SYSTEM AND METHOD FOR PERFORMING TRANSACTIONS USING UNIFIED TRANSACTION IDENTIFIER (ID)

The present disclosure discloses a method and a system for performing transactions using unified transaction identifier (ID). In the present disclosure, the method includes generating a unified ID using credentials of a user during registration process. Herein, the unified ID is linked with one or more accounts of the user, one or more debit cards, credit cards, and the like, of the user. Post registering, the user such as, a sender having a unified ID can initiate a transaction with a recipient having a unified ID. Herein, while initiating the transaction from the sender to the recipient, the sender provides unified ID of the recipient. Upon entering the unified ID of the recipient, the unified ID system determines a risk rate and a payment limit associated with the initiated transaction. Then, the determined risk rate associated with the transaction is notified to the sender to complete or terminate the transaction

End-to-End Neural Network Compression via ℓ1ℓ2\frac{\ell_1}{\ell_2} Regularized Latency Surrogates

Neural network (NN) compression via techniques such as pruning, quantization requires setting compression hyperparameters (e.g., number of channels to be pruned, bitwidths for quantization) for each layer either manually or via neural architecture search (NAS) which can be computationally expensive. We address this problem by providing an end-to-end technique that optimizes for model's Floating Point Operations (FLOPs) or for on-device latency via a novel $\frac{\ell_1}{\ell_2}$ latency surrogate. Our algorithm is versatile and can be used with many popular compression methods including pruning, low-rank factorization, and quantization. Crucially, it is fast and runs in almost the same amount of time as single model training; which is a significant training speed-up over standard NAS methods. For BERT compression on GLUE fine-tuning tasks, we achieve $50\%$ reduction in FLOPs with only $1\%$ drop in performance. For compressing MobileNetV3 on ImageNet-1K, we achieve $15\%$ reduction in FLOPs, and $11\%$ reduction in on-device latency without drop in accuracy, while still requiring $3\times$ less training compute than SOTA compression techniques. Finally, for transfer learning on smaller datasets, our technique identifies $1.2\times$-$1.4\times$ cheaper architectures than standard MobileNetV3, EfficientNet suite of architectures at almost the same training cost and accuracy

IMD-Net: A deep learning-based icosahedral mesh denoising network

In this work, we propose a novel denoising technique, the icosahedral mesh denoising network (IMD-Net) for closed genus-0 meshes. IMD-Net is a deep neural network that produces a denoised mesh in a single end-to-end pass, preserving and emphasizing natural object features in the process. A preprocessing step, exploiting the homeomorphism between genus-0 mesh and sphere, remeshes an irregular mesh using the regular mesh structure of a frequency subdivided icosahedron. Enabled by gauge equivariant convolutional layers arranged in a residual U-net, IMD-Net denoises the remeshing invariant to global mesh transformations as well as local feature constellations and orientations, doing so with a computational complexity of traditional conv2D kernel. The network is equipped with carefully crafted loss function that leverages differences between positional, normal and curvature fields of target and noisy mesh in a numerically stable fashion. In a first, two large shape datasets commonly used in related fields, ABC and ShapeNetCore , are introduced to evaluate mesh denoising. IMD-Net’s competitiveness with existing state-of-the-art techniques is established using both metric evaluations and visual inspection of denoised models. Our code is publicly available at https://github.com/jjabo/IMD-Net.DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

DESIGN OF LOW CARBON HIGH PERFORMANCE CONCRETE INCORPORATING ULTRAFINE MATERIALS

In general, high performance concrete (HPC) is associated with high strength and improved durability in comparison to normal strength concrete. However, HPC invariably involves high binder content at low water/binder ratio and its application has been limited to specialised concrete works. In this study, an attempt was made to design high performance concrete, at high water/binder ratio made with OPC content varying from 40%-80% in concrete mixes with low binder content of 280 kg/m3. Binary and quaternary, low carbon mixes were prepared by incorporating  Supplementary Cementitious Materials (SCM) and Ultrafine (UF) materials (silica fume, ultrafine GGBS, ultrafine fly ash and metakaolin) and were characterised for strength and durability parameters such as charge passed using RCPT, electrical resistivity and carbonation depth. Findings of the study shows that with appropriate choice and combination of SCM and ultrafine materials, low carbon high performance concrete mixes can be designed for strength up to 50 MPa with improved durability performance even at 45% OPC content. Overall, performance of low carbon high performance concrete mixes depends on the type and extent of SCM as well ultrafine materials such as metakaolin, ultrafine GGBS, ultrafine fly ash and silica fume use along with their compatibility