Accurate segmentation of aortic vascular structures is critical for diagnosing and treating cardiovascular diseases. Traditional Transformer-based models have shown promise in this domain by capturing long-range dependencies between vascular features. However, their reliance on fixed-size rectangular patches often influences the integrity of complex vascular structures, leading to suboptimal segmentation accuracy. To address this challenge, we propose the adaptive Morph-Patch Transformer (MPT), a novel architecture specifically designed for aortic vascular segmentation. Specifically, MPT introduces an adaptive patch partitioning strategy that dynamically generates morphology-aware patches aligned with complex vascular structures. This strategy can preserve semantic integrity of complex vascular structures within individual patches. Moreover, a Semantic Clustering Attention (SCA) method is proposed to dynamically aggregate features from various patches with similar semantic characteristics. This method enhances the model's capability to segment vessels of varying sizes, preserving the integrity of vascular structures. Extensive experiments on three open-source datasets (AVT, AortaSeg24 and TBAD) demonstrate that MPT achieves state-of-the-art performance, with improvements in segmenting intricate vascular structures.

Breast cancer remains a major global health challenge, while more efficient and low-cost diagnosis technologies are expected to explore, while Raman spectroscopy has been employed for breast cancer detection, current analytical methods relying on characteristic peak intensity or position extraction fail to capture comprehensive patterns in the original spectral data. In this field, deep learning approaches have been explored, with one-dimensional convolutional neural networks (1D-CNNs) achieving the highest accuracy. However, existing deep learning methods face challenges in effectively identifying the global characteristics of Raman spectra. Therefore, this study proposes a lightweight Transformer architecture designed to effectively capture global spectral features while preserving local characteristics, thereby enhancing the accuracy of breast cancer diagnosis. Using a four-head attention mechanism for spectral analysis, the model employs Raman spectroscopy data as input to develop a binary classification model to distinguish normal from cancerous cytopathological states. This study provides a promising technical framework for the integration of Raman spectroscopy with deep learning, with significant potential for clinical application.

Visual localization, which is a key technology to realize the automation of cell manipulation, has been widely studied. Since the depth of field of the microscope is narrow, the planar localization and depth estimation are usually coupled together. At present, most methods adopt the serial working mode of focusing first and then planar localization, but they usually do not have good real-time performance and stability. In this paper, a simultaneous depth estimation and localization network was developed for cell manipulation. The network takes a focused image and a defocus-offset image as inputs, and outputs the defocus in the depth direction and the offset in the plane at the same time after going through defocus-offset information extraction, defocus classification mapping and offset regression mapping. To train and test our network, we also create two datasets: An Adherent Cell dataset and an Injection Micropipette dataset. The experimental results demonstrated that the proposed method achieves the detection of all test samples with a frame rate of more than 40Hz, and the maximum errors of depth estimation and localization are$\boldsymbol{2.44\mu m}$and$\boldsymbol{0.49\mu m}$, respectively. The proposed method has good stability, which is mainly reflected in its strong generalization ability and anti-noise ability.

Accelerator-based computing, which has been a mainstay of System-on-Chips (SoCs) is of growing interest to a wider range of computing systems. However, the significant design effort required to identify a computational target for acceleration, design a hardware accelerator, verify the correctness of the accelerator, integrate the accelerator into the system, and rewrite applications to use the accelerator, is a major bottleneck to the widespread adoption of accelerator-based computing. The classical approach to this problem is based on top-down methodologies such as automatic HW/SW partitioning and high-level synthesis (HLS). While HLS has advanced significantly and is seeing increased adoption, it does not leverage the ability of experienced human designers to craft highly optimized RTL, nor does it leverage the growing body of already existing hardware accelerators. In this work, we propose ACCLIB, a design framework that allows software developers to utilize existing libraries of predesigned hardware accelerators automatically with no prior knowledge of the function of the accelerators, with minimal knowledge of hardware design, and with minimal design effort. To accomplish this, ACCLIB uses formal verification techniques to match a target software function with a functionally equivalent accelerator from a library of accelerators. It also generates the required HW/SW interfaces as well as the code necessary to offload the computation to the accelerator. We validate ACCLIB by applying it to accelerate six different applications using a library of hardware accelerators in just over one hour per application, demonstrating that the proposed approach has the potential to lower the barrier to adoption of accelerator-based computing.

The Kekexili region of the Tibetan Plateau has become warmer and wetter since the 1960s, resulting in a significant expansion of Zhuonai Lake (+0.46 km2/year, p <; 0.05) before an outburst flood event occurred on September 15, 2011, and mapped by the Chinese Huanjing (HJ)-A/B satellites with a two-day revisit ability and a 360-km orbit swath. The direct cause of the outburst was due to relatively heavy precipitation from May to September 2011, specifically the continuous rainfall from later August to middle September. Two nearby earthquakes that occurred two months before the outburst might have impacted the natural structure of the lakebed and moraine dam to accelerate the outburst. The outburst event of Zhuonai Lake caused large environmental impacts on the region: 1) the desertification of the exposed lakebed of Zhuonai Lake; 2) the significant expansion of the three downstream lakes Kusai, Haidingnuoer, and Salt Lakes that not only caused the grassland reduction and deteriorations but also the potential threat to the operations of the Qing-Tibet Railway and Highway; and 3) the calving relocation of Tibetan antelopes to the shore area of Kusai Lake due to the deep cutting riverbanks caused by the overflow of Zhuonai Lake. This study provides some scientific clues or alerts for local or central governments to pay some attention on this very issue so that possible future devastative disasters and environmental damages would be avoided or mitigated.

This paper describes a novel approach to real-time vehicle following. A scheme, called "symmetry axis detection and filtering based on symmetry constraints", is proposed and has been implemented. This scheme incorporates many elements of our research efforts in sensor-based control of mobile robots and manipulators. The proposed algorithm detects and tracks the rear portion of the exterior of a leading vehicle via a camera mounted on a vehicle that belongs to a platoon. Our scheme uses the symmetry property of the shape of most vehicles. In particular, our technique takes advantage of the stable contour symmetry instead of the intensity symmetry. An algorithm that employs a voting technique is proposed in order to detect the vertical symmetry axis of the leading vehicle. A filtering method based on symmetry constraints is performed to eliminate the pixels which do not contribute to the leading vehicle's contour. As a result of the filtering, a distinct symmetric contour is obtained. Robustness and real-time performance of the symmetry-based scheme are greatly enhanced by using an adaptive processing window, the local symmetry properties, and a filtering procedure.

We present a piecewise linear projection of the 3D color space that greatly reduces the computations required for using color information for robot vision tasks which we call categorical color. This 24-bit to 6-bit projection is inspired by the way humans name colors. This projection is developed to provide generic target tracking in real-time. A generic target in our system is defined by a user selecting a distinctive object in the window of a color image. The system has no a priori models of object shapes or colors. Therefore, the generic target tracking must perform robustly, in real time, using only the initial example appearance of the target object. To evaluate the performance of our piecewise linear projection on the task of generic target tracking, we compare similar RGB, intensity, and categorical color algorithms. Rather than simply observing the located target, we have developed a quantitative method for evaluating generic target tracking algorithms. By using this procedure, we show that categorical color is a better feature for generic tracking than RGB and gray-level.