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  1. Free, publicly-accessible full text available March 1, 2025
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  4. Self-supervised learning through contrastive representations is an emergent and promising avenue, aiming at alleviating the availability of labeled data. Recent research in the field also demonstrates its viability for several downstream tasks, henceforth leading to works that implement the contrastive principle through inno- vative loss functions and methods. However, despite achieving impressive progress, most methods depend on prohibitively large batch sizes and compute requirements for good performance. In this work, we propose the AUC-Contrastive Learning, a new approach to contrastive learning that demonstrates robust and competitive performance in compute-limited regimes. We propose to incorporate the contrastive objective within the AUC-maximization framework, by noting that the AUC metric is maximized upon enhancing the probability of the network’s binary prediction difference between positive and negative samples which inspires adequate embed- ding space arrangements in representation learning. Unlike standard contrastive methods, when performing stochastic optimization, our method maintains unbiased stochastic gradients and thus is more robust to batchsizes as opposed to standard stochastic optimization problems. Remarkably, our method with a batch size of 256, outperforms several state-of-the-art methods that may need much larger batch sizes (e.g., 4096), on ImageNet and other standard datasets. Experiments on transfer learning and few-shot learning tasks also demonstrate the downstream viability of our method. Code is available at AUC-CL. 
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  5. Many researchers have studied the roles of building envelope materials on UHI, such as roofs, and walls, but few of them have explored the impacts of the emergence of the solar-reflective coatings, films, and panels but well-visible transmittance that is increasingly applied to glazed building facades, especially in hot climates, for outdoor thermal environments. The question then arises: Despite the positive effects of these strong solar-reflective facades on building heating and cooling energy savings, do they have the same positive effects on the adjacent outdoor area, especially in a dense urban context? This research aims to quantify the potential UHI effects of the solar-reflective facades relative to the non-reflective ones in a dense urban context, along with the heating and cooling energy performance analysis. As such, a simulation method in terms of a series of tools including LBNL Radiance, EnergyPlus, and WINDOW software was adopted in this work to analyze the solar radiation interactions between the façade surface and the surrounding urban structures and potential temperature rise under solar-reflective and non-reflective facades. The result shows that the annual cooling energy savings by using the solar-reflective facades are about 33.8% relative to the typical double-pane clear glazed façade because of the substantial reduction of U-factor and solar heat gains; But, this preliminary work also unveils the potential adverse effects of using such materials at the urban scale, leading nearly 2 times greater solar irradiation and UHI effects than the ones by the solar-non-reflective building surfaces in an urban area. Future optimization studies on the trade-off between the building cooling energy savings and UHI effects by the solar-reflective façades need to be conducted. 
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    Free, publicly-accessible full text available October 1, 2024
  6. critical to reveal a blackbox model’s decision-making process from raw data to prediction. In this article, we use two real datasets, the MNIST handwritten digits and MIT-BIH Electrocardiogram (ECG) signals, to motivate key characteristics of discriminative features, namely adaptiveness, predictive importance and effectiveness. Then, we develop a localization framework based on adversarial attacks to effectively localize discriminative features. In contrast to existing heuristic methods, we also provide a statistically guaranteed interpretability of the localized features by measuring a generalized partial R2. We apply the proposed method to the MNIST dataset and the MIT-BIH dataset with a convolutional auto-encoder. In the first, the compact image regions localized by the proposed method are visually appealing. Similarly, in the second, the identified ECG features are biologically plausible and consistent with cardiac electrophysiological principles while locating subtle anomalies in a QRS complex that may not be discernible by the naked eye. Overall, the proposed method compares favorably with state-of-the-art competitors. Accompanying this paper is a Python library dnn-locate that implements the proposed approach. 
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    Free, publicly-accessible full text available October 1, 2024
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  9. The development of communication technologies in edge computing has fostered progress across various applications, particularly those involving vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Enhanced infrastructure has improved data transmission network availability, promoting better connectivity and data collection from IoT devices. A notable IoT application is with the Intelligent Transportation System (ITS). IoT technology integration enables ITS to access a variety of data sources, including those pertaining to weather and road conditions. Real-time data on factors like temperature, humidity, precipitation, and friction contribute to improved decision-making models. Traditionally, these models are trained at the cloud level, which can lead to communication and computational delays. However, substantial advancements in cloud-to-edge computing have decreased communication relays and increased computational distribution, resulting in faster response times. Despite these benefits, the developments still largely depend on central cloud sources for computation due to restrictions in computational and storage capacity at the edge. This reliance leads to duplicated data transfers between edge servers and cloud application servers. Additionally, edge computing is further complicated by data models predominantly based on data heuristics. In this paper, we propose a system that streamlines edge computing by allowing computation at the edge, thus reducing latency in responding to requests across distributed networks. Our system is also designed to facilitate quick updates of predictions, ensuring vehicles receive more pertinent safety-critical model predictions. We will demonstrate the construction of our system for V2V and V2I applications, incorporating cloud-ware, middleware, and vehicle-ware levels. 
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  10. This paper presents a mid-air haptic interface device enabled by a piezoelectric micromachined ultrasonic transducer (pMUT) array achieving an unprecedentedly high transmission pressure of 2900 Pa at a 15 mm distance for the first time. The structure is based on sputtered potassium sodium niobate (K,Na)NbO3 (KNN) thin film with a high piezoelectric coefficient (𝑒𝑒31 ~ 8-10 C/m2). A prototype KNN pMUT array composed of 15×15 dual-electrode circular-shape diaphragms exhibits a resonant frequency around 92.4 kHz. Testing results show a transmitting sensitivity of 120.8 Pa/cm2 per volt under only 12 Vp-p excitation at the natural focal point of 15 mm away, which is at least 3 times that of previously reported AlN pMUTs at a similar frequency. Furthermore, an instant non-contact haptic stimulation of wind-like sensation on human palms has been realized. As such, this work sheds light on a new class of pMUT array with high acoustic output pressure for human-machine interface applications, such as consumer electronics and AR/VR systems. 
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    Free, publicly-accessible full text available June 26, 2024