Local Aggregation Research Articles

In-situ observations of gelatinous zooplankton aggregations in inshore and offshore Arctic waters

AbstractGelatinous zooplankton (GZ), play a crucial role in marine food webs, nutrient cycling, and carbon sequestration, however, quantifying their abundances remains challenging due to their delicate body structure, complex life cycles and variable population dynamics. Their tendency to form sporadic, large-scale aggregations further complicate the differentiation between true ecosystem alterations and stochastic variations in their abundance. In the Arctic Ocean, our understanding of GZ aggregations remains generally incomplete. Using in-situ observations from a towed pelagic camera system, we assessed the diversity and vertical distributions of GZ in fjord and offshore environments in northern Norway and the Svalbard archipelago. We found that Atlantic water masses harbored the highest GZ abundance, while intermediate waters showed the highest diversity. We documented dense aggregations of Beroe spp. in Van Mijenfjorden in Svalbard (observed during ascent of the camera system, not quantified in ind. m−3) and Bolinopsis infundibulum in the open Barents Sea (> 2.67 ind. m−3 at 100 m). Other observed taxa included the hydrozoans Aglantha digitale, Melicertum octocostatum, Solmundella bitentaculata, Pandeidae sp. and Physonectae spp., the scyphozoan Cyanea capillata and the ctenophores Mertensia ovum and Euplokamis sp. By linking the vertical distribution and observations of local aggregations with physical and biotic factors, we described the potential drivers of the distributional patterns observed. Towed camera surveys contribute to accurate in-situ observations, thereby improving our understanding of GZ aggregations and distributions in the Arctic Ocean.

AIFormer: Adaptive Interaction Transformer for 3D Point Cloud Understanding

Recently, significant advancements have been made in 3D point cloud analysis by leveraging transformer architecture in 3D space. However, it remains challenging to effectively implement local and global learning within irregular and sparse structures of 3D point clouds. This paper presents the Adaptive Interaction Transformer (AIFormer), a novel hierarchical transformer architecture designed to enhance 3D point cloud analysis by fusing local and global features through the adaptive interaction of features. Specifically, AIFormer mainly consists of several stacked AIFormer Blocks. Each AIFormer module employs the Local Relation Aggregation Module and the Global Context Aggregation Module, respectively, to extract local details of relationships within the reference point and long-range dependencies between reference points. Then, the local and global features are fused using the Adaptive Interaction Module for adaptive interaction to optimize the point representation. Additionally, the AIFormer Block further designs geometric relation functions and contextual relative semantic encoding to enhance local and global feature extraction capabilities, respectively. Extensive experiments on three popular 3D point cloud datasets verify that AIFormer achieves state-of-the-art or comparable performances. Our comprehensive ablation study further validates the effectiveness and soundness of the AIFormer design.

Evolution of Frustrated Coordination in Eutectic Electrolyte Driven by Ligand Asymmetry toward High-Performance Zinc Batteries.

Eutectic electrolytes hold promise for aqueous zinc metal batteries in sustainable energy storage chemistries, yet improvement from perspective of molecule configurational engineering are ambiguous. Herein, we propose design strategy of increasing asymmetric molecular geometry in organic ligands to regulate frustrated coordination and disordered structure for eutectic electrolytes toward enhanced zinc metal batteries. The introduced asymmetry in eutectic component gives rise to relatively weak coordination strength and configurational disorder interaction among cation-anion-ligand, leading to suppressed local aggregation, steady eutectic phase and improved Zn2+diffusion kinetics. Such highly frustrated coordination state also enables disruption of hydrogen bonding network and reinforcement of anion participation, which results in confined side reactions, decreased water activity and the formation of inorganic-enriched solid electrolyte interphase. In comparison to highly symmetric ligands, asymmetric ligand-involved eutectic electrolytes with configurational disorderdeliver high Coulombic efficiency of 99.4 %, stabilized Zn plating/stripping of 5000 h and impressive rate capability even under harsh conditions such as small N/P, low temperature. The rationale in this work advances the deep understanding of asymmetric molecular engineering in eutectic electrolytes and showcases suitability of frustrated coordination to achieve high-performance zinc metal batteries.

Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach.

Recent progress in single-image super-resolution (SISR) has achieved remarkable performance, yet the computational costs of these methods remain a challenge for deployment on resource-constrained devices. In particular, transformer-based methods, which leverage self-attention mechanisms, have led to significant breakthroughs but also introduce substantial computational costs. To tackle this issue, we introduce the Convolutional Transformer layer (ConvFormer) and propose a ConvFormer-based Super-Resolution network (CFSR), offering an effective and efficient solution for lightweight image super-resolution. The proposed method inherits the advantages of both convolution-based and transformer-based approaches. Specifically, CFSR utilizes large kernel convolutions as a feature mixer to replace the self-attention module, efficiently modeling long-range dependencies and extensive receptive fields with minimal computational overhead. Furthermore, we propose an edge-preserving feed-forward network (EFN) designed to achieve local feature aggregation while effectively preserving high-frequency information. Extensive experiments demonstrate that CFSR strikes an optimal balance between computational cost and performance compared to existing lightweight SR methods. When benchmarked against state-of-the-art methods such as ShuffleMixer, the proposed CFSR achieves a gain of 0.39 dB on the Urban100 dataset for the x2 super-resolution task while requiring 26% and 31% fewer parameters and FLOPs, respectively. The code and pre-trained models are available at https://github.com/Aitical/CFSR.

PillarVTP: vehicle trajectory prediction method based on local point cloud aggregation and receptive field expansion

PillarVTP: vehicle trajectory prediction method based on local point cloud aggregation and receptive field expansion

Flow of temporal network properties under local aggregation and time shuffling: a tool for characterizing, comparing and classifying temporal networks

Abstract Although many tools have been developed and employed to characterize temporal networks (TNs), the issue of how to compare them remains largely open. It depends indeed on what features are considered as relevant, and on the way the differences in these features are quantified. In this paper, we propose to characterize TNs through their behavior under general transformations that are local in time: (i) a local time shuffling, which destroys correlations at time scales smaller than a given scale b, while preserving large time scales, and (ii) a local temporal aggregation on time windows of length n. By varying b and n, we obtain a flow of TNs, and flows of observable values, which encode the phenomenology of the TN on multiple time scales. We use a symbolic approach to summarize these flows into labels (strings of characters) describing their trends. These labels can then be used to compare TNs, validate models, or identify groups of networks with similar labels. Our procedure can be applied to any TN and with an arbitrary set of observables, and we illustrate it on an ensemble of data sets describing face-to-face interactions in various contexts, including both empirical and synthetic data.

Contraband detection algorithm for X‐ray security inspection images based on global semantic enhancement

AbstractAiming at the problem of low detection accuracy caused by overlapping occlusion and noise disturbance, a contraband detection algorithm for X‐ray security inspection images based on global semantic enhancement is proposed to achieve accurate contraband target detection by enhancing global semantic information. First, the disturbance suppression module is used to weaken the noise disturbance at different positions by local suppression and aggregate finer detail information. Then, the parallel cascade search module is used to capture long‐range dependencies and strengthen the representation of global semantic information, which helps the model identify contraband under overlapping occlusion. Finally, the contribution of different features is adaptively adjusted through the feature‐weighted fusion module, which promotes the effective fusion of multi‐scale features and improves the accuracy of model detection. The method in this article has been extensively evaluated and experimented on three mainstream benchmark datasets: SIXray, OPIXray, and PIDray. The mAPs of three datasets reach 93.5%, 91.9%, and 85.9%, respectively. The experimental results fully demonstrate that the method in this article has better performance compared with the latest method, which can meet the practical application requirements of real‐time target detection.

