Releasing the CRaQAn (Coreference Resolution in Question-Answering): An open-source dataset and dataset creation methodology using instruction-following models

Instruction-following language models demand robust methodologies for information retrieval to augment instructions for question-answering applications. A primary challenge is the resolution of coreferences in the context of chunking strategies for long documents. The critical barrier to experimentation of handling coreferences is a lack of open source datasets, specifically in question-answering tasks that require coreference resolution. In this work we present our Coreference Resolution in Question-Answering (CRaQAn) dataset, an open-source dataset that caters to the nuanced information retrieval requirements of coreference resolution in question-answering tasks by providing over 250 question-answer pairs containing coreferences. To develop this dataset, we developed a novel approach for creating high-quality datasets using an instruction-following model (GPT-4) and a Recursive Criticism and Improvement Loop.Comment: NeurIPS 2023 Workshop on Instruction Tuning and Instruction Followin

T-dependent B cell responses to Plasmodium induce antibodies that form a high-avidity multivalent complex with the circumsporozoite protein

The repeat region of the Plasmodium falciparum circumsporozoite protein (CSP) is a major vaccine antigen because it can be targeted by parasite neutralizing antibodies; however, little is known about this interaction. We used isothermal titration calorimetry, X-ray crystallography and mutagenesis-validated modeling to analyze the binding of a murine neutralizing antibody to Plasmodium falciparum CSP. Strikingly, we found that the repeat region of CSP is bound by multiple antibodies. This repeating pattern allows multiple weak interactions of single FAB domains to accumulate and yield a complex with a dissociation constant in the low nM range. Because the CSP protein can potentially cross-link multiple B cell receptors (BCRs) we hypothesized that the B cell response might be T cell independent. However, while there was a modest response in mice deficient in T cell help, the bulk of the response was T cell dependent. By sequencing the BCRs of CSP-repeat specific B cells in inbred mice we found that these cells underwent somatic hypermutation and affinity maturation indicative of a T-dependent response. Last, we found that the BCR repertoire of responding B cells was limited suggesting that the structural simplicity of the repeat may limit the breadth of the immune responseThis work was supported by the Bill and Melinda Gates foundation http://www. gatesfoundation.org (OPP1151018)

‘It’s all a cover up!’: An Examination of Epistemic Vices and Conspiracy Theories in the Internet Age

Conspiracy theories and fake news can be found in both off and online discourse. With COVID-19 still mutating, the same can be said for disinformation. At the beginning there were conspiracies that COVID- 19 was engineered to prevent global population growth, and now on vaccine hesitancy, with some conspiracies pointing to embryos being used as an ingredient to the vaccines. In this paper, I aim to address the era of digitality in which we live, asking and answering fundamental questions such as: what is fake news? How harmful are conspiracy theories? Has there been a shift in the conspiracy theory paradigm? And is there a way to prevent conspiracy theory growth? To answer such questions, I will focus on the philosophical discipline of vice epistemology: vicious intellectual characteristics that can give way to a loss of knowledge i.e., closed- mindedness, lack of thoroughness, dogmatism and more. Moreover, I will introduce my theory ‘Epistemic Cluster Vices’ to categorise each recognised vice into their respective groups and assess whether other epistemic vices can off-shoot from the is a central/ capital vice. It must be stated that my theory is in its embryonic stage and further academic examination is necessary to cement this idea into vice epistemology

Prediction of Smoking Risk From Repeated Sampling of Environmental Images: Model Validation

BackgroundViewing their habitual smoking environments increases smokers’ craving and smoking behaviors in laboratory settings. A deep learning approach can differentiate between habitual smoking versus nonsmoking environments, suggesting that it may be possible to predict environment-associated smoking risk from continuously acquired images of smokers’ daily environments. ObjectiveIn this study, we aim to predict environment-associated risk from continuously acquired images of smokers’ daily environments. We also aim to understand how model performance varies by location type, as reported by participants. MethodsSmokers from Durham, North Carolina and surrounding areas completed ecological momentary assessments both immediately after smoking and at randomly selected times throughout the day for 2 weeks. At each assessment, participants took a picture of their current environment and completed a questionnaire on smoking, craving, and the environmental setting. A convolutional neural network–based model was trained to predict smoking, craving, whether smoking was permitted in the current environment and whether the participant was outside based on images of participants’ daily environments, the time since their last cigarette, and baseline data on daily smoking habits. Prediction performance, quantified using the area under the receiver operating characteristic curve (AUC) and average precision (AP), was assessed for out-of-sample prediction as well as personalized models trained on images from days 1 to 10. The models were optimized for mobile devices and implemented as a smartphone app. ResultsA total of 48 participants completed the study, and 8008 images were acquired. The personalized models were highly effective in predicting smoking risk (AUC=0.827; AP=0.882), craving (AUC=0.837; AP=0.798), whether smoking was permitted in the current environment (AUC=0.932; AP=0.981), and whether the participant was outside (AUC=0.977; AP=0.956). The out-of-sample models were also effective in predicting smoking risk (AUC=0.723; AP=0.785), whether smoking was permitted in the current environment (AUC=0.815; AP=0.937), and whether the participant was outside (AUC=0.949; AP=0.922); however, they were not effective in predicting craving (AUC=0.522; AP=0.427). Omitting image features reduced AUC by over 0.1 when predicting all outcomes except craving. Prediction of smoking was more effective for participants whose self-reported location type was more variable (Spearman ρ=0.48; P=.001). ConclusionsImages of daily environments can be used to effectively predict smoking risk. Model personalization, achieved by incorporating information about daily smoking habits and training on participant-specific images, further improves prediction performance. Environment-associated smoking risk can be assessed in real time on a mobile device and can be incorporated into device-based smoking cessation interventions

A site directed fMRI approach for evaluating functional status in the anterolateral temporal lobes

NRC publication: Ye

A site directed approach for evaluating functional status in the anterolateral temporal lobes

NRC publication: Ye

T-dependent B cell responses to <i>Plasmodium</i> induce antibodies that form a high-avidity multivalent complex with the circumsporozoite protein

<div><p>The repeat region of the <i>Plasmodium falciparum</i> circumsporozoite protein (CSP) is a major vaccine antigen because it can be targeted by parasite neutralizing antibodies; however, little is known about this interaction. We used isothermal titration calorimetry, X-ray crystallography and mutagenesis-validated modeling to analyze the binding of a murine neutralizing antibody to <i>Plasmodium falciparum</i> CSP. Strikingly, we found that the repeat region of CSP is bound by multiple antibodies. This repeating pattern allows multiple weak interactions of single F_AB domains to accumulate and yield a complex with a dissociation constant in the low nM range. Because the CSP protein can potentially cross-link multiple B cell receptors (BCRs) we hypothesized that the B cell response might be T cell independent. However, while there was a modest response in mice deficient in T cell help, the bulk of the response was T cell dependent. By sequencing the BCRs of CSP-repeat specific B cells in inbred mice we found that these cells underwent somatic hypermutation and affinity maturation indicative of a T-dependent response. Last, we found that the BCR repertoire of responding B cells was limited suggesting that the structural simplicity of the repeat may limit the breadth of the immune response.</p></div