CEDR: Contextualized Embeddings for Document Ranking

Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this work, we investigate how two pretrained contextualized language modes (ELMo and BERT) can be utilized for ad-hoc document ranking. Through experiments on TREC benchmarks, we find that several existing neural ranking architectures can benefit from the additional context provided by contextualized language models. Furthermore, we propose a joint approach that incorporates BERT's classification vector into existing neural models and show that it outperforms state-of-the-art ad-hoc ranking baselines. We call this joint approach CEDR (Contextualized Embeddings for Document Ranking). We also address practical challenges in using these models for ranking, including the maximum input length imposed by BERT and runtime performance impacts of contextualized language models

Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires

Automated methods have been widely used to identify and analyze mental healthconditions (e.g., depression) from various sources of information, includingsocial media. Yet, deployment of such models in real-world healthcareapplications faces challenges including poor out-of-domain generalization andlack of trust in black box models. In this work, we propose approaches fordepression detection that are constrained to different degrees by the presenceof symptoms described in PHQ9, a questionnaire used by clinicians in thedepression screening process. In dataset-transfer experiments on three socialmedia datasets, we find that grounding the model in PHQ9's symptomssubstantially improves its ability to generalize to out-of-distribution datacompared to a standard BERT-based approach. Furthermore, this approach canstill perform competitively on in-domain data. These results and ourqualitative analyses suggest that grounding model predictions inclinically-relevant symptoms can improve generalizability while producing amodel that is easier to inspect.<br

Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires

Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications faces challenges including poor out-of-domain generalization and lack of trust in black box models. In this work, we propose approaches for depression detection that are constrained to different degrees by the presence of symptoms described in PHQ9, a questionnaire used by clinicians in the depression screening process. In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9's symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach. Furthermore, this approach can still perform competitively on in-domain data. These results and our qualitative analyses suggest that grounding model predictions in clinically-relevant symptoms can improve generalizability while producing a model that is easier to inspect

So Long Mary / words by Geo M. Cohan

Cover: photo of a maid; Publisher: F. A. Mills (New York)https://egrove.olemiss.edu/sharris_b/1063/thumbnail.jp

Improved Refolding Efficacy of Recombinant Human Interferon α-2b via pH Modulation

Purpose: To increase the refolding yield of Recombinant Human Interferon α-2b in order to achieve a highly potent product.Methods: Interferon α-2b inclusion body was dissolved in tris-HCl buffer containing 6 M guanidine-HCl and CuSO4. Different refolding buffers were employed for refolding the target protein. The refolded proteins were then purified by affinity and gel filtration chromatography. The purified proteins were subjected to circular dichroism (CD) spectropolarimetry and assayed for biological activity in vitro.Results: Increment of pH to 8.5 improved refolding efficacies from 42.28 % to 71.22 %. However, the relative potency significantly increased up to pH 8.0 (from 19353546 to 28633902, p &lt; 0.05) and then decreased to 21081305.00 at pH 8.5. The CD spectra demonstrated that by increasing pH to 8.5, the secondary structure of the protein was altered, probably due to increase in alpha-helix from 23.7 % at pH 7.0 to 28.1 %.Conclusion: Employing a low-cost and simple method, such as alteration of refolding buffer pH, results in higher refolding yield in downstream processing of rhIFN α-2b.Keywords: Recombinant human interferon α-2b, Refolding, Circular dichroism, Spectropolarimetry,Recombinant protein, pH effec

Why polls fail to predict elections

In the past decade we have witnessed the failure of traditional polls in predicting presidential election outcomes across the world. To understand the reasons behind these failures we analyze the raw data of a trusted pollster which failed to predict, along with the rest of the pollsters, the surprising 2019 presidential election in Argentina which has led to a major market collapse in that country. Analysis of the raw and re-weighted data from longitudinal surveys performed before and after the elections reveals clear biases (beyond well-known low-response rates) related to mis-representation of the population and, most importantly, to social-desirability biases, i.e., the tendency of respondents to hide their intention to vote for controversial candidates. We then propose a longitudinal opinion tracking method based on big-data analytics from social media, machine learning, and network theory that overcomes the limits of traditional polls. The model achieves accurate results in the 2019 Argentina elections predicting the overwhelming victory of the candidate Alberto Fern\'andez over the president Mauricio Macri; a result that none of the traditional pollsters in the country was able to predict. Beyond predicting political elections, the framework we propose is more general and can be used to discover trends in society; for instance, what people think about economics, education or climate change.Comment: 47 pages, 10 tables, 15 figure

Emission estimates of HCFCs and HFCs in California from the 2010 CalNex study

The CalNex 2010 (California Research at the Nexus of Air Quality and Climate Change) study was designed to evaluate the chemical composition of air masses over key source regions in California. During May to June 2010, air samples were collected on board a National Oceanic and Atmospheric Administration (NOAA) WP-3D aircraft over the South Coast Air Basin of California (SoCAB) and the Central Valley (CV). This paper analyzes six effective greenhouse gases - chlorodifluoromethane (HCFC-22), 1,1-dichloro-1-fluoroethane (HCFC-141b), 1-chloro-1,1-difluoroethane (HCFC-142b), 2-chloro-1,1,1,2-tetrafluoroethane (HCFC-124), 1,1,1,2- tetrafluoroethane (HFC-134a), and 1,1-difluoroethane (HFC-152a) - providing the most comprehensive characterization of chlorofluorocarbon (CFC) replacement compound emissions in California. Concentrations of measured HCFCs and HFCs are enhanced greatly throughout the SoCAB and CV, with highest levels observed in the SoCAB: 310 ± 92 pptv for HCFC-22, 30.7 ± 18.6 pptv for HCFC-141b, 22.9 ± 2.0 pptv for HCFC-142b, 4.86 ± 2.56 pptv for HCFC-124, 109 ± 46.4 pptv for HFC-134a, and 91.2 ± 63.9 pptv for HFC-152a. Annual emission rates are estimated for all six compounds in the SoCAB using the measured halocarbon to carbon monoxide (CO) mixing ratios and CO emissions inventories. Emission rates of 3.05 ± 0.70 Gg for HCFC-22, 0.27 ± 0.07 Gg for HCFC-141b, 0.06 ± 0.01 Gg for HCFC-142b, 0.11 ± 0.03 Gg for HCFC-124, 1.89 ± 0.43 Gg for HFC-134a, and 1.94 ± 0.45 Gg for HFC-152b for the year 2010 are calculated for the SoCAB. These emissions are extrapolated from the SoCAB region to the state of California using population data. Results from this study provide a baseline emission rate that will help future studies determine if HCFC and HFC mitigation strategies are successful. Key PointsHCFC and HFC emissions are calculated for the year 2010 for the SoCABEmissions are extrapolated to the state of CaliforniaEmissions are calculated using CalNex field measurements © 2013. American Geophysical Union. All Rights Reserved

Insatiability and Crisis: Using Interdisciplinarity to Understand (and Denaturalize) Contemporary Humans

This chapter illustrates how collaboration between different social sciences can encourage students to think critically about prevailing assumptions regarding human nature. Both the chapter and the pedagogical experience on which it is based investigate the distinctive type of human created by capitalist society. In so doing, it takes a heterodox approach to analyzing the concept of an insatiable human nature through a case study that invites students to critically assess this perspective. This discussion then leads to an investigation and critique of traditional neoclassical Economic assumptions about human behavior, which forms the basis for a case study on the causes of the global economic and financial crisis of 2008. The goal is to facilitate students’ development of a more grounded perspective on real world events