ED-Filter improves eating disorder classification on Twitter.
problem High dimensionality and extensive feature sets in Twitter data for ED classification.
method Informed branch and bound search technique with hybrid greedy-based deep learning.
result Significant improvements in classification accuracy and efficiency.
Researchers use statistical methods to infer transmission matrices in complex media.
problem Comprehending and exploiting photon scattering through disordered media.
method Pseudolikelihood decimation to learn the coupling matrix via random sampling.
result Transmission matrices can be inferred and used like normal optical elements.
Detecting depression early from social media texts.
problem Early diagnosis and prevention of depression.
method Topic analysis and learned confidence scores.
result Achieved good results compared to state of the art.
Social media has recently emerged as a premier method to disseminate information online. Through these online networks, tens of millions of individuals communicate their thoughts, personal experiences, and social ideals. We therefore explore the potential of social media to predict, even prior to onset, Major Depressiv…
Linear problems appear in a variety of disciplines and their application for the transmission matrix recovery is one of the most stimulating challenges in biomedical imaging. Its knowledge turns any random media into an optical tool that can focus or transmit an image through disorder. Here, converting an input-output …
A new model for stock price fluctuations is proposed, based upon an analogy with the motion of tracers in Gaussian random fields, as used in turbulent dispersion models and in studies of transport in dynamically disordered media. Analytical and numerical results for this model in a special limiting case of a single-sca…
Paper proposes CBPT for early depression detection on Twitter.
problem Early detection of depression on social media.
method Cost-sensitive Boosting Pruning Trees (CBPT) for depression detection.
result CBPT achieves strong classification results on multiple datasets.
Study uses interviews to automatically detect BD and BPD with good accuracy.
problem Challenges in distinguishing BD and BPD from clinical interviews.
method Developed a multi-modal dataset and used a linear classifier with selected features from interviews.
result Different sets of features characterize BD and BPD, providing insights into their differences.
A lack of information exists about the health issues of lesbian, gay, bisexual, transgender, and queer (LGBTQ) people who are often excluded from national demographic assessments, health studies, and clinical trials. As a result, medical experts and researchers lack a holistic understanding of the health disparities fa…
Mobile technologies offer opportunities for higher resolution monitoring of health conditions. This opportunity seems of particular promise in psychiatry where diagnoses often rely on retrospective and subjective recall of mood states. However, getting actionable information from these rather complex time series is cha…
Study on a pinning model with random walk increments, showing convergence to a critical disordered pinning measure.
problem Understanding the critical behavior of a disordered pinning model.
method Analyzing a disordered pinning model induced by a random walk with specific moment conditions, showing convergence to a limiting measure.
result Convergence of point-to-point partition functions to the critical disordered pinning measure in the critical window.
New minimal surfaces show stacking disorder in periodic structures.
problem Reproducing experimental twinning defects in periodic minimal surfaces.
method Constructing non-periodic minimal surfaces that lift to disordered stacking in 3D.
result Reproduced twinning defects in periodic minimal surfaces as stacking disorder.
New equations for Cosserat media motions derived from bundle automorphisms.
problem Modeling deformations in Cosserat media.
method Euler-type equations derived from SO(3)-bundle automorphisms.
result Presented new equations for Cosserat media motions.
In this paper, we propose a classification based glottal closure instants (GCI) detection from pathological acoustic speech signal, which finds many applications in vocal disorder analysis. Till date, GCI for pathological disorder is extracted from laryngeal (glottal source) signal recorded from Electroglottograph, a d…
Deep learning predicts mental disorders from audio and text samples.
problem Predicting mental disorders from speech samples.
method Multimodal deep learning structure using various pre-trained models for audio and text embeddings, transfer learning, and auxiliary corpora.
result Acceptable accuracy in predicting mental disorders through multimodal analysis.
