A timer-based data link control protocol for mobile computing

科研費報告書収録論文(課題番号:09680388・基盤研究(C)(2)・H9～H10/研究代表者:根元, 義章/情報フィルタリングを用いた大規模情報ネットワークのリアルタイム障害検出方式

IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models

The field of vision-and-language (VL) understanding has made unprecedented progress with end-to-end large pre-trained VL models (VLMs). However, they still fall short in zero-shot reasoning tasks that require multi-step inferencing. To achieve this goal, previous works resort to a divide-and-conquer pipeline. In this paper, we argue that previous efforts have several inherent shortcomings: 1) They rely on domain-specific sub-question decomposing models. 2) They force models to predict the final answer even if the sub-questions or sub-answers provide insufficient information. We address these limitations via IdealGPT, a framework that iteratively decomposes VL reasoning using large language models (LLMs). Specifically, IdealGPT utilizes an LLM to generate sub-questions, a VLM to provide corresponding sub-answers, and another LLM to reason to achieve the final answer. These three modules perform the divide-and-conquer procedure iteratively until the model is confident about the final answer to the main question. We evaluate IdealGPT on multiple challenging VL reasoning tasks under a zero-shot setting. In particular, our IdealGPT outperforms the best existing GPT-4-like models by an absolute 10% on VCR and 15% on SNLI-VE. Code is available at https://github.com/Hxyou/IdealGPTComment: 13 pages, 5 figure

Visual Features Assisted Robot Localization in Symmetrical Environment Using Laser SLAM

Localization for estimating the position and orientation of a robot in an asymmetrical environment has been solved by using various 2D laser rangefinder simultaneous localization and mapping (SLAM) approaches. Laser-based SLAM generates an occupancy grid map, then the most popular Monte Carlo Localization (MCL) method spreads particles on the map and calculates the position of the robot by a probabilistic algorithm. However, this can be difficult, especially in symmetrical environments, because landmarks or features may not be sufficient to determine the robot’s orientation. Sometimes the position is not unique if a robot does not stay at the geometric center. This paper presents a novel approach to solving the robot localization problem in a symmetrical environment using the visual features-assisted method. Laser range measurements are used to estimate the robot position, while visual features determine its orientation. Firstly, we convert laser range scans raw data into coordinate data and calculate the geometric center. Secondly, we calculate the new distance from the geometric center point to all end points and find the longest distances. Then, we compare those distances, fit lines, extract corner points, and calculate the distance between adjacent corner points to determine whether the environment is symmetrical. Finally, if the environment is symmetrical, visual features based on the ORB keypoint detector and descriptor will be added to the system to determine the orientation of the robot. The experimental results show that our approach can successfully determine the position of the robot in a symmetrical environment, while ordinary MCL and its extension localization method always fail

Visual Features Assisted Robot Localization in Symmetrical Environment Using Laser SLAM

Localization for estimating the position and orientation of a robot in an asymmetrical environment has been solved by using various 2D laser rangefinder simultaneous localization and mapping (SLAM) approaches. Laser-based SLAM generates an occupancy grid map, then the most popular Monte Carlo Localization (MCL) method spreads particles on the map and calculates the position of the robot by a probabilistic algorithm. However, this can be difficult, especially in symmetrical environments, because landmarks or features may not be sufficient to determine the robot’s orientation. Sometimes the position is not unique if a robot does not stay at the geometric center. This paper presents a novel approach to solving the robot localization problem in a symmetrical environment using the visual features-assisted method. Laser range measurements are used to estimate the robot position, while visual features determine its orientation. Firstly, we convert laser range scans raw data into coordinate data and calculate the geometric center. Secondly, we calculate the new distance from the geometric center point to all end points and find the longest distances. Then, we compare those distances, fit lines, extract corner points, and calculate the distance between adjacent corner points to determine whether the environment is symmetrical. Finally, if the environment is symmetrical, visual features based on the ORB keypoint detector and descriptor will be added to the system to determine the orientation of the robot. The experimental results show that our approach can successfully determine the position of the robot in a symmetrical environment, while ordinary MCL and its extension localization method always fail

