Multiple Instance Learning (MIL) in digital pathology Whole Slide Image (WSI) analysis has shown significant progress. However, due to data bias and unobservable confounders, this paradigm still faces challenges in terms of performance and interpretability. Existing MIL methods might identify patches that do not have true diagnostic significance, leading to false correlations, and experience difficulties in integrating multi-scale features and handling unobservable confounders. To address these issues, we propose a new Multi-Scale Frequency Domain Causal framework (MFC). This framework employs an adaptive memory module to estimate the overall data distribution through multi-scale frequency-domain information during training and simulates causal interventions based on this distribution to mitigate confounders in pathological diagnosis tasks. The framework integrates the Multi-scale Spatial Representation Module (MSRM), Frequency Domain Structure Representation Module (FSRM), and Causal Memory Intervention Module (CMIM) to enhance the model's performance and interpretability. Furthermore, the plug-and-play nature of this framework allows it to be broadly applied across various models. Experimental results on Camelyon16 and TCGA-NSCLC dataset show that, compared to previous work, our method has significantly improved accuracy and generalization ability, providing a new theoretical perspective for medical image analysis and potentially advancing the field further. The code will be released at https://github.com/WissingChen/MFC-MIL.

Encrypted traffic classification refers to the task of identifying the application, service or malware associated with network traffic that is encrypted. Previous methods mainly have two weaknesses. Firstly, from the perspective of word-level (namely, byte-level) semantics, current methods use pre-training language models like BERT, learned general natural language knowledge, to directly process byte-based traffic data. However, understanding traffic data is different from understanding words in natural language, using BERT directly on traffic data could disrupt internal word sense information so as to affect the performance of classification. Secondly, from the perspective of packet-level semantics, current methods mostly implicitly classify traffic using abstractive semantic features learned at the top layer, without further explicitly separating the features into different space of categories, leading to poor feature discriminability. In this paper, we propose a simple but effective Aggregator and Separator Network (ASNet) for encrypted traffic understanding, which consists of two core modules. Specifically, a parameter-free word sense aggregator enables BERT to rapidly adapt to understanding traffic data and keeping the complete word sense without introducing additional model parameters. And a category-constrained semantics separator with task-aware prompts (as the stimulus) is introduced to explicitly conduct feature learning independently in semantic spaces of different categories. Experiments on five datasets across seven tasks demonstrate that our proposed model achieves the current state-of-the-art results without pre-training in both the public benchmark and real-world collected traffic dataset. Statistical analyses and visualization experiments also validate the interpretability of the core modules. Furthermore, what is important is that ASNet does not need pre-training, which dramatically reduces the cost of computing power and time. The model code and dataset will be released inhttps://github.com/pengwei-iie/ASNET.

Accurate lower limb motion capture is crucial for improving performance and user experience in fields such as motion analysis, rehabilitation training, and virtual reality. Traditional motion capture systems can only provide motion information, and often face issues such as occlusion or drift due to the limitations of sensor characteristics. For this purpose, we have designed a new type of data knee pad that integrates an Inertial Measurement Unit (IMU) and five liquid metal sensors. IMU provides basic motion data, while liquid metal sensors can provide information on joint bending and muscle activity. In order to extract effective information from sensor signals, we have developed an pose estimation model. The model first uses Fast Fourier Transform (FFT) to perform time-domain and frequency-domain analysis on the signal, in order to reveal hidden features in the signal. Next, inverse FFT and feature extraction are performed using the Transformer encoder to capture key motion features in the signal. Finally, we utilize a fully connected regression network to transform the extracted features into reconstruction of lower limb movements. Our system's lower limb pose estimation performance has been validated through a series of experiments, with an average tracking error of 1.48° for the personalized model. In addition, the ability of the system to capture muscle activity signals was also verified through experiments. Our system has achieved high-precision measurement of knee joint bending angle while capturing muscle activity signals, which other existing technologies cannot achieve. This makes our system more widely applicable in fields such as motion detection and rehabilitation evaluation of muscle diseases.

