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1. Inferring #MeToo Experience Tweets using Classic and Neural Models [Inferring #MeToo Experience Tweets using Classic and Neural Models]

   https://doi.org/10.5220/0011278100003269  

   Zech, Julianne ; Singh, Lisa ; Kawintiranon, Kornraphop ; Mezey, Naomi ; Williams, Jamillah ( January 2022 , Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA)

   The #MeToo movement is one of several calls for social change to gain traction on Twitter in the past decade. The movement went viral after prominent individuals shared their experiences, and much of its power continues to be derived from experience sharing. Because millions of #MeToo tweets are published every year, it is important to accurately identify experience-related tweets. Therefore, we propose a new learning task and compare the effectiveness of classic machine learning models, ensemble models, and a neural network model that incorporates a pre-trained language model to reduce the impact of feature sparsity. We find that even with limited training data, the neural network model outperforms the classic and ensemble classifiers. Finally, we analyze the experience-related conversation in English during the first year of the #MeToo movement and determine that experience tweets represent a sizable minority of the conversation and are moderately correlated to major events. 

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2. Transfer Learning with Deep CNNs for Gender Recognition and Age Estimation

   https://doi.org/10.1109/BigData.2018.8621891  

   Smith, Philip ; Chen, Cuixian ( December 2018 , 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018, pp. 2564-2571.)

   In this project, competition-winning deep neural networks with pretrained weights are used for image-based gender recognition and age estimation. Transfer learning is explored using both VGG19 and VGGFace pretrained models by testing the effects of changes in various design schemes and training parameters in order to improve prediction accuracy. Training techniques such as input standardization, data augmentation, and label distribution age encoding are compared. Finally, a hierarchy of deep CNNs is tested that first classifies subjects by gender, and then uses separate male and female age models to predict age. A gender recognition accuracy of 98.7% and an MAE of 4.1 years is achieved. This paper shows that, with proper training techniques, good results can be obtained by retasking existing convolutional filters towards a new purpose. 

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3. An insight analysis and detection of drug-abuse risk behavior on Twitter with self-taught deep learning

   https://doi.org/https://doi.org/10.1186/s40649-019-0071-4  

   Hu, Han ; Phan, NhatHai ; Geller, James ; Chun, Soon Ae ; Jin, Ruoming ; Ding, Kele ; Kenne, Deric ; Dou, Dejing. ( November 2019 , Computational social networks)
4. Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer’s Disease Using Biophysical Modeling and Deep Learning

   https://doi.org/10.1007/s11538-024-01273-5  

   Saghafi, Soheil ; Rumbell, Timothy ; Gurev, Viatcheslav ; Kozloski, James ; Tamagnini, Francesco ; Wedgwood, Kyle C. A. ; Diekman, Casey O. ( March 2024 , Bulletin of Mathematical Biology)

   Alzheimer’s disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy exhibit altered intrinsic excitability properties. We used deep hybrid modeling (DeepHM), a recently developed parameter inference technique that combines deep learning with biophysical modeling, to map experimental data recorded from hippocampal CA1 neurons in transgenic AD mice and age-matched wildtype littermate controls to the parameter space of a conductance-based CA1 model. Although mechanistic modeling and machine learning methods are by themselves powerful tools for approximating biological systems and making accurate predictions from data, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. DeepHM addresses these shortcomings by using conditional generative adversarial networks to provide an inverse mapping of data to mechanistic models that identifies the distributions of mechanistic modeling parameters coherent to the data. Here, we demonstrated that DeepHM accurately infers parameter distributions of the conductance-based model on several test cases using synthetic data generated with complex underlying parameter structures. We then used DeepHM to estimate parameter distributions corresponding to the experimental data and infer which ion channels are altered in the Alzheimer’s mouse models compared to their wildtype controls at 12 and 24 months. We found that the conductances most disrupted by tauopathy, amyloidopathy, and aging are delayed rectifier potassium, transient sodium, and hyperpolarization-activated potassium, respectively.

    

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5. Are Fair Learning To Rank Models Really Fair? An Analysis Using Inferred Gender

   https://doi.org/10.1109/URTC56832.2022.10002233  

   Pietrick, Alexander ; Romportl, Alyssa ; Smith, Shailen ; Olulana, Oluseun ; Cachel, Kathleen ; Rundensteiner, Elke ( September 2022 , 2022 IEEE MIT Undergraduate Research Technology Conference (URTC))

   Fair Learning To Rank (LTR) frameworks require demographic information; however, that information is often unavailable. Inference algorithms may infer the missing demographic information to supply to the fair LTR model. In this study, we analyze the effect of using a trained fair LTR model with uncertain demographic inferences. We show that inferred data results in varying levels of fairness and utility depending on inference accuracy. Specifically, less accurate inferred data adversely affects the rankings’ fairness, while more accurate inferred data creates fairer rankings. Therefore, we recommend that a careful evaluation of demographic inference algorithms before use is critical. 

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