Mcleanlunde1662

Through this framework, our model also learns meaningful image representations in a lower dimensional latent space and semantically associate separate parts of the encoded vector with both the person's identity and facial attributes. This opens up the possibility of generating new faces and other transformations such as making the face thinner or chubbier. Furthermore, our model only encodes the image once and allows for multiple transformations using the encoded vector. This allows for faster transformations since it does not need to reprocess the entire image for every transformation. We show the effectiveness of our proposed method through both qualitative and quantitative evaluations, such as ablative studies, visual inspection, and face verification. N6022 ic50 Competitive results are achieved compared to the main competition (CycleGAN), however, at great space and extensibility gain by using a single model.Traditional target detection methods assume that the background spectrum is subject to the Gaussian distribution, which may only perform well under certain conditions. In addition, traditional target detection methods suffer from the problem of the unbalanced number of target and background samples. To solve these problems, this study presents a novel target detection method based on asymmetric weighted logistic metric learning (AWLML). We first construct a logistic metric-learning approach as an objective function with a positive semidefinite constraint to learn the metric matrix from a set of labeled samples. Then, an asymmetric weighted strategy is provided to emphasize the unbalance between the number of target and background samples. Finally, an accelerated proximal gradient method is applied to identify the global minimum value. Extensive experiments on three challenging hyperspectral datasets demonstrate that the proposed AWLML algorithm improves the state-of-the-art target detection performance.In this article, we focus on the task of zero-shot image classification (ZSIC) that equips a learning system with the ability to recognize visual images from unseen classes. In contrast to the traditional image classification, ZSIC more easily suffers from the class-imbalance issue since it is more concerned with the class-level knowledge transferring capability. In the real world, the sample numbers of different categories generally follow a long-tailed distribution, and the discriminative information in the sample-scarce seen classes is hard to transfer to the related unseen classes in the traditional batch-based training manner, which degrades the overall generalization ability a lot. To alleviate the class-imbalance issue in ZSIC, we propose a sample-balanced training process to encourage all training classes to contribute equally to the learned model. Specifically, we randomly select the same number of images from each class across all training classes to form a training batch to ensure that the sample-scarce classes contribute equally as those classes with sufficient samples during each iteration. Considering that the instances from the same class differ in class representativeness, we further develop an efficient semantic-guided feature fusion model to obtain the discriminative class visual prototype for the following visual-semantic interaction process via distributing different weights to the selected samples based on their class representativeness. Extensive experiments on three imbalanced ZSIC benchmark datasets for both traditional ZSIC and generalized ZSIC tasks demonstrate that our approach achieves promising results, especially for the unseen categories that are closely related to the sample-scarce seen categories. Besides, the experimental results on two class-balanced datasets show that the proposed approach also improves the classification performance against the baseline model.Vibrotactile feedback is a common form of rendered haptic feedback used for simulating stylus-texture interaction. Most state-of-the-art stylus-texture interaction vibrotactile feedback synthesizing methods are oriented toward generating signal with resemblance in spectrum in frequency domain. In this paper we set our foot backward and explore more about record-and-playback method for a subset of textures those that have obvious spatial pattern, which constitutes a significant proportion of man-made textures we interact with in daily life. We propose a method that explicitly renders the periodic vibrotactile feedback for patterned textures. The method uses Dynamic Time Warping to select the most representative signal segment from a long continuous signal captured under a certain interaction condition, and constructs a waveform segment table to store representative signal segments under different conditions. Results of similarity-comparison user study show that subjects gave generally higher similarity scores to our proposed method than to a spectrum-oriented method. The results shed light on the importance of conserving the pattern in the haptic feedback rendering for patterned textures.Both bacterial viability and concentration are significant metrics for bacterial detection. Existing miniaturized and cost-effective single-mode sensor, pH or optical, can only be skilled at detecting single information viability or concentration. This paper presents an inverter-based CMOS ion-sensitive-field-effect-transistor (ISFET) sensor array, featuring bacterial pH detection which is an indicator of viability. The proposed design realizes pH detection using the native passivation layer of CMOS process as a sensing layer and configuring an inverter-based front-end as a capacitive feedback amplifier. This sensor array is assisted by optical detection which reveals bacterial concentration, and temperature sensing. The optical detection is enabled using the leakage current of a reset switch as a response to a light source. While in reset mode, the inverter-based amplifier works as a temperature sensor that could help to reduce temperature influences on pH and optical detection. All the functionalities are realized using one single inverter-based amplifier, resulting in a compact pixel structure and largely relaxed design complexity for the sensor system. Fabricated in 0.18um standard CMOS process, the proposed CMOS sensor array system achieves an amplified pH sensitivity of 221mV/pH, an improved sensor resolution of 0.03pH through systematic noise optimization, a linear optical response, and a maximum temperature error of 0.69. The sensing capabilities of the proposed design are demonstrated through on-chip Escherichia coli (E. coli) detection. This study may be extended to a rapid and cost-effective platform that renders multiple information of bacterial samples.