Abstract:
Example systems and methods of developing a learning model are presented. In one example, a sample data set to train a first learning algorithm is accessed. A number of states for each input of the sample data set is determined A subset of the inputs is selected, and the sample data set is partitioned into a number of partitions equal to a combined number of states of the selected inputs. A second learning algorithm is created for each of the partitions, wherein each second learning algorithm receives the unselected inputs. Each of the second learning algorithms is assigned to a processor and trained using the samples of the partition corresponding to that algorithm. Decision logic is generated to direct each of a plurality of operational data units as input to one of the second learning algorithms based on states of the selected inputs of the operational data unit.
Abstract:
A filter selection technique is described for automatically selecting filters and filter parameters to apply to a given input data. The technique first receives input data and accesses a library storing information from previously analyzed data. The technique selects an entry from the library where the entry contains data that is correlated with the input data. The technique then applies a filter to the input data. The filter and filter parameters are determined by the selected entry.
Abstract:
Accelerated sub-string searches on large data sets can be performed using filtering processes that can improve or optimize run time performance. A first filtering process can include partitioning a binary tree into sections to enable an exact search to replace a substring search for part of the binary tree and for part of the binary tree to be potentially excluded from substring searching. A second filtering process can include comparing count representations of entries in the binary tree and of a received input string to potentially further exclude entries from substring searching.
Abstract:
A performance modeling tool and method permitting the user to define the elements of a distributed system (hosts, networks and response times), and examine the effect on performance of different distributions of application processes over the system at an early stage in application design. Once a user has defined a performance scenario, it is saved to a data model as a number of interdependent persistent objects that show the distribution of the application for a particular performance scenario from different views. Multiple alternates of each object can be stored. The user can construct different performance scenarios for analysis from the stored objects. Analysis can include performance simulation from which the user can obtain performance projections for an application process or several application processes over different distributions of the performance worload.
Abstract:
Example systems and methods of developing a learning model are presented. In one example, a sample data set to train a first learning algorithm is accessed. A number of states for each input of the sample data set is determined. A subset of the inputs is selected, and the sample data set is partitioned into a number of partitions equal to a combined number of states of the selected inputs. A second learning algorithm is created for each of the partitions, wherein each second learning algorithm receives the unselected inputs. Each of the second learning algorithms is assigned to a processor and trained using the samples of the partition corresponding to that algorithm. Decision logic is generated to direct each of a plurality of operational data units as input to one of the second learning algorithms based on states of the selected inputs of the operational data unit.
Abstract:
The invention relates to the technical field of gene treatment and nano targeted delivery, in particular to a siRNA delivery system compound as well as a preparation method and application thereof. The preparation method comprises the following steps of reacting Boc-protected histidine with cholesterol to generate Boc-protected histidine cholesteryl ester, and removing BOC-released protected amino in trifluoroacetic acid to generate histidine cholesteryl ester; then, enabling amino to react with carboxyl of carboxymethyl chitosan to modify long-chain carboxymethyl chitosan to generate carboxymethyl chitosan modified by histidine cholesteryl ester, and performing targeted labeling on carboxymethyl chitosan modified by histidine cholesteryl ester to generate a carboxymethyl chitosan high-molecular compound modified by histidine cholesteryl ester and a targeted marker together, then forming a stable spherical delivery vector through self-assembly above critical micelle concentration, and mixing the stable spherical delivery vector with siRNA (small interfering ribonucleic acid) to obtain the siRNA delivery system compound.
Abstract:
A filter selection technique is described for automatically selecting filters and filter parameters to apply to a given input data. The technique first receives input data and accesses a library storing information from previously analyzed data. The technique selects an entry from the library where the entry contains data that is correlated with the input data. The technique then applies a filter to the input data. The filter and filter parameters are determined by the selected entry.
Abstract:
An integrated clinical laboratory software system for testing a specimen. At least one specimen processing module is advantageously provided, each for performing particular predetermined tests on the specimen. Integrated work flow automation programming communicates with any of the plurality of specimen processing modules. The specimen processing modules can include instrument hardware and embedded process control software. The work flow automation programming includes request processing programming for processing a user request for any of the tests which are available to be performed by the specimen processing modules, and also includes functional control programming which provides functional control of any of the plurality of specimen processing modules for performing any of the tests, and which further includes result data management programming provides processing of test result data of any of the tests.
Abstract:
Example systems and methods of circular transaction path detection are presented. In one example, a directed graph comprising nodes and directed edges interconnecting the nodes is generated. The directed graph is based on information describing a plurality of parties and a plurality of transactions between the parties. A circular path length of interest is received. Strongly connected components of the directed graph are identified. Within each of the strongly connected components, each circular path having a length equal to the circular path length of interest is discovered. For each discovered circular path, the transactions represented by the directed edges of the path are denoted as related transactions.
Abstract:
A method of encoding Chinese character PINYIN into digital string and utilizing a key pad to input Chinese character to computer is described herein. The pronunciation of a Chinese character can be described by Romania form of 26 letters, and four tone indicators. In accordance with the present invention, a lookup table has been composed, allowing the 30 symbols to be grouped into ten sets. Each set is associated with group identification respectively. In operation, a key pad mapped with the lookup table allows individual symbol of PINYIN to be inputted directly using group identification as medium. Individual symbols are retrieved exclusively through the group identification and echoed step by step. A human-machine interface similar to an ASCII compatible keyboard has been established by the method for a small key pad compatible to phone set during the PINYIN inputting process. An inputted PINYIN is used to retrieve a Chinese character further.