Detecting the origin of information or infection spread in networks is a fundamental challenge with applications in misinformation tracking, epidemiology, and beyond. We study the multi-source detection problem: given snapshot observations of node infection status on a graph, estimate the set of source nodes that initiated the propagation. Existing methods either lack statistical guarantees or are limited to specific diffusion models and assumptions. We propose a novel conformal prediction framework that provides statistically valid recall guarantees for source set detection, independent of the underlying diffusion process or data distribution. Our approach introduces principled score functions to quantify the alignment between predicted probabilities and true sources, and leverages a calibration set to construct prediction sets with user-specified recall and coverage levels. The method is applicable to both single- and multi-source scenarios, supports general network diffusion dynamics, and is computationally efficient for large graphs. Empirical results demonstrate that our method achieves rigorous coverage with competitive accuracy, outperforming existing baselines in both reliability and scalability.

The remarkable progress of Vision Transformer (ViT) models has significantly advanced performance in computer vision tasks. However, the deployment of ViTs in resource-constrained environments remains a challenge, as the attention computation mechanisms within these models form a significant bottleneck, requiring substantial memory and computational resources. To address this challenge, we introduce TAFP-ViT, a tailored hardware-software co-design framework for Vision Transformers. On the software level, TAFP-ViT leverages a learnable compressor to perform multi-head shared compression on feature maps, and fuses decompression reconstruction, QKV generation and QKV processing together for calculation, thereby greatly reducing memory and computation requirements. Furthermore, TAFP-ViT combines dynamic inter-layer token pruning to eliminate unimportant tokens and hardware-friendly intra-block row pruning to diminish redundant computations. The proposed software design converts the calculations before and after SoftMax into dense and sparse triple matrix multiplication (TMM) forms respectively. On the hardware level, TAFP-ViT proposes a configurable systolic array (SA) to efficiently adapt to the QKV fusion computation pattern. The SA has flexible PE units that can effectively support general matrix multiplication (GEMM), dense and sparse TMM. The TMM and flexible dataflows allow TAFP-ViT to avoid handling transpositions and storing intermediate computation results, greatly enhancing computational efficiency. Besides, TAFP-ViT innovatively designs a Top-k engine to support dynamic pruning on the fly with high throughput and low resource consumption. Experiments show that the proposed TAFP-ViT achieves remarkable speedups of 123.91×, 29.5×, and 3.01∼ 20.65× compared to conventional CPUs, GPUs, and previous state-of-the-art works, respectively. Additionally, TAFP-ViT reaches a throughput of up to 731.5 GOP/s and an impressive energy efficiency of 77.9 GOPS/W.

The Motor Imagery Brain-Computer Interfaces (MI-BCIs) have shown considerable promise for applications in neural rehabilitation. However, improving the practicality and interpretability of MI-BCIs remains a critical challenge. Unlike previous methods that focus generally on either spatial, frequency, or temporal domains with coarse-grained segmentation schemes, this study proposes a novel fine-grained spatial-frequency-time (FGSFT) framework, aiming to enhance the efficiency and reliability of MI-BCIs. Multi-channel MI EEG recordings are firstly processed through multiscale time-frequency segmentation and spatial segmentation schemes, yielding fine-grained spatial-frequency-time segments (SFTSs). The key SFTSs are then selected with a tailored wrapper-based feature selection approach. Discriminative MI EEG features are extracted using a divergence-based common spatial pattern algorithm with intra-class regularization and classified using an efficient linear support vector machine (SVM). The proposed framework was evaluated on the BCI IV IIa and SDU-MI datasets, demonstrating state-of-the-art performance in terms of information transfer rate (ITR) Meanwhile, the proposed spatial segmentation strategy can significantly improve the performance of MI-BCIs when using a larger number of electrodes. Additionally, the fine-grained Motor Imagery Time-Frequency Reaction Map (MI-TFRM) and time-frequency topographical map can be obtained with the proposed framework enabling visualization of the subject-specific dynamic neural process during motor imagery tasks, facilitating the devising of personalized MI-BCIs. The FGSFT framework significantly advances the accuracy, ITR, and interoperability of MI-BCIs, paving the way for future neuroscientific research and clinical applications in neural rehabilitation and assistive technologies.

The face recognition of familiar and unfamiliar people is an essential part of our daily lives. However, its neural mechanism and relevant electroencephalography (EEG) features are still unclear. In this study, a new EEG-based familiar and unfamiliar faces classification method is proposed. We record the multichannel EEG with three different face-recall paradigms, and these EEG signals are temporally segmented and filtered using a well-designed filter-bank strategy. The filter-bank differential entropy is employed to extract discriminative features. Finally, the support vector machine (SVM) with Gaussian kernels serves as the robust classifier for EEG-based face recognition. In addition, the F-score is employed for feature ranking and selection, which helps to visualize the brain activation in time, frequency, and spatial domains, and contributes to revealing the neural mechanism of face recognition. With feature selection, the highest mean accuracy of 74.10% can be yielded in face-recall paradigms over ten subjects. Meanwhile, the analysis of results indicates that the EEG-based classification performance of face recognition will be significantly affected when subjects lie. The time–frequency topographical maps generated according to feature importance suggest that the delta band in the prefrontal region correlates to the face recognition task, and the brain response pattern varies from person to person. The present work demonstrates the feasibility of developing an efficient and interpretable brain–computer interface for EEG-based face recognition.

