A road quality classification technique based on vehicle system responses with experimental validation

Aiming at estimating the road surface condition with improvement of the accuracy in spatial, this paper proposes a new method to classify road surface condition by considering identification interval based on vehicle system responses. First, the response signals in different vehicle speeds are decomposed by using both Wavelet Transform (WT) and Empirical Mode Decomposition (EMD) techniques. Then characteristics of the signals in both the time and decomposed frequency domain are subsequently extracted. An Improved Distance Evaluation Technique (IDET) is used to select superior features from the characteristics. Finally, a Support Vector Machine (SVM) classifier is applied to determine the road classification. The influences of identification intervals in spatial accuracy are discussed, and an adaptive classification interval was proposed to improve accuracy. The algorithm is validated by using both simulation and experimental results

SAMAug: Point Prompt Augmentation for Segment Anything Model

This paper introduces SAMAug, a novel visual point augmentation method for the Segment Anything Model (SAM) that enhances interactive image segmentation performance. SAMAug generates augmented point prompts to provide more information about the user's intention to SAM. Starting with an initial point prompt, SAM produces an initial mask, which is then fed into our proposed SAMAug to generate augmented point prompts. By incorporating these extra points, SAM can generate augmented segmentation masks based on both the augmented point prompts and the initial prompt, resulting in improved segmentation performance. We conducted evaluations using four different point augmentation strategies: random sampling, sampling based on maximum difference entropy, maximum distance, and saliency. Experiment results on the COCO, Fundus, COVID QUEx, and ISIC2018 datasets show that SAMAug can boost SAM's segmentation results, especially using the maximum distance and saliency. SAMAug demonstrates the potential of visual prompt augmentation for computer vision. Codes of SAMAug are available at github.com/yhydhx/SAMAu

ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT

Large language models (LLMs) such as ChatGPT have recently demonstrated significant potential in mathematical abilities, providing valuable reasoning paradigm consistent with human natural language. However, LLMs currently have difficulty in bridging perception, language understanding and reasoning capabilities due to incompatibility of the underlying information flow among them, making it challenging to accomplish tasks autonomously. On the other hand, abductive learning (ABL) frameworks for integrating the two abilities of perception and reasoning has seen significant success in inverse decipherment of incomplete facts, but it is limited by the lack of semantic understanding of logical reasoning rules and the dependence on complicated domain knowledge representation. This paper presents a novel method (ChatABL) for integrating LLMs into the ABL framework, aiming at unifying the three abilities in a more user-friendly and understandable manner. The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format. Similarly, perceptual module provides necessary reasoning examples for LLMs in natural language format. The variable-length handwritten equation deciphering task, an abstract expression of the Mayan calendar decoding, is used as a testbed to demonstrate that ChatABL has reasoning ability beyond most existing state-of-the-art methods, which has been well supported by comparative studies. To our best knowledge, the proposed ChatABL is the first attempt to explore a new pattern for further approaching human-level cognitive ability via natural language interaction with ChatGPT

Comparative proteomic and clinicopathological analysis of breast adenoid cystic carcinoma and basal-like triple-negative breast cancer

BackgroundAdenoid cystic carcinoma (ACC) is a rare type of triple-negative breast cancer that has an indolent clinical behavior. Given the substantial overlapping morphological, immunohistochemical, and molecular features with other basal-like triple-negative breast cancer (BL-TNBC), accurate diagnosis of ACC is crucial for effective clinical treatment. The integrative analysis of the proteome and clinicopathological characteristics may help to distinguish these two neoplasms and provide a deep understanding on biological behaviors and potential target therapy of ACC.MethodsWe applied mass spectrometry-based quantitative proteomics to analyze the protein expression in paired tumor and adjacent normal breast tissue of five ACC and five BL-TNBC. Bioinformatic analyses and the clinicopathological characteristics, including histological features, immunohistochemistry, and FISH results, were also collected to get comprehensive information.ResultsA total of 307 differentially expressed proteins (DEPs) were identified between ACC and BL-TNBC. Clustering analysis of DEPs clearly separated ACC from BL-TNBC. GSEA found downregulation of the immune response of ACC compared with BL-TNBC, which is consistent with the negative PD-L1 expression of ACC. Vesicle-mediated transport was also inhibited, while ECM organization was enriched in ACC. The top upregulated proteins in DEPs were ITGB4, VCAN, and DPT. Moreover, in comparison with normal breast tissue, ACC showed elevated ribosome biogenesis and RNA splicing activity.ConclusionThis study provides evidence that ACC presents a substantially different proteomic profile compared with BL-TNBC and promotes our understanding on the molecular mechanisms and biological processes of ACC, which might be useful for differential diagnosis and anticancer strategy

