ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning

We introduce ECHo (Event Causality Inference via Human-Centric Reasoning), a diagnostic dataset of event causality inference grounded in visio-linguistic social scenarios. ECHo employs real-world human-centric deductive information building on a television crime drama. ECHo requires the Theory-of-Mind (ToM) ability to understand and reason about social interactions based on multimodal information. Using ECHo, we propose a unified Chain-of-Thought (CoT) framework to assess the reasoning capability of current AI systems. Our ToM-enhanced CoT pipeline accommodates various large foundation models in both zero-shot and few-shot visio-linguistic reasoning. We use this framework to scrutinize recent large foundation models such as InstructGPT and MiniGPT-4 on three diagnostic human-centric tasks. Further analysis demonstrates ECHo as a challenging dataset to expose imperfections and inconsistencies in reasoning. Our data and code are publicly available at https://github.com/YuxiXie/ECHo.Comment: Findings of EMNLP 2023. 10 pages, 6 figures, 5 tables (22 pages, 8 figures, 15 tables including references and appendices

Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding

We endow Large Language Models (LLMs) with fine-grained self-evaluation to refine multi-step reasoning inference. We propose an effective prompting approach that integrates self-evaluation guidance through stochastic beam search. Our approach explores the reasoning search space using a well-calibrated automatic criterion. This enables an efficient search to produce higher-quality final predictions. With the self-evaluation guided stochastic beam search, we also balance the quality-diversity trade-off in the generation of reasoning chains. This allows our approach to adapt well with majority voting and surpass the corresponding Codex-backboned baselines by $6.34\%$, $9.56\%$, and $5.46\%$ on the GSM8K, AQuA, and StrategyQA benchmarks, respectively, in few-shot accuracy. Analysis of our decompositional reasoning finds it pinpoints logic failures and leads to higher consistency and robustness. Our code is publicly available at https://github.com/YuxiXie/SelfEval-Guided-Decoding.Comment: Our code is publicly available at https://github.com/YuxiXie/SelfEval-Guided-Decodin

Potential Therapeutic Applications of Exosomes in Bone Regenerative Medicine

The ability of bone regeneration is relatively robust, which is crucial for fracture healing, but delayed healing and nonunion are still common problems in clinical practice. Fortunately, exciting results have been achieved for regenerative medicine in recent years, especially in the area of stem cell-based treatment, but all these cell-based approaches face challenging problems, including immune rejection. For this reason, exosomes, stem cell-derived small vesicles of endocytic origin, have attracted the attention of many investigators in the field of bone regeneration. One of the attractive features of exosomes is that they are small and can travel between cells and deliver bioactive products, including miRNA, mRNA, proteins, and various other factors, to promote bone regeneration, with undetectable immune rejection. In this chapter, we intend to briefly update the recent progressions, and discuss the potential challenges in the target areas. Hopefully, our discussion would be helpful not only for the clinicians and the researchers in the specific disciplines but also for the general audiences as well

A convex geometry based blind source separation method for separating nonnegative sources

This paper presents a convex geometry (CG)-based method for blind separation of nonnegative sources. First, the unaccessible source matrix is normalized to be column-sum-to-one by mapping the available observation matrix. Then, its zero-samples are found by searching the facets of the convex hullspanned by the mapped observations. Considering these zerosamples, a quadratic cost function with respect to each row of the unmixing matrix, together with a linear constraint in relation to the involved variables, is proposed. Upon which, an algorithm is presented to estimate the unmixing matrix by solving a classical convex optimization problem. Unlike the traditional blind source separation (BSS) methods, the CG-based method does not require the independence assumption, nor the uncorrelation assumption. Compared with the BSS methods that are specifically designed to distinguish between nonnegative sources, the proposed method requires a weaker sparsity condition. Provided simulation results illustrate the performance of our method