Key Points Question Is the increasing use of promotional language (often referred to as hype) in National Institutes of Health (NIH) funding applications associated with a similar shift in journal abstracts reporting the results of NIH-funded research? Findings This cross-sectional study of 2 394 480 journal abstracts reporting the results of NIH-funded research from 1985 to 2020 found that the use of 133 out of 139 hype adjectives increased and that these trends were positively correlated with previously reported trends in related funding applications. Meaning These results suggest that increasing salesmanship in the reporting of research is in part a downstream effect of language choices made during the stage of funding application.

Promotional Language (Hype) in Abstracts of Publications of National Institutes of Health–Funded Research, 1985-2020

MultiHU-TD: Multi-feature Hyperspectral Unmixing Based on Tensor Decomposition

Abstract We report on changes in the expression of epistemic stance in competitive funding applications—that is, applicants’ confidence and certainty towards knowledge and beliefs. We analysed abstracts describing all projects funded by the US National Institutes of Health during the period 1985–2020 for 140 stance features. Trends that we identify indicate that applicants adopt a stance less cautious and less tentative, and increasingly confident, optimistic, and promissory. This is evidenced, for example, by a consistent decline in weak possibility/probability, as expressed by modal verbs (e.g. may, might, should), by epistemic status verbs (indicate, seem) and adverbs (e.g. possible, probable, perhaps); and an increase among features that convey certainty, importance, and empiricism—for example, status verbs (e.g. demonstrate, establish, reveal), and adverbs that emphasize frequency/degree (usually, widely, almost). We argue that (i) these shifts are best understood in relation to increasing salesmanship and structural and cultural shifts within the research ecosystem, and (ii) trends in this dataset are better analysed at the level of individual features, rather than at the level of metadiscoursal categories. 

Trends in the Expression of Epistemic Stance in NIH Research Funding Applications: 1985–2020

The scientific communication ecosystem: the responsibility of investigators

This paper proposes a new video representation for subspace-based action recognition. Traditional subspace-based methods represent a video as a subspace by applying principal component analysis (PCA) to its frames. However, this subspace might lead to an imprecise representation of actions, as PCA loses the temporal information of frames. Therefore, we introduce the slow feature subspace based on the slow feature analysis (SFA). SFA extracts a set of slow features of an input video by projecting the video frames onto the weight vectors that minimize the data variance over time. Motivated by these properties, several methods based on SFA were proposed for action recognition. However, they do not explicitly consider the distribution of the slow features, which represents essential action information. Therefore, our key idea is to capture this distribution through a low-dimensional subspace called slow feature subspace. Our subspace is generated by applying PCA to several weight vectors corresponding to the slowest components obtained by SFA. Our framework replaces the subspaces of several traditional mutual subspace methods with our slow feature subspace to improve their results in action recognition. This approach transforms the comparison between two videos into the comparison between two slow feature subspaces using the canonical angles between them, avoiding vector concatenation and data aggregation. The effectiveness of our framework is demonstrated through extensive experiments with various datasets. Our results show that our slow feature subspace can improve the traditional subspace-based methods and achieve competitive performance compared to different methods, including state-of-the-art neural networks. 

Slow feature subspace: A video representation based on slow feature analysis for action recognition

Key Points Question Has the use of “hype” (promotional language) in the abstracts of successful National Institutes of Health applications increased since 1985? Findings This cross-sectional study of 901 717 National Institutes of Health abstracts from 1985 to 2020 shows that applicants described their work in increasingly subjective terms and relied on promotional language and appeals to emotion (ie, 130 adjective forms identified as hype increased in frequency). Meaning This study suggests that applicants, reviewers, and funding agencies should be aware of the increasing prevalence of promotional language in funding applications.

/pdf/trends-in-the-use-of-promotional-language-hype-in-abstracts-vn188cgp.pdf

Trends in the Use of Promotional Language (Hype) in Abstracts of Successful National Institutes of Health Grant Applications, 1985-2020

In this paper, we propose Temporal-Stochastic Product Grassmann Manifold (TS-PGM), an efficient method for tensor classification in tasks such as gesture and action recognition. Our approach builds on the idea of representing tensors as points on Product Grassmann Manifold (PGM). This is achieved by mapping tensor modes to linear subspaces, where each subspace can be seen as a point on a Grassmann Manifold (GM) of the corresponding mode. Subsequently, it is possible to unify factor manifolds of respective modes in a natural way via PGM. However, this approach possibly discards discriminative information by treating all modes equally, and not considering the nature of temporal tensors such as videos. Therefore, we introduce Temporal-Stochastic Tensor features (TST features) to extract temporal information from tensors and encode them in a sequence-preserving TST subspace. These features and regular tensor modes can then be simultaneously used on PGM. Our framework addresses the problem of classification of temporal tensors while inheriting the unified mathematical interpretation of PGM because the TST subspace can be naturally integrated into PGM as a new factor manifold. Additionally, we enhance our method in two ways: (1) we improve the discrimination ability by projecting subspaces onto a Generalized Difference Subspace, and (2) we utilize kernel mapping to construct kernelized subspaces able to handle nonlinear data distribution. Experimental results on gesture and action recognition datasets show that our methods based on subspace representation with explicit TST features outperform pure spatio-temporal approaches. 

Temporal-stochastic tensor features for action recognition

Growing abundance of multi-dimensional data creates a need for efficient data exploration and analysis. In this paper, we address this need by tackling the task of tensor dataset visualization and clustering, as tensors are a natural form of multi-dimensional data. Previous work has shown that representing individual tensor modes via respective linear subspaces and unifying them on the product Grassmann manifold (PGM) is an effective and memory-efficient way of representation. However, such representation may lead to loss of valuable temporal information. To address this issue, we model temporal tensor modes with a Hankel-like matrix, preserving sequence information and encoding it with a linear subspace, fully compatible with PGM. Unifying regular tensor modes and Hankel-like representation of regular tensor modes then enriches representation on the PGM, with minimal increase in computational complexity. By relying on geodesic distance on the manifold, we facilitate analysis of multi-dimensional datasets in two ways: 1) by enabling straightforward visualizations using algorithms such as t-SNE; and 2) by fostering clustering of data using distance- or similarity-based methods such as spectral clustering. We evaluate our approach on hand gesture and action recognition datasets as exemplars of temporal tensor datasets.

Analysis of Temporal Tensor Datasets on Product Grassmann Manifold

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