1,293 research outputs found
Die Rollen der sauren Sphingomyelinase und des Tenascin C in der Pathogenese der Alzheimer-Krankheit
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
Die Rollen der sauren Sphingomyelinase und des Tenascin C in der Pathogenese der Alzheimer-Krankheit
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
, , and 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
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