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.
AgABC improves ABC algorithm by balancing exploration and exploitation.
Computational swarm intelligence consists of multiple artificial simple agents exchanging information while exploring a search space. Despite a rich literature in the field, with works improving old approaches and proposing new ones, the mechanism by which complex behavior emerges in these systems is still not well und…
Deep learning autoencoder detects bee colony anomalies.
Math model helps bees decide between winter survival and raising young.
Tracking large numbers of densely-arranged, interacting objects is challenging due to occlusions and the resulting complexity of possible trajectory combinations, as well as the sparsity of relevant, labeled datasets. Here we describe a novel technique of collective tracking in the model environment of a 2D honeybee hi…
This paper uses decolonial theory to improve AI's ethical development.
This paper argues for decolonizing AI alignment by incorporating open-source Hinduism concepts.
MAYA learns bee foraging decisions with limited memory.
This study shows how social insects and machine learning methods share a common mathematical framework.
Image classification system identifies bumble bee species from images.
Ant colonies and boosting algorithms both reduce bias and variance through adaptive mechanisms.
Ant colony optimization for clustering with improved K-means.
A fuzzy expert system selects stocks for BSE using AI techniques.
This paper studies the interrelation between spot and futures prices in the two major rice markets in prewar Japan from the perspective of market efficiency. Applying a non-Bayesian time-varying model approach to the fundamental equation for spot returns and the futures premium, we detect when efficiency reductions in …
HSBC grew from colonial China through wars, surviving unethical practices.
Multi-layer optical film has been found to afford important applications in optical communication, optical absorbers, optical filters, etc. Different algorithms of multi-layer optical film design has been developed, as simplex method, colony algorithm, genetic algorithm. These algorithms rapidly promote the design and …
This study analyzes how colonial rice trade in prewar Japan affected its rice market, considering several government interventions in the two rice futures exchanges in Tokyo and Osaka. We explore the interventions in the futures markets using two procedures. First, we measure the joint degree of efficiency in the marke…
The waggle dance that honeybees perform is an astonishing way of communicating the location of food source. After over 60 years of its discovery, researchers still use manual labeling by watching hours of dance videos to detect different transitions between dance components thus extracting information regarding the dis…
LAAT detects multiple low-density manifolds in noisy data.
QC-ST and CoCo methods correct batch effects in metabolomics data.
Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait-matching for determining species in…
Model learns evolving network relationships over time.
Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utilizes a hiera…
Hidden Markov Models (HMM) have been used for several years in many time series analysis or pattern recognitions tasks. HMM are often trained by means of the Baum-Welch algorithm which can be seen as a special variant of an expectation maximization (EM) algorithm. Second-order training techniques such as Variational Ba…
New clustering methods for binary data using combinatorial optimization.
In this paper we propose DeepSwarm, a novel neural architecture search (NAS) method based on Swarm Intelligence principles. At its core DeepSwarm uses Ant Colony Optimization (ACO) to generate ant population which uses the pheromone information to collectively search for the best neural architecture. Furthermore, by us…
Metaheuristics improve yield curve estimation for Costa Rica.
AI learns market manipulation through simulation, suggesting regulation.
Artificial intelligence has impacted many aspects of human life. This paper studies the impact of artificial intelligence on economic theory. In particular we study the impact of artificial intelligence on the theory of bounded rationality, efficient market hypothesis and prospect theory.
PAMS is a Python-based platform for simulating artificial markets.
The paper discusses regularization properties of artificial data for deep learning. Artificial datasets allow to train neural networks in the case of a real data shortage. It is demonstrated that the artificial data generation process, described as injecting noise to high-level features, bears several similarities to e…
The paper solves European option pricing under Heston model using artificial boundary method.
In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy with respect to the original dataset. We use generative adversarial network to draw privacy-preserving artificial data samples and derive an empirical method to assess the risk of infor…
Study evaluates saliency maps on artificial data with different backgrounds.
The paper reports the construction of artificial stock market that emerges the similar statistical facts with real data in Indonesian stock market. We use the individual but dominant data, i.e.: PT TELKOM in hourly interval. The artificial stock market shows standard statistical facts, e.g.: volatility clustering, the …
Survey on AI math foundations, focusing on neural networks.
We present chemlambda (or the chemical concrete machine), an artificial chemistry with the following properties: (a) is Turing complete, (b) has a model of decentralized, distributed computing associated to it, (c) works at the level of individual (artificial) molecules, subject of reversible, but otherwise determinist…
A test measures artificial agents' human-like behavior in video games.
Study uses artificial counterfactuals to show lockdowns reduced US case and death counts.
In this work, we propose a generalized likelihood ratio method capable of training the artificial neural networks with some biological brain-like mechanisms,.e.g., (a) learning by the loss value, (b) learning via neurons with discontinuous activation and loss functions. The traditional back propagation method cannot tr…
Interactive steering improves hierarchical clustering for diverse user needs.
Proposes a new jackknife method for time series hyperparameter selection.
Sequential processing biases asset allocation in artificial stock markets.
AI improves credit rating predictions over traditional methods.
The paper monitors artificial neural networks using embeddings and multivariate control charts.
Survey compares methods for generating artificial outliers.
Study examines how taxes affect wealth inequality in economic models.
We extend the empirical results published in article "Empirical Evidence on Arbitrage by Changing the Stock Exchange" by means of machine learning and advanced econometric methodologies based on Smooth Transition Regression models and Artificial Neural Networks.