The so-called “conciliatory” norm in epistemology and meta-ethics requires that an agent, upon encountering peer disagreement with her judgment, lower her confidence about that judgment. But whether agents actually abide by this norm is unclear. Although confidence is excessively researched in the empirical sciences, possible effects of disagreement on confidence have been understudied. Here, we target this lacuna, reporting a study that measured confidence about moral beliefs before and after exposure to moral discourse about a controversial issue. Our findings indicate (...) that participants do not abide by the conciliatory norm. Neither do they conform to a rival “steadfast” norm that demands their confidence to remain the same. Instead, moral discourse seems to boost confidence. Interestingly, we also find a confidence boost for factual beliefs, and a correlation between the extremity of moral views and confidence. One possible explanation of our findings is that when engaging in moral discourse participants become more extreme in their opinions, which leads them to become more confident about them, or vice versa: they become more confident and in turn more extreme. Although our work provides initial evidence for the former mechanism, further research is needed for a better understanding of confidence and moral discourse. (shrink)

We present our method for tackling a legal case retrieval task by introducing our method of encoding documents by summarizing them into continuous vector space via our phrase scoring framework utilizing deep neural networks. On the other hand, we explore the benefits from combining lexical features and latent features generated with neural networks. Our experiments show that lexical features and latent features generated with neural networks complement each other to improve the retrieval system performance. Furthermore, our experimental results suggest the (...) importance of case summarization in different aspects: using provided summaries and performing encoded summarization. Our approach achieved F1 of 65.6% and 57.6% on the experimental datasets of legal case retrieval tasks. (shrink)

Metacognitive mental states are mental states about mental states. For example, I may be uncertain whether my belief is correct. In social discourse, an interlocutor’s metacognitive certainty may constitute evidence about the reliability of their testimony. For example, if a speaker is certain that their belief is correct, then we may take this as evidence in favour of their belief, or its content. This paper argues that, if metacognitive certainty is genuine evidence, then it is disproportionate evidence for extreme beliefs. (...) In support of the argument, we report findings from five studies with different participant samples, designs, and measures. These studies show that, the more extreme an agent’s belief (positive or negative), the more certain they are about it, and vice versa. This relationship might contribute to moralism, virtue signalling, and polarisation, which in turn may be epistemically and morally problematic. Therefore, we caution against taking metacognitive certainty as genuine evidence. (shrink)

Cải cách tiền lương là một trong những nội dung quan trọng sẽ được thảo luận tại Kỳ họp thứ 6, Quốc hội khóa XV và đây cũng là một trong những chìa khoá để mở ra các chính sách đãi ngộ nhằm góp phần xây dựng, phát triển đội ngũ trí thức nước nhà, đáp ứng yêu cầu phát triển đất nước nhanh và bền vững trong giai đoạn mới như nội dung mà Hội nghị Trung ương 8 khoá (...) XIII, Ban Chấp hành Trung ương Đảng đã thảo luận. (shrink)

Case law retrieval is the task of locating truly relevant legal cases given an input query case. Unlike information retrieval for general texts, this task is more complex with two phases (legal case retrieval and legal case entailment) and much harder due to a number of reasons. First, both the query and candidate cases are long documents consisting of several paragraphs. This makes it difficult to model with representation learning that usually has restriction on input length. Second, the concept of (...) relevancy in this domain is defined based on the legal relation that goes beyond the lexical or topical relevance. This is a real challenge because normal text matching will not work. Third, building a large and accurate legal case dataset requires a lot of effort and expertise. This is obviously an obstacle to creating enough data for training deep retrieval models. In this paper, we propose a novel approach called supporting model that can deal with both phases. The underlying idea is the case–case supporting relation and the paragraph–paragraph as well as the decision-paragraph matching strategy. In addition, we propose a method to automatically create a large weak-labeling dataset to overcome the lack of data. The experiments showed that our solution has achieved the state-of-the-art results for both case retrieval and case entailment phases. (shrink)

Legal text retrieval serves as a key component in a wide range of legal text processing tasks such as legal question answering, legal case entailment, and statute law retrieval. The performance of legal text retrieval depends, to a large extent, on the representation of text, both query and legal documents. Based on good representations, a legal text retrieval model can effectively match the query to its relevant documents. Because legal documents often contain long articles and only some parts are relevant (...) to queries, it is quite a challenge for existing models to represent such documents. In this paper, we study the use of attentive neural network-based text representation for statute law document retrieval. We propose a general approach using deep neural networks with attention mechanisms. Based on it, we develop two hierarchical architectures with sparse attention to represent long sentences and articles, and we name them Attentive CNN and Paraformer. The methods are evaluated on datasets of different sizes and characteristics in English, Japanese, and Vietnamese. Experimental results show that: (i) Attentive neural methods substantially outperform non-neural methods in terms of retrieval performance across datasets and languages; (ii) Pretrained transformer-based models achieve better accuracy on small datasets at the cost of high computational complexity while lighter weight Attentive CNN achieves better accuracy on large datasets; and (iii) Our proposed Paraformer outperforms state-of-the-art methods on COLIEE dataset, achieving the highest recall and F2 scores in the top-N retrieval task. (shrink)

This study aims to predict the shear strength of reinforced concrete deep beams based on artificial neural network using four training algorithms, namely, Levenberg–Marquardt, quasi-Newton method, conjugate gradient, and gradient descent. A database containing 106 results of RC deep beam shear strength tests is collected and used to investigate the performance of the four proposed algorithms. The ANN training phase uses 70% of data, randomly taken from the collected dataset, whereas the remaining 30% of data are used for the algorithms’ (...) evaluation process. The ANN structure consists of an input layer with 9 neurons corresponding to 9 input parameters, a hidden layer of 10 neurons, and an output layer with 1 neuron representing the shear strength of RC deep beams. The performance evaluation of the models is performed using statistical criteria, including the correlation coefficient, root mean square error, mean absolute error, and mean absolute percentage error. The results show that the ANN-CG model has the best prediction performance with R = 0.992, RMSE = 14.02, MAE = 14.24, and MAPE = 6.84. The results of this study show that the ANN-CG model can accurately predict the shear strength of RC deep beams, representing a promising and useful alternative design solution for structural engineers. (shrink)

UPtSn is known as a semimetal with a bandgap in the electronic density of states. We report the results of measurements of the cubic lattice parameter a , coefficient of thermal expansion f , magnetic susceptibility h , magnetization † , specific heat C and electrical resistivity „ . We give arguments that this compound is antiferromagnetically ordered with T N = 35 K, although this is not confirmed by neutron diffraction. For carefully annealed samples of UPtSn we provide a (...) phenomenological description of electron transport properties in terms of the fact that the temperature-dependent bandgap is closed at T = 0 K. Results of electrical measurements on the isostructural compounds ThPtSn and ZrPtSn provide evidence for bandgap formation in these two compounds as well. (shrink)