arXiv research

Chaos in cerebellar cells enhances complexity of neural patterns.

Neural HMM with AGA captures multi-scale dynamics in financial markets.

Network analysis reveals distinct financial relationships among Euro Area banks.

GBOC detects anomalies in time series data using granular-ball vectors.

GACAN combines multi-granularity time series for traffic forecasting.

We introduce a microscopic model for the dynamics of the order book to study how the lack of liquidity influences price fluctuations. We use the average density of the stored orders (granularity $g$) as a proxy for liquidity. This leads to a Price Impact Surface which depends on both volume $ω$ and $g$. The dependence …

The paper proposes a principle for dynamically adjusting the granularity of reinforcement learning abstractions.

We propose and document the evidence for an analogy between the dynamics of granular counter-flows in the presence of bottlenecks or restrictions and financial price formation processes. Using extensive simulations, we find that the counter-flows of simulated pedestrians through a door display many stylized facts obser…

Higher granularity in MoE models boosts expressivity exponentially.

BGNNs model particle-boundary interactions efficiently.

Using the analogy with inelastic granular gasses we introduce a model for wealth exchange in society. The dynamics is governed by a kinetic equation, which allows for self-similar solutions. The scaling function has a power-law tail, the exponent being given by a transcendental equation. In the limit of continuous trad…

Big spatio-temporal datasets, available through both open and administrative data sources, offer significant potential for social science research. The magnitude of the data allows for increased resolution and analysis at individual level. While there are recent advances in forecasting techniques for highly granular te…

Model quantifies cyber-attacks' impact on firms and insurers.

Currently, the diagnosis of Autism Spectrum Disorder (ASD) is dependent upon a subjective, time-consuming evaluation of behavioral tests by an expert clinician. Non-invasive functional MRI (fMRI) characterizes brain connectivity and may be used to inform diagnoses and democratize medicine. However, successful construct…

New method detects anomalies in computing centers' logs.

Machine learning uncovers hidden correlations in granular material behavior.

Existing attention mechanisms are trained to attend to individual items in a collection (the memory) with a predefined, fixed granularity, e.g., a word token or an image grid. We propose area attention: a way to attend to areas in the memory, where each area contains a group of items that are structurally adjacent, e.g…

Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is…

Masanao Aoki developed a new methodology for a basic problem of economics: deducing rigorously the macroeconomic dynamics as emerging from the interactions of many individual agents. This includes deduction of the fractal / intermittent fluctuations of macroeconomic quantities from the granularity of the mezo-economic …

INGB improves oversampling for noisy imbalanced datasets.

In this paper, we develop the continuous time dynamic topic model (cDTM). The cDTM is a dynamic topic model that uses Brownian motion to model the latent topics through a sequential collection of documents, where a "topic" is a pattern of word use that we expect to evolve over the course of the collection. We derive an…

We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data set…

Revisits granular models explaining firm growth rates and sizes.

We introduce a hierarchical architecture for video understanding that exploits the structure of real world actions by capturing targets at different levels of granularity. We design the model such that it first learns simpler coarse-grained tasks, and then moves on to learn more fine-grained targets. The model is train…

Graph Neural Networks model 3D granular flow simulations.

New method predicts bankruptcy by imputing missing data with granular semantics.

Motivated by the practical demands for simplification of data towards being consistent with human thinking and problem solving as well as tolerance of uncertainty, information granules are becoming important entities in data processing at different levels of data abstraction. This paper proposes a method to construct c…

Tree++ graph kernel captures similarities at multiple granularities.

Study uses trajectory embedding to measure place function similarity at fine spatial granularity.

The clustering ensemble technique aims to combine multiple clusterings into a probably better and more robust clustering and has been receiving an increasing attention in recent years. There are mainly two aspects of limitations in the existing clustering ensemble approaches. Firstly, many approaches lack the ability t…

Paper tackles cross-granularity few-shot learning with meta-embedder.

Develops MgCSL for discovering causal structures in high-dimensional data.

New method learns fusion rules from few images using granular ball priors.

Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock the clustering measure captures the degree of trading overlap among any two investors in that stock. We investigate the ef…

This paper proposes a web-based visual graph analytics platform for interactive graph mining, visualization, and real-time exploration of networks. GraphVis is fast, intuitive, and flexible, combining interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoni…

Reducing ICD-10 code granularity improves cost model accuracy and stability.

The study analyzes the performance of a nonparametric estimator for dynamical systems.

Reduces high granularity and dimensionality in hierarchical categorical variables.

A scalable method for econometric inference using machine learning for big data.

Understanding how housing values evolve over time is important to policy makers, consumers and real estate professionals. Existing methods for constructing housing indices are computed at a coarse spatial granularity, such as metropolitan regions, which can mask or distort price dynamics apparent in local markets, such…

OMBA learns product and user representations for better online market basket analysis.

Explainable recommendation is far from being well solved partly due to three challenges. The first is the personalization of preference learning, which requires that different items/users have different contributions to the learning of user preference or item quality. The second one is dynamic explanation, which is cru…

Employs granular data to create a multilayer network for euro area banks, revealing distinct risk patterns.

Networks evolve continuously over time with the addition, deletion, and changing of links and nodes. Such temporal networks (or edge streams) consist of a sequence of timestamped edges and are seemingly ubiquitous. Despite the importance of accurately modeling the temporal information, most embedding methods ignore it …

Nested learning improves model performance on multi-granular tasks.

Money analyzed as a multidimensional tensor for better economic policy.

Fuzzy eIX method evolves classifiers for online data streams.

A method uses Wasserstein clustering to simplify financial data analysis.