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arXiv 2025年

作者： Ding, Yuheng Wang, Yusong Qiang, Bo Yu, Jie Li, Qi Zhou, Yiran Liu, Zhenmin State Key Laboratory of Natural and Biomimetic Drugs School of Pharmaceutical Sciences Peking University Beijing100191 China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Paul G. Allen School of Computer Science & Engineering University of Washington SeattleWA98105 United States

Natural products, as metabolites from microorganisms, animals, or plants, exhibit diverse biological activities, making them crucial for drug discovery. Nowadays, existing deep learning methods for natural products research primarily rely on supervised learning approaches designed for specific downstream tasks. However, such one-model-for-a-task paradigm often lacks generalizability and leaves significant room for performance improvement. Additionally, existing molecular characterization methods are not well-suited for the unique tasks associated with natural products. To address these limitations, we have pre-trained a foundation model for natural products based on their unique properties. Our approach employs a novel pretraining strategy that is especially tailored to natural products. By incorporating contrastive learning and masked graph learning objectives, we emphasize evolutional information from molecular scaffolds while capturing side-chain information. Our framework achieves state-of-the-art (SOTA) results in various downstream tasks related to natural product mining and drug discovery. We first compare taxonomy classification with synthesized molecule-focused baselines to demonstrate that current models are inadequate for understanding natural synthesis. Furthermore, by diving into a fine-grained analysis at both the gene and microbial levels, NaFM demonstrates the ability to capture evolutionary information. Eventually, our method is experimented with virtual screening, illustrating informative natural product representations that can lead to more effective identification of potential drug candidates. © 2025, CC BY.

关键词： Metabolites

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Microbial Communities in Coastal Salt Marshes Along the Southeast Coast of China and their Effects on Carbon Storage

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Current Analytical Chemistry 2025年

作者： Lv, Chunguang Li, Yan Zhang, Huining Wang, Chenyi Ren, Min Zhang, Chiqian Zhejiang Wanli University College of Biological & Environmental Sciences 315000 China NingboTech University School of Civil Engineering 315000 China Marine Environmental Monitoring Centre of Ningbo 315100 China Civil Engineering Program College of Engineering & Computer Science Arkansas State University AR72467 United States

Background: Wetlands are vital carbon sinks, with coastal salt marshes being particularly effective in carbon sequestration. Understanding how different vegetation types influence soil carbon storage and microbial composition can enhance our knowledge of these ecosystems’ roles in global carbon cycling. Methods: This study investigates soil physicochemical properties, soil carbon storage, and microbial community composition and diversity at three depths (10, 30, and 60 cm) in five salt marsh plots from five coastal salt marshes: Bare flat (a non-vegetated marsh) and plots dominated by Imperata cylindrica, Solidago canadensis, Suaeda spp., and Phragmites australis. Carbon storage was evaluated by measuring soil organic carbon (SOC) content. At the same time, high-throughput sequencing was employed to analyze microbial communities, aiming to elucidate their relationships with soil carbon storage and wetland vegetation. Results: The average SOC contents in the five plots were in the order of S. canadensis (13.33 g·kg–1) > Bare flat (11.45 g·kg–1) > Suaeda spp. (8.10 g·kg–1) > I. cylindrica (6.15 g·kg–1) > P. australis (5.80 g·kg–1). S. canadensis and Bare flat marshes had the highest carbon content, which is mainly attributed to the presence of the most abundant carbon-fixation microbes, Fulvivirgaceae (family) and Acidobacteria RB41 (genus), in the soil of these two plots. Both organic and dissolved organic carbon contents at 10 cm were higher than those at the other two depths for all five marshes. Conclusion: The Bare flat exhibited strong carbon sequestration capability, second only to the invasive S. canadensis plot among the five plots surveyed. It also showed the highest microbial abundance and the greatest number of carbon-sequestration-related functional genes. The S. canadensis plot exhibited the lowest microbial community diversity and abundance despite having the highest carbon storage capacity. As an invasive species, S. canadensis should be removed to mit

关键词： Vegetation

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Fixed Frequency, Variable Amplitude (FiFVA) Actuation Systems for Micro Aerial Vehicles

Fixed Frequency, Variable Amplitude (FiFVA) Actuation System...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Ratti, Jayant Jones, Emanuel Vachtsevanos, George Robotics Program School of Electrical and Computer Engineering USA

ISBN: (纸本)9781612843858

Micro Aerial Vehicles (MAVs) have gained a significant amount of research lately, with a number of universities and industry sponsors paving the way with micro flying robots to perform Intelligence, Surveillance and Reconnaissance (ISR) Missions. However, much of the work done in flapping wing MAVs till date has not shown performance improvements over their Vertical Take Off and Landing (VTOL), rotary-wing counterparts. Research and development over the years has shown that insects and birds have unmatched flying capabilities in the Low Reynolds Number Regime. Their phenomenal flight performance is attributed to among others the highly energy efficient actuation systems and their power to weight ratios. The paper proposes and illustrates new and energy-efficient actuation mechanisms and incorporating them into MAVs. The paper begins by elaborating on the background of our research and the need for energy efficiency in MAVs;followed by showcasing resonant, elastic/restorative actuation mechanisms and their control methodology to reduce flight-actuation energy requirements. In conclusion, the paper contrasts and compares all the actuation systems for power intake, scalability and mechanical complexity.

