Reconstructing high-resolution (HR) 3D Gaussian Splatting (3DGS) models from low-resolution (LR) inputs remains challenging due to the lack of fine-grained textures and geometry. Existing methods typically rely on pre-trained 2D super-resolution (2DSR) models to enhance textures, but suffer from 3D Gaussian ambiguity arising from cross-view inconsistencies and domain gaps inherent in 2DSR models. We propose IE-SRGS, a novel 3DGS SR paradigm that addresses this issue by jointly leveraging the complementary strengths of external 2DSR priors and internal 3DGS features. Specifically, we use 2DSR and depth estimation models to generate HR images and depth maps as external knowledge, and employ multi-scale 3DGS models to produce cross-view consistent, domain-adaptive counterparts as internal knowledge. A mask-guided fusion strategy is introduced to integrate these two sources and synergistically exploit their complementary strengths, effectively guiding the 3D Gaussian optimization toward high-fidelity reconstruction. Extensive experiments on both synthetic and real-world benchmarks show that IE-SRGS consistently outperforms state-of-the-art methods in both quantitative accuracy and visual fidelity.

As semiconductor manufacturing advances toward smaller technology nodes, triple-patterning lithography (TPL) has become indispensable. Existing TPL-aware routers address manufacturability constraints too late, leading to numerous stitches, conflicts, and mask density imbalances. To overcome this, we propose a "shift-TPL-left" strategy that, for the first time, integrates TPL awareness into the track assignment (TA) stage and tightly coordinates it with detailed routing (DR). This approach guides the TPL-aware detailed routing from a more macroscopic level, resulting in faster convergence and fewer DRC violations. Experimental results demonstrate that our method achieves DRC clean in 80% of cases on the ISPD’18 dataset, outperforms the state-of-the-art TPL-aware routing method by 7 × in mask balance score, and achieves a 6 × speedup in runtime.

Computer-aided diagnosis greatly enhances personalized treatment planning and diagnostic efficiency by providing accurate dental anatomy through teeth segmentation. However, it still constrained by the scarcity of high-quality annotated dental datasets. To address this issue, this paper presents a dataset combining both 2D panoramic X-rays with over 6,500 images and 3D CBCT with over 580 volumes (88,500+ slices) to support the Semi-supervised Teeth Segmentation (STS) Challenge, which includes partially meticulous annotations and covers all age groups. Moreover, multi-phase semi-supervised teeth segmentation algorithms and high-confidence pseudo-labels refinement strategies were proposed by competitors during this challenge. Algorithms were verified on this proposed dataset and good segmentation performance were achieved, over 93+ and 80+ Dice score were obtained for top three 2D and 3D participants, demonstrating the high quality of this proposed dataset. This paper also summarizes the diverse methods employed by the top-ranking teams in the MICCAI 2023 STS Challenge. Our dataset is publicly accessible through Zenodo ( https://zenodo.org/records/10597292 ), and the participants’ code is hosted on GitHub ( https://github.com/ricoleehduu/STS-Challenge ).

Triple patterning lithography (TPL) has been recognized as one of the most promising solutions to print critical features in advanced technology nodes. A critical challenge within TPL is the effective assignment of the layout to masks. Recently, various layout decomposition methods and TPL-aware routing methods have been proposed to consider TPL. However, these methods typically result in numerous conflicts and stitches, and are mainly designed for 2-pin nets. This paper proposes a multipin net routing method in triple patterning lithography, called Mr.TPL. Experimental results demonstrate that Mr.TPL reduces color conflicts by 81.17%, decreases stitches by 76.89%, and achieves up to $5.4 \times$ speed improvement compared to the state-of-the-art TPL-aware routing method.

