A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Classifies defects in ordered media using homotopy theory.
Extends knotted defect classification to bounded domains using handlebodies.
Study travel time tomography for transversely isotropic media using modified pseudodifferential calculus.
A Lie groupoid, called \textit{second-order non-holonomic material Lie groupoid}, is associated in a natural way to any Cosserat media. This groupoid is used to give a new definition of homogeneity which does not depend on a reference crystal. The corresponding Lie algebroid, called \textit{second-order non-holonomic m…
EmTract extracts emotions from financial social media text.
Deep learning predicts fluid flow in porous media, accelerating simulations by orders of magnitude.
CNNs predict porosity, permeability, and tortuosity from porous media images.
The damaging effects of hate speech on social media are evident during the last few years, and several organizations, researchers and social media platforms tried to harness them in various ways. Despite these efforts, social media users are still affected by hate speech. The problem is even more apparent to social gro…
New equations for Cosserat media motions derived from bundle automorphisms.
There is a large amount of interest in understanding users of social media in order to predict their behavior in this space. Despite this interest, user predictability in social media is not well-understood. To examine this question, we consider a network of fifteen thousand users on Twitter over a seven week period. W…
IntraVascular UltraSound (IVUS) is one of the most effective imaging modalities that provides assistance to experts in order to diagnose and treat cardiovascular diseases. We address a central problem in IVUS image analysis with Fully Convolutional Network (FCN): automatically delineate the lumen and media-adventitia b…
With the proliferation of social media, fashion inspired from celebrities, reputed designers as well as fashion influencers has shortened the cycle of fashion design and manufacturing. However, with the explosion of fashion related content and large number of user generated fashion photos, it is an arduous task for fas…
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…
Study compares sentiment spillover networks from news and social media in tech companies.
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…
The paper introduces new uniformity and homogeneity concepts for Cosserat media.
Social media enhances or diminishes scientific status, depending on usage.
Media tone around earnings announcements predicts stock returns.
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.
Social media reduces individual investors' disposition effect through negative information.
Deep learning ensemble detects social media rumors faster.
The tools of presymplectic geometry are used to study light rays trajectories in anisotropic media.
Deep learning predicts stock movements using social media data.
Study finds meme stocks have unique price and social media dynamics.
The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors. In this work, we focus on the set of cryptocurrency news, which recently became of emerging interest to the general and financial audience. In order to track relevant news in real-time, we (i…
Paper improves gender detection on social media using deep learning.
Study finds social media investor emotions predict stock prices.
Study uses social media to analyze COVID-19 impact.
Study shows social media impacts shareholder returns on ESG risks.
Model financial markets with social media influences using hierarchical networks.
Well-established methods for the solution of stochastic partial differential equations (SPDEs) typically struggle in problems with high-dimensional inputs/outputs. Such difficulties are only amplified in large-scale applications where even a few tens of full-order model runs are impracticable. While dimensionality redu…
The combination of large open data sources with machine learning approaches presents a potentially powerful way to predict events such as protest or social unrest. However, accounting for uncertainty in such models, particularly when using diverse, unstructured datasets such as social media, is essential to guarantee t…
Sentiment analysis from news and social media predicts forex market movements.
Systematic review of ML models for detecting social media deception.
A new model for defective media using two scales.
Study uses multiple online media to predict crude oil prices.
Online harassment is a significant social problem. Prevention of online harassment requires rapid detection of harassing, offensive, and negative social media posts. In this paper, we propose the use of word embedding models to identify offensive and harassing social media messages in two aspects: detecting fast-changi…
Detecting depression early from social media texts.
A variety of approaches have been proposed to automatically infer the profiles of users from their digital footprint in social media. Most of the proposed approaches focus on mining a single type of information, while ignoring other sources of available user-generated content (UGC). In this paper, we propose a mechanis…
The notions of uniformity and homogeneity of elastic materials are reviewed in terms of Lie groupoids and frame bundles. This framework is also extended to consider the case Functionally Graded Media, which allows us to obtain some homogeneity conditions.
In this paper, we study the discrete Morse flow for the Ricci flow on football, which is the 2-sphere with removed north and south poles and with the metric of constant scalar curvature, and and for Porous media equation on a bounded regular domain in the plane. We show that with a suitable assumption about $g(0)…
The rise in popularity of major social media platforms have enabled people to share photos and textual information about their daily life. One of the popular topics about which information is shared is food. Since a lot of media about food are attributed to particular locations and restaurants, information like spatio-…
Researchers create topologically protected knots in a realizable system.
In this work, we analyze the flow filtration process of slightly compressible fluids in porous media containing man made fractures with complex geometries. We model the coupled fracture-porous media system where the linear Darcy flow is considered in porous media and the nonlinear Forchheimer equation is used inside th…
Deep Gaussian processes reduce uncertainty in porous media flow modeling.
Paper uses queue theory to model financial signals with relativistic delay.
GCAN detects fake news on social media with explanations.