Abstract Medical image synthesis plays a crucial role in providing anatomically accurate images for diagnosis and treatment. Hallux valgus (HV), which affects approximately 19% of the global population, often requires frequent weight-bearing X-rays for assessment, which can be costly and logistically challenging in resource-limited healthcare settings due to the scarcity of horizontal foot imaging systems. Existing X-ray models often struggle to balance image fidelity, skeletal consistency, and physical constraints, particularly in diffusion-based methods that lack skeletal guidance. We propose the skeletal-constrained conditional diffusion model (SCCDM) along with two evaluation metrics, keypoint confidence-completeness (KCC) and keypoint structural consistency (KSC), to assess the anatomical plausibility of generated X-rays based on keypoint confidence and structural consistency. SCCDM incorporates multi-scale feature extraction and attention mechanisms, improving the structural similarity index by 5.72% (0.794) and the peak signal-to-noise ratio by 18.34% (21.40 dB). When combined with KCC and KSC, this method achieves average scores of 0.85 and 0.84, respectively, facilitating the clinical assessment of HV deformity and enabling more accurate surgical intervention. The code is available at https://github.com/midisec/SCCDM.

Marine litter detection remains a critical environmental challenge. Building on our previous evaluation of the PlastOPol dataset and object detection models, this research addresses key gaps in real-time detection and dataset coverage. While our earlier study demonstrated YOLOv5’s superior performance on low-power devices, it revealed limitations in existing datasets’ material-based classifications. We address these gaps by introducing a comprehensive marine litter dataset with material-type annotations and evaluating YOLOv11 for real-time detection on lowpower devices. Our advancements significantly enhance environmental monitoring capabilities and support citizen science initiatives.

Retrieval-Augmented Generation (RAG) systems in enterprise environments face challenges including semantic overlap, ambiguous requirements, and precise knowledge retrieval difficulties.Despite Agent-based methodological advances, limitations persist in collaborative efficiency and contextual adaptability.To address these challenges,this paper introduces the Orion framework, a multi-Agent collaborative RAG architecture for Enterprise Guidance Q&A Systems.The framework integrates three specialized Agents: Information Amplification Specialist enhances content presentation to resolve similarity-based errors; Interaction Analyst optimizes query formulation to address non-expert articulation deficiencies; and Query Complexity Evaluator selects appropriate language models based on query characteristics.Empirical evaluation demonstrates 93.43% question-answering accuracy in enterprise scenarios, significantly outperforming existing systems.This multi-Agent framework enhances knowledge quality, improves retrieval precision, and generates responses better aligned with user requirements, establishing novel pathways for enterprise knowledge management and guidance question-answering systems.

As robots become integral to public services, addressing harmful user behaviors like bullying is crucial. Existing research often overlooks the gradual nature of human bullying. This study fills this gap by exploring how robots can counter bullying through optimized responses. Using a simulated human-robot interaction study, we manipulated robot response behaviors and styles across escalating bullying severity. Results show that empathetic verbal responses promptly reduce users’ bullying tendencies by eliciting remorse and redirecting attention to social awareness. However, users’ underlying dispositions may override these reflexive reactions, emphasizing the need for a holistic understanding. In conclusion, a comprehensive approach is essential, involving immediate reaction optimization, emotional state assessment, and ongoing behavioral adjustment through empathetic dialogue. By implementing such strategies, we can transform human-robot relationships from potential bullying situations to harmonious interactions. This study provides an empirical foundation for response protocols that discourage bullying and enhance mutual understanding.

The general aim of multi-focus image fusion is to gather focused regions of different images to generate a unique all-in-focus fused image. Deep learning based methods become the mainstream of image fusion by virtue of its powerful feature representation ability. However, most of the existing deep learning structures failed to balance fusion quality and end-to-end implementation convenience. End-to-end decoder design often leads to unrealistic result because of its non-linear mapping mechanism. On the other hand, generating an intermediate decision map achieves better quality for the fused image, but relies on the rectification with empirical post-processing parameter choices. In this work, to handle the requirements of both output image quality and comprehensive simplicity of structure implementation, we propose a cascade network to simultaneously generate decision map and fused result with an end-to-end training procedure. It avoids the dependence on empirical post-processing methods in the inference stage. To improve the fusion quality, we introduce a gradient aware loss function to preserve gradient information in output fused image. In addition, we design a decision calibration strategy to decrease the time consumption in the application of multiple images fusion. Extensive experiments are conducted to compare with 19 different state-of-the-art multi-focus image fusion structures with 6 assessment metrics. The results prove that our designed structure can generally ameliorate the output fused image quality, while implementation efficiency increases over 30\% for multiple images fusion.

In online display advertising, guaranteed contracts and real-time bidding (RTB) are two major ways to sell impressions for a publisher. For large publishers, simultaneously selling impressions through both guaranteed contracts and in-house RTB has become a popular choice. Generally speaking, a publisher needs to derive an impression allocation strategy between guaranteed contracts and RTB to maximize its overall outcome (e.g., revenue and/or impression quality). However, deriving the optimal strategy is not a trivial task, e.g., the strategy should encourage incentive compatibility in RTB and tackle common challenges in real-world applications such as unstable traffic patterns (e.g., impression volume and bid landscape changing). In this paper, we formulate impression allocation as an auction problem where each guaranteed contract submits virtual bids for individual impressions. With this formulation, we derive the optimal bidding functions for the guaranteed contracts, which result in the optimal impression allocation. In order to address the unstable traffic pattern challenge and achieve the optimal overall outcome, we propose a multi-agent reinforcement learning method to adjust the bids from each guaranteed contract, which is simple, converging efficiently and scalable. The experiments conducted on real-world datasets demonstrate the effectiveness of our method.

