We take interest in the early assessment of risk for depression in social media users. We focus on the eRisk 2018 dataset, which represents users as a sequence of their written online contributions. We implement four RNN-based systems to classify the users. We explore several aggregations methods to combine predictions on individual posts. Our best model reads through all writings of a user in parallel but uses an attention mechanism to prioritize the most important ones at each timestep.
The @cite_21 is a simpler version of the that we used, that only takes into account the target hidden state. It is stated as such :
@cite_21
2949335953
The problem of estimating the number of hidden states in a hidden Markov model is considered. Emphasis is placed on cross-validated likelihood criteria. Using cross-validation to assess the number of hidden states allows to circumvent the well-documented technical difficulties of the order identification problem in mixture models. Moreover, in a predictive perspective, it does not require that the sampling distribution belongs to one of the models in competition. However, computing cross-validated likelihood for hidden Markov models for which only one training sample is available, involves difficulties since the data are not independent. Two approaches are proposed to compute cross-validated likelihood for a hidden Markov model. The first one consists of using a deterministic half-sampling procedure, and the second one consists of an adaptation of the EM algorithm for hidden Markov models, to take into account randomly missing values induced by cross-validation. Numerical experiments on both simulated and real data sets compare different versions of cross-validated likelihood criterion and penalised likelihood criteria, including BIC and a penalised marginal likelihood criterion. Those numerical experiments highlight a promising behaviour of the deterministic half-sampling criterion. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Bell’s theorem [1] refutes local theories based on Einstein, Podolsky, and Rosen’s (EPR’s) “elements of reality” [2]. A recently introduced proof without inequalities [3] presents the same logical structure as that of Hardy’s proof [4], but exhibits a greater contradiction between EPR local elements of reality and quantum mechanics. Here a simpler version of the proof in [3] will be introduced. This new version parallels Mermin’s reformulation [5] of Greenberger, Horne, and Zeilinger’s (GHZ’s) proof [6] and, besides being simpler, it emphasizes the fact that [3] is also an “all versus nothing” [7] or GHZ-type proof of Bell’s theorem, albeit with only two observers. In addition, this new approach will allow us to derive an inequality between correlation functions which is violated by quantum mechanics. Moreover, this new version will also constitute the basis for a new stateindependent proof of the Kochen-Specker (KS) theorem [8]. The whole set of new results provides a wider perspective on the relations between the most relevant proofs of no hidden variables. Consider four qubits, labeled 1, 2, 3, 4, prepared in the state jc1234 1 j0011 2 j0110 2 j1001 1 j1100 , (1) which, as can be easily checked, is the product of two singlet states, jc 2 13 ≠j c 2 24. Let us suppose that qubits 1 and 2 fly apart from qubits 3 and 4, and that an observer, Alice, performs measurements on qubits 1 and 2, while in a spacelike separated region a second observer, Bob, performs measurements on qubits 3