Solar evaporation of liquid marbles with composite nanowire arrays

Solar interfacial evaporation is a sustainable technology to produce fresh water. The synergistic realization of broadband light absorption and good thermal insulation is crucial to improving the thermal efficiency. Here, we propose a new structure over the surface of liquid marbles, which via nanowire arrays assembled with composite nanomaterials (Fe3O4/CNT nanoparticles) to enhance the evaporation efficiency. The composite materials can effectively absorb light across the entire solar spectrum. Multiple reflections of light over nanowire array further improve light absorption. The narrow spaces and local particle aggregation of the nanowire array enhance thermal insulation to improve the evaporation efficiency. A series of experiments were conducted to verify the concept. The results showed that liquid marbles with upward Fe3O4/CNT nanowire arrays (Fe3O4/CNT-ULM) enhance the light harvesting ability and thermal insulation. Fe3O4/CNT-ULM at a volume ratio 1:2 provides the highest evaporation rate of 12.25 μg/s, which is about 1.16 and 1.53 times higher than that of Fe3O4 and CNT, respectively. Numerical analysis further illustrates that the combination of composite materials and nanowire arrays enhances concentration of electromagnetic field and heat at the air/water interfaces. This promotes rapid evaporation at the thermal boundary region, which has important implications for the structure design of solar interfacial evaporator.

The impact of particle size and concentration on the characteristics of solid–fluid two-phase flow in vertical pipe under pulsating flow

Vertical pipe hydraulic conveying is currently considered the most promising method for deep-sea mining. The conveying velocity at the inlet of the vertical pipe is often close to a pulsating form due to the influence of the flexible pipe and the marine environment. This study employs a combined Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) approach to investigate the impact of various particle sizes and concentrations on the flow behavior of solid–fluid two-phase flow in a vertical pipe under pulsating flow. The study reveals that fluid velocities exhibit periodic fluctuations within the vertical pipe under pulsating inlet flow. Moreover, the periodic phenomenon of local increases in particle velocity and concentration becomes more pronounced as particle size and concentration increase. Larger particle sizes increase particle inertia, reducing the ability to follow the fluid and leading to more localized particle aggregation. Therefore, the flow pattern within the vertical pipe transitions from homogeneous flow to plug flow as particle size increases. Variations in fluid–solid interaction forces and particle collision forces are explored to explain this phenomenon. When the particle size exceeds a critical threshold, the collision force surpasses the fluid–solid interaction force, leading to the transition.

A Secure and Fair Federated Learning Framework Based on Consensus Incentive Mechanism

Federated learning facilitates collaborative computation among multiple participants while safeguarding user privacy. However, current federated learning algorithms operate under the assumption that all participants are trustworthy and their systems are secure. Nonetheless, real-world scenarios present several challenges: (1) Malicious clients disrupt federated learning through model poisoning and data poisoning attacks. Although some research has proposed secure aggregation methods to address this issue, many methods have inherent limitations. (2) Clients may refuse or passively participate in the training process due to considerations of self-interest, and may even interfere with the training process due to competitive relationships. To overcome these obstacles, we have devised a reliable federated framework aimed at ensuring secure computing throughout the entirety of federated task processes. Initially, we propose a method for detecting malicious models to safeguard the integrity of model aggregation. Furthermore, we have proposed a fair contribution assessment method and awarded the right to write blocks to the creator of the optimal model, ensuring the active participation of participants in both local training and model aggregation. Finally, we establish a computational framework grounded in blockchain and smart contracts to uphold the integrity and fairness of federated tasks. To assess the efficacy of our framework, we conduct simulations involving various types of client attacks and contribution assessment scenarios using multiple open-source datasets. Results from these experiments demonstrate that our framework effectively ensures the credibility of federated tasks while achieving impartial evaluation of client contributions.

DEVELOPMENT OF A METHODOLOGY FOR DATA NORMALISATION AND AGGREGATION TO ENHANCE SECURITY LEVELS IN INTERNET OF THINGS INTERACTIONS

The number of interacting devices is increasing every day, and with this constant innovation, serious security challenges arise. The concept of the Internet of Things is being actively applied in both domestic and industrial settings. Researchers are increasingly highlighting the challenges and importance of network security. Data preprocessing plays an important role in security by transforming the input data corresponding to algorithmic criteria and thereby contributing to the prediction accuracy. The data preprocessing process is determined by many factors, including the processing algorithm, the data, and the application. Moreover, in Internet of Things interactions, data normalisation and aggregation can significantly improve security and reduce the amount of data used further decision making. This paper discusses the challenges of data normalisation and aggregation in the IoT to handle large amounts of data generated by multiple connected IoT devices. A secure data normalisation and aggregation method promotes successful minimised data transfer over the network and provides scalability to meet the increasing demands of IoT deployment. The proposed work presents approaches used in data aggregation protocols that address interference, fault tolerance, security and mobility issues. A local aggregation approach using the run-length encoding algorithm is presented. The proposed technique consists of data acquisition, data preprocessing, data normalisation and data aggregation steps. Data normalisation was performed via the Z-score algorithm, and the LEACH algorithm was used for data aggregation. In the experimental study, the percentage of faulty nodes reached 35%. The performance of the proposed solution was 0.82. The results demonstrate a reduction in resource consumption while maintaining the value and integrity of the data.

