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.
Study finds inventory inaccuracies are linked to store activity and product perishability.
Sufficient physical activity and restful sleep play a major role in the prevention and cure of many chronic conditions. Being able to proactively screen and monitor such chronic conditions would be a big step forward for overall health. The rapid increase in the popularity of wearable devices provides a significant new…
Twitter promotes cryptocurrency pump-and-dumps, affecting trading behavior and returns.
Understanding and predicting the popularity of online items is an important open problem in social media analysis. Considerable progress has been made recently in data-driven predictions, and in linking popularity to external promotions. However, the existing methods typically focus on a single source of external influ…
Recent seminal work at the intersection of deep neural networks practice and random matrix theory has linked the convergence speed and robustness of these networks with the combination of random weight initialization and nonlinear activation function in use. Building on those principles, we introduce a process to trans…
Paper tackles fairness in insurance machine learning models using active learning.
Encouraging sustainable mobility patterns is at the forefront of policymaking at all scales of governance as the collective consciousness surrounding climate change continues to expand. Not every community, however, possesses the necessary economic or socio-cultural capital to encourage modal shifts away from private m…
This paper forecasts renewable energy prospects in South America through cross-border interconnection.
Paper tackles active learning for GNNs, reducing annotation costs.
Research activities of Kyoto Econophysics Group is reviewed. Strong emphasis has been placed on real economy. While the initial stage of research was a first high-definition data analysis on personal income, it soon progressed to firm dynamics, growth rate distribution and establishment of Pareto's law and Gibrat's law…
Tumblr, as a leading content provider and social media, attracts 371 million monthly visits, 280 million blogs and 53.3 million daily posts. The popularity of Tumblr provides great opportunities for advertisers to promote their products through sponsored posts. However, it is a challenging task to target specific demog…
Magnetoencephalography and electroencephalography (M/EEG) are non-invasive modalities that measure the weak electromagnetic fields generated by neural activity. Estimating the location and magnitude of the current sources that generated these electromagnetic fields is a challenging ill-posed regression problem known as…
Exact solutions reveal how unbalanced initializations promote rapid feature learning in neural networks.
This paper studies activation sparsity in large language models, finding key trends and implications.
Study uses time series analysis to predict player churn and conversion in games.
In this paper, we consider the problem of recovering a sparse signal based on penalized least squares formulations. We develop a novel algorithm of primal-dual active set type for a class of nonconvex sparsity-promoting penalties, including , bridge, smoothly clipped absolute deviation, capped and mini…
ResNets promote smoother interpolations than MLPs, enhancing generalization.
New MMM captures hierarchical marketing effects and sign restrictions.
Researchers infer gene activity in dividing cells, accounting for protein inheritance and division history.
We propose to optimize the activation functions of a deep neural network by adding a corresponding functional regularization to the cost function. We justify the use of a second-order total-variation criterion. This allows us to derive a general representer theorem for deep neural networks that makes a direct connectio…
Computational identification of promoters is notoriously difficult as human genes often have unique promoter sequences that provide regulation of transcription and interaction with transcription initiation complex. While there are many attempts to develop computational promoter identification methods, we have no reliab…
Tool detects tax evasion on social media using multi-modal deep learning.
Gradient boosting predicts promotion efficiency using multiple performance indicators.
Margin trading and short selling boost green tech innovation in China.
Public debates are a common platform for presenting and juxtaposing diverging views on important issues. In this work we propose a methodology for tracking how ideas flow between participants throughout a debate. We use this approach in a case study of Oxford-style debates---a competitive format where the winner is det…
Active learning reduces labeling efforts for QDE models.
A novel framework IMBoost improves outlier detection by leveraging the inlier memorization effect.
Dynamic promotion optimization for e-commerce platforms within financial constraints.
Promoter is a short region of DNA which is responsible for initiating transcription of specific genes. Development of computational tools for automatic identification of promoters is in high demand. According to the difference of functions, promoters can be of different types. Promoters may have both intra and inter cl…
Automated quality control for seismic data reduces human labor and time.
Motivated by recent activity in low-dimensional topology, we provide a new criterion for left-orderability of a group under the assumption that the group is circularly-orderable: A group is left-orderable if and only if is circularly-orderable for all . This implies that eve…
New method selects variables in groups with few nonzeros, improving support recovery.
New neural architectures with multivariate nonlinearities are optimal in function space.
Paper develops a method to learn optimal sparsity-promoting regularizers for linear inverse problems.
Visually predicting the stability of block towers is a popular task in the domain of intuitive physics. While previous work focusses on prediction accuracy, a one-dimensional performance measure, we provide a broader analysis of the learned physical understanding of the final model and how the learning process can be g…
Centralized exchanges influence staking behavior and decentralization in Proof of Stake blockchain ecosystems.
Gamification enhances law enforcement training on terrorism financing.
A new framework converts EEG signals between subjects and tasks.
In classification applications, we often want probabilistic predictions to reflect confidence or uncertainty. Dropout, a commonly used training technique, has recently been linked to Bayesian inference, yielding an efficient way to quantify uncertainty in neural network models. However, as previously demonstrated, conf…
A variation of the Minority Game has been applied to study the timing of promotional actions at retailers in the fast moving consumer goods market. The underlying hypotheses for this work are that price promotions are more effective when fewer than average competitors do a promotion, and that a promotion strategy can b…
Framework improves data-driven ROMs for complex systems using Bayesian operator inference.
The paper challenges the validity of cluster validity measures in unsupervised learning.
Research funding agencies routinely use a proportion of their total revenues to support internal administration and marketing costs. The ratio of administration to total costs, referred to as the administration ratio, is highly variable and within any single fund depends on many factors including the number and average…
In the last decade, the digital age has sharply redefined the way we study human behavior. With the advancement of data storage and sensing technologies, electronic records now encompass a diverse spectrum of human activity, ranging from location data, phone and email communication to Twitter activity and open-source c…
Our goal is to estimate causal interactions in multivariate time series. Using vector autoregressive (VAR) models, these can be defined based on non-vanishing coefficients belonging to respective time-lagged instances. As in most cases a parsimonious causality structure is assumed, a promising approach to causal discov…
New method controls false detections in brain activity localization.
Net-Promoter Score (NPS) is now ubiquitous as an easily-collected market research metric, having displaced many serious market research processes. Unfortunately, this has been its sole success. It possesses few, if any, of the characteristics that might be regarded as highly desirable in a high-level market research me…
The paper tackles batch policy learning in Markov Decision Processes, focusing on average reward maximization.