Automatic seizure detection is of great significance for epilepsy diagnosis and alleviating the massive burden caused by manual inspection of long-term EEG. At present, most seizure detection methods are highly patient-dependent and have poor generalization performance. In this study, a novel patient-independent approach is proposed to effectively detect seizure onsets. First, the multi-channel EEG recordings are preprocessed by wavelet decomposition. Then, the Convolutional Neural Network (CNN) with proper depth works as an EEG feature extractor. Next, the obtained features are fed into a Bidirectional Long Short-Term Memory (BiLSTM) network to further capture the temporal variation characteristics. Finally, aiming to reduce the false detection rate (FDR) and improve the sensitivity, the postprocessing, including smoothing and collar, is performed on the outputs of the model. During the training stage, a novel channel perturbation technique is introduced to enhance the model generalization ability. The proposed approach is comprehensively evaluated on the CHB-MIT public scalp EEG database as well as a more challenging SH-SDU scalp EEG database we collected. Segment-based average accuracies of 97.51% and 93.70%, event-based average sensitivities of 86.51% and 89.89%, and average AUC-ROC of 90.82% and 90.75% are yielded on the CHB-MIT database and SH-SDU database, respectively.

In this paper, a novel denoising method for electrocardiogram (ECG) signal is proposed to improve performance and availability under multiple noise cases. The method is based on the framework of conditional generative adversarial network (CGAN), and we improved the CGAN framework for ECG denoising. The proposed framework consists of two networks: a generator that is composed of the optimized convolutional auto-encoder (CAE) and a discriminator that is composed of four convolution layers and one full connection layer. As the convolutional layers of CAE can preserve spatial locality and the neighborhood relations in the latent higher-level feature representations of ECG signal, and the skip connection facilitates the gradient propagation in the denoising training process, the trained denoising model has good performance and generalization ability. The extensive experimental results on MIT-BIH databases show that for single noise and mixed noises, the average signal-to-noise ratio (SNR) of denoised ECG signal is above 39 dB, and it is better than that of the state-of-the-art methods. Furthermore, the denoised classification results of four cardiac diseases show that the average accuracy increased above 32 % under multiple noises under SNR=0 dB. So, the proposed method can remove noise effectively as well as keep the details of the features of ECG signals.

This paper presents a lightweight (<;250 g) and low-cost (<;350 USD) biomimetic prosthetic hand with two actuators embedded in the palm. One of them is employed for flexion/extension of the five digits, and the other one is used for the adduction/abduction of thumb. Thus, the hand can achieve major grasping tasks that account for about 85% of activities in daily life. The unique transmission provides various advantages such as a compact structure, weight saving, and short driven distance. Furthermore, by using 3D printing technology, most parts of the prosthetic hand were made to be much lighter and have a humanlike appearance, compared with conventionally manufactured artificial hand. Finally, the performance and practical applicability of the proposed design was verified experimentally through both of a motion verification and an intuitive control test by a healthy subject and a transradial amputee.

Piezoelectrets are polymer-foam based space-charge electrets with strong piezoelectric effect. The piezoelectricity in piezoelectrets occurs due to the elastic heterogeneous cellular structure and the regularly arranged dipolar space charges stored therein. Some polymers have been experimented for piezoelectret preparation, where polypropylene (PP) is the mostly applied material at present. PP piezoelectrets have several promising features, such as large piezoelectric d33coefficient, small thickness, light weight, low cost, large area scale, as well as flexibility and even stretchability, which would enable them very suitable for applications in signal sensing and energy harvesting. In this work, the electromechanical properties of flexible and stretchable PP piezoelectrets are introduced and some of their possible applications as wearable physiological-signal sensors and micro-energy harvesters are demonstrated by experiments.

Wearable systems based on continuously monitoring of vital physiological signals without interfering with user's daily life much are desired urgently in health care. Similarly, the limb amputees who need to wear their myoelectric prostheses for a long time daily expect a comfortable and reliable prosthetic system. It is inconvenient in clinical application of a myoelectric prosthesis to use the commonly used gel electrode for electromyography (EMG) recording over all day. Textile electrode with characteristics of ventilation, flexibility, and folding, may be an ideal selection of physiological signal monitoring in clinical applications. In this study, the textile electrodes made using screen printing technology were used for EMG recordings and the real-time performance of the textile-electrode EMG in myoelectric control of multifunctional prostheses was investigated in transradial amputees and able-bodied subjects for comparison purpose. The results over seven able-bodied subjects showed that the textile electrode could achieve similar performance as conventional metal electrodes for both the off-line classification accuracy and the real-time motion completion rate in operating a virtual hand. With the textile electrodes, the average off-line classification accuracy of 73.4% and the real-time motion completion rate of 81.9% within a 5 s time limit were achieved in three transradial amputees. These pilot results suggested that the textile electrodes might be feasible for EMG recordings in control of myoelectric prostheses.

Band-pass filter bank is an essential part in cochlear implant (CI). Its characteristics are important for synthesized sound quality because the outputs of filter bank contain a lot of fine structure cues which need to be encoded and transmitted to implanted electrode and then to stimulate auditory nerves. All phase DFT (APDFT) filter is a novel and high efficient digital filter, which possesses concurrent merits, such as zero-phase (or all-phase), abrupt cut-off characteristic. This paper presents the principle and the design method of APDFT filter bank and applies this filter into CI signal processing. Under the same acoustic simulation conditions, based on the continuous interleaved sampling (CIS) strategy, we analyze and evaluate the simulation results. Comparing with classical Butterworth filter bank adopted in CI processors, some objective waveform and spectra show that the output signal from APDFT filter bank is much closer to the original sound. For the normal-hearing (NH) listener, the synthetic speech and music by the APDFT filter bank have higher quality, and perceptual tests on six NH listeners primarily verify the improvement of intelligibility in noise. Thus, the APDFT filter could potentially improve the hearing quality for CI user.