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654 results about "Tree node" patented technology

Detecting Dangling Pointers and Memory Leaks Within Software

Dangling pointers and memory leak locations within software are detected. As the software allocates and deallocates memory, lists of pointers referencing the memory, and pointer status, are maintained. As the software writes new addresses within pointers and reads addresses referenced by the pointers, the pointer lists are maintained to determine whether the pointers are dangling and to detect memory leak locations. A balanced binary tree having a number of nodes can be maintained. The nodes represent heap or stack records. Each heap record corresponds to heap memory that has been allocated and has a list of pointers referencing the heap memory. Each stack record corresponds to a stack within which a stack frame is allocated each time a function is entered. The stack record has frame records corresponding to the stack frames. Each frame record has a list of pointers referencing the corresponding stack frame.
Owner:IBM CORP

Restoring a database using a fully hydrated backup

A backup of a database is determined to be performed. A backup of at least a portion of contents of a storage volume that includes data of the database is performed. The backup includes a step of creating a new metadata tree root node. Creating the new metadata tree root node includes copying from another metadata tree root node of a tree data structure corresponding to a previous backup instance, one or more references to one or more lower tier metadata tree nodes associated with the tree data structure corresponding to the previous backup instance.
Owner:COHESITY

Distributed multiresolution geometry modeling system and method

A distributed multiresolution modeling system has a database management system on a first server. The database management system provides access to a hierarchical tree representation of surfaces of geometric models. Client programs executing on computers connected to the first server accesses and updates the model by traversing the hierarchical tree representation until a terminating criterion has been satisfied. During the traversal the client programs request quad-tree nodes from the database management system.
Owner:SCHLUMBERGER TECH CORP

Webpage loading method based on layout zoning

The invention provides a webpage loading method based on layout zoning, belonging to the technical field of browsers. The webpage loading method comprises the following steps that: a client sends a request to a server; the client establishes TCP (Transmission Control Protocol) connection together with the server and obtains an HTML (Hyper Text Mark-up Language) file, a CSS (Cascading Style Sheet) file and various resource files; a browser analyzes various kinds of files to respectively generate a DOM (Document Object Model) tree and a style sheet, and generates a render tree; a layout manager carries out layout on the render tree, and starts to generate render layout tree nodes; and when one render layout tree node is generated, a browser interface is drawn by using an increment drawing mode until the whole render layout tree is completely drawn. According to the method, by fully utilizing the characteristic of a small screen of a mobile device, the method of carrying out loading and layout on all webpage contents and then displaying is changed, increment drawing is carried out when one render layout tree node is generated, thus long-time interface blank caused by loading all webpages when a user opens the webpages is avoided, the waiting time is greatly reduced, and the user experience is greatly promoted.
Owner:SHANDONG UNIV

Probabilistic boosting tree framework for learning discriminative models

InactiveUS20070053563A1Image enhancementImage analysisProbability propagationKnowledge combination
A probabilistic boosting tree framework for computing two-class and multi-class discriminative models is disclosed. In the learning stage, the probabilistic boosting tree (PBT) automatically constructs a tree in which each node combines a number of weak classifiers (e.g., evidence, knowledge) into a strong classifier or conditional posterior probability. The PBT approaches the target posterior distribution by data augmentation (e.g., tree expansion) through a divide-and-conquer strategy. In the testing stage, the conditional probability is computed at each tree node based on the learned classifier which guides the probability propagation in its sub-trees. The top node of the tree therefore outputs the overall posterior probability by integrating the probabilities gathered from its sub-trees. In the training stage, a tree is recursively constructed in which each tree node is a strong classifier. The input training set is divided into two new sets, left and right ones, according to the learned classifier. Each set is then used to train the left and right sub-trees recursively.
Owner:SIEMENS MEDICAL SOLUTIONS USA INC

Quick file comparison method, system and client side

The invention relates to a quick file comparison method, a quick file comparison system and a quick file comparison client side. The quick file comparison method comprises the steps of: e scanning and analyzing a local file directory by the client side; according to the variation of the local file system, updating a first identification tree maintained by the client side; corresponding a node in the first identification tree to each local directory of the client side, wherein node information comprises a node identification and relevant directory information; comparing a second identification tree maintained by server side equipment with the first identification tree node by node by the client side; recording a difference node and a variation file corresponding to the difference node; and according to the difference node and the variation file, providing data to the server side equipment section by the client side so as to update the second identification tree maintained by server side equipment and a stored file. According to the quick file comparison method, a mode of comparing node information for reflecting the local file system of the client side is adopted, and due to the mode, the quickness is realized, the mode is more suitable for bigger and a more complex file system, and the file comparison efficiency and performance can be effectively improved.
Owner:ZHENGJIANG PUBLIC INFORMATION

Data summarization method and device based on tree block chain network, equipment and medium

InactiveCN109635165ABreakthrough performanceBreak through the storage bottleneckOther databases indexingData switching networksStructure of Management InformationPooling data
The embodiment of the invention discloses a data summarization method and device based on a tree block chain network, equipment and a medium. The method comprises: when it is detected that a pre-deployed summary contract is triggered, the node attribute of a tree node corresponding to a target block chain network where the tree node is located in a tree hierarchical structure is acquired; processing the data in the target block chain according to a data summarization strategy matched with the node attribute to form target summarized data; and if it is determined that the node attribute is a non-root node, sending the target summarized data to a parent block chain network of the target block chain network through a cross-chain protocol. According to the technical scheme provided by the embodiment of the invention, based on cross-chain interaction between parent and child block chain networks and a tree layering technology, the performance and storage bottleneck of a traditional single-chain structure are broken through, the problem of information islands incompatible with each other between different block chain networks is solved, and extensible application scenarios such as multi-service chains and polymorphic chains can be supported.
Owner:北京磁云数字科技有限公司

Method and System for Object Detection Using Probabilistic Boosting Cascade Tree

A method and system for object detection using a probabilistic boosting cascade tree (PBCT) is disclosed. A PBCT is a machine learning based classifier having a structure that is driven by training data and determined during the training process without user input. In a PBCT training method, for each node in the PBCT, a classifier is trained for the node based on training data received at the node. The performance of the classifier trained for the node is then evaluated based on the training data. Based on the performance of the classifier, the node is set to either a cascade node or a tree node. If the performance indicates that the data is relatively easy to classify, the node can be set as a cascade node. If the performance indicates that the data is relatively difficult to classify, the node can be set as a tree node. The trained PBCT can then be used to detect objects or classify data. For example, a trained PBCT can be used to detect lymph nodes in CT volume data.
Owner:SIEMENS MEDICAL SOLUTIONS USA INC
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