficient and responsive operation. By identifying major challenges and outlining future research directions, this work serves as a cornerstone for developing next-generation low-altitude intelligent systems.

Zhenan Shao, Linjian Ma, Yiqing Zhou et al.

Humans effortlessly navigate the dynamic visual world, yet deep neural networks (DNNs), despite excelling at many visual tasks, are surprisingly vulnerable to minor image perturbations. Past theories suggest that human visual robustness arises from a representational space that evolves along the ventral visual stream (VVS) of the brain to increasingly tolerate object transformations. To test whether robustness is supported by such progression as opposed to being confined exclusively to specialized higher-order regions, we trained DNNs to align their representations with human neural responses from consecutive VVS regions while performing visual tasks. We demonstrate a hierarchical improvement in DNN robustness: alignment to higher-order VVS regions leads to greater improvement. To investigate the mechanism behind such robustness gains, we test a prominent hypothesis that attributes human robustness to the unique geometry of neural category manifolds in the VVS. We first reveal that more desirable manifold properties, specifically, smaller extent and better linear separability, indeed emerge across the human VVS. These properties can be inherited by neurally aligned DNNs and predict their subsequent robustness gains. Furthermore, we show that supervision from neural manifolds alone, via manifold guidance, is sufficient to qualitatively reproduce the hierarchical robustness improvements. Together, these results highlight the critical role of the evolving representational space across VVS in achieving robust visual inference, in part through the formation of more linearly separable category manifolds, which may in turn be leveraged to develop more robust AI systems.

Yiqing Zhou, Yu Lei, Shuzheng Si et al.

Managing extensive context remains a critical bottleneck for Large Language Models (LLMs), particularly in applications like long-document question answering and autonomous agents where lengthy inputs incur high computational costs and introduce noise. Existing compression techniques often disrupt local coherence through discrete token removal or rely on implicit latent encoding that suffers from positional bias and incompatibility with closed-source APIs. To address these limitations, we introduce the EDU-based Context Compressor, a novel explicit compression framework designed to preserve both global structure and fine-grained details. Our approach reformulates context compression as a structure-then-select process. First, our LingoEDU transforms linear text into a structural relation tree of Elementary Discourse Units (EDUs) which are anchored strictly to source indices to eliminate hallucination. Second, a lightweight ranking module selects query-relevant sub-trees for linearization. To rigorously evaluate structural understanding, we release StructBench, a manually annotated dataset of 248 diverse documents. Empirical results demonstrate that our method achieves state-of-the-art structural prediction accuracy and significantly outperforms frontier LLMs while reducing costs. Furthermore, our structure-aware compression substantially enhances performance across downstream tasks ranging from long-context tasks to complex Deep Search scenarios.

Yiqing Zhou, Xule Zhou, Zecan Cheng et al.

In WSN/IoT, node localization is essential to long-running applications for accurate environment monitoring and event detection, often covering a large area in the field. Due to the lower time resolution of typical WSN/IoT platforms (e.g., 1 microsecond on ESP32 platforms) and the jitters in timestamping, packet-level localization techniques cannot provide meter-level resolution. For high-precision localization as well as world-wide interoperability via 2.4-GHz ISM band, a new variant of LoRa, called LoRa 2.4 GHz, was proposed by semtech, which provides a radio frequency (RF) time of flight (ToF) ranging method for meter-level localization. However, the existing datasets reported in the literature are limited in their coverages and do not take into account varying environmental factors such as temperature and humidity. To address these issues, LoRa 2.4 GHz RF ToF ranging data was collected on a sports field at the XJTLU south campus, where three LoRa nodes logged samples of ranging with a LoRa base station, together with temperature and humidity, at reference points arranged as a 3x3 grid covering 400 square meter over three weeks and uploaded all measurement records to the base station equipped with an ESP32-based transceiver for machine and user communications. The results of a preliminary investigation based on a simple deep neural network (DNN) model demonstrate that the environmental factors, including the temperature and humidity, significantly affect the accuracy of ranging, which calls for advanced methods of compensating for the effects of environmental factors on LoRa RF ToF ranging outdoors.

Ningzhe Shi, Yiqing Zhou, Ling Liu et al.

Integrated sensing and communication (ISAC) can enhance artificial intelligence-generated content (AIGC) networks by providing efficient sensing and transmission. Existing AIGC services usually assume that the accuracy of the generated content can be ensured, given accurate input data and prompt, thus only the content generation quality (CGQ) is concerned. However, it is not applicable in ISAC-based AIGC networks, where content generation is based on inaccurate sensed data. Moreover, the AIGC model itself introduces generation errors, which depend on the number of generating steps (i.e., computing resources). To assess the quality of experience of ISAC-based AIGC services, we propose a content accuracy and quality aware service assessment metric (CAQA). Since allocating more resources to sensing and generating improves content accuracy but may reduce communication quality, and vice versa, this sensing-generating (computing)-communication three-dimensional resource tradeoff must be optimized to maximize the average CAQA (AvgCAQA) across all users with AIGC (CAQA-AIGC). This problem is NP-hard, with a large solution space that grows exponentially with users. To solve the CAQA-AIGC problem with low complexity, a linear programming (LP) guided deep reinforcement learning (DRL) algorithm with an action filter (LPDRL-F) is proposed. Through the LP-guided approach and the action filter, LPDRL-F can transform the original three-dimensional solution space to two dimensions, reducing complexity while improving the learning performance of DRL. Simulations show that compared to existing DRL and generative diffusion model algorithms without LP, LPDRL-F converges faster by over 60% and finds better resource allocation solutions, improving AvgCAQA by more than 14%. With LPDRL-F, CAQA-AIGC can achieve an improvement in AvgCAQA of more than 50% compared to existing schemes focusing solely on CGQ.