Showing papers on "Tree (data structure) published in 2022"

Visual Foresight Trees for Object Retrieval From Clutter With Nonprehensile Rearrangement

Abstract: This letter considers the problem of retrieving an object from many tightly packed objects using a combination of robotic pushing and grasping actions. Object retrieval in dense clutter is an important skill for robots to operate in households and everyday environments effectively. The proposed solution, Visual Foresight Tree ( VFT ), intelligently rearranges the clutter surrounding a target object so that it can be grasped easily. Rearrangement with nested nonprehensile actions is challenging as it requires predicting complex object interactions in a combinatorially large configuration space of multiple objects. We first show that a deep neural network can be trained to accurately predict the poses of the packed objects when the robot pushes one of them. The predictive network provides visual foresight and is used in a tree search as a state transition function in the space of scene images. The tree search returns a sequence of consecutive push actions yielding the best arrangement of the clutter for grasping the target object. Experiments in simulation and using a real robot and objects show that the proposed approach outperforms model-free techniques as well as model-based myopic methods both in terms of success rates and the number of executed actions, on several challenging tasks. A video introducing VFT , with robot experiments, is accessible at https://youtu.be/7cL-hmgvyec . The full source code is available at https://github.com/arc-l/vft .

A Highly-Available Move Operation for Replicated Trees

Abstract: Replicated tree data structures are a fundamental building block of distributed filesystems, such as Google Drive and Dropbox, and collaborative applications with a JSON or XML data model. These systems need to support a move operation that allows a subtree to be moved to a new location within the tree. However, such a move operation is difficult to implement correctly if different replicas can concurrently perform arbitrary move operations, and we demonstrate bugs in Google Drive and Dropbox that arise with concurrent moves. In this article we present a CRDT algorithm that handles arbitrary concurrent modifications on trees, while ensuring that the tree structure remains valid (in particular, no cycles are introduced), and guaranteeing that all replicas converge towards the same consistent state. Our algorithm requires no synchronous coordination between replicas, making it highly available in the face of network partitions. We formally prove the correctness of our algorithm using the Isabelle/HOL proof assistant, and evaluate the performance of our formally verified implementation in a geo-replicated setting.

Changes of Tree and Stand Growth: Review and Implications

Abstract: In this chapter, we review the current long-term growth trends and short-term growth reaction to single or repeated stress events on tree and stand level in Europe. Based on growth trend analyses, the chapter reveals the strong human footprint on forest ecosystems.

FR-Tree: A novel rare association rule for big data problem

Abstract: In some situations, finding the rare association rule is of higher importance than the frequent itemset. Unique rules represent rare cases, activities, or events in real-world applications. It is essential to extract exceptional critical activity from vast routine data. This paper proposes a new algorithm called FR-Tree to mine the association rules and produce essential rules. This work aims to demonstrate that this algorithm is suitable for extracting rare association rules with high confidence. The proposed algorithm generates, filters, and classifies the all-important rules, either frequent or rare. The rare rules were produced without needing to set an additional threshold. Therefore, the proposed algorithm has an advantage incomparable with the other rare association rule techniques. The generated rules were tested using well-known datasets, and the performance was compared with the other rare association rule techniques. The results proved that our method outperformed the existing rare association rule techniques.

Integration of aggressive bound tightening and Mixed Integer Programming for Cost-sensitive feature selection in medical diagnosis

Abstract: Silent diseases is an umbrella term that captures a spectrum of chronic illnesses that produce no clinically obvious signs and are diagnosed at advanced stages when the damage is irreversible. Current diagnostic strategies of silent diseases depend on self-reported symptoms and observed behavior through extended periods of time, and until now there are no specific clinical tests to diagnose silent diseases. Scientific research suggests the importance of early diagnosis to restore the functionality and reduce diseases-related complications. Previous studies primarily focused on feature selection methods to aid in medical diagnosis. Traditional feature selection methods are primarily focused on correct classification and often ignore features’ costs; the cost of clinical tests required to acquire the feature value. However, in medical diagnosis, features have different associated costs. Because ignoring features’ costs may result in a high cost diagnostic strategy that cannot be used in practice, developing a low-cost diagnostic strategy remains a subject of much interest. In this paper, new Mixed Integer Programming (MIP) models, namely, Cost-sensitive Support Vector Machine (CS-SVM) and Cost-sensitive Multi-surface Method Tree (CS-MSMT) that allow for simultaneous selection of low-cost and informative features are proposed. The CS-SVM and CS-MSMT are superior because they have the ability to account for shared costs. The CS-SVM and CS-MSMT were modified to embed shared costs across feature groups, and are termed Discounted CS-SVM (dCS-SVM) and Discounted CS-MSMT (dCS-MSMT), respectively. Computationally effective algorithm that integrates aggressive bound tightening with the MIP formulation is proposed. To demonstrate the effectiveness of the proposed models, different analysis paradigms are conducted on six UCI medical datasets; Chronic Kidney Disease, Hepatitis, Heart Disease, Thyroid, Diabetes and Leukemia. The results demonstrate the efficiency and robustness of the CS-SVM and CS-MSMT (and consequently the dCS-SVM and dCS-MSMT) under various conditions. The CS-SVM and CS-MSMT improved accuracy by 10.3% and 3.4% and reduced costs by 94.3% and 72.4% in the leukemia dataset, respectively.

A Practical Framework for Secure Document Retrieval in Encrypted Cloud File Systems

Abstract: With the development of cloud computing, more and more data owners are motivated to outsource their documents to the cloud and share them with the authorized data users securely and flexibly. To protect data privacy, the documents are generally encrypted before being outsourced to the cloud and hence their searchability decreases. Though many privacy-preserving document search schemes have been proposed, they cannot reach a proper balance among functionality, flexibility, security and efficiency. In this paper, a new encrypted document retrieval system is designed and a proxy server is integrated into the system to alleviate data owner's workload and improve the whole system's security level. In this process, we consider a more practical and stronger threat model in which the cloud server can collude with a small number of data users. To support multiple document search patterns, we construct two AVL trees for the filenames and authors, and a Hierarchical Retrieval Features tree (HRF tree) for the document vectors. A depth-first search algorithm is designed for the HRF tree and the Enhanced Asymmetric Scalar-Product-Preserving Encryption (Enhanced ASPE) algorithm is utilized to encrypt the HRF tree. All the three index trees are linked with each other to efficiently support the search requests with multiple parameters. Theoretical analysis and simulation results illustrate the security and efficiency of the proposed framework.

Air Combat Decision Based on Genetic Fuzzy Tree

Abstract: To solve the issue that the accurate model of complex air combat process is difficult to establish and air combat is high real-time, an air combat decision-making based on genetic fuzzy tree is proposed. Taking a dual cooperative silent attack scenario as an example, the corresponding cascade fuzzy tree model is established, the parameter coding method of the fuzzy tree model is studied, and simulation verification is performed in the set air combat environment. The simulation results show that the method of genetic fuzzy tree is effective for air combat decision-making.

Efficacy of Trans-geographic Observational Network Design for Revelation of Growth Pattern in Mountain Forests Across Europe

Abstract: Understanding tree and stand growth dynamics in the frame of climate change calls for large-scale analyses. For analysing growth patterns in mountain forests across Europe, the CLIMO consortium compiled a network of observational plots across European mountain regions. Here, we describe the design and efficacy of this network of plots in monospecific European beech and mixed-species stands of Norway spruce, European beech, and silver fir.