Accurate point tracking in surgical environments remains challenging due to complex visual conditions, including smoke occlusion, specular reflections, and tissue deformation. While existing surgical tracking datasets provide coordinate information, they lack the semantic context necessary to understand tracking failure mechanisms. We introduce VL-SurgPT, the first large-scale multimodal dataset that bridges visual tracking with textual descriptions of point status in surgical scenes. The dataset comprises 908 in vivo video clips, including 754 for tissue tracking (17,171 annotated points across five challenging scenarios) and 154 for instrument tracking (covering seven instrument types with detailed keypoint annotations). We establish comprehensive benchmarks using eight state-of-the-art tracking methods and propose TG-SurgPT, a text-guided tracking approach that leverages semantic descriptions to improve robustness in visually challenging conditions. Experimental results demonstrate that incorporating point status information significantly improves tracking accuracy and reliability, particularly in adverse visual scenarios where conventional vision-only methods struggle. By bridging visual and linguistic modalities, VL-SurgPT enables the development of context-aware tracking systems crucial for advancing computer-assisted surgery applications that can maintain performance even under challenging intraoperative conditions.

Transformer models are widely used in AI applications such as Natural Language Processing (NLP), Computer Vision (CV), etc. However, enormous computation workload be-comes an obstacle to train large transformer models efficiently. Recently, some methods focus on reducing the computation workload during the training by skipping some layers. How-ever, these methods use simple probability distribution and coarse-grained probability calculation, which significantly affect the model accuracy. To address the issue, in this paper we propose a novel method to accelerate training—Sensitivity-Based Layer Dropping (SBLD). SBLD uses lay-er-wise sensitivity data to switch on/off transformer layers in proper order to keep high accuracy. Besides, we adjust the probability of skipping transformer layers with a scheduler to accelerate training speed and get faster convergence. Our results show that SBLD solves the accuracy drop issue com-pared with prior layer dropping methods. Our SBLD method can decrease end-to-end training time by 19.67% during training of GPT-3 Medium model, the same time increasing the accuracy by 1.65% w.r.t. baseline. Furthermore, for SwinV2-L model the obtained Top-1 and Top-5 accuracies are also higher vs. the baseline. Thus, the proposed method is efficient and practical to improve the large transformer model training.

Wireless communications aided by unmanned aerial vehicles (UAVs) have emerged as a promising technology for serving numerous terminal devices of the Internet of Things. To support such a broad range of emerging applications, millimeter-wave (mm-Wave) is an effective solution because it has the characteristics of high transmission efficiency, wide frequency band, and very low latency. Meanwhile, a UAV-assisted communication system also requires a global positioning system reception (1.575 GHz) and ground control unit (2.4 GHz). This article investigates a highly integrated multiband antenna design to operate at the microwave and mm-Wave bands for robust and flexible UAV communications. Each antenna operating band utilizes the technology of a substrate-integrated waveguide cavity-backed antenna. By strategically engineering an mm-Wave antenna into a 2.4 GHz band antenna to share the aperture, besides showing high aperture reuse efficiency with a large frequency ratio, the proposed antenna has also maintained the geometrical advantages of compactness, planar configuration, and ease of integration with other circuits. Furthermore, the proposed antenna can provide flexible radiation patterns with upper/lower-space signal coverage according to the mission demands of the UAV. Experimental results of the prototype show that the antenna can fully cover the 1.575 GHz, 2.4 GHz, and fifth-generation mm-Wave bands of n257, n258, and n261. Multiple bands and stable radiation characteristics indicate the proposed antenna's suitability for deploying a UAV-supported wireless network.

The advances of single-cell technologies provide un-precedented opportunities to investigate heterogeneity of cellular processes. Although population modelling is a powerful tool to describe different cellular dynamics in a number of cells, it is a challenge and computational burden to infer different sets of parameters in the mathematical models based on the observations in different cells. To address this issue, this work proposes a population Monte Carlo framework to increase the acceptance rates in the approximate Bayesian computation algorithms. We first test a prior distribution and distinct tolerance threshold sequence for the dataset of the first cell. After applying the Bayesian inference methods, we obtain the estimated model parameters of the first cell. This inferred parameter set will be used to construct the prior distribution and tolerance threshold sequence of the following cells. This adaptive approach will be repeatedly applied for the inference of the following cells. The accuracy and efficacy of the proposed algorithms is rigorously examined by using two mathematical models for genetic networks. Numerical results shows that the proposed algorithm improve the efficiency substantially and provide a powerful tool to obtain more accurate inference results for large-scale regulatory networks.