Graph-based collaborative filtering has advanced by modeling higher-order interactions, yet performance remains constrained by underlying geometric assumptions and propagation schemes. User-item interaction graphs typically exhibit pronounced topological heterogeneity, whereas existing methods rely on a fixed, homogeneous geometry and employ tangent space aggregation. To address these fundamental limitations, this paper introduces Adaptive Geometric Collaborative Filtering (AGCF), a novel method rooted in Hamiltonian dynamics, which reframes representation learning as a physical process evolving on a time-varying manifold. AGCF is distinguished by an integrated design comprising: (1) a learnable, node-dependent Riemannian metric that construct a continuous heterogeneous manifold aligned with local topology; (2) unified dynamic trajectories that achieve intrinsic propagation without tangent space approximations; (3) a channel-wise metric that captures semantic anisotropy in the feature space. We rigorously prove global existence and uniqueness of the induced dynamics and explain the mechanism enabling long-range information propagation. Extensive experiments on five benchmark datasets show consistent gains over representative baselines.

The application of Reinforcement Learning (RL) in Electronic Design Automation (EDA), particularly for chip placement, has attracted considerable attention in recent years. While existing machine learning (ML)-based approaches have achieved notable progress, they predominantly focus on generating optimal layouts in a single attempt, often producing solutions that require subsequent refinement. To address this limitation, we propose RollPlace, a novel and generalized macro placement framework. RollPlace adopts a two-stage optimization strategy: generating initial placement solutions via machine learning methods or heuristic-based strategies, and refining these layouts efficiently by adjusting specific macros derived from the initial stage. This strategy circumvents the sequential generation constraints inherent in traditional RL-based placement methods. Furthermore, RollPlace seamlessly integrates Monte Carlo Tree Search (MCTS) to balance exploration and exploitation, and employs a rollout mechanism for efficient local search. Extensive experiments on the ISPD 2005 benchmark demonstrate that RollPlace outperforms state-of-the-art methods. Additionally, end-to-end experimental results based on OpenROAD across 19 benchmarks show that RollPlace excels in multiple metrics. The proposed framework offers a robust and scalable solution for addressing the growing complexity of modern chip design challenges.

Context-aware Multiple Instance Learning (MIL) is gaining popularity in Whole Slide Image (WSI) classification. Existing methods typically convert instances in a WSI into one-dimensional sequences and learn the long-range contextual dependencies among instances. However, due to the extremely large size of WSIs and the morphological similarities within tissue structures, the enormous number of redundant instances significantly increases computational overhead in the context learning paradigm. Additionally, the rearrangement of instances into one dimension loses the inherent spatial information involved in image patches, further compromising the classification performance of pathological images. Consequently, efficiently modeling contextual dependencies in WSIs remains a crucial challenge. In this paper, we propose a novel Semantic Anchor-based Context-aware Multiple Instance Learning (SeCoMIL) framework. This framework partitions the WSI into a series of regions and encodes the coordinates of instances within these regions to preserve their spatial relationships. Subsequently, SeCoMIL identifies the most representative instances from each region as semantic anchors. By capturing both the local context around these anchors and the global context across different anchors, the framework efficiently summarizes the critical pathological information of the WSI, enabling precise classification. Extensive experiments on four public datasets (CAMELYON16, CAMELYON17, TCGA-NSCLC, and TCGA-RCC) demonstrate the robustness of our method, with superior performance compared to state-of-the-art methods.

Automated classification of lymph node metastasis (LNM) plays an important role in the diagnosis and prognosis. However, it is very challenging to achieve satisfactory performance in LNM classification, because both the morphology and spatial distribution of tumor regions should be taken into account. To address this problem, this article proposes a two-stage dMIL-Transformer framework, which integrates both the morphological and spatial information of the tumor regions based on the theory of multiple instance learning (MIL). In the first stage, a double Max-Min MIL (dMIL) strategy is devised to select the suspected top-K positive instances from each input histopathology image, which contains tens of thousands of patches (primarily negative). The dMIL strategy enables a better decision boundary for selecting the critical instances compared with other methods. In the second stage, a Transformer-based MIL aggregator is designed to integrate all the morphological and spatial information of the selected instances from the first stage. The self-attention mechanism is further employed to characterize the correlation between different instances and learn the bag-level representation for predicting the LNM category. The proposed dMIL-Transformer can effectively deal with the thorny classification in LNM with great visualization and interpretability. We conduct various experiments over three LNM datasets, and achieve 1.79%-7.50% performance improvement compared with other state-of-the-art methods.

Since drug-target data have neither class labels nor the cluster number information, they are not suitable for clustering algorithms that require predefined parameters determined by comparing clustering results with real class labels. Density peaks clustering (DPC) is a density-based clustering alg orithm that can determine the number of clusters without requiring class labels. However, the predefined cutoff distance of local density limits its wide application. Therefore, this paper proposes an improved local density method based on a cutoff distance sequence that overcomes the limitations of DPC and can be successful applied to drug-target data. We also introduce multiple-dimensional scaling based on drug and target similarity and perform intuitive graph analysis of the two most significant differentiation features. Drugs of the Enzyme, GPCR, Ion Channel, and Nuclear Receptor 4 standard datasets are identified as 6, 6, 3, and 5 clusters by an improved algorithm, respectively, and similarly, their targets are identified be 5, 5, 8, and 4 clusters. Drug-target data clustering results of the improved algorithm are more reasonable than the results of the fast K-medoids and hierarchical clustering algorithms.

As a representative deep learning model, Convolutional Neural Networks (CNNs) can provide good features to represent the objects in image, and has made a great achievement in image classification and object detection. However, CNNs requires resizing the input images to a fixed size, which may affect the performance of the model due to information loss and distortion. To overcome the limitation, we replace the last pooling layer with topic model-LDA (Latent Dirichlet Allocation) to get a fixed-size output without resizing the input images, and we call it Topic Network. With Topic Network, the input images can be images of an arbitrary size and ratio without resizing, but the output is a k-dimension vector which represents the distribution of topics in image (k is the number of topics). Topic Network performs well in image classification task on Caltech101 and VOC2007 datasets.

This paper presents a computer vision system for tracking high-speed non-rigid skaters over a large playing area in short track speeding skating competitions. The outputs of the tracking system are spatio-temporal trajectories of the players which can be further processed and analyzed by sport experts. Given very fast and non-smooth camera motions to capture highly complex and dynamic scenes of skating, tracking amorphous skaters should be a challenging task. We propose a new method of (1) automatically computing the transformation matrices to map each frame of the imagery to the globally consistent model of the rink and (2) incorporating the hierarchical model based on the contextual knowledge and multiple cues into the unscented Kalman filter to improve the tracking performance when occlusion occurs. Experimental results show that the proposed algorithm is very efficient and effective on video recorded live by the authors in the World Short Track Speed Skating Championships.