10,528 research outputs found
How well are we doing relative to other States? Interstate Highway Bridge Spending and Performance Comparisons across the States of the Union
Networked fairness in cake cutting
We introduce a graphical framework for fair division in cake cutting, where comparisons between agents are limited by an underlying network structure. We generalize the classical fairness notions of envy-freeness and proportionality to this graphical setting. Given a simple undirected graph G, an allocation is envy-free on G if no agent envies any of her neighbor's share, and is proportional on G if every agent values her own share no less than the average among her neighbors, with respect to her own measure. These generalizations open new research directions in developing simple and efficient algorithms that can produce fair allocations under specific graph structures. On the algorithmic frontier, we first propose a moving-knife algorithm that outputs an envy-free allocation on trees. The algorithm is significantly simpler than the discrete and bounded envy-free algorithm recently designed in [Aziz and Mackenzie, 2016a] for complete graphs. Next, we give a discrete and bounded algorithm for computing a proportional allocation on descendant graphs, a class of graphs by taking a rooted tree and connecting all its ancestor-descendant pairs
Singular Effects of Spin-Flip Scattering on Gapped Dirac Fermions
We investigate the effects of spin-flip scattering on the Hall transport and
spectral properties of gapped Dirac fermions. We find that in the weak
scattering regime, the Berry curvature distribution is dramatically compressed
in the electronic energy spectrum, becoming singular at band edges. As a result
the Hall conductivity has a sudden jump (or drop) of when the Fermi
energy sweeps across the band edges, and otherwise is a constant quantized in
units of . In parallel, spectral properties such as the density of
states and spin polarization are also greatly enhanced at band edges. Possible
experimental methods to detect these effects are discussed
â™ SPADE: A Big Five-Mturk Dataset of Argumentative Speech Enriched with Socio-Demographics for Personality Detection
Toward explainable AI (XAI) for mental health detection based on language behavior
Advances in artificial intelligence (AI) in general and Natural Language Processing (NLP) in particular are paving the new way forward for the automated detection and prediction of mental health disorders among the population. Recent research in this area has prioritized predictive accuracy over model interpretability by relying on deep learning methods. However, prioritizing predictive accuracy over model interpretability can result in a lack of transparency in the decision-making process, which is critical in sensitive applications such as healthcare. There is thus a growing need for explainable AI (XAI) approaches to psychiatric diagnosis and prediction. The main aim of this work is to address a gap by conducting a systematic investigation of XAI approaches in the realm of automatic detection of mental disorders from language behavior leveraging textual data from social media. In pursuit of this aim, we perform extensive experiments to evaluate the balance between accuracy and interpretability across predictive mental health models. More specifically, we build BiLSTM models trained on a comprehensive set of human-interpretable features, encompassing syntactic complexity, lexical sophistication, readability, cohesion, stylistics, as well as topics and sentiment/emotions derived from lexicon-based dictionaries to capture multiple dimensions of language production. We conduct extensive feature ablation experiments to determine the most informative feature groups associated with specific mental health conditions. We juxtapose the performance of these models against a “black-box” domain-specific pretrained transformer adapted for mental health applications. To enhance the interpretability of the transformers models, we utilize a multi-task fusion learning framework infusing information from two relevant domains (emotion and personality traits). Moreover, we employ two distinct explanation techniques: the local interpretable model-agnostic explanations (LIME) method and a model-specific self-explaining method (AGRAD). These methods allow us to discern the specific categories of words that the information-infused models rely on when generating predictions. Our proposed approaches are evaluated on two public English benchmark datasets, subsuming five mental health conditions (attention-deficit/hyperactivity disorder, anxiety, bipolar disorder, depression and psychological stress)
Pushing on Personality Detection from Verbal Behavior: A Transformer Meets Text Contours of Psycholinguistic Features
Research at the intersection of personality psychology, computer science, and
linguistics has recently focused increasingly on modeling and predicting
personality from language use. We report two major improvements in predicting
personality traits from text data: (1) to our knowledge, the most comprehensive
set of theory-based psycholinguistic features and (2) hybrid models that
integrate a pre-trained Transformer Language Model BERT and Bidirectional Long
Short-Term Memory (BLSTM) networks trained on within-text distributions ('text
contours') of psycholinguistic features. We experiment with BLSTM models (with
and without Attention) and with two techniques for applying pre-trained
language representations from the transformer model - 'feature-based' and
'fine-tuning'. We evaluate the performance of the models we built on two
benchmark datasets that target the two dominant theoretical models of
personality: the Big Five Essay dataset and the MBTI Kaggle dataset. Our
results are encouraging as our models outperform existing work on the same
datasets. More specifically, our models achieve improvement in classification
accuracy by 2.9% on the Essay dataset and 8.28% on the Kaggle MBTI dataset. In
addition, we perform ablation experiments to quantify the impact of different
categories of psycholinguistic features in the respective personality
prediction models.Comment: accepted at WASSA 202
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