Sequential recommendation systems aim to predict users' future interests based on their historical interactions and are widely applied in domains such as e-commerce and social media. However, in real-world scenarios, recommendation data often exhibits a long-tail distribution, resulting in extremely sparse interactions for a large number of users. Traditional methods face significant challenges in modeling such user preferences, thereby compromising recommendation performance and personalization. In recent years, Large Language Models (LLMs) have shown great promise in sparse scenarios due to their powerful language understanding and generation capabilities, offering new opportunities for user interest modeling. Nevertheless, existing approaches often rely on a single prompt or focus solely on item-side semantic information, neglecting users' multi-interest structures and collaborative behavior signals, which limits the reasoning potential of LLMs. To address these issues, this paper proposes the PERec (Prompt-enhanced Semantic Modeling with Large Language Models for Long-tail Recommendation) framework, aiming to enhance semantic modeling and recommendation performance for long-tail users. PERec builds a prompt-driven multi-interest semantic enhancement structure. Specifically, it first generates multiple personalized prompts from users' behavioral histories to guide LLMs in learning diverse preference-aware semantic representations. It then constructs semantic and collaborative dual-view representations and employs a contrastive alignment mechanism to enhance inter-view consistency. Finally, a view fusion strategy is used to generate robust user representations, thereby improving recommendation performance in sparse longtail scenarios. We conduct empirical studies on three real-world datasets-Amazon Beauty, Amazon Clothing, and Yelp. The results demonstrate that PERec significantly outperforms representative baseline methods in both overall recommendation performance and long-tail user accuracy, verifying its effectiveness and practical value in sparse recommendation settings.

Prognostic risk prediction is pivotal for clinicians to appraise the patient's esophageal squamous cell cancer (ESCC) progression status precisely and tailor individualized therapy treatment plans. Currently, CT-based multi-modal prognostic risk prediction methods have gradually attracted the attention of researchers for their universality, which is also able to be applied in scenarios of preoperative prognostic risk assessment in the early stages of cancer. However, much of the current work focuses only on CT images of the primary tumor, ignoring the important role that CT images of lymph nodes play in prognostic risk prediction. Additionally, it is important to consider and explore the inter-patient feature similarity in prognosis when developing models. To solve these problems, we proposed a novel multi-modal population-graph based framework leveraging CT images including primary tumor and lymph nodes combined with clinical, hematology, and radiomics data for ESCC prognostic risk prediction. A patient population graph was constructed to excavate the homogeneity and heterogeneity of inter-patient feature embedding. Moreover, a novel node-level multi-task joint loss was proposed for graph model optimization through a supervised-based task and an unsupervised-based task. Sufficient experimental results show that our model achieved state-of-the-art performance compared with other baseline models as well as the gold standard on discriminative ability, risk stratification, and clinical utility.

This paper presents an approach to solving the physician scheduling problem in fever clinics by combining Benders decomposition and column generation. The Benders decomposition involves iterating between a master problem that computes the physician staffing requirements and a subproblem that allocates physicians’ schedules to meet these requirements. We suggest an approach based on column generation for an effective solution to the subproblem. In addition, several acceleration strategies are provided to enhance the solution’s efficiency. Based on data collected from fever clinics in Shanghai, the numerical study confirms that the proposed method can control patient queue length and physician working hours. It is also demonstrated that the method can effectively optimize physician scheduling under severe epidemics. The models and algorithms developed from this research can assist fever clinics in their operation and management during an epidemic.Note to Practitioners—Our study is motivated by our collaboration with a fever clinic in a large hospital in Shanghai, China. Since 2019, the Covid-19 virus has spread worldwide, burdening the healthcare system immensely. In China, fever clinics are on the front line in the fight against Covid-19, providing services to patients at high risk of infection. Because of several specific constraints, the physician scheduling process in such clinics is different and more complex. It is challenging for medical managers to provide physicians with high-quality schedules. In order to solve this problem, we proposed a series of methods. In particular, an approach combining Benders decomposition and column generation is designed to solve the problem exactly, and several acceleration strategies are proposed to solve the problem more effectively. Based on real-life hospital data, we demonstrate that the methods presented in this article may help hospital managers obtain more reasonable scheduling solutions, thereby improving patient service quality without increasing physician workload.