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arXiv research

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.

168,742 papers · 148 categories

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count% of corpus
0111 · Feb 201319922001200920172026
4 results for “Repression”
JFR-rg: A New Macroeconomic Framework for High-Debt, Low-Growth Economies under Financial Repressionecon.EM

JFR-rg model explains Japan's stable debt despite high interest rates and low growth.

problem Understanding Japan's stable government debt despite high interest rates and low growth.
method Formalizes financial repression channels through JFR-rg model, incorporating financial repression bias and exchange-rate channel.
result Identifies Normalization Trap and Captive Financial System Parameter, showing debt dynamics under financial repression.
JapanFinancial RepressionDebt DynamicsStabilityEconomic Framework
2026-03-30abs ↗pdf ↗
A Mixed-Methods Analysis of Repression and Mobilization in Bangladesh's July Revolution Using Machine Learning and Statistical Modelingstat.AP

The July Revolution in Bangladesh was fueled by state violence, which paradoxically strengthened the movement.

problem Understanding how state repression can paradoxically lead to increased mobilization during civil resistance.
method Mixed-methods approach combining qualitative narrative and quantitative analysis using machine learning and statistical modeling.
result The July Revolution was driven by a contingent, non-linear backfire effect triggered by specific catalytic moral shocks and accelerated by the viral reaction to state brutality.
BangladeshJuly RevolutionCivil ResistanceRepressionMobilization
2025-10-05abs ↗pdf ↗
Models and Selection Criteria for Regression and Classificationcs.LG

When performing regression or classification, we are interested in the conditional probability distribution for an outcome or class variable Y given a set of explanatoryor input variables X. We consider Bayesian models for this task. In particular, we examine a special class of models, which we call Bayesian regression…

2013-02-06abs ↗pdf ↗
Ensemble emotion recognizing with multiple modal physiological signalscs.LG

Model improves emotion recognition using multiple physiological signals.

problem Single physiological signal is insufficient for accurate emotion recognition.
method Fused multiple modal physiological signals (EEG, EMG, EOG) for emotion classification.
result Best classification accuracy of 94.42% on arousal and 94.02% on valence in two-class tasks.
emotion recognitionphysiological signalsensemble learningEEGEMG
2020-01-01abs ↗pdf ↗