In Vivo Lifetime Imaging of the Internal O2 Dynamics in Corals with near-Infrared-Emitting Sensor Nanoparticles.

Mapping of O2 with luminescent sensors within intact animals is challenging due to attenuation of excitation and emission light caused by tissue absorption and scattering as well as interfering background fluorescence. Here we show the application of luminescent O2 sensor nanoparticles (∼50-70 nm) composed of the O2 indicator platinum(II) tetra(4-fluoro)phenyltetrabenzoporphyrin (PtTPTBPF) immobilized in poly(methyl methacrylate-co-methacrylic acid) (PMMA-MA). We injected the sensor nanoparticles into the gastrovascular system of intact colony fractions of reef-building tropical corals that harbor photosynthetic microalgae in their tissues. The sensor nanoparticles are excited by red LED light (617 nm) and emit in the near-infrared (780 nm), which enhances the transmission of excitation and emission light through biological materials. This enabled us to map the internal O2 concentration via time-domain luminescence lifetime imaging through the outer tissue layers across several coral polyps in flowing seawater. After injection, nanoparticles dispersed within the coral tissue for several hours. While luminescence intensity imaging showed some local aggregation of sensor particles, lifetime imaging showed a more homogeneous O2 distribution across a larger area of the coral colony. Local stimulation of symbiont photosynthesis in corals induced oxygenation of illuminated tissue areas and formation of lateral O2 gradients toward surrounding respiring tissues, which were dissipated rapidly after the onset of darkness. Such measurements are key to improving our understanding of how corals regulate their internal chemical microenvironment and metabolic activity, and how they are affected by environmental stress such as ocean warming, acidification, and deoxygenation. Our experimental approach can also be adapted for in vivo O2 imaging in other natural systems such as biofilms, plant and animal tissues, as well as in organoids and other cell constructs, where imaging internal O2 conditions are relevant and challenging due to high optical density and background fluorescence.

3D Point Cloud Semantic Segmentation based on Multi-scale Dense Nested Networks

Aiming at the problem that the relationship between geometric features and semantic features is ignored in the point cloud data downsampling process, which leads to inaccurate segmentation of object boundaries and structural details, this paper proposes a 3D point cloud semantic segmentation network based on multi-scale dense nested type. Firstly, a dense nested network architecture is constructed by nesting multiple multi-scale feature fusion modules to fuse multi-scale features of different directions between encoder-decoder paths, so as to effectively propagate local ge-ometric context information and enhance the ability of cross-scale information interaction. Secondly, a local feature aggregation unit is constructed in the multi-scale feature fusion module, which strengthens the structural awareness within the local point set based on graph convolution and attention mechanism, and promotes the complementarity of local geometric features and abstract semantic information. Then, the cross-layer multi-loss supervision module is combined to further optimize the multi-scale feature propagation, which makes the network training more stable and improves the point cloud segmentation accuracy. Finally, this paper verified the proposed network on the S3DIS dataset. The experimental results show that the proposed network has the mean intersection over Union of 71.2% and the overall accuracy of 88.7%, which is 1.2 and 0.7 percentage points higher than RandLA-Net, respectively, which proves that the proposed network can effectively improve the accuracy of 3D point cloud semantic segmentation.