关键词： Actuators Coils Couplings Frequency modulation Gears Springs Switches

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ERetinex: Event Camera Meets Retinex Theory for Low-Light Image Enhancement

arXiv

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arXiv 2025年

作者： Guo, Xuejian Tian, Zhiqiang Wang, Yuehang Li, Siqi Jiang, Yu Du, Shaoyi Gao, Yue the School of Software Engineering Xi’an Jiaotong University Xi’an710049 China the College of Computer Science and Technology Jilin University Changchun130012 China the National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China BNRist THUIBCS KLISS BLBCI School of Software Tsinghua University Beijing100084 China

Low-light image enhancement aims to restore the under-exposure image captured in dark scenarios. Under such scenarios, traditional frame-based cameras may fail to capture the structure and color information due to the exposure time limitation. Event cameras are bio-inspired vision sensors that respond to pixel-wise brightness changes asynchronously. Event cameras’ high dynamic range is pivotal for visual perception in extreme low-light scenarios, surpassing traditional cameras and enabling applications in challenging dark environments. In this paper, inspired by the success of the retinex theory for traditional frame-based low-light image restoration, we introduce the first methods that combine the retinex theory with event cameras and propose a novel retinex-based low-light image restoration framework named ERetinex. Among our contributions, the first is developing a new approach that leverages the high temporal resolution data from event cameras with traditional image information to estimate scene illumination accurately. This method outperforms traditional image-only techniques, especially in low-light environments, by providing more precise lighting information. Additionally, we propose an effective fusion strategy that combines the high dynamic range data from event cameras with the color information of traditional images to enhance image quality. Through this fusion, we can generate clearer and more detail-rich images, maintaining the integrity of visual information even under extreme lighting conditions. The experimental results indicate that our proposed method outperforms state-of-the-art (SOTA) methods, achieving a gain of 1.0613 dB in PSNR while reducing FLOPS by 84.28%. The code is available at https://***/lodew920/ERetinex. © 2025, CC BY.

关键词： Cameras

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Mixotrophic Chlorella pyrenoidosa biofilm with enhanced biomass production, microalgal activity, and nutrient removal from nutrient-rich wastewater

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Journal of Environmental sciences (China) 2025年 157卷 366-377页

作者： Wu, Xiongwei Jin, Cong Zhang, Chiqian Li, Ping Huang, Jim Junhui Wu, Jing Wu, Jinhua Hu, Zhiqiang School of Environment and Energy South China University of Technology Guangzhou510006 China Zhongshan Institute of Modern Industrial Technology of South China University of Technology Zhongshan528400 China Civil Engineering Program College of Engineering & Computer Science Arkansas State University JonesboroAR72467 United States Environmental Research Institute National University of Singapore Singapore117411 Singapore Department of Civil and Environmental Engineering University of Missouri-Columbia 65211 United States

Microalgae possess significant advantages in nitrogen and phosphorus removal from nutrient-rich wastewater that are highly efficient and independent of the C/N ratio. However, challenges such as low biomass productivity, high variability in nutrient removal under different trophic types, and difficulty in harvesting biomass limits the large-scale application of microalgae wastewater treatment. This study attempted to employ mixotrophic microalgae biofilm to address these issues. The biomass production, microalgal activity, and nutrient removal of Chlorella pyrenoidosa biofilms with different trophic types were compared for nutrient-rich wastewater treatment. The results showed that the biomass productivity of the mixotrophic microalgal biofilm (0.215 g/(L·d)) was 2.3, 8.6, and 6.0 times higher than that of photoautotrophic microalgal biofilm, heterotrophic microalgal biofilm, and photoautotrophic suspended microalga, respectively. Additionally, the dehydrogenase activity (DHA), indicating microalgal activity, of the mixotrophic biofilm was 2.3 and 16.5 times higher than that of photoautotrophic and heterotrophic biofilms, respectively. Meanwhile, the mixotrophic biofilm removed 96.0 % of NH4+-N and 99.2 % of PO43–-P, more efficient than that with other types of biofilms and suspended microalgae. In an open-ended air-lift photobioreactor, the mixotrophic microalgal biofilm produced biomass at 0.12 g/(L·d) and removed 90.0 % of NH4+-N and 97.6 % of PO43–-P. This study suggests that the mixotrophic microalgal biofilm shows promise in treating nutrient-rich wastewater and producing microalgal biomass for value-added products. © 2024

