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Text-driven diffusion models have achieved remarkable success in image editing, but a crucial component in these models-text embeddings-has not been fully explored. The entanglement and opacity of text embeddings present significant challenges to achieving precise image editing. In this paper, we provide a comprehensive and in-depth analysis of text embeddings in Stable Diffusion XL, offering three key insights. First, while the 'aug_embedding' captures the full semantic content of the text, its contribution to the final image generation is relatively minor. Second, 'BOS' and 'Padding_embedding' do not contain any semantic information. Lastly, the 'EOS' holds the semantic information of all words and contains the most style features. Each word embedding plays a unique role without interfering with one another. Based on these insights, we propose a novel approach for controllable image editing using a free-text embedding control method called PSP (Prompt-Softbox-Prompt). PSP enables precise image editing by inserting or adding text embeddings within the cross-attention layers and using Softbox to define and control the specific area for semantic injection. This technique allows for obejct additions and replacements while preserving other areas of the image. Additionally, PSP can achieve style transfer by simply replacing text embeddings. Extensive experimental results show that PSP achieves significant results in tasks such as object replacement, object addition, and style transfer.

Recently, various illustrative examples have shown the impressive ability of generative large language models (LLMs) to perform NLP related tasks. ChatGPT undoubtedly is the most representative model. We empirically evaluate ChatGPT's performance on requirements information retrieval (IR) tasks to derive insights into designing or developing more effective requirements retrieval methods or tools based on generative LLMs. We design an evaluation framework considering four different combinations of two popular IR tasks and two common artifact types. Under zero-shot setting, evaluation results reveal ChatGPT's promising ability to retrieve requirements relevant information (high recall) and limited ability to retrieve more specific requirements information (low precision). Our evaluation of ChatGPT on requirements IR under zero-shot setting provides preliminary evidence for designing or developing more effective requirements IR methods or tools based on LLMs.

This paper concerns the simultaneous reconstruction of a sound-soft cavity and its excitation sources from the total-field data. Using the single-layer potential representations on two measurement curves, this co-inversion problem can be decoupled into two inverse problems: an inverse cavity scattering problem and an inverse source problem. This novel decoupling technique is fast and easy to implement since it is based on a linear system of integral equations. Then the uncoupled subproblems are respectively solved by the modified optimization and sampling method. We also establish the uniqueness of this co-inversion problem and analyze the stability of our method. Several numerical examples are presented to demonstrate the feasibility and effectiveness of the proposed method., Comment: 21 pages, 7 figures

This paper is concerned with the inverse acoustic scattering problems by an obstacle or a cavity with a sound-soft or a sound-hard boundary. A direct imaging method relying on the boundary conditions is proposed for reconstructing the shape of the obstacle or cavity. First, the scattered fields are approximated by the Fourier-Bessel functions with the measurements on a closed curve. Then, the indicator functions are established by the superpositions of the total fields or their derivatives to the incident point sources. We prove that the indicator functions vanish only on the boundary of the obstacle or cavity. Numerical examples are also included to demonstrate the effectiveness of the method., Comment: 28 pages, 7 figures

Pretrained language models (PLM) achieve surprising performance on the Choice of Plausible Alternatives (COPA) task. However, whether PLMs have truly acquired the ability of causal reasoning remains a question. In this paper, we investigate the problem of semantic similarity bias and reveal the vulnerability of current COPA models by certain attacks. Previous solutions that tackle the superficial cues of unbalanced token distribution still encounter the same problem of semantic bias, even more seriously due to the utilization of more training data. We mitigate this problem by simply adding a regularization loss and experimental results show that this solution not only improves the model's generalization ability, but also assists the models to perform more robustly on a challenging dataset, BCOPA-CE, which has unbiased token distribution and is more difficult for models to distinguish cause and effect., Comment: ACL2021, Main Conference, Short Paper

This paper is concerned with the uniqueness of inverse acoustic scattering problem for cavities with the modulus of the near-fields. With the aid of the reference ball technique and the superpositions of two point sources as the incident waves, we rigorously prove that the location and shape of the cavity as well as its boundary condition can be uniquely determined by the modulus of near-fields at an admissible surface. To our knowledge, this is the first uniqueness result in inverse cavity scattering problems with phaseless near-field data. In this paper, we make use of the phaseless near-field data incurred by the cavity and the point sources, and thus the configuration is more feasible in practice., Comment: 10 pages, 1 figure. arXiv admin note: text overlap with arXiv:1812.03291 and substantial text overlap with arXiv:1905.08242