Geo-Spatio-Temporal Information Based 3D Cooperative Positioning in LOS/NLOS Mixed Environments

We propose a geographic and spatio-temporal information based distributed cooperative positioning (GSTICP) algorithm for wireless networks that require three-dimensional (3D) coordinates and operate in the line-of-sight (LOS) and nonline-of-sight (NLOS) mixed environments. First, a factor graph (FG) is created by factorizing the a posteriori distribution of the position-vector estimates and mapping the spatial-domain and temporal-domain operations of nodes onto the FG. Then, we exploit a geographic information based NLOS identification scheme to reduce the performance degradation caused by NLOS measurements. Furthermore, we utilize a finite symmetric sampling based scaled unscented transform (SUT) method to approximate the nonlinear terms of the messages passing on the FG with high precision, despite using only a small number of samples. Finally, we propose an enhanced anchor upgrading (EAU) mechanism to avoid redundant iterations. Our GSTICP algorithm supports any type of ranging measurement that can determine the distance between nodes. Simulation results and analysis demonstrate that our GSTICP has a lower computational complexity than the state-of-the-art belief propagation (BP) based localizers, while achieving an even more competitive positioning performance.Comment: 6 pages, 5 figures, accepted to appear on IEEE Globecom, Aug. 2022. arXiv admin note: text overlap with arXiv:2208.1185

Understanding the Determinants of Review Helpfulness in Online Review Sites: An Empirical Study

Online review websites play an important role in customer’s purchase decision-making process for the useful product knowledge contained in the customer-generated reviews. However, the increasing information volume also makes it difficult for customers to identify and consider those attributes relevant to their decision. Based on Information Processing Theory (IPT) and Multiple Pathway Anchoring and Adjustment Model (MPAAM), we proposed three characteristics of online reviews affecting review helpfulness (e.g., attractiveness, representational sufficiency and functional sufficiency) and examined the moderating influences of information volume on these relationships. A large-scale review dataset from Yelp.com are collected and text analysis technique are applied to validate our research model. Our work, which illustrates the disturbance effect of information volume, has implications for both online word-of-mouth and information processing research

Distributed Spatio-Temporal Information Based Cooperative 3D Positioning in GNSS-Denied Environments

A distributed spatio-temporal information based cooperative positioning (STICP) algorithm is proposed for wireless networks that require three-dimensional (3D) coordinates and operate in the global navigation satellite system (GNSS) denied environments. Our algorithm supports any type of ranging measurements that can determine the distance between nodes. We first utilize a finite symmetric sampling based scaled unscented transform (SUT) method for approximating the nonlinear terms of the messages passing on the associated factor graph (FG) with high precision, despite relying on a small number of samples. Then, we propose an enhanced anchor upgrading mechanism to avoid any redundant iterations. Our simulation results and analysis show that the proposed STICP has a lower computational complexity than the state-of-the-art belief propagation based localizer, despite achieving an even more competitive positioning performance

SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation

The Segment Anything Model (SAM) is a powerful foundation model that has revolutionised image segmentation. To apply SAM to surgical instrument segmentation, a common approach is to locate precise points or boxes of instruments and then use them as prompts for SAM in a zero-shot manner. However, we observe two problems with this naive pipeline: (1) the domain gap between natural objects and surgical instruments leads to poor generalisation of SAM; and (2) SAM relies on precise point or box locations for accurate segmentation, requiring either extensive manual guidance or a well-performing specialist detector for prompt preparation, which leads to a complex multi-stage pipeline. To address these problems, we introduce SurgicalSAM, a novel end-to-end efficient-tuning approach for SAM to effectively integrate surgical-specific information with SAM's pre-trained knowledge for improved generalisation. Specifically, we propose a lightweight prototype-based class prompt encoder for tuning, which directly generates prompt embeddings from class prototypes and eliminates the use of explicit prompts for improved robustness and a simpler pipeline. In addition, to address the low inter-class variance among surgical instrument categories, we propose contrastive prototype learning, further enhancing the discrimination of the class prototypes for more accurate class prompting. The results of extensive experiments on both EndoVis2018 and EndoVis2017 datasets demonstrate that SurgicalSAM achieves state-of-the-art performance while only requiring a small number of tunable parameters. The source code will be released at https://github.com/wenxi-yue/SurgicalSAM.Comment: Technical Report. The source code will be released at https://github.com/wenxi-yue/SurgicalSA

Robust Audio Anti-Spoofing with Fusion-Reconstruction Learning on Multi-Order Spectrograms

Robust audio anti-spoofing has been increasingly challenging due to the recent advancements on deepfake techniques. While spectrograms have demonstrated their capability for anti-spoofing, complementary information presented in multi-order spectral patterns have not been well explored, which limits their effectiveness for varying spoofing attacks. Therefore, we propose a novel deep learning method with a spectral fusion-reconstruction strategy, namely S2pecNet, to utilise multi-order spectral patterns for robust audio anti-spoofing representations. Specifically, spectral patterns up to second-order are fused in a coarse-to-fine manner and two branches are designed for the fine-level fusion from the spectral and temporal contexts. A reconstruction from the fused representation to the input spectrograms further reduces the potential fused information loss. Our method achieved the state-of-the-art performance with an EER of 0.77% on a widely used dataset: ASVspoof2019 LA Challenge

Effects of oxycodone hydrochloride and dezocine on hemodynamics and levels of inflammatory factors in patients receiving gynecological laparoscopic surgery under general anesthesia

We aimed to compare the effects of oxycodone hydrochloride and dezocine on hemodynamics and inflammatory factors in patients receiving gynecological laparoscopic surgery under general anesthesia. A total of 246 patients were divided into group A and B (n=123). Hemorheology indices were recorded 5 min after anesthesia (T0), 1 min after pneumoperitoneum (T1), when position was changed 5 min after pneumoperitoneum (T2), 15 min after pneumoperitoneum (T3), 1 min (T4) and 5 min (T5) after position was restored. Visual analogue scale scores 1, 2, 6, 12, 24 and 48 h after operation were recorded. Postoperative adverse reactions and visceral pain were observed. The expression levels of inflammatory factors were detected by enzyme-linked immunosorbent assay 12 h after operation. Compared with group A, group B had higher heart rate and mean arterial pressure at T2, lower central venous pressure and cardiac output at T1-T3, and higher systemic vascular resistance at T1-T5 (P<0.05). The incidence rate of pain syndrome in group A was lower (P<0.05). Group A had lower tumor necrosis factor-alpha and interleukin-6 expression levels and higher interleukin-10 level than those of group B (P<0.05). For gynecological laparoscopic surgery, oxycodone preemptive analgesia has superior outcomes to those of dezocine