Zero-shot object navigation presents a highly challenging task in embodied AI, requiring an agent to interpret natural language instructions, perceive complex visual environments, and plan actions without any task-specific training. While recent approaches have introduced large language models (LLMs) as high-level planners, they often rely on static, one-shot inference and struggle with ambiguous or partially observable scenes. This paper proposes DebateNav, a novel multi-agent decision framework that integrates multiple vision-language model (VLM) experts under the supervision of a central LLM controller. Each VLM is assigned a unique expert role (e.g., object detection, risk assessment, spatial reasoning), and together they engage in structured multi-round debates when perception conflicts arise. The LLM controller performs task decomposition, memory-guided exploration, and final arbitration based on expert arguments. To enhance the perception and decision process, DebateNav incorporates a multimodal image fusion module combining RGB, depth, and segmentation inputs, as well as a map memory and trajectory tracking system that helps avoid redundant exploration and supports long-horizon planning. The system is evaluated on a subset of the HM3D dataset with approximately 5,000 tasks, achieving a$\mathbf{5 2. 3 \%}$success rate and$\mathbf{1 5. 5}$SPL under strict zero-shot conditions. Extensive ablation studies confirm the effectiveness of the expert debate mechanism, multi-modal fusion, memory system, and LLM-based arbitration. The results demonstrate that DebateNav outperforms several recent baselines and establishes a new perspective on collaborative, interpretable planning for zero-shot embodied navigation.

As the social environment is growing more complex and collaboration is deepening, factors affecting the healthy development of service ecosystem are constantly changing and diverse, making its governance a crucial research issue. Applying the scenario analysis method and conducting scenario rehearsals by constructing an experimental system before managers make decisions, losses caused by wrong decisions can be largely avoided. However, it relies on predefined rules to construct scenarios and faces challenges such as limited information, a large number of influencing factors, and the difficulty of measuring social elements. These challenges limit the quality and efficiency of generating social and uncertain scenarios for the service ecosystem. Therefore, we propose a scenario generator design method, which adaptively coordinates three Large Language Model (LLM) empowered agents that autonomously optimize experimental schemes to construct an experimental system and generate high quality scenarios. Specifically, the Environment Agent (EA) generates social environment including extremes, the Social Agent (SA) generates social collaboration structure, and the Planner Agent (PA) couples task-role relationships and plans task solutions. These agents work in coordination, with the PA adjusting the experimental scheme in real time by perceiving the states of each agent and these generating scenarios. Experiments on the ProgrammableWeb dataset illustrate our method generates more accurate scenarios more efficiently, and innovatively provides an effective way for service ecosystem governance related experimental system construction.

The interpretability of Convolutional Neural Networks (CNNs) has garnered significant attention in computer vision. Integrated Gradients (IG), as a widely used feature attribution method, quantifies the contribution of input features (pixels) to model predictions by accumulating gradients along an interpolation path. However, the linear interpolation path employed by IG can result in inflated attribution scores for irrelevant (unimportant) pixels, introducing noise into saliency maps and reducing their reliability. To address this issue, this paper proposes an improved feature attribution method called Dynamic Weighted Path Integration (DWPI). DWPI incorporates a dynamic interpolation path strategy, prioritizing the movement of the least important pixels to minimize interference from irrelevant pixels in attribution results. Additionally, DWPI leverages the model output rate to weight gradients, amplifying the influence of high-quality gradients and further enhancing the reliability of saliency maps. Experimental results on the ImageNet validation set demonstrate that DWPI outperforms other methods across various models and four standard perturbation test metrics. Ablation study further confirms the effectiveness of the dynamic interpolation path strategy and gradient weighting mechanism.