Automated detection of voice disorders with computational methods is a recent research area in the medical domain since it requires a rigorous endoscopy for the accurate diagnosis. Efficient screening methods are required for the diagnosis of voice disorders so as to provide timely medical facilities in minimal resourc…
Paper presents a method to diagnose schizophrenia using fMRI dynamics from healthy controls.
problem Early diagnosis of mental disorders like schizophrenia using fMRI.
method Self-supervised pre-training on fMRI dynamics of healthy controls for transfer learning.
result Effective classification of schizophrenia using fMRI dynamics with small datasets.
Whole MILC learns brain disorder dynamics from unlabeled data.
problem Learning spatio-temporal brain disorder dynamics from unlabeled data.
method Self-supervised pre-training of whole MILC on unlabeled healthy control data.
result Whole MILC outperforms existing methods and provides diagnostic insights.
The study predicts how discussions in mental disorder Reddit communities affect users' emotional states.
problem Improving mental health conditions through social support analysis.
method Text embedding techniques and RNNs for predicting emotional tone shifts.
result Users' emotional states can improve due to social support, as evidenced by positive comments following negative posts.
This article asks how planning scholarship may effectively gain impact in planning practice through media exposure. In liberal democracies the public sphere is dominated by mass media. Therefore, working with such media is a prerequisite for effective public impact of planning research. Using the example of megaproject…
The opioid epidemic in the United States claims over 40,000 lives per year, and it is estimated that well over two million Americans have an opioid use disorder. Over-prescription and misuse of prescription opioids play an important role in the epidemic. Individuals who are prescribed opioids, and who are diagnosed wit…
Research uses activity analysis to identify mental health symptoms.
problem Identifying mental health symptoms using objective activity metrics.
method Proposes a framework for mHealth monitoring of psychiatric patients based on physical activity time series.
result Identifies distinct behavioural phenotypes and measures for mood assessment.
Study compares sentiment spillover networks from news and social media in tech companies.
problem Understanding how sentiment information flows between companies through news and social media.
method Network-based transfer entropy method to measure and compare sentiment spillover.
result News shows stronger information flow among tech companies after COVID-19.
As a highlighting research topic in the multimedia area, cross-media retrieval aims to capture the complex correlations among multiple media types. Learning better shared representation and distance metric for multimedia data is important to boost the cross-media retrieval. Motivated by the strong ability of deep neura…
New algorithm optimizes matrix reordering for noisy disordered matrices.
problem Optimizing matrix reordering for noisy disordered matrices in single-cell biology and metagenomics.
method Proposed a polynomial-time adaptive sorting algorithm to improve upon spectral seriation.
result Our algorithm achieves superior performance compared to existing methods in real datasets.
The paper introduces new uniformity and homogeneity concepts for Cosserat media.
problem Characterizing uniformity and homogeneity in Cosserat media.
method Using groupoids and smooth distributions, the authors derive three canonical equations to characterize uniformity and homogeneity.
result The paper provides a unique and maximal division of Cosserat media into uniform and second-grade parts.
Social media enhances or diminishes scientific status, depending on usage.
problem Impact of social media on scientific stratification and mobility.
method Logistic Attribution Analysis combining statistical and machine learning methods.
result Social media promotes stratification and mobility, but beyond a threshold, it negatively impacts status.
Vocal disorders have affected several patients all over the world. Due to the inherent difficulty of diagnosing vocal disorders without sophisticated equipment and trained personnel, a number of patients remain undiagnosed. To alleviate the monetary cost of diagnosis, there has been a recent growth in the use of data a…
Deep-learning model detects ASD from MRI data with high accuracy.
problem Challenges in diagnosing ASD due to subjective behavioral assessments and informant biases.
method Integrates deep-learning and SVM techniques to classify ASD brain scans.
result Highly accurate classification of ASD brain scans from neurotypical scans.
Media tone around earnings announcements predicts stock returns.
problem Determining if media tone around earnings announcements provides useful information for stock prices.
method Conducted an event study on media tone around earnings announcements for nonfinancial S&P 500 firms.
result Media tone around earnings announcements predicts abnormal stock returns.
EEG measures brain activity to diagnose ASD more efficiently.
problem Lack of objective measures for early ASD diagnosis.
method Use EEG to classify ASD using machine learning.
result EEG can be a biomarker for ASD.
Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life for patients and potentially supports the development of new treatments. Many studies have been conducted on machine learning techniques that seek brain imaging data for specific biomarkers of disorders. These studies hav…
Model predicts cannabis use disorder risk for adolescents and young adults.
problem Predicting cannabis use disorder progression in adolescents and young adults.
method Bayesian machine learning model trained on longitudinal data.
result Model provides personalized risk assessment with AUC of 0.68-0.75 and E/O ratio of 0.95-1.
EmTract extracts emotions from financial social media text.
problem Understanding investor emotions in financial markets.
method Annotated data, DistilBERT model, embedding space augmentation.
result EmTract outperforms existing emotion classifiers.
Online social networks offer a new way to investigate financial markets' dynamics by enabling the large-scale analysis of investors' collective behavior. We provide empirical evidence that suggests social media and stock markets have a nonlinear causal relationship. We take advantage of an extensive data set composed o…
This paper studies how social media posts, especially by executives, affect stock prices.
problem Predicting stock market movements using social media data.
method Integrated sentiment analysis of Twitter and Reddit posts with historical stock data using time series models and deep learning.
result Improvements in stock price prediction when social media data, especially executive posts, are included.
Investigates stochastic networks on disordered lattices, converging to Brownian web in 2D.
problem Stochastic networks on disordered lattices.
method Directed spanning forests on randomly perturbed lattices.
result DSF converges to Brownian web in 2D under diffusive scaling.
Deep learning predicts opioid use disorder risk in patients.
problem Identifying patients at high risk of opioid use disorder.
method Applied LSTM models to analyze electronic health records of opioid users.
result LSTM model outperformed other methods with F1 score of 0.8023 and AUCROC of 0.9369.
Paper proposes using word embeddings to detect trolls in social media debates.
problem Preventing online harassment through rapid detection of offensive posts.
method Word embedding models for identifying fast-changing topics and negative content.
result GloVe model helps in discovering new keywords for trolling detection.
ASD-DiagNet uses fMRI data to improve ASD diagnosis accuracy.
problem Difficult diagnosis of Autism Spectrum Disorder (ASD) due to symptom observation.
method Hybrid learning approach combining autoencoder and single layer perceptron.
result Improved classification accuracy up to 80% with 20% increase over state-of-the-art methods.
Social media reduces individual investors' disposition effect through negative information.
problem The disposition effect in individual investors selling profitable assets too early and holding onto losing assets for too long.
method Analysis of post data and trading data from Xueqiu.com.
result Social media information significantly reduces the disposition effect.
Analyze food popularity trends using social media data.
problem Identify trends and popularity of cuisine types across space and time.
method Use off-the-shelf machine learning techniques, Kernel Density Estimation, and Bayesian Networks.
result Model dependencies among food cuisines popularity.
Deep learning ensemble detects social media rumors faster.
problem Timely detection of rumors on social media platforms.
method Ensemble model using deep neural networks and time-series tweet data.
result Improved rumor detection performance by 7.9%.
New framework uses social media images to estimate wildlife populations.
problem Lack of basic data for wildlife species due to inadequate traditional methods.
method Developed a new computer vision tool to account for social media bias.
result Showed that wildlife population size estimates are learnable from social media.
Study models fractures in porous media using geometric analysis.
problem Analyzing fluid flow in fractures with complex geometries.
method Developed a geometric model using Riemannian manifold and Laplace Beltrami operators.
result Reduced model accurately approximates flow in complex fractures.
The tools of presymplectic geometry are used to study light rays trajectories in anisotropic media.
The paper proposes a method to learn user representations invariant to social media behavior changes.
problem Difficulty in comparing users over time due to evolving behavior.
method Learning a mapping from user activity to a vector space capturing invariant features.
result The learned mapping enables efficient comparisons of users not seen at training time.
Study travel time tomography for transversely isotropic media using modified pseudodifferential calculus.
problem Travel time tomography problem for transversely isotropic media.
method Modified scattering pseudodifferential calculus to solve the tomography problem.
result Construction and use of modified pseudodifferential calculus to solve the tomography problem.