Systems and methods are provided for mitigating effects of hash collisions in hardware data compression, for example reducing or avoiding the side effects of hash collisions, or reducing or avoiding slow downs caused by hash collisions. In an aspect, a processor-implemented method includes: hashing an input data byte sequence to produce a hash value, the input data byte sequence being located at a sequence address within an input data stream; and storing, in a hash table at a hash address corresponding to the hash value, the sequence address and a portion of the input data byte sequence. In an aspect, to further avoid hash collisions, hash memory accesses are distributed among a plurality of parallel hash banks to increase the throughput. Another aspect virtually extends a hash depth by extending a data match search around broken hash links, going backward in the data sequence.

A method for recognizing an input pattern in at least one time series is provided including a. providing the time series; b. generating associated time series sections of a specific length on the basis of the time series by a combination of statistical approaches or a machine learning model; c. indexing each time series section; d. assigning each time series section to an applicable key value index; e. recognizing the input pattern in at least one time series of the plurality of time series by identifying at least one time series section that matches or is similar to the input pattern by a similarity search approach on the basis of the plurality of indexed time series sections; and f. providing the at least one identified time series section as an output pattern that matches or is similar to the input pattern if a match or similarity is detected.

The present application discloses a cache access method and an associated graph neural network system. The graph neural network processor is used for performing computation upon a graph neural network. The graph neural network is stored in the memory in compressed sparse row format. The method includes: receiving an address corresponding to a node of the graph neural network and a type of the address; in response to the type is one of a first type or a second type, performing lookup by comparing the address with a tag field of a degree lookup table to at least obtain a degree of the node; determining whether the degree is greater than a predetermined value to obtain a determination result; and determining whether to perform lookup on a region of the cache corresponding to the type according to the determination result.

A monitoring system and a monitoring method for a planter includes a computer having an unit speed fusion module, a seeding flow rate monitoring module, and a decision module for theoretical rotation speed of a drive motor, a rotation speed deviation inference module, a controlling parameter tuning module, an adjusting module for rotation speed of a seeding shaft, a controlling module for rotation speed of seeding shaft and a positioning signal receiver fixed on a top of a cab for receiving a geographic position signal. The system monitors operating state parameters of the planter and accurately controls seed amount.

Disclosed herein are embodiments of systems and methods for synchronizing file-catalog table with a file stage. In an embodiment, a data platform receives a notification of a modification to one or more files in a file stage. The file stage includes data storage having a storage location. The data platform updates, based on receiving the notification of the modification, a first file-catalog table for the file stage based on the modification. The first file-catalog table includes a row for each file in the file stage, as well as a column for each of one or more metadata properties of the one or more files in the file stage.

A direct digital synthesis transmitter that uses a programmable digital circuit to generate a digital signal representing at least one radio frequency signal, the generated signal is filtered, amplified by an amplifier, and provided to a transmission antenna without upconversion. The transmitter generating the digital signal at a desired output frequency range such that a frequency upconverter is not needed to produce signals in the desired radio frequency range.

The present disclosure discloses a method and an apparatus for generating a vector representation of a knowledge graph, and relates to a field of a field of artificial intelligence technologies. The detailed implementing solution is: obtaining a knowledge graph, the knowledge graph including a plurality of entity nodes; obtaining a context type and context data corresponding to the knowledge graph; and generating vector representations corresponding to the plurality of entity nodes by a context model based on the context data and the context type.

Described are methods and systems for improved cardinality estimation. A method may include obtaining a data-query, obtaining a row, generating a hash value, determining a cardinality of leading zeros in the hash value, identifying a bucket with respect to the hash value, including a bucket identifier and the cardinality of leading zeros in a representation, determining the approximate unique count, and outputting the approximate unique count as results data responsive to the portion of the data-query.

A method of controlling an operational system by a rules management system comprising a processor and a memory, and a computing apparatus comprising a processor and a memory are provided. The processor is programmed to execute rules from a rules repository stored on a memory in response to a request. The computing apparatus further comprises a high rules repository storing one or more high level rules, wherein each high level rule, when executed by the processor, modifies the effect of execution of one or more rules R.sub.m in the rules repository; and a high rules conditions module that when executed by the processor identifies and executes the high level rules that apply to the request.

Disclosed are methods, systems, and computer-readable medium for providing query results. The query results may be provided more accurately and faster than via distributed tables. Disclosed techniques include receiving a plurality of tables comprising attributes and attribute data, the attribute data comprising attribute forms and attribute related information, identifying attributes and corresponding attribute data from each of the plurality of tables, applying a categorization machine learning model to categorize each attribute data as an attribute form or an attribute related information, generating the lookup table based on the attributes and the corresponding attribute forms, generating the relationship table based on the attributes and the corresponding attribute related information, receiving a query for attribute data, and generating a query response based on at least one of the lookup table or the relationship table.