In online display advertising, advertisers usually participate in real-time bidding to acquire ad impression opportunities. In most advertising platforms, a typical impression acquiring demand of advertisers is to maximize the sum value of winning impressions under budget and some key performance indicators constraints, (e.g. maximizing clicks with the constraints of budget and cost per click upper bound). The demand can be various in value type (e.g. ad exposure/click), constraint type (e.g. cost per unit value) and constraint number. Existing works usually focus on a specific demand or hardly achieve the optimum. In this paper, we formulate the demand as a constrained bidding problem, and deduce a unified optimal bidding function on behalf of an advertiser. The optimal bidding function facilitates an advertiser calculating bids for all impressions with only m parameters, where m is the constraint number. However, in real application, it is non-trivial to determine the parameters due to the non-stationary auction environment. We further propose a reinforcement learning (RL) method to dynamically adjust parameters to achieve the optimum, whose converging efficiency is significantly boosted by the recursive optimization property in our formulation. We name the formulation and the RL method, together, as Unified Solution to Constrained Bidding (USCB). USCB is verified to be effective on industrial datasets and is deployed in Alibaba display advertising platform.

Distributed peer-to-peer power energy markets are emerging quickly. Due to central governance and lack of effective information aggregation mechanisms, energy trading cannot be efficiently scheduled and tracked. We devise a new distributed energy transaction system over the energy Industrial Internet of Things based on predictive analytics, blockchain, and smart contract technologies. We propose a solution for scheduling distributed energy sources based on the Minimum Cut Maximum Flow theory. Blockchain is used to record transactions and reach consensus. Payment clearing for the actual power consumption is executed via smart contracts. Experimental results on real data show that our solution is practical and achieves a lower total cost for power energy consumption.

Constructing Sustainable Smart Water Supply systems are facing serious challenges all around the world with the fast expansion of modern cities. Water quality is influencing our life ubiquitously and prioritizing all the urban management. Traditional urban water quality control mostly focused on routine tests of quality indicators, which include physical, chemical, and biological groups. However, the inevitable delay for biological indicators has increased the health risk and leads to accidents such as massive infections in many big cities. In this paper, we first analyze the problem, technical challenges, and research questions. Then, we provide a possible solution by building a risk analysis framework for the urban water supply system. It takes indicator data we collected from industrial processes to perceive water quality changes, and further for risk detection. In order to provide explainable results, we propose an Adaptive Frequency Analysis (Adp-FA) method to resolve the data using indicators' frequency domain information for their inner relationships and individual prediction. We also investigate the scalability properties of this method from indicator, geography, and time domains. For the application, we select industrial quality data sets collected from a Norwegian project in four different urban water supply systems, as Oslo, Bergen, Strømmen, and Ålesund. We employ the proposed method to test spectrogram, prediction accuracy, and time consumption, comparing with classical Artificial Neural Network and Random Forest methods. The results show our method better perform in most of the aspects. It is feasible to support industrial water quality risk early warnings and further decision support.

Urban Water Supply (UWS) is one of the most critical and sensitive systems to sustain overall city operations. The European Union (EU) has strict water quality regulations that currently depend on periodic laboratory tests of selected parameters in most of the cases. The tests of some biological parameters can take up to 48 hours, which leads to the delay of risk detection and lengthens the response time for taking countermeasures in UWS. This situation increases the risk of negative impacts to the health of mass population. To address this challenge, we propose a data-driven risk analysis method which is low-cost and efficient. First, we build a framework for risk evaluation and prediction, within which a risk evaluation model is introduced considering the Quantitative Microbiological Risk Assessment (QMRA) process suggested by the World Health Organization (WHO). Second, we present a collaborative method to analyze biological risk features and similarities across different locations. Third, we propose a new risk prediction algorithm. We apply this method on the real-world data collected from 4 UWS systems in Norway. The preliminary results are depicted in risk maps and prediction accuracies are compared with different strategies. The application results show that our method is practical with good accuracy and explainability.

Real-Time Bidding (RTB) is an important paradigm in display advertising, where advertisers utilize extended information and algorithms served by Demand Side Platforms (DSPs) to improve advertising performance. A common problem for DSPs is to help advertisers gain as much value as possible with budget constraints. However, advertisers would routinely add certain key performance indicator (KPI) constraints that the advertising campaign must meet due to practical reasons. In this paper, we study the common case where advertisers aim to maximize the quantity of conversions, and set cost-per-click (CPC) as a KPI constraint. We convert such a problem into a linear programming problem and leverage the primal-dual method to derive the optimal bidding strategy. To address the applicability issue, we propose a feedback control-based solution and devise the multivariable control system. The empirical study based on real-word data from Taobao.com verifies the effectiveness and superiority of our approach compared with the state of the art in the industry practices.

Real-time bidding (RTB) is an important mechanism in online display advertising, where a proper bid for each page view plays an essential role for good marketing results. Budget constrained bidding is a typical scenario in RTB where the advertisers hope to maximize the total value of the winning impressions under a pre-set budget constraint. However, the optimal bidding strategy is hard to be derived due to the complexity and volatility of the auction environment. To address these challenges, in this paper, we formulate budget constrained bidding as a Markov Decision Process and propose a model-free reinforcement learning framework to resolve the optimization problem. Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint. Therefore, we innovate a reward function design methodology for the reinforcement learning problems with constraints. Based on the new reward design, we employ a deep neural network to learn the appropriate reward so that the optimal policy can be learned effectively. Different from the prior model-based work, which suffers from the scalability problem, our framework is easy to be deployed in large-scale industrial applications. The experimental evaluations demonstrate the effectiveness of our framework on large-scale real datasets.