Data & Analytics

DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers (arXiv: 2608.20258)

**arXiv ID:** 2608.18555v1 **Authors:** Deepak Kanneganti, Sajib Mistry, Sheik Mohammad Mostakim Fattah, Erik Elmroth, Aneesh Krishna, Monowar Bhuyan **URL:** http://arxiv.org/abs/2608.18555v1 **Utility Score:** 1.00

**arXiv ID:** 2608.06001v1 **Authors:** Muhammad Riaz Hasib Hossain, Rafiqul Islam, Shawn R. McGrath, Md Zahidul Islam, David W. Lamb **URL:** http://arxiv.org/abs/2608.06001v1 **Utility Score:** 1.00

Hybrid Machine Learning Framework for Herd-Level Cattle Growth Pattern and Weight Gain Forecasting in Grazing-Based Production Systems (arXiv: 2608.06001)

MS-MLB: An Open Machine Learning Benchmark for Blood-Based MS Classification (arXiv: 2608.05196)

This work presents a novel approach for adapting neural network architecture along the depth based on a posteriori error estimation. By formulating neural network training as a continuous-time optimal. Based on arXiv:2607.07637.

**arXiv ID:** 2001.10178 **Authors:** Benjamin Patrick Evans, Bing Xue, Mengjie Zhang **Published:** 2020-01-28T05:44:53Z **Abstract:** A common claim of evolutionary computation methods is that they can achieve good results without the need for human intervention. However, one criticism of this is that there are still hyperparameters which must be tuned in order to achieve good performance. In this work, we propose a near "parameter-free" genetic programming approach, which adapts the hyperp...

**arXiv ID:** 2010.08158 **Authors:** Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb, Pablo Montero-Manso **Published:** 2020-10-16T04:29:09Z **Abstract:** Many businesses and industries require accurate forecasts for weekly time series nowadays. However, the forecasting literature does not currently provide easy-to-use, automatic, reproducible and accurate approaches dedicated to this task. We propose a forecasting method in this domain to fill this gap, leveraging state-of-the-art...

**arXiv ID:** 2302.13019 **Authors:** Yanqi Chen, Zhengyu Ma, Wei Fang, Xiawu Zheng, Zhaofei Yu, Yonghong Tian **Published:** 2023-02-25T08:16:14Z **Abstract:** Soft threshold pruning is among the cutting-edge pruning methods with state-of-the-art performance. However, previous methods either perform aimless searching on the threshold scheduler or simply set the threshold trainable, lacking theoretical explanation from a unified perspective. In this work, we reformulate soft threshold pruning...

**arXiv ID:** 2306.01991 **Authors:** Andrei Velichko, Petr Boriskov, Maksim Belyaev, Vadim Putrolaynen **Published:** 2023-06-03T03:36:47Z **Abstract:** The study presents a bio-inspired chaos sensor model based on the perceptron neural network for the estimation of entropy of spike train in neurodynamic systems. After training, the sensor on perceptron, having 50 neurons in the hidden layer and 1 neuron at the output, approximates the fuzzy entropy of a short time series with high accuracy,...

Functional ensembles as units of computation in deep spiking networks. First-order functionally-connected (1FC) groups based on pairwise correlations, aggregate cofiring predicts downstream responses, ReLU-like input-output relationship with ensemble-size scaling, rare high-coordination events encode information. Activation: functional ensemble, SNN computation, functional connectivity, 1FC group, ensemble cofiring, deep spiking network analysis.

Functional Ensembles as Units of Computation in Deep Spiking Networks. 1FC (first-order functionally-connected) ensembles framework for analyzing information encoding in SNNs through rare coordinated firing events.

Statistical benchmarking methodology for EEG motor-imagery BCI decoders using Friedman-Nemenyi tests. Proves no single decoding pipeline dominates across subjects — personalized model selection adds ~7% accuracy over best fixed choice. Use when evaluating BCI decoders, comparing multi-classifier performance, or designing subject-aware BCI systems.

Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity. Use when: building neurological biomarker discovery pipelines, applying foundation models to fMRI/EEG data, analyzing dynamic functional connectivity for disease detection, developing robust cross-subject biomarkers. Triggers: brain biomarker foundation model, dynamic functional connectivity biomarker, neurological disorder detection, robust biomarker discovery, fMRI foundation ...

Novel approach to dictionary learning on fMRI data that explicitly accounts for individual brain geometry variability using optimal transport (Fused Gromov-Wasserstein distance) with amortized optimization for computational efficiency.

Tail-certified quantum metrology for quenched sensors — Fisher-zero integrability transition, no-go theorem on averaged Fisher data, universal design laws (safe windows, nondegenerate portfolios, Fisher reserves, Fisher-cut criteria). Activation: quantum metrology, Fisher information, quenched environments, tail certification, NV centers, superconducting qubits, Fisher glass, QFI certification

First-in-human quantum entanglement imaging methodology using J-PET plastic scintillator scanner. Measures polarization correlations of annihilation photons from positron-electron annihilation in vivo for clinical diagnostics. Use when: quantum PET imaging, entanglement-based medical imaging, J-PET scanner design, polarization-correlated tomography, quantum entanglement degree as biomarker, 68Ga radiopharmaceutical quantum imaging.

Finite-temperature quantum Krylov method for computing thermal properties of quantum many-body systems from real-time overlaps. Use when analyzing quantum many-body systems at finite temperatures, computing thermal observables, or avoiding thermal state preparation in quantum simulations.

Comprehensive stock technical analysis system for fetching data, calculating indicators (KDJ, MACD, RSI, BOLL), generating visualizations and reports. Use when user asks about stock analysis, 股票分析, technical analysis, 技术分析, k-line, or stock scoring.

Regime-aware Continual Adaptive Portfolio management (ReCAP) — integrating continual learning into portfolio management via adaptive regime detection, policy libraries, and regime-gated policy combination. Accepted by KDD 2026. Activation: regime detection, portfolio management, continual learning, adaptive trading, ReCAP, market regime, policy library, regime shift.

Critical analysis methodology for quantum data encoding — identifies how naive amplitude encoding (psi=sqrt(P)) abelianizes the Hilbert space and fails to achieve genuine quantum advantage in QML/finance. Advocates for Dynamical Hamiltonian Encoding (DHE) where data generates non-commutative evolution.

Robust volatility updates for Hierarchical Gaussian Filtering (HGF). Improves stability and convergence of uncertainty estimation in perceptual inference. Activation: hierarchical gaussian filter, volatility update, perceptual inference, active inference, uncertainty estimation.

**arXiv ID:** 1805.11232 **Authors:** Gonçalo Abreu, Rui Neves, Nuno Horta **Published:** 2018-05-29T03:36:34Z **Abstract:** Technical analysis is used to discover investment opportunities. To test this hypothesis we propose an hybrid system using machine learning techniques together with genetic algorithms. Using technical analysis there are more ways to represent a currency exchange time series than the ones it is possible to test computationally, i.e., it is unfeasible to search the whole ...

Fermi-Dirac quantization methodology for neural networks — reinterprets classical neurons as parameterized Hamiltonians and replaces variables with quantum operators. BQP-complete for certain decision problems. Use when: designing quantum neural architectures, quantizing activation functions (ReLU, GeLU, sigmoid), building hybrid quantum-classical neural algorithms, analyzing quantum advantage in neural computation, or studying the quantum-classical boundary in machine learning.