This paper presents a novel robust method for single target tracking in RGB-D images, and also contributes a substantial new benchmark dataset for evaluating RGB-D trackers. While a target object's color distribution is reasonably motion-invariant, this is not true for the target's depth distribution, which continually varies as the target moves relative to the camera. It is therefore nontrivial to design target models which can fully exploit (potentially very rich) depth information for target tracking. For this reason, much of the previous RGB-D literature relies on color information for tracking, while exploiting depth information only for occlusion reasoning. In contrast, we propose an adaptive range-invariant target depth model, and show how both depth and color information can be fully and adaptively fused during the search for the target in each new RGB-D image. We introduce a new, hierarchical, two-layered target model (comprising local and global models) which uses spatio-temporal consistency constraints to achieve stable and robust on-the-fly target relearning. In the global layer, multiple features, derived from both color and depth data, are adaptively fused to find a candidate target region. In ambiguous frames, where one or more features disagree, this global candidate region is further decomposed into smaller local candidate regions for matching to local-layer models of small target parts. We also note that conventional use of depth data, for occlusion reasoning, can easily trigger false occlusion detections when the target moves rapidly toward the camera. To overcome this problem, we show how combining target information with contextual information enables the target's depth constraint to be relaxed. Our adaptively relaxed depth constraints can robustly accommodate large and rapid target motion in the depth direction, while still enabling the use of depth data for highly accurate reasoning about occlusions. For evaluation, we introduce a new RGB-D benchmark dataset with per-frame annotated attributes and extensive bias analysis. Our tracker is evaluated using two different state-of-the-art methodologies, VOT and object tracking benchmark, and in both cases it significantly outperforms four other state-of-the-art RGB-D trackers from the literature.

Currently, several convolutional neural network (CNN)-based methods have been proposed for computer-aided COVID-19 diagnosis based on lung computed tomography (CT) scans. However, the lesions of pneumonia in CT scans have wide variations in appearances, sizes, and locations in the lung regions, and the manifestations of COVID-19 in CT scans are also similar to other types of viral pneumonia, which hinders the further improvement of CNN-based methods. Delineating infection regions manually is a solution to this issue, while excessive workload of physicians during the epidemic makes it difficult for manual delineation. In this article, we propose a CNN called dense connectivity network with parallel attention module (PAM-DenseNet), which can perform well on coarse labels without manually delineated infection regions. The parallel attention module automatically learns to strengthen informative features from both channelwise and spatialwise simultaneously, which can make the network pay more attention to the infection regions without any manual delineation. The dense connectivity structure performs feature maps reuse by introducing direct connections from previous layers to all subsequent layers, which can extract representative features from fewer CT slices. The proposed network is first trained on 3530 lung CT slices selected from 382 COVID-19 lung CT scans, 372 lung CT scans infected by other pneumonia, and 200 normal lung CT scans to obtain a pretrained model for slicewise prediction. We then apply this pretrained model to a CT scans dataset containing 94 COVID-19 CT scans, 93 other pneumonia CT scans, and 93 normal lung scans, and achieve patientwise prediction through a voting mechanism. The experimental results show that the proposed network achieves promising results with an accuracy of 94.29%, a precision of 93.75%, a sensitivity of 95.74%, and a specificity of 96.77%, which is comparable to the methods that are based on manually delineated infection regions.

The building block of in-memory computing with spintronic devices is mainly based on the magnetic tunnel junction with perpendicular interfacial anisotropy (p-MTJ). The resulting asymmetric write and readout operations impose challenges in downscaling and direct cascadability of p-MTJ devices. Here, we propose that a previously unimplemented symmetric write and readout mechanism can be realized in perpendicular-anisotropy spin-orbit (PASO) quantum materials based on Fe ₃ GeTe ₂ and WTe ₂ . We demonstrate that field-free and deterministic reversal of the perpendicular magnetization can be achieved using unconventional charge–to– z -spin conversion. The resulting magnetic state can be readily probed with its intrinsic inverse process, i.e., z -spin–to–charge conversion. Using the PASO quantum material as a fundamental building block, we implement the functionally complete set of logic-in-memory operations and a more complex nonvolatile half-adder logic function. Our work highlights the potential of PASO quantum materials for the development of scalable energy-efficient and ultrafast spintronic computing.

Styrene-butadiene-styrene-modified asphalt binder (SBSMA), crumb rubber–modified asphalt binder (CRMA), and high-viscosity-modified asphalt binder (HVMA) have been widely used in porous asphalt concrete. However, the aging of the modified asphalt binder significantly affects the performance of the porous asphalt concrete. The main aim of this research is to quantitatively assess the aging degree of finished product–modified asphalt binders of SBSMA, CRMA, and HVMA at high, medium, and low temperatures, and to explore the aging mechanism at the microscopic level. This study used rolling thin-film oven and pressurized aging vessel to simulate the aging of asphalt binders. Dynamic shear rheometer, bending beam rheometer, Fourier transform infrared spectrometry, thin-layer chromatography with flame ionization detection, and scanning electron microscopy were employed to investigate how aging affects the rheological and microscopic properties of finished product–modified asphalt binder. The results showed that at high temperatures, short-term aging significantly impacted HVMA [change rate of the rutting factor $(\mathrm{CRRF})=18.9\%$ ], while long-term aging had an enormous influence on CRMA ($\mathrm{CRRF}=179.5\%$ ). Short-term aging had the greatest effect on the fatigue property at medium temperature of CRMA [change rate of $N_f$ $(\mathrm{CR}{N}_f)=59.9\%$ ], followed by SBSMA ($\mathrm{CR}{N}_f=46.1\%$ ) and HVMA ($\mathrm{CR}{N}_f=26.2\%$ ). However, the effect of long-term aging on the fatigue properties of HVMA ($\mathrm{CR}{N}_f=99.2\%$ ) is the largest, followed by that of CRMA ($\mathrm{CR}{N}_f=95.8\%$ ) and SBSMA ($\mathrm{CR}{N}_f=56.6\%$ ). At low temperature, compared with CRMA and HVMA, aging had the greatest influence on the low-temperature rheological properties of SBSMA. In addition, the three finished product–modified asphalt binders exhibited similar changes in microscopic level after aging.