Mobile crowdsourcing platforms leverage a mass of mobile users and their vehicles to learn massive point-of-interest (PoI) information while traveling and share it as a public good. Given that the crowdsourced users mind their travel costs and have diverse usage preferences for PoI information along different paths, we formulate the problem as a novel non-atomic multi-path routing game. This game features positive network externalities from social information sharing, distinguishing it from the traditional routing game literature that primarily focuses on congestion control of negative externalities among users. Our price of anarchy (PoA) analysis shows that in the absence of any incentive design, users’ selfish routing on the lowest-cost path will significantly limit PoI diversity leading to an arbitrarily large efficiency loss from the social optimum with a PoA of 0. This motivates us to design effective incentive mechanisms to remedy while upholding desirable properties including individual rationality (IR), incentive compatibility (IC), and budget balance (BB) to ensure practical feasibility. Without knowing a specific user’s path preference, we first present a non-monetary mechanism called Adaptive Information Restriction (AIR) that satisfies all the desirable properties. Our AIR indirectly penalizes non-cooperative users by adaptively reducing their access to the public good, according to actual user flows along different paths. It achieves a significant PoA of 1/4 with low complexityO(klogk+ logm), wherekandmrepresent the numbers of paths and user preference types, respectively. When the system can support pricing/billing for users, we further propose a new monetary mechanism called Adaptive Side-Payment (ASP), which adaptively charges and rewards users based on their chosen paths. Our ASP achieves a better PoA of 1/2 with an even lower complexity ofO(klogk). Finally, our theoretical findings are well corroborated by our experimental results using a real-world dataset.

To promote viral marketing, major social platforms (e.g., Facebook Marketplace and Pinduoduo) repeatedly select and invite different users (as seeds) in online social networks to share fresh information about a product or service with their friends. Thereby, we are motivated to optimize a multi-stage seeding process of viral marketing in social networks, and adopt the recent notions of the peak and the average age of information (AoI) to measure the timeliness of promotion information received by network users. Our problem is different from the literature on information diffusion in social networks, which limits to one-time seeding and overlooks AoI dynamics or information replacement over time. As a critical step, we manage to develop closed-form expressions that characterize and trace AoI dynamics over any social network. For the peak AoI problem, we first prove the NP-hardness of our multi-stage seeding problem by a highly non-straightforward reduction from the dominating set problem, and then present a new polynomial-time algorithm that achieves good approximation guarantees (e.g., less than 2 for linear network topology). To minimize the average AoI, we also prove that our problem is NP-hard by properly reducing it from the set cover problem. Benefiting from our two-sided bound analysis on the average AoI objective, we build up a new framework for approximation analysis and link our problem to a much simplified sum-distance minimization problem. This intriguing connection inspires us to develop another polynomial-time algorithm that achieves a good approximation guarantee. Additionally, our theoretical results are well corroborated by experiments on a real social network.

BACKGROUND: Hepatic metastases (HM) and primary liver malignant tumor (PLMC) share partially similar pathological foundations, which can make it difficult to differentiate them based on visual imaging findings. This study will explore the value of multi-algorithm radiomics machine learning models that integrate ultrasound imaging and inflammation-immune features in the identification of HM. METHODS: Patients with hepatic malignancies who had undergone ultrasound-guided biopsy and image acquisition were retrospectively included. A total of 104 patients were randomly divided into training and internal validation cohorts at a 6:4 ratio, while other 45 patients were assigned as the external validation cohort. The PyRadiomics package was utilized to extract 107 radiomics original features. The Wilcoxon test was employed to identify high-value features significantly associated with HM. Univariate analysis was employed to identify inflammation-immune risk features related to HM for model development. 12 machine learning algorithms were integrated to combine radiomics features with inflammation-immune features. Model performance was comprehensively evaluated through receiver operating characteristic curves, and Shapley additive explanations (SHAP) method. RESULTS: The Wilcoxon test identified 15 radiomics features significantly associated with HM (P < 0.001), which were subsequently presented to 12 machine learning algorithms to develop 98 models using two or more features. Among these, 68% of the models achieved moderate identify performance [area under the curve (AUC), 0.73-0.82] in the internal validation cohort. The optimal algorithm radiomics model (random forest + gradient boosting machine) was developed using 4 selected features, yielding AUCs of 0.77 in the training cohort and 0.82 in the internal validation cohort, 0.70 in the external validation cohort, respectively. Univariate analysis further identified platelet-to-lymphocyte ratio (PLR) and platelet-to-albumin ratio (PAR) as high-risk inflammation-immune features for HM. While integrating radiomic features with PLR/PAR enhanced identification performance in the training cohort (AUC = 0.84), this improvement was not significantly replicated in the internal validation cohort (AUC = 0.82), but there was an improvement in the external validation cohort (AUC = 0.74). SHAP analysis revealed that PAR contributed most to the integrated model's predictions, followed by the aforementioned 4 radiomic features. CONCLUSION: This study revealed the potential clinical utility of radiomics features and inflammation-immune features in HM identification. CLINICAL TRIAL NUMBER: Not applicable.

To promote viral marketing, major social platforms (e.g., Facebook Marketplace and Pinduoduo) repeatedly select and invite different users (as seeds) in online social networks to share fresh information about a product or service with their friends. Thereby, we are motivated to optimize a multi-stage seeding process of viral marketing in social networks, and adopt the recent notions of the peak and the average age of information (AoI) to measure the timeliness of promotion information received by network users. Our problem is different from the literature on information diffusion in social networks, which limits to one-time seeding and overlooks AoI dynamics or information replacement over time. As a critical step, we manage to develop closed-form expressions that characterize and trace AoI dynamics over any social network. For the peak AoI problem, we first prove the NP-hardness of our multi-stage seeding problem by a highly non-straightforward reduction from the dominating set problem, and then present a new polynomial-time algorithm that achieves good approximation guarantees (e.g., less than 2 for linear network topology). For minimizing the average AoI, we also prove that our problem is NP-hard by properly reducing it from the set cover problem. Benefiting from our two-side bound analysis on the average AoI objective, we build up a new framework for approximation analysis and link our problem to a much simplified sum-distance minimization problem. This intriguing connection inspires us to develop another polynomial-time algorithm that achieves a good approximation guarantee. Additionally, our theoretical results are well corroborated by experiments on a real social network.