Aerial-terrestrial network (ATN) framework is currently the dominant method of the Internet of unmanned agents (IUAs) for integrating both aerial vehicles and terrestrial sensors. In ATN scenarios, existing work shows that route planning utilizing deep reinforcement learning (DRL) is important as it can conserve energy for aerial vehicles or diminish network latency. However, utilizing DRL approaches in a multiagent ATN environment may lead to inefficiencies when sharing original interaction data. In addition, direct data exchange between agents can raise significant privacy concerns. To address these challenges, this work develops a novel federated reinforcement learning (FRL) approach called FRIGE, which takes advantage of ensemble learning for DRL-based methods. Specifically, this article develops a prioritized gradient ensemble technique for local DRL agents, which constructs twin networks to obtain prioritized gradients without incurring additional interactive costs. After aggregating local models on the server, the latest iteration of the global model is used to update the networks of both agents and their twin networks for subsequent rounds of training. Extensive experiments are conducted on two typical ATN route planning tasks to validate FRIGE’s advancement and generalization capabilities. The numerical results demonstrate that FRIGE not only enhances data sharing efficiency among all agents but also maintains privacy while delivering superior solutions for ATN route planning tasks.

Graph Neural Networks (GNNs) have significantly advanced recommendation systems by capturing complex interplays between user-item relationships and dependencies. However, inherent noise in user behaviors, manifesting as random clicks and diverse browsing patterns, disrupts the structural integrity of graphs, thereby degrading the accuracy and reliability of GNN-based recommendation systems. Traditional graph pruning methods, which remove or reweight connections, often fail to adequately address this noise because they neglect the deeper causal factors influencing user choices, resulting in biased outcomes. To confront these challenges, this paper presents the GNN-based Causal Denoising Framework (GCDF). GCDF employs causal relationships to filter out noisy connections, thus enhancing the performance of GNNs. By utilizing a denoised graph that more accurately reflects the causal interactions among items, GCDF significantly improves the accuracy and reliability of recommendations, as evidenced by comprehensive empirical evaluations.

Crowdsourcing has been widely adopted in various domains for problem-solving, idea generation and data collection, leveraging distributed networks for efficient outcomes. Crowdsourcing platforms harness workers’ collective productivity by assigning tasks to a diverse pool of workers. However, all these tasks are assigned to registered high-quality workers on the platform, which is prone to task congestion. Moreover, the mechanical scheduling of workers ignores workers’ productivity fluctuations, and fails to make full use of the productivity of high-quality workers who are not currently online. In order to solve these problems, we design a Self-organizing Collaborative Crowdsourcing Framework (SoCCF) to support worker collaboration. Specifically, we propose a two-stage strategy, including a Reputation-driven Collaborative Partner Selection (RCPS) algorithm to expand the pool of collaborative workers and a Lyapunov optimization-based Task Acceptance and Sub-Delegation (LTASD) algorithm to guide worker to make workload decisions that meet emotional needs. Extensive experiments based on simulated crowdsourcing scenarios demonstrate that SoCCF consistently achieves higher overall service utility, while ensuring that workers can achieve 90.2% of the benefits of traditional algorithms with only 82.5% effort on average.

Single-cell RNA sequencing (scRNA-seq) data are typically with a large number of missing values, which often results in the loss of critical gene signaling information and seriously limit the downstream analysis. Deep learning-based imputation methods often can better handle scRNA-seq data than shallow ones, but most of them do not consider the inherent relations between genes, and the expression of a gene is often regulated by other genes. Therefore, it is essential to impute scRNA-seq data by considering the regional gene-to-gene relations. We propose a novel model (named scGGAN) to impute scRNA-seq data that learns the gene-to-gene relations by Graph Convolutional Networks (GCN) and global scRNA-seq data distribution by Generative Adversarial Networks (GAN). scGGAN first leverages single-cell and bulk genomics data to explore inherent relations between genes and builds a more compact gene relation network to jointly capture the homogeneous and heterogeneous information. Then, it constructs a GCN-based GAN model to integrate the scRNA-seq, gene sequencing data and gene relation network for generating scRNA-seq data, and trains the model through adversarial learning. Finally, it utilizes data generated by the trained GCN-based GAN model to impute scRNA-seq data. Experiments on simulated and real scRNA-seq datasets show that scGGAN can effectively identify dropout events, recover the biologically meaningful expressions, determine subcellular states and types, improve the differential expression analysis and temporal dynamics analysis. Ablation experiments confirm that both the gene relation network and gene sequence data help the imputation of scRNA-seq data.

