International Business Machines Corporation

Stmicroelectronics International N.v.

Go Daddy Operating Company, LLC

Google Inc.

Oracle International Corporation

Western Digital Technologies, Inc.

Sandisk Technologies Inc.

Accenture Global Solutions Limited

Stmicroelectronics Pvt. Ltd.

Other

Cornell University

Cisco Technology, Inc.

Synopsys, Inc.

Symbol Technologies, LLC

Stmicroelectronics (Crolles 2) Sas

Nitin Kumar Gupta

Venkatesh Aravamudhan

Tapan Kamdar

Raghuvamshi Vasudev Singh Thakur

Abhinandan Sujit Das

Shiv Harit Mathur

Ramakrishnan Subramanian

Garrett Matsudaira

Edward J Karcher, Iii

Steven D Baker

Srinivasan Venkatachary

Saswata Lahiri

Anwis Das

Tapas Nandy

Hima Patel

Stacy SteinKuller

Abhirup Lahiri

Brad Lee Herold

Shashank Mujumdar

Hemant Manaharlal Kakkad

Vijay Ekambaram

Arnold Neil Blinn

Sameep Mehta

Shashikiran Bhalachandra Mahalank

Mehulkumar Patel

Bhavin Odedara

Abhishek Bansal

Jayakumar N Sankarannair

Deepak Chandrashekhar Shetty

Rahul Mahajan

Anand Kumar

Jeet Narayan Tiwari

Anup Shivarajapura

Abul Azim

Renshen Wang

Nipun Agarwal

Walter Cabri

Paul Haahr

Yonghui Wu

Himanshu Gupta

Pranay Kumar Lohia

Michael E Flaster

Nitin Jain

Ruhi Sharma Mittal

Nilendu Panda

Ravi Murthy

Sivasankaran Chandrasekar

Pedro Miguel Barbas

Pratyush Kumar

Vinayak Ashok Ghatawade

Richard Merdinger

Ravi Kumar Reddy Kanamatareddy

Johannes Gehrke

Ankit Gupta

Bhuwan Bhaskar Mishra

Shaikh Shahriar Quader

Prashutosh Gupta

Yi Liu

Yan Li

Jeffrey Adgate Dean

Sam Idicula

Norman William Finn

Simon Tong

Eric Sedlar

Noam M Shazeer

Pankaj Satyanarayan Dayama

Rahul Jain

Venkatesh Ramachandra

Kallol Chatterjee

Daniel Ernesto Barreto

Lukasz Mieczyslaw Kaiser

Yedendra Shrinivasan

Jayant Madhavan

Manjit Singh Sodhi

Sreeram Viswanath Balakrishnan

Arvind Agarwal

Alon Yitzchak Halevy

Dmitry Vaysman

Vikas V Joshi

Sangram Alapati

Emanuel Taropa

Sundaresan Sundaram

Marek Olszewski

Elkana Richter

Saugata Das Purkayastha

Gagan Midha

Kawal Sapra

Nitin Agarwal

Rene Reinsberg

Pierre Dautriche

Diego Federici

Madhavan Srinivasan

Vijay Chinchole

Jayanth Mysore Thimmaiah

Nandakumar Sarun Madarakal

Rajmohan Chandrahasan

Moninder Jheeta

Amit Garg

Manish Garg

Christopher Ambler

Methods, systems, and computer program products for automatically improving data annotations by processing annotation properties and user feedback are provided herein. A computer-implemented method includes obtaining data annotation pairs, each comprising an input data annotation in a first format and a corresponding output data annotation in a second format; determining, within at least a portion of the data annotation pairs, one or more non-diffs; identifying, across the at least a portion of data annotation pairs, data annotation properties associated with multiple intents by processing the non-diffs using property-related rules; modifying at least a portion of the data annotation pairs based on the identified data annotation properties; outputting the modified data annotation pairs to at least one user; and generating a final collection of data annotation pairs by processing at least a portion of the modified data annotation pairs and user feedback received in response to the outputting.

Automatically improving data annotations by processing annotation properties and user feedback

Aspects of a storage device are provided for producing an oscillator clock from a host reference clock in a storage device lacking a crystal oscillator. The storage device includes a memory die, and an oscillator circuit that generates a clock based on a host reference clock and outputs an output clock to the memory die. While the host reference clock is available, the output clock includes a frequency that is identical to a frequency or a frequency division factor of the host reference clock. In response to loss of the host reference clock, the oscillator circuit reduces the frequency of the output clock to a frequency of the generated clock within a clock cycle following the loss of the host reference clock. In response to re-availability of the host reference clock, the oscillator circuit increases the frequency of the output clock back to the frequency of the host reference clock.

SSD with reference clock loss tolerant oscillator

A method, computer program, and computer system are provided for collecting and annotating data based on user preference. Unlabeled data corresponding to one or more entries within a dataset is received. Pseudo-labeled data is generated based on the unlabeled data. Based on one or more quality metrics, each entry from among the pseudo-labeled data is determining to be included within a final dataset. A user is prompted for annotations corresponding to entries of the pseudo-labeled data included within the final dataset. A determination is made as to whether additional data is needed based on comparing the final dataset to the one or more quality metrics, and the additional information is collected if the final dataset does not meet the quality metrics.

Annotating and collecting data-centric AI quality metrics considering user preferences

A cleansing operation defined for a data structure of a database managed by a database management system is obtained. The cleansing operation is performed on data of the data structure to obtain clean data. The cleansing operation that is defined for the data structure and performed on data of the data structure is performed by the database management system.

In-database data cleansing

A cleansing operation is performed on data of a data structure to obtain clean data. The clean data is stored as part of the data structure; however, the clean data is independent of the data. A mapping is performed to provide a set of ordered data that includes the data and the clean data.

