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We show that the use of 1,3-diaminopropane (DAP) as a chemical modifier at the perovskite/electron-transport layer (ETL) interface enhances the power conversion efficiency (PCE) of 1.7 eV bandgap FACs mixed-halide perovskite single-junction cells, pr

Externí odkaz: http://arxiv.org/abs/2411.18756

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LIBMoE: A Library for comprehensive benchmarking Mixture of Experts in Large Language Models

Autor: Nguyen, Nam V., Doan, Thong T., Tran, Luong, Nguyen, Van, Pham, Quang

Mixture of Experts (MoEs) plays an important role in the development of more efficient and effective large language models (LLMs). Due to the enormous resource requirements, studying large scale MoE algorithms remain in-accessible to many researchers

Externí odkaz: http://arxiv.org/abs/2411.00918

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Class-Aware Contrastive Optimization for Imbalanced Text Classification

Autor: Khvatskii, Grigorii, Moniz, Nuno, Doan, Khoa, Chawla, Nitesh V

The unique characteristics of text data make classification tasks a complex problem. Advances in unsupervised and semi-supervised learning and autoencoder architectures addressed several challenges. However, they still struggle with imbalanced text c

Externí odkaz: http://arxiv.org/abs/2410.22197

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Chambered invariants of real Cauchy-Riemann operators

Autor: Doan, Aleksander, Walpuski, Thomas

Motivated by counting pseudo-holomorphic curves in symplectic Calabi-Yau $3$-folds, this article studies a chamber structure in the space of real Cauchy-Riemann operators on a Riemann surface, and constructs three chambered invariants associated with

Externí odkaz: http://arxiv.org/abs/2410.21057

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Bearing-Only Solution for Fermat-Weber Location Problem: Generalized Algorithms

Autor: Le-Phan, Nhat-Minh, Nguyen, Phuoc Doan, Ahn, Hyo-Sung, Trinh, Minh Hoang

This paper presents novel algorithms for the Fermat-Weber Location Problem, guiding an autonomous agent to the point that minimizes the weighted sum of Euclidean distances to some beacons using only bearing measurements. The existing results address

Externí odkaz: http://arxiv.org/abs/2410.18470

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Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation

Autor: Bui, Anh, Vuong, Long, Doan, Khanh, Le, Trung, Montague, Paul, Abraham, Tamas, Phung, Dinh

Diffusion models excel at generating visually striking content from text but can inadvertently produce undesirable or harmful content when trained on unfiltered internet data. A practical solution is to selectively removing target concepts from the m

Externí odkaz: http://arxiv.org/abs/2410.15618

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Simultaneous Diffusion Sampling for Conditional LiDAR Generation

Autor: Faulkner, Ryan, Haub, Luke, Ratcliffe, Simon, Doan, Anh-Dzung, Reid, Ian, Chin, Tat-Jun

By enabling capturing of 3D point clouds that reflect the geometry of the immediate environment, LiDAR has emerged as a primary sensor for autonomous systems. If a LiDAR scan is too sparse, occluded by obstacles, or too small in range, enhancing the

Externí odkaz: http://arxiv.org/abs/2410.11628

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