MOTIVATION: High-resolution annotation of gene functions is a central task in functional genomics. Multiple proteoforms translated from alternatively spliced isoforms from a single gene are actual function performers and greatly increase the functional diversity. The specific functions of different isoforms can decipher the molecular basis of various complex diseases at a finer granularity. Multi-instance learning (MIL)-based solutions have been developed to distribute gene(bag)-level Gene Ontology (GO) annotations to isoforms(instances), but they simply presume that a particular annotation of the gene is responsible by only one isoform, neglect the hierarchical structures and semantics of massive GO terms (labels), or can only handle dozens of terms. RESULTS: We propose an efficacy approach IsofunGO to differentiate massive functions of isoforms by GO embedding. Particularly, IsofunGO first introduces an attributed hierarchical network to model massive GO terms, and a GO network embedding strategy to learn compact representations of GO terms and project GO annotations of genes into compressed ones, this strategy not only explores and preserves hierarchy between GO terms but also greatly reduces the prediction load. Next, it develops an attention-based MIL network to fuse genomics and transcriptomics data of isoforms and predict isoform functions by referring to compressed annotations. Extensive experiments on benchmark datasets demonstrate the efficacy of IsofunGO. Both the GO embedding and attention mechanism can boost the performance and interpretability. AVAILABILITYAND IMPLEMENTATION: The code of IsofunGO is available at http://www.sdu-idea.cn/codes.php?name=IsofunGO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

With the current raging spread of the COVID19, early forecasting of the future epidemic trend is of great significance to public health security. The COVID-19 is virulent and spreads widely. An outbreak in one region often triggers the spread of others, and regions with relatively close association would show a strong correlation in the spread of the epidemic. In the real world, many factors affect the spread of the outbreak between regions. These factors exist in the form of multimodal data, such as the time-series data of the epidemic, the geographic relationship, and the strength of social contacts between regions. However, most of the current work only uses historical epidemic data or single-modal geographic location data to forecast the spread of the epidemic, ignoring the correlation and complementarity in multi-modal data and its impact on the disease spread between regions. In this paper, we propose a Multimodal InformatioN fusion COVID-19 Epidemic forecasting model (MINE). It fuses inter-regional and intra-regional multi-modal information to capture the temporal and spatial relevance of the COVID-19 spread in different regions. Extensive experimental results show that the proposed method achieves the best results compared to state-of-art methods on benchmark datasets.

As considerable amounts of POI check-in data have been accumulated, successive point-of-interest (POI) recommendation is increasingly popular. Existing successive POI recommendation methods only predict where user will go next, ignoring when this behavior will occur. In this work, we focus on predicting POIs that will be visited by users in the next 24 hours. As check-in data is very sparse, it is challenging to accurately capture user preferences in temporal patterns. To this end, we propose a category-aware deep model CatDM that incorporates POI category and geographical influence to reduce search space to overcome data sparsity. We design two deep encoders based on LSTM to model the time series data. The first encoder captures user preferences in POI categories, whereas the second exploits user preferences in POIs. Considering clock influence in the second encoder, we divide each user’s check-in history into several different time windows and develop a personalized attention mechanism for each window to facilitate CatDM to exploit temporal patterns. Moreover, to sort the candidate set, we consider four specific dependencies: user-POI, user-category, POI-time and POI-user current preferences. Extensive experiments are conducted on two large real datasets. The experimental results demonstrate that our CatDM outperforms the state-of-the-art models for successive POI recommendation on sparse check-in data.