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 identifies NFT whales driving the market with consistent high returns.
Automatically detecting sound units of humpback whales in complex time-varying background noises is a current challenge for scientists. In this paper, we explore the applicability of Convolution Neural Network (CNN) method for this task. In the evaluation stage, we present 6 bi-class classification experimentations of …
Paper forecasts extreme Bitcoin volatility spikes using whale transactions and CryptoQuant data.
Modeling vessel speed to balance efficiency and environmental risks in Arctic shipping.
A Deep Zero-Inflated Model for Detecting North Atlantic Right Whale Presence
Study shows how high-budget agents can manipulate prediction markets.
Bitcoin reacts positively to USDT minting but not burning, showing state-dependence.
In 2012, JPMorgan accumulated a USD~6.2 billion loss on a credit derivatives portfolio, the so-called `London Whale', partly as a consequence of de-correlations of non-perfectly correlated positions that were supposed to hedge each other. Motivated by this case, we devise a factor model for correlations that allows for…
Research into automated systems for detecting and classifying marine mammals in acoustic recordings is expanding internationally due to the necessity to analyze large collections of data for conservation purposes. In this work, we present a Convolutional Neural Network that is capable of classifying the vocalizations o…
Method combines latent space exploration and causal inference to interpret unknown data.
Nowadays, video game developers record every virtual action performed by their players. As each player can remain in the game for years, this results in an exceptionally rich dataset that can be used to understand and predict player behavior. In particular, this information may serve to identify the most valuable playe…
Wind power as a renewable source of energy, has numerous economic, environmental and social benefits. In order to enhance and control renewable wind power, it is vital to utilize models that predict wind speed with high accuracy. Due to neglecting of requirement and significance of data preprocessing and disregarding t…
This paper presents a spermwhale' localization architecture using jointly a bag-of-features (BoF) approach and machine learning framework. BoF methods are known, especially in computer vision, to produce from a collection of local features a global representation invariant to principal signal transformations. Our idea …
Paper introduces SCI to distinguish market signals from coordination.
Robust X-Learner improves HTE estimation in imbalanced and heavy-tailed data.
Bayesian X-Learner calibrates uncertainty and robustness for CATE estimation under heavy-tailed data.
Dictionary based classifiers are a family of algorithms for time series classification (TSC), that focus on capturing the frequency of pattern occurrences in a time series. The ensemble based Bag of Symbolic Fourier Approximation Symbols (BOSS) was found to be a top performing TSC algorithm in a recent evaluation, as w…
LLM sandbox and persona dynamics create unethical reality gaps that shift risk to users.
This study examines non-retail trading on Polymarket, revealing unique behavior patterns and structural limitations.