Acquiring labeled data for deep learning tasks such as medical image classification is inherently costly, often requiring expert-level annotation. The scarcity of high-quality labels poses a significant challenge to traditional supervised methods, as models trained on small, naively selected datasets can suffer from distributional bias and poorly defined decision boundaries. Active Learning (AL) offers a compelling solution by strategically querying critical samples for annotation, thereby optimizing model generalization within limited annotation constraints. However, a critical limitation of conventional AL frameworks is that they predominantly emphasize sampling heuristics while defaulting to basic supervised objectives like standard cross-entropy. Due to the complex and ambiguous feature distributions inherent in medical imaging, this approach is insufficient. It fails to learn robust feature representations, leading to poor generalization and unreliable performance in real-world clinical scenarios. To address this gap, we propose a novel synergistic learning framework. Instead of inventing a new sampling method, our approach enhances the learning objective itself. We introduce a coordinated training goal that couples standard supervision with consistency regularization. This dual-objective approach creates a synergistic effect, ensuring that the model not only learns to differentiate between classes but also builds a more structured and robust feature space from the selectively acquired labels. We conduct extensive experiments on two challenging medical imaging datasets. Our results demonstrate that the proposed framework serves as a powerful and general-purpose training strategy that consistently and significantly improves the classification performance and stability of mainstream active learning algorithms across diverse sampling budgets. Code and datasets are available at https://github.com/ddfs430/Synergistic-Learning .

Visual Place Recognition (VPR) is essential for robotics and autonomous navigation, yet most methods rely on heavy task-specific training. Existing approaches fall into two main paradigms: single-stage models that learn compact global descriptors, and two-stage pipelines that combine coarse global retrieval with local feature or geometric verification. While effective, both require large annotated datasets and carefully tuned optimization, limiting scalability and cross-domain reuse. We introduce TF-VPR, a new benchmark that tackles a more challenging setting: VPR performed entirely without additional training, where descriptors are generated, refined and matched only at test time. Enabled by recent Vision Foundation Models (VFMs), TF-VPR systematically evaluates how far pretrained VFMs can be pushed for place recognition when used as-is, and provides a standardized protocol for fairly comparing arbitrary VFMs without fine-tuning. To support this, we unify major VPR datasets covering diverse real-world conditions and propose two lightweight, training-free modules: Training-Free Graph-Attention Graph Module (TF-GAM) and Training-Free Cross-Attention Module (TF-CAM). These plug-and-play modules enhance descriptor discriminability and retrieval robustness. Experiments show that TF-VPR exposes new challenges and reveals previously unexplored strengths of VFMs for training-free place recognition. Code and datasets are available at https://github.com/ddfs430/TF-VPR .

Devices on the Internet of Things (IoT) often have constrained resources and operate in diverse environments, making them vulnerable to unauthorized access and cyber threats. Electrocardiogram (ECG) signals have emerged as a promising biometric for authenticating users in such settings. However, current ECG-based authentication studies lack a standardized evaluation framework tailored to resource-limited IoT contexts and long-term usage, making it difficult to assess their practical reliability. In this paper, we introduce a new evaluation framework for ECG-based authentication on IoT devices and construct a standardized dataset to facilitate rigorous testing. We categorize performance metrics into four key dimensions: scalability, adaptability, efficiency, and cancelability. Using this framework, we evaluate four representative ECG authentication algorithms for IoT devices. The results show that these algorithms struggle to maintain consistent performance under cross-session authentication scenarios. These findings highlight the critical importance of addressing the temporal variability of ECG signals and the current gap in robust ECG-based authentication for IoT devices. We believe the proposed framework will guide future research toward more resilient and secure ECG authentication systems for the IoT.

Recent developments in mobile computing power and wireless communication speeds have significantly improved the efficiency of medical systems. This paper focuses on passive wearable sensor devices, which are integral to noninvasive monitoring of physiological data in healthcare observation. Beyond data collection, some wearables play an active role in patient treatment, underscoring the critical importance of protecting their security and privacy. Breach in these areas can severely affect patient health. However, the distinctive characteristics of wearable technologies introduce unique security and privacy challenges, including the potential for unauthorized access to sensitive location, medical, and physiological data. This review delves into the security and privacy concerns associated with wearable devices and proposes potential remedies. Its value lies in providing insights for researchers and manufacturers, aiming to advance the development of safer and more effective wearable medical technologies.

Electrocardiogram(ECG)-based biometric authentication has become a promising method to improve security in wearable devices due to its inherent uniqueness and difficulty to replicate. However, no studies currently demonstrate that ECG authentication can resist modern attack techniques employed against biometric authentication. In this paper, we present a novel dictionary attack against ECG authentication systems, which poses a significant threat. In contrast to conventional targeted attacks, this approach utilizes random pairing to breach a vast number of users, without requiring specific information about their biometric data. Our approach leverages adversarial optimization and clustering to generate synthetic ECG waveforms capable of bypassing authentication mechanisms of various systems, revealing critical vulnerabilities in the current implementation of ECG-based biometrics. We comprehensively evaluate the effectiveness of this attack across different ECG authentication models, demonstrating that despite the intrinsic uniqueness of ECG signals, a substantial number of users are vulnerable. Our attack method can bypass the authentication system of an average of 20% of users even at the most stringent false acceptance rate of 1%. With up to five attack attempts allowed, our method can bypass up to 62% of users’ ECG authentication models.

