Newest and Lowest Rated Papers
Individual differences in susceptibility to false memories for COVID-19 fake news
Ciara M. Greene, Gillian Murphy
Cognitive Research: Principles and Implications·2020·32 citations
<jats:title>Abstract</jats:title><jats:p>Exposure to ‘fake news’ can result in false memories, with possible consequences for downstream behaviour. Given the sharp rise in online misinformation during the coronavirus pandemic, it is important to understand the factors that influence the development of false memories. The present study measured susceptibility to false memories following exposure to fabricated news stories about the pandemic in a sample of 3746 participants. We investigated the effect of individual differences in (1) knowledge about COVID-19, (2) engagement with media or discussion about the coronavirus, (3) anxiety about COVID-19 and (4) analytical reasoning. Notably, objectively and subjectively assessed knowledge about COVID-19 were not significantly correlated. Objectively assessed knowledge was associated with fewer false memories but more true memories, suggesting a true discrimination between true and fake news. In contrast, participants who merely believed themselves to be very knowledgeable were more likely to report a memory for true stories, but showed no reduction in false memories. Similarly, individuals who reported high levels of media engagement or anxiety about COVID-19 reported an increase in true (but not false) memories. Finally, higher levels of analytical reasoning were associated with fewer memories for both true and fabricated stories, suggesting a stricter threshold for reporting a memory for any story. These data indicate that false memories can form in response to fake COVID-19 news and that susceptibility to this misinformation is affected by the individual’s knowledge about and interaction with COVID-19 information, as well as their tendency to think critically.</jats:p>
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View paper →Causal Quartets: Different Ways to Attain the Same Average Treatment Effect
Andrew Gelman, Jessica Hullman, Lauren Kennedy
The American Statistician·2023·5 citations
No abstract available
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View paper →Still Dying on the Inside: Examining Women's Deaths In Prison
INQUEST
inquest.org.uk·2018
No abstract available
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View paper →The Cultural Negotiation of Practice Boundaries Through New Rituals: The Case of Self-Care Consumption - ProQuest
R. E. Hochstein
proquest.com
No abstract available
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View paper →International State of Safety Tech Report 2025
PUBLIC
view.publitas.com
18 INTERNATIONAL STATE OF SAFETY TECH 2025 Human-Machine Interaction in Physical Space This refers to how people engage with autonomous or semi-autonomous
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View paper →Butterfly Traction: A New Standardized Approach for Gallbladder Bed Detachment in Laparoscopic Cholecystectomy.
Daisuke Noguchi, Takahiro Ito, Aoi Hayasaki, Yusuke Iizawa, Takehiro Fujii, Akihiro Tanemura, Yasuhiro Murata, Naohisa Kuriyama, Masashi Kishiwada, Shugo Mizuno
Asian journal of endoscopic surgery·2025
Gallbladder bed detachment (GBB-D) in laparoscopic cholecystectomy (LC) lacks a standardized technique. We developed butterfly traction (BT) to perform optimal GBB-D. This study evaluates its effectiveness and potential for standardization. BT aligns the dissection axis with the surgeon's dominant hand by positioning the gallbladder upright and using a left-sided lateral approach. This improves right-side mobility, enabling horizontal traction and avoiding axis interference. BT has been adopted in all LCs at our institution since July 2023. Among 118 LCs performed from 2019 to 2024, three open conversion cases and 13 bailout cases were excluded. Operative outcomes in the remaining patients were compared between the BT group (n = 50) and the nonstandardized (NS) group (n = 52). No significant differences were observed in age, gender, comorbidities, or preoperative gallbladder drainage between groups. However, the BT group had more surgeons with ≤ 5 years of experience (52% vs. 29%, p = 0.017). Operative times were comparable (p = 0.363), but bile contamination due to gallbladder perforation during GBB-D was significantly lower in the BT group (4% vs. 17%, p = 0.030), as was blood loss (0 vs. 1 g, p = 0.003). BT was particularly effective in higher difficulty cases (Grade B/C from Tokyo Guidelines 2018), with shorter operative times (95 vs. 116 min, p = 0.095), less blood loss (0 vs. 2 g, p < 0.001), and fewer perforations (6% vs. 24%, p = 0.046). BT improved surgical outcomes and minimized bile contamination, even when performed by less experienced surgeons. It shows strong potential as a standard technique for GBB-D in LC.
