All papers

The paper presents circuit and electromagnetic models to simulate bulk current injection testing across complex wiring harnesses.

The paper implements high-frequency injection sensorless control on an FPGA-driven silicon carbide inverter for permanent magnet synchronous machines.

The paper examines security risks and protocol vulnerabilities when deploying commercial IT networking technologies in industrial communication systems.

The paper demonstrates reducing laser amplitude and phase noise by injecting femtosecond squeezed vacuum into ultrafast light sources.

The paper evaluates the storage reliability of an engine control circuit module.

The paper presents a zero-failure reliability demonstration method using degradation data for systems.

This paper introduces a multimodal federated learning framework that combines spectrograms and network metrics for on-device wireless jamming detection.

This paper presents an ensemble machine learning framework for tracking student knowledge and recommending multimodal content in tutoring systems.

This paper analyzes visual token utilization in high-resolution vision-language models to optimize token pruning across network layers.

This paper evaluates an ensemble deep learning framework of two convolutional networks for robust and efficient deepfake video detection.

This paper evaluates an ensemble deep learning framework of two convolutional networks for robust and efficient deepfake video detection.

This paper evaluates an ensemble deep learning framework of two convolutional networks for robust and efficient deepfake video detection.

This paper introduces a dual-branch hybrid deep learning model combining spatial features and facial embeddings for deepfake detection.

This paper presents AVNet, an optimized convolutional network designed for efficient FPGA deployment to classify circuit board defects.

This paper presents a mid-training method, corpus, and benchmark to help language models understand and reason about time series data.

This paper presents a mid-training method, corpus, and benchmark to help language models understand and reason about time series data.

This paper presents a mid-training method, corpus, and benchmark to help language models understand and reason about time series data.

This paper presents a mid-training method, corpus, and benchmark to help language models understand and reason about time series data.

This paper presents a mid-training method, corpus, and benchmark to help language models understand and reason about time series data.

This paper compares the quality and coverage of unit test cases generated by ChatGPT against an automated generation tool.

This paper compares the quality and coverage of unit test cases generated by ChatGPT against an automated generation tool.

This paper compares the quality and coverage of unit test cases generated by ChatGPT against an automated generation tool.

This paper introduces an open-source infrastructure and benchmark for multi-fidelity AI-assisted photonic simulation and inverse design.

This paper introduces an open-source infrastructure and benchmark for multi-fidelity AI-assisted photonic simulation and inverse design.

This paper introduces an open-source infrastructure and benchmark for multi-fidelity AI-assisted photonic simulation and inverse design.

This paper introduces an open-source infrastructure and benchmark for multi-fidelity AI-assisted photonic simulation and inverse design.

This paper provides design guidelines and a selection tool to help build augmented reality systems for industrial assembly support.

This paper examines challenges and recent machine learning approaches for automating FPGA high-level synthesis hardware design.

The paper introduces a system layer that separates control flow from untrusted data to protect language model agents against prompt injection attacks.

The paper introduces a multimodal dataset combining physiological sensor signals and video recordings to study human social stress.

The paper presents a multimodal deep learning method using PET and CT scans to predict brain metastasis in non-small cell lung cancer.

The summary text is too thin to judge, containing mostly page navigation text instead of the research abstract.

The paper proposes a modular vision-and-language navigation framework combining dynamic memory pruning and language-model spatial reasoning.

The paper introduces a decomposed attention mechanism for vision-language models to separate visual and textual token processing.

The paper compares traditional cloud encryption methods with artificial intelligence based encryption techniques across several performance metrics.

The paper proposes a persistent homology metric to evaluate the topological and structural stability of time-varying social networks.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.