This minimax regime is well matched with the core idea of adversarial learning, empowering our model with transferability as well as discriminability for domain adaptation tasks. Also, AEO is flexible and compatible with different deep networks and domain adaptation frameworks. Experiments on five data sets show that our method can achieve state-of-the-art performance across diverse domain adaptation tasks.With the memory-resource-limited constraints, class-incremental learning (CIL) usually suffers from the ``catastrophic forgetting'' problem when updating the joint classification model on the arrival of newly added classes. To cope with the forgetting problem, many CIL methods transfer the knowledge of old classes by preserving some exemplar samples into the size-constrained memory buffer. To utilize the memory buffer more efficiently, we propose to keep more auxiliary low-fidelity exemplar samples, rather than the original real-high-fidelity exemplar samples. Such a memory-efficient exemplar preserving scheme makes the old-class knowledge transfer more effective. However, the low-fidelity exemplar samples are often distributed in a different domain away from that of the original exemplar samples, that is, a domain shift. To alleviate this problem, we propose a duplet learning scheme that seeks to construct domain-compatible feature extractors and classifiers, which greatly narrows down the above domain gap. As a result, these low-fidelity auxiliary exemplar samples have the ability to moderately replace the original exemplar samples with a lower memory cost. In addition, we present a robust classifier adaptation scheme, which further refines the biased classifier (learned with the samples containing distillation label knowledge about old classes) with the help of the samples of pure true class labels. Experimental results demonstrate the effectiveness of this work against the state-of-the-art approaches. We will release the code, baselines, and training statistics for all models to facilitate future research.In this article, we present a comprehensive scheme for the quality assessment of compressed vibrotactile signals with human assessors. Inspired by the multiple stimulus test with hidden reference and anchors (MUSHRA) from the audio domain, we designed a method in which each compressed signal is compared to its original signal and rated on a numerical scale. For each signal tested, the hidden reference and two anchor signals are used to validate the results and provide assessor screening criteria. Differing from previous approaches, our method is hierarchically structured and strictly timed in a sequential manner to avoid experimental confounds and provide precise psychophysical assessments. We validated our method in an experiment with 20 human participants in which we compared two state-of-the-art lossy codecs. The results show that, with our approach, the performance of different codecs can be compared effectively. Furthermore, the method also provides a measure of subjective quality at different data compression rates. The proposed procedure can be easily adapted to evaluate other vibrotactile codecs.Contractures are generally assessed by a physician or physical therapist through palpation. However, contracture palpation requires skill and experience. The frictional vibration, which has a pulse-like vibration due to sliding disturbances around the affected area during palpation, is important in assessing the degree of contracture progression. This paper aims to enhance the perceptual sensitivity of frictional vibration for contracture palpation using a vibrotactile feedback system. We previously proposed an evaluation system for palpation with a wearable skin vibration sensor that detects skin-propagated vibration, allowing touch with a bare fingertip. In this paper, we propose the vibrotactile feedback system that presents the tactile information of the fingertip detected by the wearable tactile sensor to the temples with a vibrotactile display. A stimulator that gives vibrations similar to those during the palpation, which include pulse-like vibration and small vibration, was assembled. Then, psychophysical experiments on the vibrotactile feedback system were conducted using this stimulator. Perifosine The results showed that the detection sensitivity of the pulse-like vibration was significantly enhanced with the feedback.A significant research problem of recent interest is the localization of targets like vessels, surgical needles, and tumors in photoacoustic (PA) images.To achieve accurate localization, a high photoacoustic signal-to-noise ratio (SNR) is required. However, this is not guaranteed for deep targets, as optical scattering causes an exponential decay in optical fluence with respect to tissue depth. To address this, we develop a novel deep learning method designed to explicitly exhibit robustness to noise present in photoacoustic radio-frequency (RF) data. More precisely, we describe and evaluate a deep neural network architecture consisting of a shared encoder and two parallel decoders. One decoder extracts the target coordinates from the input RF data while the other boosts the SNR and estimates clean RF data. The joint optimization of the shared encoder and dual decoders lends significant noise robustness to the features extracted by the encoder, which in turn enables the network to contain detailed information about deep targets that may be obscured by noise. Additional custom layers and newly proposed regularizers in the training loss function (designed based on observed RF data signal and noise behavior) serve to increase the SNR in the cleaned RF output and improve model performance. To account for depth-dependent strong optical scattering, our network was trained with simulated photoacoustic datasets of targets embedded at different depths inside tissue media of different scattering levels. The network trained on this novel dataset accurately locates targets in experimental PA data that is clinically relevant with respect to the localization of vessels, needles, or brachytherapy seeds. We verify the merits of the proposed architecture by outperforming the state of the art on both simulated and experimental datasets.