A decision-making model for joint energy and reserve scheduling of wind power producers with local intraday demand response exchange market

In this paper, a three-stage stochastic bi-level optimization framework is presented for optimal participation of wind power producers (WPPs) in day-ahead (DA), intraday, and balancing markets. In this framework, to leverage demand response (DR) services, a peer-to-peer (P2P) energy trading platform is implemented that allows local load aggregators (LAs) to contribute to the intraday markets improving both LAs’ and WPP’s benefits. Participating in the intraday DR exchange (IDRX) market enables WPP to purchase DR services from LAs, to reduce the penalty cost on the deviation between the day-head bidding and the real-time dispatch. A Stackelberg game for the bi-level decision-making model captures the conflict of interests between the WPP and LAs, in which, the upper level seeks to maximize WPP profit, while the lower level aims to maximize LAs’ economic surplus. The bi-level model is converted into its equivalent single-level mixed-integer quadratic problem (MIQP) employing the Karush-Kuhn-Tucker (KKT) conditions and strong duality theorem. Simulation results show that participation of the WPP in the IDRX market and employing spinning reserve and DR services for compensating the uncertainties are greatly dependent on its risk preferences and increase its expected profit in all conditions, significantly.

Rational Design of Untranslated Regions to Enhance Gene Expression

How to improve gene expression by optimizing mRNA structures is a crucial question for various medical and biotechnological applications. Previous efforts focus largely on investigation of the 5′ UTR hairpin structures. In this study, we present a rational strategy that enhances mRNA stability and translation by engineering both the 5′ and 3′ UTR sequences. We have successfully demonstrated this strategy using green fluorescent protein (GFP) as a model in Escherichia coli and across different expression vectors. We further validated it with luciferase and Plasmodium falciparum lactate dehydrogenase (PfLDH). To elucidate the underlying mechanism, we have quantitatively analyzed both protein, mRNA levels and half-life time. We have identified several key aspects of UTRs that significantly influence mRNA stability and protein expression in our system: (1) The optimal length of the single-stranded spacer between the stabilizer hairpin and ribosome binding site (RBS) in the 5′ UTR is 25–30 nucleotide (nt) long. An optimal 32% GC content in the spacer yielded the highest levels of GFP protein production. (2) The insertion of a homodimerdizable, G-quadruplex structure containing RNA aptamer, “Corn”, in the 3′ UTR markedly increased the protein expression. Our findings indicated that the carefully engineered 5′ UTRs and 3′ UTRs significantly boosted gene expression. Specifically, the inclusion of 5 × Corn in the 3′ UTR appeared to facilitate the local aggregation of mRNA, leading to the formation of mRNA condensates. Aside from shedding light on the regulation of mRNA stability and expression, this study is expected to substantially increase biological protein production.

Salt Gradient-Induced Phoresis of Vesicles and Enhanced Membrane Fusion in a Crowded Milieu.

Phoresis of biocolloidal objects in response to chemical gradients is a matter of interest among diverse scientific disciplines owing to their importance in the spatiotemporal orchestration of biochemical processes. Although there are reports of soft matter transport/phoresis in the gradient of ions or salts in the aqueous system, their phoretic behavior in the presence of macromolecular crowder is largely unexplored. Notably, cellular cytoplasm is illustrated as a crowded milieu and thereby understanding biomolecular phoresis in the presence of polymeric macromolecules would endorse phoretic behavior in a biomimetic environment. Here, we report the phoresis-induced enhanced aggregation and fusion of vesicles in gradients of monovalent (NaCl) and divalent salt (MgCl2), in the presence of polymeric crowder, polyethylene glycol of molecular weight 400 (PEG 400). Apart from diffusiophoresis, depletion force plays a crucial factor in crowded environments to control localized vesicle aggregation in a salt gradient. This demonstration will potentially show the pathway to future research related to spatiotemporally correlated liposomal transport and membrane-dependent function (such as content mixing and signaling) in a physiologically relevant crowded environment.

