Tata Consultancy Services Patente

This disclosure relates generally to neural network inferencing, and more particularly, to a method and system for neural network inferencing in logarithmic domain. The conventional techniques include training a neural network in logarithmic domain and performing inferencing. This leads to less accuracy, challenge in converting large models and unable to perform optimization. The present disclosure converts a pre-trained neural network into logarithmic domain using a bit manipulation based logarithm number system technique wherein the neural network is pre-trained in real time or in logarithmic domain. The method converts the weights, neural network layers and activation function into logarithmic domain. The method uses a 32-bit integer variable to store a logarithm number which leads to memory efficiency. The disclosed method is used for inferencing of convolutional neural network for natural language processing, image recognition and so on.

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This disclosure relates generally to safety improvement in a workspace. The method includes creating a context model associated with the workspace by defining contextual domain and a plurality of safety violation rules for safety standard operating procedures (SOPs) of digitally augmented assets corresponding to the physical entities present in the workspace. Further, the method includes monitoring, in real-time, states of each of the digitally augmented assets by obtaining sensor-based data from a sensor devices deployed in a smart space environment implemented in the workspace. SOP violations are triggered as events on a comparison of the states of digitally augmented assets with the safety violation rules using the context model.

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The present disclosure estimates items to be returned based on nature of items picked online in association with the context. The context includes time, shopper details, local events and the like. It is addressed by mapping return spread and similarity spread or linkage spread in unique format. Further, the intrinsic mechanism that result returns are captured by training a multivariate Machine Learning (ML) model using the actual return spread, the similarity spread, or the linkage spread and the customer profile data. The captured return mechanism is leveraged to pre-empt the returns online in the form of return spread at the time of ordering in real time.

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The embodiments of the present disclosure herein address unresolved problem of obtaining price recommendation not only for the immediate moment but also for a continuous period until a market-driven price trigger necessitates change. Embodiments herein provide a framework for dynamic pricing of one or more products in an e-commerce retail using at least one contextual Bandit (CB) technique. The framework provides an ensemble of contextual bandits for dynamic pricing in e-commerce. The framework amalgamates strengths of one or more CB models. The framework incorporates an ensemble layer to critically review immediate pricing suggestions, ensuring their long-term applicability, thereby balancing margin stability and customer satisfaction over time.

Use of ultrasonic guided waves for damage identification and localization is not new in Non-Destructive Testing/Evaluation. However, most of the time it is performed with high voltage pulse excitations that use several hundreds of volts in the form of a short burst, thus making it unsafe and unsustainable for defect localization in large structures. Present disclosure provides a method and a system for damage localization using low power ultrasonic guided waves. The system of the present disclosure uses a Vector network analyzer (VNA) sweep of a defined frequency range of low signal amplitude on a structure to form guided wave resonance spectra. Then, the system performs an Inverse Fast Fourier transform (IFFT) on the guided wave resonance spectra to obtain a time domain pulse propagation picture. Thereafter, the system uses a pulse echo based analysis technique based on time domain pulse propagation picture to locate damage position in the structure.

High-performance deployment of DNN recommendation models heavily rely on embedding tables, and their performance bottleneck lies in the latency of embedding access. To optimize the deployment of RMs, the method and system is disclosed, which leverages heterogeneous memory types on FPGAs to improve the overall performance by maximizing the availability of frequently accessed data in faster memory. The system, using a optimizer dynamically allocates table partitions of the embedding tables based on history of input access history. A pre-optimizer block disclosed determines whether smaller tables should be partitioned or placed entirely in smaller memories, improving overall efficiency. The performance of RM is improved with improvement in average embedding fetch latency and effectively inference latency via modified Round Trip computation.

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Data inaccuracy and insufficiency are critical aspects to be analyzed to improve batch predictions, specifically in context of SLA jobs as they are foremost in affecting deliverables. Embodiments of the present disclosure provide a method and system for enhancing batch predictions by localizing jobs contributing to time deviation and generating fix recommendations by fixing data inaccuracy and insufficiency. The term fix recommendation refers to recommending a list of plausible fixes to identified causes that reduce batch prediction errors enhancing accuracy of predictions. The localization is performed by bottom-up traversing of a batch graph representing a batch process, if the batch process has a Service level Agreement (SLA) job, by narrowing down to the SLA job that has end time inaccuracies. The localization enables identifying the origin or real contributors and root cause analysis is performed for the localized jobs to generate effective fix recommendations by fixing data inaccuracy and insufficiency.

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With the growing use of large-scale datasets containing participants' genomics and clinical data for research and studies purposes, it is important to ensure the privacy of the participants by generating a secure representation of genome data observations. Embodiments herein provide a system and method to perform DNA analysis on an encrypted genome without the need for decryption and to ensure users' genome privacy. Herein, a stream of input genome is divided into chunks called sketches, where each k-mer in the sketch is hashed to get a sampling level and a counter value for the k-mer in the sketch. A privacy-enhancing technique is designed over the representations using Fully Homomorphic Encryption (FHE). Genomic data is analyzed with identified efficient algorithms for real-world deployment in the encrypted domain.

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Amyloid deposition is considered a viable biomarker for Alzheimer's disease which is expensive and less used modality to study amyloid spatial distribution in brain. The present disclosure addresses problems of conventional approaches which exhibit trade-off between performance and mode collapse. This disclosure provides a system and method for subject-specific amyloid position emission tomography (PET) translation using conditioned diffusion-based generative model. The present disclosure discloses an architecture for synthesizing subject-specific amyloid images with a diffusion model. First effectiveness of a relationship between Fluorodeoxyglucose (FDG) and amyloid PET images is identified. Further, a framework is provided to synthesize Amyloid PET by utilizing its connection to FDG PET of same subject and cross-subject trends in amyloid deposition by incorporating age, gender and disease status information in learning process. In the other words, a diffusion model inspired image translation is provided to synthesize Amyloid PET from FDG PET and cross-subject amyloid deposition patterns.

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