Ethical Use of Machine Learning in Higher Education Admission

A machine learning model called GRADE was used for PhD admission at UT Austin from the year 2013-2020. The model was trained a small set of past admission decisions which are already bias and was used immediately without further tuning or human validation. The model will score all applicants and the decision is made without further human assessment for applicants with the highest and lowest score. Only 362/588 full human reviews are conducted with a few people admitted and the majority of the rest being rejected by algorithm.https://digitalcommons.hamilton.edu/posters/1017/thumbnail.jp

Testing the pain paradox: a longitudinal study on PTSD from past trauma, alexithymia, mindfulness, and psychological distress

Although the negative impact of posttraumatic stress disorder (PTSD) on psychological distress is broadly consistent across the literature, the psychological mechanisms underpinning this relationship need further exploration. Pain paradox theory has postulated the important role of PTSD, avoidance strategies, mindfulness, and distress following trauma. However, a more comprehensive study is needed to understand their interactive effects over time. This current longitudinal study aimed to examine the associations between these factors. 201 participants completed the questionnaire survey (i.e., the PTSD Checklist for DSM-5, Toronto Alexithymia Scale, Mindfulness Attention Awareness Scale, and the General Health Questionnaire) at two time points nine months apart. An autoregressive cross-lagged panel with structural equation modelling was used for data analysis. Initial PTSD symptoms predicted subsequent psychological distress. Initial mindfulness was significantly negatively correlated with subsequent alexithymia, PTSD, and distress outcomes. Furthermore, initial alexithymia was significantly positively associated with subsequent PTSD and distress. Following trauma exposure, individuals may develop PTSD that impairs mental health. However, individuals with higher levels of mindfulness tend to experience less alexithymia and PTSD symptoms, which in turn may lead to lower levels of psychological distress over time. Meanwhile, individuals with lower levels of difficulty identifying and describing emotions are less likely to develop PTSD symptoms and experience psychological distress over time

Network analysis on the relationship between posttraumatic stress disorder, psychiatric co-morbidity and posttraumatic growth among Chinese adolescents

Background This study investigated the association between posttraumatic stress disorder (PTSD), psychiatric co-morbidity and posttraumatic growth (PTG) among Chinese adolescents using network analysis. Methods 867 Chinese adolescents (male = 424, female = 443) were recruited from three secondary schools. They completed the Posttraumatic Stress Disorder Checklist for DSM-5, the Posttraumatic Growth Inventory, and the General Health Questionnaire-28. Results Domains of each construct mainly clustered within their respective communities with several bridging edges identified. The prominent roles of bridging nodes and edges (positive and negative) were highlighted. Key bridging nodes were negative alterations in cognitions and mood for PTSD, anxiety and insomnia for psychiatric co-morbidity and appreciation of life for PTG. Limitations The cross-sectional nature of the present study may preclude the identification of real causal relationships between nodes. Conclusions Following a trauma, adolescents displayed posttraumatic stress along with general psychological disorder symptoms. These distress reactions could affect the way they appreciated life and their motivation to seek future life possibilities. Findings from the current study may provide some clue for the facilitation of posttraumatic growth among clinical patients

FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding

Large-scale cross-lingual language models (LM), such as mBERT, Unicoder and XLM, have achieved great success in cross-lingual representation learning. However, when applied to zero-shot cross-lingual transfer tasks, most existing methods use only single-language input for LM finetuning, without leveraging the intrinsic cross-lingual alignment between different languages that proves essential for multilingual tasks. In this paper, we propose FILTER, an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. Specifically, FILTER first encodes text input in the source language and its translation in the target language independently in the shallow layers, then performs cross-language fusion to extract multilingual knowledge in the intermediate layers, and finally performs further language-specific encoding. During inference, the model makes predictions based on the text input in the target language and its translation in the source language. For simple tasks such as classification, translated text in the target language shares the same label as the source language. However, this shared label becomes less accurate or even unavailable for more complex tasks such as question answering, NER and POS tagging. To tackle this issue, we further propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language. Extensive experiments demonstrate that FILTER achieves new state of the art on two challenging multilingual multi-task benchmarks, XTREME and XGLUE.Comment: Accepted to AAAI 2021; Top-1 Performance on XTREME (https://sites.research.google/xtreme, September 8, 2020) and XGLUE (https://microsoft.github.io/XGLUE, September 14, 2020) benchmar

A Bayesian semi-parametric model for thermal proteome profiling.

