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.
Machine learning uncovers hidden correlations in granular material behavior.
The paper develops a method to discover causal relations and predict material laws with uncertainty quantification.
New modularity function improves clustering of spatially embedded networks.
GBOC detects anomalies in time series data using granular-ball vectors.
Higher granularity in MoE models boosts expressivity exponentially.
GACAN combines multi-granularity time series for traffic forecasting.
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…
Chaos in cerebellar cells enhances complexity of neural patterns.
New method detects anomalies in computing centers' logs.
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 ) as a proxy for liquidity. This leads to a Price Impact Surface which depends on both volume and . The dependence …
INGB improves oversampling for noisy imbalanced datasets.
Unified framework for non-uniform materials evolving over time.
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…
Paper proposes a new method for designing materials using deep learning.
Network analysis reveals distinct financial relationships among Euro Area banks.
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.
A groupoid called material groupoid is naturally associated to any simple body . The material distribution is introduced due to the (possible) lack of differentiability of the material groupoid. Thus, the inclusion of these new objects in the theory of material bodies opens th…
Dual energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition operates directly on CT images using linear matrix inversion, but the decomposed material images can be severely degraded by noise and artifact…
New method learns fusion rules from few images using granular ball priors.
Neural HMM with AGA captures multi-scale dynamics in financial markets.
Graph-structured data arise ubiquitously in many application domains. A fundamental problem is to quantify their similarities. Graph kernels are often used for this purpose, which decompose graphs into substructures and compare these substructures. However, most of the existing graph kernels do not have the property of…
A Lie groupoid, called \textit{material Lie groupoid}, is associated in a natural way to any elastic material. The corresponding Lie algebroid, called \textit{material algebroid}, is used to characterize the uniformity and the homogeneity properties of the material. The relation to previous results in terms of stru…
A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials datab…
Reducing ICD-10 code granularity improves cost model accuracy and stability.
Integrated Computational Materials Engineering (ICME) aims to accelerate optimal design of complex material systems by integrating material science and design automation. For tractable ICME, it is required that (1) a structural feature space be identified to allow reconstruction of new designs, and (2) the reconstructi…
Reduces high granularity and dimensionality in hierarchical categorical variables.
Recognizing an object's material can inform a robot on the object's fragility or appropriate use. To estimate an object's material during manipulation, many prior works have explored the use of haptic sensing. In this paper, we explore a technique for robots to estimate the materials of objects using spectroscopy. We d…
We present a learning-based system for rapid mass-scale material synthesis that is useful for novice and expert users alike. The user preferences are learned via Gaussian Process Regression and can be easily sampled for new recommendations. Typically, each recommendation takes 40-60 seconds to render with global illumi…
New algorithm improves materials discovery using max K-Armed Bandit.
The paper introduces new uniformity and homogeneity concepts for Cosserat media.
FlowLLM uses LLMs and flow matching to efficiently generate novel materials.
Lie groupoids and their associated algebroids arise naturally in the study of the constitutive properties of continuous media. Thus, Continuum Mechanics and Differential Geometry illuminate each other in a mutual entanglement of theory and applications. Given any material property, such as the elastic energy or an inde…
Computational materials screening studies require fast calculation of the properties of thousands of materials. The calculations are often performed with Density Functional Theory (DFT), but the necessary computer time sets limitations for the investigated material space. Therefore, the development of machine learning …
4-dim intrinsic (material) Riemannian metric of the material 4-D space-time continuum is utilized as the characteristic of the aging processes developing in the material. Manifested through variation of basic material characteristics such as density, moduli of elasticity, yield stress, strength, and toughness.,…
In this study, we establish a basis for selecting similarity measures when applying machine learning techniques to solve materials science problems. This selection is considered with an emphasis on the distinctiveness between materials that reflect their nature well. We perform a case study with a dataset of rare-earth…
Method reveals dissimilarity in alloys' Curie temperatures.
We propose a machine learning approach to address a key challenge in materials science: predicting how fractures propagate in brittle materials under stress, and how these materials ultimately fail. Our methods use deep learning and train on simulation data from high-fidelity models, emulating the results of these mode…
We enhance autonomous materials research with problem-aware models.
Study material evolution using groupoids to track intrinsic properties.
A new method optimizes material discovery by balancing exploration and exploitation.