Finger motion tracking is a significant challenge in the field of motion capture. However, existing technology for finger motion tracking often requires the wearing of a heavy device and a laborious calibration process to track the bending angle of each joint; this can be challenging, particularly because the motion of each finger has a high coupling characteristic. To address this issue, in this work, we have proposed a compressed sensing-based tracking (CST) framework that enables the estimation of the bending angle of all hand joints using sensors smaller than the number of hand joints. Our framework also integrates a real-time calibration function, which significantly simplifies the calibration process. We developed a glove with multiple liquid metal sensors and an inertial measurement unit to evaluate the effectiveness of our CST framework. The experimental results show that our CST framework can achieve high-speed and accurate hand arbitrary motion capture with only 12 sensors. The motion-tracking gloves developed on this basis are user-friendly and particularly suitable for human–computer interaction applications in robot control, the metaverse and other fields.

In the field of pathology, the scarcity of certain diseases and the difficulty of annotating images hinder the development of large, high-quality datasets, which in turn affects the advancement of deep learning-assisted diagnostics. Few-shot learning has demonstrated unique advantages in modeling tasks with limited data, yet explorations of this method in the field of pathology remain in the early stages. To address this issue, we present a dual-channel prototype network (DCPN), a novel few-shot learning approach for efficiently classifying pathology images with limited data. The DCPN leverages self-supervised learning to extend the pyramid vision transformer (PVT) to few-shot classification tasks and combines it with a convolutional neural network to construct a dual-channel network for extracting multi-scale, high-precision pathological features, thereby substantially enhancing the generalizability of prototype representations. Additionally, we design a soft voting classifier based on multi-scale features to further augment the discriminative power of the model in complex pathology image classification tasks. We constructed three few-shot classification tasks with varying degrees of domain shift using three publicly available pathological datasets-CRCTP, NCTCRC, and LC25000-to emulate real-world clinical scenarios. The results demonstrated that the DCPN outperformed the prototypical network across all metrics, achieving the highest accuracies in same-domain tasks-70.86% for 1-shot, 82.57% for 5-shot, and 85.2% for 10-shot setups-corresponding to improvements of 5.51%, 5.72%, and 6.81%, respectively, over the prototypical network. Notably, in the same-domain 10-shot setting, the accuracy of the DCPN (85.2%) surpassed that of the PVT-based supervised learning model (85.15%), confirming its potential to diagnose rare diseases within few-shot learning frameworks.

Blindspot is a 2D web-based game that tells a story of a Chinese student newly arrived at an American university who experiences microaggressions in her college life. A prologue gives background information of the protagonist, followed by three chapters in different campus settings complete with puzzle games whose mechanics emphasize the microaggression topic of the chapter. Based on player dialogue choices, strategies for dealing with microaggressions may be followed or not, leading to different puzzles, with a hopeful ending presented to all. The narrative and game mechanics foster empathy with the protagonist, providing benefits for players in both marginalized and non-marginalized groups.

The principle and procedure of coding a convolutional neural network (CNN) in terms of the spectral transmittance of a programmable optical filter are proposed and discussed. They exhibit an intrinsic link between the CNNs and the optical filters, which leads to a methodology by which optical imaging through such spectral transmittance can be seen as equivalent to the results of hyperspectral data numerically postprocessed by the CNN. In such a manner, hyperspectral data acquisition and CNN postprocessing can be implemented simultaneously by the physical process of optical imaging in a snapshot; thus, more intelligent, informative, and real-time optical detection and sensing in the remote sensing applications can be achieved.

The cover image is based on the Original Article Sketch-based Shape-constrained Fireworks Simulation in Head Mounted Virtual Reality by Xiaogang Jin et al., https://doi.org/10.1002/cav.1920.

Abstract In this paper we present a novel shape‐constrained fireworks simulation method with rich textures in an HMD (Helmet Mounted Display) virtual environment using sketched feature lines as input. Our approach first retrieves an object from a three‐dimensional (3D) model database using a sketch‐based 3D shape retrieval algorithm. Then, in order to approximate models with complex structures, we introduce a novel point sampling algorithm based on Gaussian curvatures, which stores not only the positions of the selected vertices but also the texture (UV) coordinates information for texture display. In addition, we introduce a multilevel explosion process so that the fireworks can dynamically form specific, visually pleasing shapes. Through our experiments, we demonstrate that our approach can produce better results than state‐of‐the‐art approaches.