Funder: Wellcome TrustThe thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets

Cross-thought for sentence encoder pre-training

In this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks such as question answering. Instead of using the original signals of full sentences, we train a Transformer-based sequence encoder over a large set of short sequences, which allows the model to automatically select the most useful information for predicting masked words. Experiments on question answering and textual entailment tasks demonstrate that our pre-trained encoder can outperform state-of-the-art encoders trained with continuous sentence signals as well as traditional masked language modeling baselines. Our proposed approach also achieves new state of the art on HotpotQA (full-wiki setting) by improving intermediate information retrieval performance.Comment: Accepted by EMNLP 202

Identification of a shared gene signature and biological mechanism between diabetic foot ulcers and cutaneous lupus erythemnatosus by transcriptomic analysis

Diabetic foot ulcers (DFU) and cutaneous lupus erythematosus (CLE) are both diseases that can seriously affect a patient’s quality of life and generate economic pressure in society. Symptomatically, both DLU and CLE exhibit delayed healing and excessive inflammation; however, there is little evidence to support a molecular and cellular connection between these two diseases. In this study, we investigated potential common characteristics between DFU and CLE at the molecular level to provide new insights into skin diseases and regeneration, and identify potential targets for the development of new therapies. The gene expression profiles of DFU and CLE were obtained from the Gene Expression Omnibus (GEO) database and used for analysis. A total of 41 common differentially expressed genes (DEGs), 16 upregulated genes and 25 downregulated genes, were identified between DFU and CLE. GO and KEGG analysis showed that abnormalities in epidermal cells and the activation of inflammatory factors were both involved in the occurrence and development of DFU and CLE. Protein-protein interaction network (PPI) and sub-module analysis identified enrichment in seven common key genes which is KRT16, S100A7, KRT77, OASL, S100A9, EPGN and SAMD9. Based on these seven key genes, we further identified five miRNAs(has-mir-532-5p, has-mir-324-3p,has-mir-106a-5p,has-mir-20a-5p,has-mir-93-5p) and7 transcription factors including CEBPA, CEBPB, GLI1, EP30D, JUN,SP1, NFE2L2 as potential upstream molecules. Functional immune infiltration assays showed that these genes were related to immune cells. The CIBERSORT algorithm and Pearson method were used to determine the correlations between key genes and immune cells, and reverse key gene-immune cell correlations were found between DFU and CLE. Finally, the DGIbd database demonstrated that Paquinimod and Tasquinimod could be used to target S100A9 and Ribavirin could be used to target OASL. Our findings highlight common gene expression characteristics and signaling pathways between DFU and CLE, indicating a close association between these two diseases. This provides guidance for the development of targeted therapies and mutual interactions

RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling

Recent advances in large-scale pre-training such as GPT-3 allow seemingly high quality text to be generated from a given prompt. However, such generation systems often suffer from problems of hallucinated facts, and are not inherently designed to incorporate useful external information. Grounded generation models appear to offer remedies, but their training typically relies on rarely-available parallel data where information-relevant documents are provided for context. We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal. The model learns to reward retrieval of the documents with the highest utility in generation, and attentively combines them using a Mixture-of-Experts (MoE) ensemble to generate follow-on text. We demonstrate that both generator and retriever can take advantage of this joint training and work synergistically to produce more informative and relevant text in both prose and dialogue generation.Comment: accepted by AAAI-22, camera ready versio

CrossBind: Collaborative Cross-Modal Identification of Protein Nucleic-Acid-Binding Residues

Accurate identification of protein nucleic-acid-binding residues poses a significant challenge with important implications for various biological processes and drug design. Many typical computational methods for protein analysis rely on a single model that could ignore either the semantic context of the protein or the global 3D geometric information. Consequently, these approaches may result in incomplete or inaccurate protein analysis. To address the above issue, in this paper, we present CrossBind, a novel collaborative cross-modal approach for identifying binding residues by exploiting both protein geometric structure and its sequence prior knowledge extracted from a large-scale protein language model. Specifically, our multi-modal approach leverages a contrastive learning technique and atom-wise attention to capture the positional relationships between atoms and residues, thereby incorporating fine-grained local geometric knowledge, for better binding residue prediction. Extensive experimental results demonstrate that our approach outperforms the next best state-of-the-art methods, GraphSite and GraphBind, on DNA and RNA datasets by 10.8/17.3% in terms of the harmonic mean of precision and recall (F1-Score) and 11.9/24.8% in Matthews correlation coefficient (MCC), respectively. We release the code at https://github.com/BEAM-Labs/CrossBind.Comment: Accepted to AAAI-2