An efficient federated learning solution for the artificial intelligence of things

Federated Learning (FL) has gained popularity due to its advantages over centralized learning. However, existing FL research has primarily focused on unconstrained wired networks, neglecting the challenges posed by wireless Internet of Things (IoT) environments. The successful integration of FL into IoT networks requires tailored adaptations to address unique constraints, especially in computation and communication. This paper introduces Communication-Aware Federated Averaging (CAFA), a novel algorithm designed to enhance FL operations in wireless IoT networks with shared communication channels. CAFA primarily leverages the latent computational capacities during the communication phase for local training and aggregation. Through extensive and realistic evaluations in dedicated FL-IoT framework, our method demonstrates significant advantages over state-of-the-art approaches. Indeed, CAFA achieves up to a 4x reduction in communication costs and accelerates FL training by as much as 70%, while preserving model accuracy. These achievements position CAFA as a promising solution for the efficient implementation of FL in constrained wireless networks.

Structural transitions in liquid semiconductor alloys: A molecular dynamics study with a neural network potential.

Liquid-liquid phase transitions hold a unique and profound significance within condensed matter physics. These transitions, while conceptually intriguing, often pose formidable computational challenges. However, recent advances in neural network (NN) potentials offer a promising avenue to effectively address these challenges. In this paper, we delve into the structural transitions of liquid CdTe, CdS, and their alloy systems using molecular dynamics simulations, harnessing the power of an NN potential named LaspNN. Our investigations encompass both pressure and temperature effects. Through our simulations, we uncover three primary liquid structures around melting points that emerge as pressure increases: tetrahedral, rock salt, and close-packed structures, which greatly resemble those of solid states. In the high-temperature regime, we observe the formation of Te chains and S dimers, providing a deeper understanding of the liquid's atomic arrangements. When examining CdSxTe1-x alloys, our findings indicate that a small substitution of S by Te atoms for S-rich alloys (x > 0.5) exhibits a structural transition much different from CdS, while a large substitution of Te by S atoms for Te-rich alloys (x < 0.5) barely exhibits a structural transition similar to CdTe. We construct a schematic diagram for liquid alloys that considers both temperature and pressure, providing a comprehensive overview of the alloy system's behavior. The local aggregation of Te atoms demonstrates a linear relationship with alloy composition x, whereas that of S atoms exhibits a nonlinear one, shedding light on the composition-dependent structural changes.

RTIA-Mono: Real-time lightweight self-supervised monocular depth estimation with global-local information aggregation

Self-supervised monocular depth estimation has attracted significant attention in computer vision, especially for applications like autonomous driving and robotics. Recently, CNNs and Transformers have achieved tremendous success in this task. However, existing research primarily focuses on improving estimation accuracy, increasing model complexity poses challenges for deployment on edge computing devices. Shallow CNNs aid lightweight network construction but suffer limited receptive fields, hindering fusion of local geometric features and global semantic information. To address these issues, we propose an efficient real-time lightweight self-supervised architecture, RTIA-Mono, for monocular depth estimation. Firstly, we design a cross-stage feature fusion structure promoting feature aggregation and fusion across stages. Secondly, in each stage, we propose a Global Local Information Aggregation (GLIA) module integrating advantages of CNNs and Transformers to aggregate local and global features. Additionally, we introduce a Directional Feature Enhancement (DFE) module supplementing spatial structure information to mitigate spatial information loss from downsampling. Through sophisticated design, the proposed approach outperforms state-of-the-art methods on KITTI benchmark with the least parameters, and achieves a good balance between accuracy, complexity and inference speed. Furthermore, RTIA-Mono demonstrates excellent generalization on other datasets.

NLA-GNN: Non-local information aggregated graph neural network for heterogeneous graph embedding

Heterogeneous graphs are ubiquitous in the real world. Recent methods aim to obtain meaningful low-dimensional node embeddings from heterogeneous graphs to facilitate downstream applications. However, most existing methods tend to consider the local information but ignore the non-local information. This paper proposes a novel Non-Local Information Aggregated Graph Neural Network (NLA-GNN) that aggregate not only the local information from neighbor nodes but also non-local information from distant nodes. Specifically, Local aggregation modules in NLA-GNN utilize the attention mechanism to generate potentially valuable metapaths and use them to aggregate local information. In contrast, non-local aggregation modules adopt a two-step approach, and each step uses an attention-guided method to sort nodes into node sequences and aggregate information with the methods designed for sequential data. Experiment results on three heterogeneous graph datasets demonstrate the performance of NLA-GNN over state-of-the-arts and the necessity of non-local aggregation in heterogeneous graphs.