关键词： Microalgae

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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DiffEGG: Diffusion-Driven Edge Generation as a Pixel-Annotation-Free Alternative for Instance Annotation

arXiv

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arXiv 2025年

作者： Jo, Sanghyun Lee, Ziseok Lee, Wooyeol Kim, Kyungsu OGQ Seoul Korea Republic of Department of Biomedical Sciences Seoul National University Seoul Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of School of Transdisciplinary Innovations Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Achieving precise panoptic segmentation heavily relies on pixel-wise instance annotations, but obtaining such datasets is costly. Unsupervised instance segmentation (UIS) eliminates annotation requirements but struggles with adjacent instance merging and single-instance fragmentation, largely due to the limitations of DINO-based backbones, which lack strong instance separation cues. Weakly-supervised panoptic segmentation (WPS) reduces annotation costs by using sparse labels (e.g., points, boxes), yet these annotations remain expensive and introduce human bias and boundary errors, limiting performance. To address these challenges, we propose DiffEGG (Diffusion-Driven EdGe Generation), a fully annotation-free method that extracts instance-aware features from pretrained diffusion models to generate precise instance edge maps. DiffEGG fundamentally differs from prior UIS and WPS approaches by leveraging pretrained diffusion models as a backbone. Unlike DINO-based UIS methods, diffusion models inherently capture fine-grained, instance-aware features, enabling more precise boundary delineation. DiffEGG further enhances edge connectivity through self-supervised fine-tuning, progressively improving instance separation. For WPS, DiffEGG completely eliminates annotation costs and human bias by operating without any form of manual supervision, addressing the key limitations of prior best methods. Additionally, we introduce RIP, a post-processing technique that fuses DiffEGG’s edge maps with segmentation masks in a task-agnostic manner. This broad applicability allows DiffEGG to be seamlessly integrated into various segmentation frameworks. When applied to UIS, DiffEGG and RIP achieve an average +4.4 AP improvement over prior best UIS methods. When combined with weakly-supervised semantic segmentation (WSS), DiffEGG enables WPS without instance annotations, outperforming prior best point-supervised WPS methods by +1.7 PQ. These results demonstrate that DiffEGG’s edge maps serv

关键词： Semantic Segmentation

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Probabilistic genotype-phenotype maps reveal mutational robustness of RNA folding, spin glasses, and quantum circuits

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Physical Review Research 2025年第1期7卷 013118-013118页

作者： Anna Sappington Vaibhav Mohanty Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Harvard-MIT Program in Health Sciences and Technology Harvard Medical School Boston Massachusetts 02115 USA and Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Harvard/MIT MD-PhD Program Harvard Medical School Boston Massachusetts 02115 USA and Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Chemistry and Chemical Biology Harvard University Cambridge Massachusetts 02138 USA

Recent studies of genotype-phenotype maps have reported universally enhanced phenotypic robustness to genotype mutations, a feature essential to evolution. Virtually all of these studies make a simplifying assumption that each genotype—represented as a sequence—maps deterministically to a single phenotype, such as a discrete structure. Here we introduce probabilistic genotype-phenotype (PrGP) maps, where each genotype maps to a vector of phenotype probabilities, as a more realistic and universal language for investigating robustness in a variety of physical, biological, and computational systems. We study three model systems to show that PrGP maps offer a generalized framework which can handle uncertainty emerging from various physical sources: (1) thermal fluctuation in RNA folding, (2) external field disorder in the spin-glass ground state search problem, and (3) superposition and entanglement in quantum circuits, which are realized experimentally on IBM quantum computers. In all three cases, we observe a biphasic robustness scaling which is enhanced relative to random expectation for more frequent phenotypes and approaches random expectation for less frequent phenotypes. We derive an analytical theory for the behavior of PrGP robustness, and we demonstrate that the theory is highly predictive of empirical robustness.

关键词： Quantum computers

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency

arXiv

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arXiv 2025年

作者： Shen, Jiangrong Xu, Qi Pan, Gang Chen, Badong Faculty of Electronic and Information Engineering Xi’an Jiaotong University China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China State Key Lab of Brain-Machine Intelligence Zhejiang University China National Key Lab of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University China School of Computer Science Dalian University of Technology China

The human brain utilizes spikes for information transmission and dynamically reorganizes its network structure to boost energy efficiency and cognitive capabilities throughout its lifespan. Drawing inspiration from this spike-based computation, Spiking Neural Networks (SNNs) have been developed to construct event-driven models that emulate this efficiency. Despite these advances, deep SNNs continue to suffer from over-parameterization during training and inference, a stark contrast to the brain’s ability to self-organize. Furthermore, existing sparse SNNs are challenged by maintaining optimal pruning levels due to a static pruning ratio, resulting in either under or over-pruning. In this paper, we propose a novel two-stage dynamic structure learning approach for deep SNNs, aimed at maintaining effective sparse training from scratch while optimizing compression efficiency. The first stage evaluates the compressibility of existing sparse subnetworks within SNNs using the PQ index, which facilitates an adaptive determination of the rewiring ratio for synaptic connections based on data compression insights. In the second stage, this rewiring ratio critically informs the dynamic synaptic connection rewiring process, including both pruning and regrowth. This approach significantly improves the exploration of sparse structures training in deep SNNs, adapting sparsity dynamically from the point view of compression efficiency. Our experiments demonstrate that this sparse training approach not only aligns with the performance of current deep SNNs models but also significantly improves the efficiency of compressing sparse SNNs. Crucially, it preserves the advantages of initiating training with sparse models and offers a promising solution for implementing Edge AI on neuromorphic hardware. © 2025, CC BY-NC-ND.

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