Associations between genetic variants in mRNA splicing-related genes and risk of lung cancer: a pathway-based analysis from published GWASs

mRNA splicing is an important mechanism to regulate mRNA expression. Abnormal regulation of this process may lead to lung cancer. Here, we investigated the associations of 11,966 single-nucleotide polymorphisms (SNPs) in 206 mRNA splicing-related genes with lung cancer risk by using the summary data from six published genome-wide association studies (GWASs) of Transdisciplinary Research in Cancer of the Lung (TRICL) (12,160 cases and 16,838 controls) and another two lung cancer GWASs of Harvard University (984 cases and 970 controls) and deCODE (1,319 cases and 26,380 controls). We found that a total of 12 significant SNPs with false discovery rate (FDR) ≤0.05 were mapped to one novel gene PRPF6 and two previously reported genes (DHX16 and LSM2) that were also confirmed in this study. The six novel SNPs in PRPF6 were in high linkage disequilibrium and associated with PRPF6 mRNA expression in lymphoblastoid cells from 373 Europeans in the 1000 Genomes Project. Taken together, our studies shed new light on the role of mRNA splicing genes in the development of lung cancer

II IPV6 TECHNOLOGY AND DNS SETUP

Over the past few years, based on a concern that the Internet address space would soon be exhausted, a new version of Internet Protocol (IP), called IP Version 6 (IPv6) is in the process of standardization, and is expected to supersede the current IP version IPv4 in the near future. This report first introduces some Internet standards-based IPv6 concepts: the features of IPv6, IPv6 addressing, IPv6 address autoconfiguration. Then it describes proposed transition strategies from IPv4 to IPv6: Dual IP Layer Operation for communication between IPv6 and IPv4 nodes, some proposed tunneling mechanisms for communication of IPv6 islands over IPv4 routing infrastructure, and some other proposed mechanisms such as DSTM, NAT-PT, SOCKS, and BIS. Finally, this report gives a brief introduction to the IPv6 project in Auburn University. Because of the prevalent use of the names (rather than addresses) to refer to network resources in these days, DNS upgrading is an urgent and important task for the smoothing transition from IPv4 to IPv6, this report gives a brief introduction to the DNS, and detailed description for DNS server setting up and test to support IPv6. Key Word: IPv6, IPv4, Autoconfiguration, Tunneling, Dual IP, DNS, BIND, HTTP2, Linux IV ACKNOWLEDGMENTS I would like to express my deepest appreciation to Dr. Carlisle for his tremendous support and guidance on this project. I would also like to thank my committee members, Dr. Chang and Dr. Lee for their useful comments on the report, assistance in scheduling the defense date. Also, thanks to Mr. Kelly Price for his help with the project. Without their assistance, this valuable educational experience at Auburn University would not have been possible! V TABLE OF CONTENTS LIST OF FIGURES .....

Vision-based navigation of an unmanned surface vehicle with object detection and tracking abilities

The paper discusses autocalibration, object detection, and object tracking for unmanned surface vehicles. Input data are recorded with a wide-baseline stereo vision system providing accuracy for distance estimations. The paper reports about followed ways and novel contributions for ensuring a working system solution. Automatic self-calibration is used for the wide-baseline stereo vision system. Robust sea surface estimation and the detection of the horizon support the understanding of the given scene environment. Long-range (i.e. up to 500 m) object detection and tracking are supported by the used wide-baseline stereo system. The paper informs about the complete system design, informs about applied or designed methods, and also about experiments which verify that the system achieved an operational state

Green resource allocation for mobile edge computing

We investigate the green resource allocation to minimize the energy consumption of the users in mobile edge computing systems, where task offloading decisions, transmit power, and computation resource allocation are jointly optimized. The considered energy consumption minimization problem is a non-convex mixed-integer non-linear programming problem, which is challenging to solve. Therefore, we develop a joint search and Successive Convex Approximation (SCA) scheme to optimize the non-integer variables and integer variables in the inner loop and outer loop, respectively. Specifically, in the inner loop, we solve the optimization problem with fixed task offloading decisions. Due to the non-convex objective function and constraints, this optimization problem is still non-convex, and thus we employ the SCA method to obtain a solution satisfying the Karush-Kuhn-Tucker conditions. In the outer loop, we optimize the offloading decisions through exhaustive search. However, the computational complexity of the exhaustive search method is greatly high. To reduce the complexity, a heuristic scheme is proposed to obtain a sub-optimal solution. Simulation results demonstrate the effectiveness of the developed schemes

Auxiliary constrained control of a class of fault-tolerant systems

This paper is concerned with the robust constrained control problem for a class of fault-tolerant time-varying systems against actuator faults and input amplitude saturation. An adaptive technique is proposed to compensate for the impacts of actuator bias faults and partial loss of control effectiveness, as well as to eliminate the effects of unknown time-varying parameters of the systems. An auxiliary system is developed to ensure that the actuator behaves within the limitation of the actuator amplitude. Based on the compensation strategies and auxiliary signals, a novel adaptive fault-tolerant constrained controller is constructed to guarantee the convergence of the system states into a small stability domain in the presence of actuator faults, actuator amplitude limitations, and unknown system parameters. An example of F-18 flight control systems is given to illustrate the proposed procedures and its effectiveness