Salient Preprocessing: Robotic ICP Pose Estimation Based on SIFT Features

The pose estimation can be effectively solved according to the feature point matching relationship in RGB-D. However, the extraction and matching process based on the whole image’s feature point is very computationally intensive and lacks robustness, which is the bottleneck of the traditional ICP algorithm. This paper proposes representing the whole image’s feature points by the salient objects’ robustness SIFT feature points through the salient preprocessing, and further solving the pose estimation. The steps are as follows: (1) salient preprocessing; (2) salient object’s SIFT feature extraction and matching; (3) RANSAC removes mismatching salient feature points; (4) ICP pose estimation. This paper proposes salient preprocessing aided by RANSAC processing based on the SIFT feature for pose estimation for the first time, which is a coarse-to-fine method. The experimental results show that our salient preprocessing algorithm can coarsely reduce the feature points’ extractable range and interfere. Furthermore, the results are processed by RANSAC good optimization, reducing the calculation amount in the feature points’ extraction process and improving the matching quality of the point pairs. Finally, the calculation amount of solving R, t based on all the matching feature points is reduced and provides a new idea for related research

To Act or Not to Act: Are Natural Landscapes a Key Force in the Resilience of Historic Urban Landscapes?

Ignoring the function of natural landscapes in the rapid development of urbanization, and especially in the conservation of historic urban landscapes, is still obvious today, and this has caused a large decrease in natural space, loss of habitats, and an increase in disasters. The resilience of a whole city and parts of it, such as historic urban areas where the historical process of man and nature have been recorded, as well as the interaction between nature, economy, and culture, is not strong enough to maintain the stability of urban ecosystems. It is misleading to think that the resilience can be built in a historic urban area without a natural landscape. We question whether this is true. Using a semantic differential analysis method from a historical perspective, this paper aims to answer this question through research on the correlation between resilience and man and nature through a case study of Yudai Trench historic urban landscape in Guangzhou, a historic urban area with 1000 years of history. A total of 212 pieces of evidence were extracted from 59 historical sources. The results showed that the cultural and economic conditions were in the same step and cycles as nature, which were influenced strongly by climate change, and that the natural landscape has a correlation on and is a dominant force in the resilience of historic urban landscapes

DataSheet_1_A novel therapeutic strategy of combined camrelizumab and apatinib for the treatment of advanced hepatocellular carcinoma.docx

Methods83 patients with hepatocellular carcinoma (HCC) admitted to the interventional oncology department were randomly divided into two groups. Apatinib and camrelizumab were administered to 42 patients in group A, whereas sorafenib was administered to 41 patients in group B for three months. The clinical efficacy was evaluated in terms of objective response rate (ORR), and disease control rate (DCR). Certain tumor markers like alpha-fetoprotein (AFP), carbohydrate antigen 199 (CA199), carcinoembryonic antigen (CEA), hypoxia-inducible factor (HIF-1), immune function T lymphocyte subsets (CD3+, CD4+, CD8+, CD4+/CD8+) were determined before and after treatment. The serum levels of vascular endothelial growth factor (VEGF), osteopontin (OPN), aspartate aminotransferase (AST), and epidermal growth factor 7 (EGF7)] were observed. The survival time between the two groups was compared, such as progression-free survival (PFS) and median survival (MS). Finally, the toxicity and side effects data were also obtained.ResultsThe ORR and DCR of group A were 69.05% and 88.10%, respectively, which were significantly higher (P0.05). The serum level of VEGF, OPN, EGF-7 and AST indexes of group A&B were decreased significantly (P0.05).ConclusionIn treating HCC, combining apatinib and camrelizumab can reduce tumor markers, enhance the immune system and curative effect, and prolong patient survival. The underline mechanism is related to the down-regulation of VEGF, OPN and HIF-1 indexes.</p