Object detection in High-Resolution Wide (HRW) shots, or gigapixel images, presents unique challenges due to extreme object sparsity and vast scale variations. State-of-the-art methods like SparseFormer have pioneered sparse processing by selectively focusing on important regions, yet they apply a uniform computational model to all selected regions, overlooking their intrinsic complexity differences. This leads to a suboptimal trade-off between performance and efficiency. In this paper, we introduce GigaMoE, a novel backbone architecture that pioneers adaptive computation for this domain by replacing the standard Feed-Forward Networks (FFNs) with a Mixture-of-Experts (MoE) module. Our architecture first employs a shared expert to provide a robust feature baseline for all selected regions. Upon this foundation, our core innovation---a novel Sparsity-Guided Routing mechanism---insightfully repurposes importance scores from the sparse backbone to provide a "computational bonus,'' dynamically engaging a variable number of specialized experts based on content complexity. The entire system is trained efficiently via a loss-free load-balancing technique, eliminating the need for cumbersome auxiliary losses. Extensive experiments show that GigaMoE sets a new state-of-the-art on the PANDA benchmark, improving detection accuracy by 1.1% over SparseFormer while simultaneously reducing the computational cost (FLOPs) by a remarkable 32.3%.

Recently, enhancing image quality in the original RAW domain has garnered significant attention, with denoising and reconstruction emerging as fundamental tasks. Although some works attempt to couple these tasks, they primarily focus on cascade learning while neglecting task associativity within a broader parameter space, leading to suboptimal performance. This work introduces a novel approach by rethinking denoising and reconstruction from a "backbone-head" perspective, leveraging the stronger shared parameter space offered by the backbone, compared to the encoder used in existing works. We derive task-specific heads with fewer parameters to mitigate learning pressure. By incorporating chromaticity-and-noise perception module into the backbone and introducing task-specific supervision during training, we enable simultaneous high-quality results for reconstruction and denoising. Additionally, we design a dual-head interaction module to capture the latent correspondence between the two tasks, significantly enhancing multi-task accuracy. Extensive experiments validate the superiority of the proposed method. Code is available at: https://github.com/csmty/CANS.

Diffusion-based super-resolution methods have achieved impressive results under normal lighting conditions. However, their performance in low-light scenarios faces fundamental limitations due to two inherent challenges. First, the characteristic noise patterns and complex degradation features in severely underexposed images create significant obstacles for diffusion models to establish reliable noise prediction mechanisms. Second, these methods often fail to establish effective coupling between the degradation priors of low-light observations and the reconstruction process, resulting in compromised detail recovery and unrealistic texture synthesis.To address these limitations, we propose Degradation-aware Adaptation with Representation Embedding (DARE) method, a novel one-step diffusion framework specifically designed for super-resolution in dark environments. DARE employs a degradation-aware low-rank adaptation strategy that dynamically adjusts model parameters conditioned on degradation-specific features, effectively addressing compound degradations such as low-light, blur, and noise. Furthermore, we introduce a content-sensitive representation embedding mechanism, integrating complementary spatial and frequency domain priors through a bilinear cross-attention module. This module explicitly captures second-order statistical correlations, enriching semantic understanding and detail recovery during the denoising process. Extensive experiments across diverse low-light scenarios demonstrate that DARE outperforms state-of-the-art methods in terms of both visual quality and perceptual accuracy. The code is available at https://github.com/csmty/DARE.

In the context of complex dynamic environments, a robust tracking loop is essential for achieving accurate positioning with GNSS receivers. The tracking loop based on the Kalman filter significantly improves the tracking accuracy and dynamic performance. However, Kalman filtering relies on accurate prior knowledge to achieve optimal estimation. In practical applications, satellite signals are subject to various uncertainties, which can severely degrade tracking performance. To enhance tracking robustness, this paper proposes an adaptive $\boldsymbol{H}_{\infty}$ unscented Kalman filter (AHUKF) based on robust control theory. By utilizing the correlator output as measurement information, a tracking loop is devised based on AHUKF to effectively mitigate the impact of unknown signal noise statistics in complex scenarios. Field vehicle experiment demonstrates that the proposed method effectively suppresses the impact of external interference on the tracking loop, thereby indirectly enhancing receiver accuracy.

In this work, we propose an effective multi-stage neural ranking system for the clinical trial matching problem. First, we introduce NQS, a neural query synthesis method that leverages a zero-shot document expansion model to generate multiple sentence-long queries from lengthy patient descriptions. These queries are independently issued to a search engine and the results are fused. We find that on the TREC 2021 Clinical Trials Track, this method outperforms strong traditional baselines like BM25 and BM25 + RM3 by about 12 points in [email protected], a relative improvement of 34%. This simple method is so effective that even a state-of-the-art neural relevance ranking method trained on the medical subset of MS MARCO passage, when reranking the results of NQS, fails to improve on the ranked list. Second, we introduce a two-stage neural reranking pipeline trained on clinical trial matching data using tailored ranking templates. In this setting, we can train a pointwise reranker using just 1.1k positive examples and obtain effectiveness improvements over NQS by 24 points. This end-to-end multi-stage system demonstrates a 20% relative effectiveness gain compared to the second-best submission at TREC 2021, making it an important step towards better automated clinical trial matching.

The application of e-commerce to agricultural products circulation has the characteristic of leading role. It is not only helpful to its own development, but also improves the efficiency of agricultural products circulation. E-commerce allows many uses that can be combined in many ways. For example, displays may be static or animated, search for specific products may be assisted by a search function, sound may be added, payment by credit card may be possible, encryption may enhance the security of transactions, etc.. The result is an evolving, diverse set of e-commerce uses and business models. This article explored the successful model of agricultural products e-commerce on the basis of analysing the characteristics of agricultural products circulation, and put forward the thinking of developing agricultural products e-commerce.