Multiple Instance Learning (MIL) consists of two stages: feature encoding of instances and feature fusion of instances. This paper identifies two issues in the feature fusion stage of MIL when applied to pathological image classification, which creates performance bottlenecks. First, previous MIL methods lack positional encoding in the feature fusion stage. However, the number of positive instances is critical for pathological diagnosis. This makes it difficult for earlier MIL models to perceive positive instances, limiting their ability to capture the semantic correlation between the number of positive instances and the disease. Second, previous MIL methods base both inference and training on a single Whole Slide Image (WSI), failing to utilize cross-slide information effectively. To address these issues, this paper proposes a novel attention mechanism called CORE Attention (Contextual Position Encoding-based Retrievable Attention) during the instance fusion stage of MIL, and develops the COREMIL model based on it. CORE Attention consists of two modules: contextual position encoding and cross-slide retrieval-based attention fine-tuning. The contextual position encoding captures better contextual information, especially for counting tasks. Cross-slide retrieval-based attention fine-tuning allows the model to leverage previously learned historical information to guide the attention in the current feature fusion process. This paper validated COREMIL’s classification performance on several public datasets and a private dataset of pathological images. Extensive experiments demonstrate that COREMIL outperforms other current MIL models in terms of F1 Score and AUC on most datasets, with improvements of up to 14.414%. This CORE Attention-based approach offers an efficient solution for slide-level classification problems. Our code will be accessed shortly.

Accurate diagnosis of cerebral tumors is crucial for effective clinical therapeutics and prognosis. However, limitations in brain biopsy tissues and the scarcity of pathologists specializing in cerebral tumors hinder comprehensive clinical tests for precise diagnosis. To address these challenges, we first established a brain tumor dataset of 3,520 cases collected from multiple centers. We then proposed a novel Hierarchically Optimized Multiple Instance Learning (HOMIL) method for classifying six common brain tumor types, glioma grading, and predicting the origin of brain metastatic cancers. The feature encoder and aggregator in HOMIL were trained alternately based on specific datasets and tasks. Compared to other multiple instance learning (MIL) methods, HOMIL achieved state-of-the-art performance with impressive accuracies: 93.29% / 85.60% for brain tumor classification, 91.21% / 96.93% for glioma grading, and 86.36% / 79.28% for origin determination on internal/external datasets. Additionally, HOMIL effectively located multi-scale regions of interest, enabling an in-depth analysis through features and heatmaps. Extensive visualization demonstrated HOMIL's ability to cluster features within the same type while establishing distinct boundaries between tumor types. It also identified critical areas on pathological slides, regardless of tumor size.

Modern manufacturing strategy requires the robotic infrastructure to be able to adapt to new products or to accomplish new tasks quickly. In order to respond to this demand, teaching a robot to realize a task by demonstration has regained popularity in recent years, especially for dual-arm or humanoid robots. One of the main issues using this method is to adapt the captured motion from the human demonstration to the robot's specific kinematics and control. In this paper we present a method where the motion and grasping adaptation is tackled during the capture. We demonstrate the validity of this method with an experiment where a humanoid robot realizes an assembly previously demonstrated by a user wearing a Head Mounted Display (HMD) performing an assembly task in a virtual environment.

Cloud computing is emerging as an alternative computing platform to bridge the gap between scientists' growing computational demands and their computing capabilities. A scientist who wants to run HPC applications can obtain massive computing resources 'in the cloud' quickly (in minutes), as opposed to days or weeks it normally takes under traditional business processes. Due to the popularity of Amazon EC2, most HPC-in-the-cloud research has been conducted using EC2 as a target platform. Previous work has not investigated how results might depend upon the cloud platform used. In this paper, we extend previous research to three public cloud computing platforms. In addition to running classical benchmarks, we also port a 'full-size' NASA climate prediction application into the cloud, and compare our results with that from dedicated HPC systems. Our results show that 1) virtualization technology, which is widely used by cloud computing, adds little performance overhead; 2) most current public clouds are not designed for running scientific applications primarily due to their poor networking capabilities. However, a cloud with moderately better network (vs. EC2) will deliver a significant performance improvement. Our observations will help to quantify the improvement of using fast networks for running HPC-in-the-cloud, and indicate a promising trend of HPC capability in future private science clouds. We also discuss techniques that will help scientists to best utilize public cloud platforms despite current deficiencies.