In-memory computing (IMC) has emerged as a promising solution to address both computation and data-movement challenges, by performing computation on data in-place directly in the memory array. IMC typically relies on analog operation, which makes analog-to-digital converters (ADCs) necessary, for converting results back to the digital domain. However, ADCs maintain computational efficiency by having limited precision, leading to substantial quantization errors in compute outputs. This work proposes RAOQ (Reshape and Adapt for Output Quantization) to overcome this issue, which comprises two classes of mechanisms including: 1) mitigating ADC quantization error by adjusting the statistics of activations and weights, through an activation-shifting approach (A-shift) and a weight reshaping technique (W-reshape); 2) adapting AI models to better tolerate ADC quantization through a bit augmentation method (BitAug), complemented by the introduction of ADC-LoRA, a low-rank approximation technique, to reduce the training overhead. RAOQ demonstrates consistently high performance across different scales and domains of neural network models for computer vision and natural language processing (NLP) tasks at various bit precisions, achieving state-of-the-art results with practical IMC implementations.

In recent times, electrocardiogram signals have been leveraged for biometric verification. The efficacy of such authentication is reliant on the feature extraction from the electrocardiogram signals. A number of electrocardiogram feature extraction methods are currently available, but these methods may not be universally applicable in different dataset collection scenarios. To tackle this issue, this paper introduces a clustering-based framework to assess the feature extraction techniques for electrocardiogram biometrics. In this paper, the effectiveness of the framework is validated by using different electrocardiogram feature extraction techniques and different electrocardiogram databases. The framework provides important insights into electrocardiogram signal.

With the increasing importance of data-intensive workloads, such as AI, in-memory computing (IMC) has demonstrated substantial energy/throughput benefits by addressing both compute and data-movement/accessing costs, and holds significant further promise by its ability to leverage emerging forms of highly-scaled memory technologies. However, IMC fundamentally derives its advantages through parallelism, which poses a trade-off with SNR, whereby variations and noise in nanoscaled devices directly limit possible gains. In this work, we propose novel training approaches to improve model tolerance to noise via a contrastive loss function and a progressive training procedure. We further propose a methodology for modeling and calibrating hardware noise, efficiently at the level of a macro operation and through a limited number of hardware measurements. The approaches are demonstrated on a fabricated MRAM-based IMC prototype in 22nm FD-SOI, together with a neural network training framework implemented in PyTorch. For CIFAR-10/100 classifications, model performance is restored to the level of ideal noise-free execution, and generalized performance of the trained model deployed across different chips is demonstrated.

Machine learning inference is of broad interest, increasingly in energy-constrained applications. However, platforms are often pushed to their energy limits, especially with deep learning models, which provide state-of-the-art inference performance but are also computationally intensive. This has motivated algorithmic co-design, where flexibility in the model and model parameters, derived from training, is exploited for hardware energy efficiency. This work extends a model-training algorithm referred to as Stochastic Data-Driven Hardware Resilience (S-DDHR) to enable statistical models of computations, amenable for energy/throughput aggressive hardware operating points as well as emerging variation-prone device technologies. S-DDHR itself extends the previous approach of DDHR by incorporating the statistical distribution of hardware variations for model-parameter learning, rather than a sample of the distributions. This is critical to developing accurate and composable abstractions of computations, to enable scalable hardware-generalized training, rather than hardware instanceby-instance training. S-DDHR is demonstrated and evaluated for a bit-scalable MRAM-based in-memory computing architecture, whose energy/throughput trade-offs explicitly motivate statistical computations. Using foundry data to model MRAM device variations, S-DDHR is shown to preserve high inference performance for benchmark datasets (MNIST, CIFAR-10, SVHN) as variation parameters are scaled to high levels, exhibiting less than 3.5% accuracy drop at 10× the nominal variation level.

Machine-learning algorithms are being employed in an increasing range of applications, spanning high-performance and energy-constrained platforms. It has been noted that the statistical nature of the algorithms can open up new opportunities for throughput and energy efficiency, by moving hardware into design regimes not limited to deterministic models of computation. This work aims to enable high accuracy in machine-learning inference systems, where computations are substantially affected by hardware variability. Previous work has overcome this by training inference model parameters for a particular instance of variation-affected hardware. Here, training is instead performed for the distribution of variation-affected hardware, eliminating the need for instance-by-instance training. The approach is referred to as Stochastic Data-Driven Hardware Resilience (S-DDHR), and it is demonstrated for an in-memory-computing architecture based on magnetoresistive random-access memory (MRAM). S-DDHR successfully address different samples of stochastic hardware, which would otherwise suffer degraded performance due to hardware variability.