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1. Does Cross-Race Contact Improve Cross-Race Face Perception? A Meta-Analysis of the Cross-Race Deficit and Contact

   https://doi.org/10.1177/01461672211024463  

   Singh, Balbir ; Mellinger, Christopher ; Earls, Holly A. ; Tran, Janis ; Bardsley, Brighid ; Correll, Joshua ( June 2021 , Personality and Social Psychology Bulletin)

   null (Ed.)

   Contact with racial outgroups is thought to reduce the cross-race recognition deficit (CRD), the tendency for people to recognize same-race (i.e., ingroup) faces more accurately than cross-race (i.e., outgroup) faces. In 2001, Meissner and Brigham conducted a meta-analysis in which they examined this question and found a meta-analytic effect of r = −.13. We conduct a new meta-analysis based on 20 years of additional data to update the estimate of this relationship and examine theoretical and methodological moderators of the effect. We find a meta-analytic effect of r = −.15. In line with theoretical predictions, we find some evidence that the magnitude of this relationship is stronger when contact occurs during childhood rather than adulthood. We find no evidence that the relationship differs for measures of holistic/configural processing compared with normal processing. Finally, we find that the magnitude of the relationship depends on the operationalization of contact and that it is strongest when contact is manipulated. We consider recommendations for further research on this topic. 

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2. Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks

   https://doi.org/10.18653/v1/2021.eacl-main.204  

   Sun, Jimin ; Ahn, Hwijeen ; Park, Chan Young ; Tsvetkov, Yulia ; Mortensen, David R. ( January 2021 , The 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL))
3. Enabling Photoactivated Cross-Linking Mass Spectrometric Analysis of Protein Complexes by Novel MS-Cleavable Cross-Linkers

   https://doi.org/10.1016/j.mcpro.2021.100084  

   Gutierrez, Craig ; Salituro, Leah J. ; Yu, Clinton ; Wang, Xiaorong ; DePeter, Sadie F. ; Rychnovsky, Scott D. ; Huang, Lan ( January 2021 , Molecular & Cellular Proteomics)

   null (Ed.)
4. Cross-Linked Polyimide/ZIF-8 Mixed-Matrix Membranes by In Situ Formation of ZIF-8: Effect of Cross-Linking on Their Propylene/Propane Separation

   https://doi.org/10.3390/membranes12100964  

   Park, Sunghwan ; Jeong, Hae-Kwon ( October 2022 , Membranes)

   Despite their potential for the scalable production of mixed-matrix membranes (MMMs), the MMMs prepared by the polymer-modification-enabled in situ metal–organic framework formation (PMMOF) process showed a considerable reduction in gas permeability as the filler loading increased. It was hypothesized that a correlation existed between the decrease in permeability and the change in the properties of the polymer, such as free volume and chain flexibility, upon in situ MOF formation. Herein, we aim to address the permeability reduction by using a cross-linked polyimide (6FDA-DAM:DABA (3:2)). It was found the degree of cross-linking affected not only the properties of the polymer, but also the in situ formation of the ZIF-8 filler particles in the cross-linked polymer. The proper degree of cross-linking resulted in suppressing C3H6 permeability reduction, suggesting a possible strategy to overcome the issue of PMMOF. The swelling of the polymer followed by chain rearrangement during the PMMOF, as well as the structural rigidity of the polymer, were found to be critical in mitigating permeability reduction. 

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5. The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study

   https://doi.org/10.1145/3543507.3583354  

   Zhang, Yu ; Jin, Bowen ; Zhu, Qi ; Meng, Yu ; Han, Jiawei ( April 2023 , WWW '23: Proceedings of the ACM Web Conference 2023)

   Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature. Scientific literature tagging is beyond a pure multi-label text classification task because papers on the Web are prevalently accompanied by metadata information such as venues, authors, and references, which may serve as additional signals to infer relevant tags. Although there have been studies making use of metadata in academic paper classification, their focus is often restricted to one or two scientific fields (e.g., computer science and biomedicine) and to one specific model. In this work, we systematically study the effect of metadata on scientific literature tagging across 19 fields. We select three representative multi-label classifiers (i.e., a bag-of-words model, a sequence-based model, and a pre-trained language model) and explore their performance change in scientific literature tagging when metadata are fed to the classifiers as additional features. We observe some ubiquitous patterns of metadata’s effects across all fields (e.g., venues are consistently beneficial to paper tagging in almost all cases), as well as some unique patterns in fields other than computer science and biomedicine, which are not explored in previous studies. 

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