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Research areas
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February 27, 2025Prototype is the first realization of a scalable, hardware-efficient quantum computing architecture based on bosonic quantum error correction.
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Featured news
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ICME 20252025In adaptive bitrate streaming, resolution cross-over refers to the point on the convex hull where the encoding resolution should switch to achieve better quality. Accurate cross-over prediction is crucial for streaming providers to optimize resolution at given bandwidths. Most existing works rely on objective Video Quality Metrics (VQM), particularly VMAF, to determine the resolution cross-over. However
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Quantum Science and Technology2025Running quantum algorithms protected by quantum error correction requires a real time, classical decoder. To prevent the accumulation of a backlog, this decoder must process syndromes from the quantum device at a faster rate than they are generated. Most prior work on real time decoding has focused on an isolated logical qubit encoded in the surface code. However, for surface code, quantum programs of utility
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AAAI 2025 Workshop on AI for Time Series Analysis2025Time series forecasting has long been a focus of research across diverse fields, including economics, energy, healthcare, and traffic management. Recent works have introduced innovative architectures for time series models, such as the Time-Series Mixer (TSMixer), which leverages multilayer perceptrons (MLPs) to enhance prediction accuracy by effectively capturing both spatial and temporal dependencies
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IEEE Symposium on Security and Privacy 20252025We propose plausible post-quantum (PQ) oblivious pseudorandom functions (OPRFs) based on the Power-Residue PRF (Damgård CRYPTO’88), a generalization of the Legendre PRF. For security parameter λ, we consider the PRF Goldk(x) that maps an integer x modulo a public prime p = 2λ ·g+ 1 to the element (k + x) g mod p, where g is public and log g ≈ 2λ. At the core of our constructions are efficient novel methods
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CVPR 2025 Workshop on Efficient Large Vision Models2025Diffusion models enables high-quality virtual try-on (VTO) with their established image synthesis abilities. Despite the extensive end-to-end training of large pre-trained models involved in current VTO methods, real-world applications often prioritize limited training and inferencing/serving/deployment budgets for VTO. To solve this obstacle, we apply Doob’s h-transform efficient fine-tuning (DEFT) for
Academia
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