Unsupervised cross-domain image retrieval aims to retrieve semantically consistent images across domains with significant domain gaps, which poses substantial challenges under the absence of annotations in both domains. Existing approaches primarily rely on internally derived supervision signals for representation learning and cross-domain alignment. However, such internally induced supervision tends to impose an upper bound on achievable retrieval performance, as it lacks stable semantic references to support reliable category-level correspondence across domains. To address these limitations, we propose Selective Constraint Learning (SCL), a framework that introduces external semantic guidance as a stable prior for unsupervised cross-domain image retrieval. Leveraging a pre-trained foundation model, SCL constructs a dual-scope constraint bank to capture high-confidence positive and negative semantic relations within and across domains. Based on this, we design a generic constraint loss to jointly facilitate intra-domain compactness and inter-domain alignment. In addition, prototypical geometry regularization is designed to enhance in-domain structural stability through prototype-centered pull-and-push forces. Extensive experiments on multiple benchmarks demonstrate that SCL consistently outperforms state-of-the-art methods.

This paper presents a high-speed global-shutter readout integrated circuit (ROIC), which is designed to capture bursts at up to 5 million frames per second from a 32V×32Hpixel infrared focal plane array (FPA) detector. The ROIC hosts a 1000-frame on-chip analog burst memory bank, and has additional hardware capabilities for on-chip spatial or temporal compression to extend the supported burst recording duration. The system is intended to operate inside a cryostat at liquid nitrogen temperatures, to support high speed thermal imaging in the mid-wavelength infrared (MWIR) band.

Granular-ball support vector machine (GBSVM) is a significant attempt to construct a classifier using the coarse-to-fine granularity of a granular ball as input, rather than a single data point. It is the first classifier whose input contains no points. However, the existing model has some errors, and its dual model has not been derived. As a result, the current algorithm cannot be implemented or applied. To address these problems, we fix the errors of the original model of the existing GBSVM and derive its dual model. Furthermore, a particle swarm optimization (PSO) algorithm is designed to solve the dual problem. The sequential minimal optimization (SMO) algorithm is also carefully designed to solve the dual problem. The latter is faster and more stable. The experimental results on the UCI benchmark datasets demonstrate that GBSVM is more robust and efficient. All codes have been released in the open source library available at: http://www.cquptshuyinxia.com/GBSVM.html or https://github.com/syxiaa/GBSVM.

Traditional fuzzy set methods, designed around the finest granularity of inputs-individual points and their membership degrees-often struggle with inefficiencies and label noise. To overcome these challenges, we introduce granular-ball computing into the fuzzy set, creating the new granular-ball fuzzy set framework. This approach uses granular-ball inputs rather than single points, significantly reducing the number of entities and minimizing susceptibility to the noise affecting individual sample points. As a result, our framework enhances both efficiency and robustness compared to traditional methods and is applicable across various domains of fuzzy data processing. Furthermore, we apply this framework to fuzzy support vector machines (FSVMs), developing the Granular-ball Fuzzy Support Vector Machine (GBFSVM). Experimental tests on UCI benchmark datasets show that GBFSVM surpasses traditional models in efficiency and robustness.

Naive Bayes has been widely used in many applications because of its simplicity and ability in handling both numerical data and categorical data. However, lack of modeling of correlations between features limits its performance. In addition, noise and outliers in the real-world dataset also greatly degrade the classification performance. In this paper, we propose a feature augmentation method employing a stack auto-encoder to reduce the noise in the data and boost the discriminant power of naive Bayes. The proposed stack auto-encoder consists of two auto-encoders for different purposes. The first encoder shrinks the initial features to derive a compact feature representation in order to remove the noise and redundant information. The second encoder boosts the discriminant power of the features by expanding them into a higher-dimensional space so that different classes of samples could be better separated in the higher-dimensional space. By integrating the proposed feature augmentation method with the regularized naive Bayes, the discrimination power of the model is greatly enhanced. The proposed method is evaluated on a set of machine-learning benchmark datasets. The experimental results show that the proposed method significantly and consistently outperforms the state-of-the-art naive Bayes classifiers.

This paper presents a new family of temperature and supply voltage sensors based on an improved current-mode relaxation oscillator. The proposed sensor circuit overcomes the finite input impedance of earlier current-mode relaxation oscillators, and it is paired with a novel opamp-free bandgap current reference. Two examples are presented: a VDD- controlled oscillator, and a temperature-controlled oscillator. The temperature-controlled oscillator operates at 11.3 MHz and consumes 11.14 uW while reaching a temperature nonlinearity error less than +0.85/-0.94°C, a resolution figure of merit of 11.5 pJ·K2, and an efficiency of 240 pJ/conv. The VDD-controlled oscillator has a nonlinearity error less than +28.7/-30.0mV from 1.2-1.8 V after two-point calibration.

Semiconductor devices are fundamentally sensitive to process variation, supply voltage, and temperature, which impacts both the design and performance of modern integrated circuits. On-chip sensors are typically designed and calibrated to measure temperature and voltage independently, but designing independent sensors has implicit power and area costs. Here we propose a new simultaneous estimation of temperature and supply voltage using an ensemble of digital logic delays and nonlinear regression models. This approach is inherently digital and compatible with modern digital design flows. Using simulations in commercial 12nm FinFET and 65nm bulk CMOS processes, we show the feasibility and efficiency of this approach, and consider its benefits and potential limitations.