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View paper →The research is clear: Solitary confinement causes long-lasting harm | Prison Policy Initiative
Prison Policy Initiative
prisonpolicy.org
At the International Symposium on Solitary Confinement, researchers and formerly incarcerated people made it clear that isolation causes severe and permanent damage.
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View paper →No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance
Udandarao, Vishaal, Prabhu, Ameya, Ghosh, Adhiraj, Sharma, Yash, Torr, Philip H., Bibi, Adel, Albanie, Samuel, Bethge, Matthias
proceedings.neurips.cc
No abstract available
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View paper →Impact of Pretraining Term Frequencies on Few-Shot Reasoning
Yasaman Razeghi, Robert L. Logan, Matt Gardner, Sameer Singh
arXiv·2022
Pretrained Language Models (LMs) have demonstrated ability to perform numerical reasoning by extrapolating from a few examples in few-shot settings. However, the extent to which this extrapolation relies on robust reasoning is unclear. In this paper, we investigate how well these models reason with terms that are less frequent in the pretraining data. In particular, we examine the correlations between the model performance on test instances and the frequency of terms from those instances in the pretraining data. We measure the strength of this correlation for a number of GPT-based language models (pretrained on the Pile dataset) on various numerical deduction tasks (e.g., arithmetic and unit conversion). Our results consistently demonstrate that models are more accurate on instances whose terms are more prevalent, in some cases above $70\%$ (absolute) more accurate on the top 10\% frequent terms in comparison to the bottom 10\%. Overall, although LMs exhibit strong performance at few-shot numerical reasoning tasks, our results raise the question of how much models actually generalize beyond pretraining data, and we encourage researchers to take the pretraining data into account when interpreting evaluation results.
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View paper →Towards Out-Of-Distribution Generalization: A Survey
Jiashuo Liu, Zheyan Shen, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, Peng Cui
arXiv·2021
Traditional machine learning paradigms are based on the assumption that both training and test data follow the same statistical pattern, which is mathematically referred to as Independent and Identically Distributed ($i.i.d.$). However, in real-world applications, this $i.i.d.$ assumption often fails to hold due to unforeseen distributional shifts, leading to considerable degradation in model performance upon deployment. This observed discrepancy indicates the significance of investigating the Out-of-Distribution (OOD) generalization problem. OOD generalization is an emerging topic of machine learning research that focuses on complex scenarios wherein the distributions of the test data differ from those of the training data. This paper represents the first comprehensive, systematic review of OOD generalization, encompassing a spectrum of aspects from problem definition, methodological development, and evaluation procedures, to the implications and future directions of the field. Our discussion begins with a precise, formal characterization of the OOD generalization problem. Following that, we categorize existing methodologies into three segments: unsupervised representation learning, supervised model learning, and optimization, according to their positions within the overarching learning process. We provide an in-depth discussion on representative methodologies for each category, further elucidating the theoretical links between them. Subsequently, we outline the prevailing benchmark datasets employed in OOD generalization studies. To conclude, we overview the existing body of work in this domain and suggest potential avenues for future research on OOD generalization. A summary of the OOD generalization methodologies surveyed in this paper can be accessed at http://out-of-distribution-generalization.com.
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View paper →Are Vision Transformers Robust to Spurious Correlations?
Soumya Suvra Ghosal, Yixuan Li
International Journal of Computer Vision·2023·10 citations
Deep neural networks may be susceptible to learning spurious correlations that hold on average but not in atypical test samples. As with the recent emergence of vision transformer (ViT) models, it remains unexplored how spurious correlations are manifested in such architectures. In this paper, we systematically investigate the robustness of different transformer architectures to spurious correlations on three challenging benchmark datasets. Our study reveals that for transformers, larger models and more pre-training data significantly improve robustness to spurious correlations. Key to their success is the ability to generalize better from the examples where spurious correlations do not hold. Further, we perform extensive ablations and experiments to understand the role of the self-attention mechanism in providing robustness under spuriously correlated environments. We hope that our work will inspire future research on further understanding the robustness of ViT models to spurious correlations.
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View paper →Collective action on artificial intelligence: A primer and review
Robert de Neufville, Seth D. Baum
Technology in Society·2021·23 citations
No abstract available
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