In-database data cleansing and independent store of clean data

A method for resolving priorities among network function (NF) instances includes, at a priority resolver implemented using at least one processor, providing a priority resolution application programming interface (API) that allows NF instances to request priority resolution. The method further includes receiving, via the API and from a plurality of NF instances, requests for priority resolution. The method further includes using a priority resolution algorithm to determine a relative priority for each of the NF instances. The method further includes generating and transmitting to each of the NF instances a priority resolution response including the relative priority determined for the NF instance.

Methods, systems, and computer readable media for providing priority resolver for resolving priorities among network function (NF) instances

Automatically selecting data for machine learning datasets is provided. The method comprises receiving an input dataset and user-specified data quality metrics. The input dataset is matched to a subset of candidate datasets in a repository according to schema characteristics. A second subset of candidate datasets having a distance from the input dataset above a specified threshold is selected from the first subset of candidate datasets. The second subset of candidate datasets are merged into a merged dataset. Top ranked samples above a specified second threshold are identified from the merged dataset based on the user-specified data quality metrics. The input dataset, augmented with the top ranked samples, is returned to the user.

Automatically selecting relevant data based on user specified data and machine learning characteristics for data integration

A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include extracting a first multiple dimensional dataset and a second multiple dimensional dataset and encoding the first multiple dimensional dataset into a first encoded dataset and the second multiple dimensional dataset into a second encoded dataset. The operations may include combining the first encoded dataset and the second encoded dataset into a combined encoded dataset and conveying the combined encoded dataset to a user.

Optimized transmission and consumption of digital content

An interface circuit that can operate in toggle mode at data high transfer rates while reducing the self-induced noise is presented. The high speed toggle mode interface supplies a data signal to a data line or other transfer line by a driver circuit. The driver circuit includes a pair of series connected transistors connected between a high supply level and a low supply level, where the data line is supplied from a node between the two transistors. A resistor is connected between one or both of the transistors and one of the supply levels, with a capacitor connected between the low supply level and a node between the resistor and the transistor. The resistor helps to isolate the transistor from the supply level while the capacitor can act as current reservoir to boost the current to the transistor during data transition, reducing the noise seen by the voltage supply.

High speed toggle mode transmitter with capacitive boosting

A computer implemented method transforms data. Responsive to receiving a data transformation of an input string to an output string, a computer system identifies mappable tokens in the input string that are mappable to the output string. The computer system creates a set of initial mappings for a set of common tokens in the mappable tokens. The set of initial mappings maps the set of common tokens from the input string to the output string. The computer system creates a set of user mappings that maps the mappable tokens from input string to the output string using a user input to the set of initial mappings. The computer system generates program code that transform input strings to output strings using the set of user mappings that maps the mappable tokens from input string to the output string, wherein the program code is used to transform input strings to output strings.

Data transformation methodology using generated program code and token mappings

One embodiment provides a method, including: receiving a sample set for training a machine-learning model, wherein the sample set includes a plurality of classes, wherein classes within the plurality of classes have an imbalance in a number of samples; creating an enlarged minority class by generating new samples from the samples within the minority class and adding the new samples to the minority class; selecting subset samples from both the samples within the enlarged minority class and the majority class; weighting each of the subset samples based upon user input defining goals for attributes of a training sample set to be used in training the machine-learning model; and generating, using the neural network, the training sample set by re-running the selecting in view of the weighting.

Training sample set generation from imbalanced data in view of user goals

A method for establishing message priority in network slices based on quality of service (QoS) parameters includes receiving, by a network slice configuration service provider entity and from a network slice management service provider entity, a network slice creation notification message containing at least a QoS profile corresponding to a created network slice, utilizing the QoS profile to derive a service based interface (SBI) message priority (SMP) associated with the network slice, and providing the SMP to a requesting access and mobility management function (AMF) entity in response to receiving a network slice selection request message from the AMF entity, wherein the AMF entity assigns the SMP level value to network traffic directed to the network slice.

Methods, systems, and computer readable media for establishing message priority in network slices based on quality of service parameters

Various apparatuses, systems, methods, and media are disclosed to provide over-voltage protection to a data interface of a multi-protocol memory card that includes a first communication interface and a second communication interface that enable communication using different protocols. An interface voltage protection circuit includes a control circuit configured to receive a first supply voltage for operating the first communication interface. The interface voltage protection circuit further includes a pull-down circuit operatively connected with the control circuit, configured to pull down a voltage at a supply voltage rail of the second communication interface such that a voltage at a plurality of connector terminals of the second communication interface is lower than the first supply voltage.

High voltage protection for high-speed data interface

A method, computer system, and a computer program product for generating deep learning model insights using provenance data is provided. Embodiments of the present invention may include collecting provenance data. Embodiments of the present invention may include generating model insights based on the collected provenance data. Embodiments of the present invention may include generating a training model based on the generated model insights. Embodiments of the present invention may include reducing the training model size. Embodiments of the present invention may include creating a final trained model.

Deep learning model insights using provenance data

Snapback ESD protection circuits that include an Input/Output pad, a ground source, a first and a second NMOS transistor, and trigger circuit, pad bias circuit, and gate bias circuit. The first transistor drain connects to the pad. The second transistor drain connects to the first transistor source. The second transistor source connects to ground. The trigger circuit connects to the pad and a reference voltage to detect an ESD event at the pad. The pad bias circuit connects to the pad, the trigger circuit, ground, and the reference voltage to manage a voltage level for the reference voltage. The gate bias circuit connects to the reference voltage, a supply voltage, ground, and the gates of the first and second transistor to dynamically control the voltage of each gate of the first and a second NMOS transistor.

Snapback electrostatic discharge protection for electronic circuits

Growing community of inventors

Ghaziabad, India

Nitin Kumar Gupta