In the world of digital systems, encoders and decoders play a crucial role in converting incoming information into appropriate binary forms for processing and output. Encoders transform data into binary codes suitable for display, while decoders ensure that binary data is correctly interpreted and u

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The research introduces a novel knowledge distillation (KD) method for transitioning from non-streaming to streaming ASR encoders by incorporating auxiliary non-streaming layers and a special KD loss function. This approach enhances feature extraction, improves robustness to frame misalignment, and

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Decoders and multiplexers are essential components in computer architecture, converting binary information efficiently. Integrated circuits house digital gates, enabling the functioning of these circuits. A decoder's purpose is to generate binary combinations, with examples like the 3-to-8-line deco

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Neural models have evolved from RNN/LSTM, designed for language processing tasks, to Transformers with enhanced context modeling. Transformers introduce features like attention, encoder-decoder architecture (e.g., BERT/GPT), and fine-tuning techniques for training. Pretrained models like BERT and GP

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Convolutional codes provide an efficient alternative to linear block coding by grouping data into smaller blocks and encoding them into output bits. These codes are defined by parameters (n, k, L) and realized using a convolutional structure. Generators play a key role in determining the connections

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Efficiently learning an encoder that classifies token replacements accurately using ELECTRA method, which involves replacing some input tokens with samples from a generator instead of masking. The key idea is to train a text encoder to distinguish input tokens from negative samples, resulting in bet

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This content delves into the concept of decoding in natural language generation (NLG) using RNN Encoder-Decoder models. It discusses decoding approaches such as greedy decoding, sampling from probability distributions, and beam search in RNNs. It also explores applications of decoding and machine tr

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In this study by Misaki Matsuura, the effectiveness of CLIP (contrastive language-image pre-training) and LLaVA (large language-and-vision assistant) on zero-shot classification is explored. CLIP, with 63 million parameters, retrieves textual labels based on internet image-text pairs. On the other h

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Autoencoders are neural networks designed to reproduce their input, with Variational Autoencoders (VAE) adding a probabilistic aspect to the encoding and decoding process. VAE makes use of encoder and decoder models that work together to learn probabilistic distributions for latent variables, enabli

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Solar Energy Generator design includes a prototype system mounted in a Pelican case with various peripherals. The system features a Laser Cut Delrin Panel covering all electronics with display, buttons, and a rotary encoder. External connections are facilitated through Souriau UTS circular connector

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This work focuses on generating sense-specific example sentences using BART (Bidirectional and AutoRegressive Transformers) by conditioning on the target word and its contextual representation from another sentence with the desired sense. The approach involves two components: a contextual word encod

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The research discusses the challenge of recovering a colour image from a grey-level image efficiently. It presents a solution involving parametric curve optimization in the encoder and decoder sides, minimizing errors and encapsulating colour data. The Parametric Curve maps grayscale values to colou

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Explore the Line Encoding Lab 6 from 2019/1440, featuring Polar NRZ-L, RZ, and MAN techniques with decoders. Dive into Simulink settings and output results for each encoding method. Discover how to modify binary number generators and pulse generators to enhance encoding. Conclude with a thank you me

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The ZEN model improves pre-training procedures by incorporating n-gram representations, addressing limitations of existing methods like BERT and ERNIE. By leveraging n-grams, ZEN enhances encoder training and generalization capabilities, demonstrating effectiveness across various NLP tasks and datas

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Large amount of parallel speech-text data is not available in most languages, leading to the development of wav2vec for ASR systems. The training process involves self-supervised pretraining and low-resource finetuning. The model architecture includes a multi-layer convolutional feature encoder, qua

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This document delves into the investigation of LDPC (Low-Density Parity-Check) improvements within IEEE 802.11-24 standards. It discusses the history of LDPC codes in 802.11 networks, current FEC (Forward Error Correction) details, a new proposal for LDPC codes, recent LDPC code developments in vari

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In the domain of neural networks, the Transformer architecture has revolutionized sequence-to-sequence translation tasks. This involves attention mechanisms, multi-head attention, transformer encoder layers, and positional embeddings to enhance the translation process. Additionally, Encoder-Decoder

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Explore OWSM-CTC, an innovative encoder-only model for diverse language speech-to-text tasks inspired by Whisper and OWSM. Learn about its non-autoregressive approach and implications for multilingual ASR, ST, and LID.

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This presentation by Y.C. Chou and H.S. Liu on "Towards Completely Automatic Decoder Synthesis" covers topics such as motivation, preliminary concepts, main algorithms, and experimental results in the field of communication and cryptography systems. The content delves into notation, SAT solvers, Cra

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The 8085 microprocessor with 16 address lines can access 216 locations in physical memory. Utilizing a 74LS138 address decoder, chip select signals are generated for memory block selection. The interfacing involves decoding address lines to enable memory access, with distinctions between RAM and ROM

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Cutting-edge progress in automatic decoder synthesis for communication and cryptography systems, presented in a comprehensive study covering motivation, prior work, preliminary notations, SAT solver algorithms, and experimental findings.

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The BIKE cryptosystem is a code-based KEM in the NIST PQC standardization process, utilizing the Niederreiter variant of the McEliece Construction with a QC-MDPC code. It ensures security against IND-CPA, and efforts are made to further confirm or disconfirm its estimates for IND-CCA security requir

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Development of a high-speed hit decoder for the RD53B chip by Donavan Erickson from MSEE ACME Lab, focusing on data streams, hitmap encoding, ROM splitting, decode engine building, and more. The process involves encoding methods, ROM setup with borrowed software look-up tables, and buffer systems fo

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Explore the integration of CPU and FPGA for a highly efficient and flexible video encoder. Learn about the motivation, industry trends, discussions, Xilinx Zynq architecture, design process, H.264 baseline profile, and more to achieve high throughput, low power consumption, and easy upgrading.

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This presentation delves into the intricacies of Neural Image Captioning, focusing on a model known as Neural Image Caption (NIC). The NIC's primary goal is to automatically generate descriptive English sentences for images. Leveraging the Encoder-Decoder structure, the NIC uses a deep CNN as the en

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Explore the Rowhammering.BIKE cryptosystem, its parameters, and the black-grey flip decoder. Learn about the bitflipping algorithm, prior analysis of DFR in QC-MDPC decoders, and strategies for key recovery and decoding in this innovative system. Discover how understanding the error patterns related

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The BIKE (Bit-Flipping Key Exchange) protocol, presented by Ray Perlner, offers variants based on MDPC codes like McEliece and Niederreiter with a focus on quasi-cyclic structures. These non-algebraic codes show promise for key reduction, targeting IND-CPA security. The protocol features competitive

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