The real story behind China’s technology triumph
China's tech dominance stems more from globalization than policy, suggesting economic shifts rather than technological breakthroughs drive this change and its implications for the global economy.
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Math & Economics
Mathematics, statistics, and economic research.
Latest
Cost-benefit analysis of deceased donor organ transplantation: an economic evaluation
A study found that organ transplants, despite high upfront costs, can save money in the long run by reducing the need for other expensive medical interventions for patients.
preprint
Erasure Thresholds for Hyperbolic and Semi-Hyperbolic Surface Codes
Researchers developed more efficient quantum error correction codes that can tolerate higher rates of data loss, a key step toward reliable quantum computing.
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Inflation with the Gauss-Bonnet term in the Palatini formulation
A new study suggests that including a specific mathematical term in theories of the early universe could change our understanding of inflation, though the full implications are unclear from the limited information provided.
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AdaMuS: Adaptive Multi-view Sparsity Learning for Dimensionally Unbalanced Data
Researchers developed a new algorithm to combine data from disparate sources, which could improve machine learning on complex real-world datasets.
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A Dual Certificate Approach to Sparsity in Infinite-Width Shallow Neural Networks
Researchers developed a new machine learning technique that can efficiently train shallow neural networks to be sparse, which could lead to faster and more energy-efficient AI models.
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New research explores the paradox of firms' unique technologies
Firms with unique tech see both upsides and downsides - it can be an advantage, but also isolating and costly. This paradox highlights the trade-offs companies face in developing specialized tech.
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Beyond Polarity: Multi-Dimensional LLM Sentiment Signals for WTI Crude Oil Futures Return Prediction
Researchers developed a new machine learning technique that uses multi-dimensional sentiment analysis on news articles to better predict fluctuations in oil futures prices, which could help investors and consumers plan for volatile energy markets.
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Multivariate GARCH and portfolio variance prediction: A forecast reconciliation perspective
Researchers found combining different forecasting models can improve predictions of portfolio risk, which helps investors better manage financial risk.
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A Controlled Comparison of Deep Learning Architectures for Multi-Horizon Financial Forecasting: Evidence from 918 Experiments
Researchers tested different deep learning models for predicting stock prices over multiple time horizons. This could help investors and traders make better financial decisions.
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Moments in the CFT Landscape
Researchers developed a new mathematical technique to study the broad landscape of possible fundamental physics theories. This could help uncover deep insights about the nature of reality.
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Hardness of High-Dimensional Linear Classification
Researchers found that classifying high-dimensional data with linear models is fundamentally difficult, setting limits on how accurate these models can be for real-world problems like image recognition.
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Limited jobs block social mobility opportunities for young people in coastal and rural areas, study shows
Young people in coastal and rural areas have fewer job opportunities, limiting their chances to get better-paying, skilled jobs and improve their social status.
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Exponents and front fluctuations in the quenched Kardar-Parisi-Zhang universality class of one and two dimensional interfaces
Researchers studied how random surfaces like crumpled paper or flowing water can behave in certain mathematical models. This could lead to better understanding of complex phenomena in nature and physics.
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Stronger core results with multidimensional prices
Researchers developed a new algorithm that can find stable matchings in economic scenarios without money, solving a longstanding challenge. This work could aid in the design of fairer allocation systems for limited resources.
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Toward Better Temporal Structures for Geopolitical Events Forecasting
Researchers have developed new ways to forecast geopolitical events using temporal knowledge graphs and large language models. This could help predict and understand global political shifts that impact people's daily lives.
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The effects of bar strength and kinematics on galaxy evolution II: The global and local impacts of slow-strong bars
Slow and strong galactic bars can significantly influence galaxy evolution, either accelerating or contributing to processes that cause galaxies to stop forming new stars.
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Musk's Twitter takeover highlights danger of owner-dominated social media platforms
Elon Musk's control over Twitter highlights the risk of social media platforms being dominated by a single owner who can push their own agenda, threatening free speech and open discourse.
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The Spillover Effects of Peer AI Rinsing on Corporate Green Innovation
Enterprises are increasingly using AI as a marketing tactic rather than for real innovation, which could undermine genuine efforts to develop green technologies.
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In Focus in this category
Inference in Regression Discontinuity Designs with Clustered Data
Researchers have a new way to analyze data from clustered studies, which can help draw better conclusions about things like policy effects on communities. This could lead to more accurate and impactful findings from real-world studies.
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AdaMuS: Adaptive Multi-view Sparsity Learning for Dimensionally Unbalanced Data
Researchers developed a new algorithm to combine data from disparate sources, which could improve machine learning on complex real-world datasets.
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Minimum-Action Learning: Energy-Constrained Symbolic Model Selection for Physical Law Identification from Noisy Data
Researchers developed a new AI method to automatically identify physical laws from noisy data, which could help scientists better understand complex natural phenomena.
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Uncertainty Relation for Entropy and Temperature of Gibbs States
Researchers found a fundamental relationship between temperature, entropy, and quantum systems. This advances our understanding of quantum physics and how it governs the behavior of microscopic systems.
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Can LLM generate interesting mathematical research problems?
Large language models have shown they can generate novel mathematical research problems, suggesting they may assist human mathematicians in discovering new frontiers.
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Toward Better Temporal Structures for Geopolitical Events Forecasting
Researchers have developed new ways to forecast geopolitical events using temporal knowledge graphs and large language models. This could help predict and understand global political shifts that impact people's daily lives.
preprint
Matrix Product States for Modulated Symmetries: SPT, LSM, and Beyond
Researchers developed a new mathematical framework for understanding quantum phases of matter, with potential applications in quantum computing. This could lead to improved understanding and control of exotic quantum systems.
preprint
Cost-benefit analysis of deceased donor organ transplantation: an economic evaluation
A study found that organ transplants, despite high upfront costs, can save money in the long run by reducing the need for other expensive medical interventions for patients.
preprint
Hamiltonian Simulation and Linear Combination of Unitary Decomposition of Structured Matrices
Researchers discovered a new technique to efficiently simulate quantum systems using standard computers. This could lead to breakthroughs in fields like chemistry and materials science by allowing complex quantum problems to be solved on regular computers.
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Erasure Thresholds for Hyperbolic and Semi-Hyperbolic Surface Codes
Researchers developed more efficient quantum error correction codes that can tolerate higher rates of data loss, a key step toward reliable quantum computing.
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Fine-Grained Uncertainty Quantification via Collisions
A new way to measure uncertainty in AI models by tracking when the same input is classified differently. This could help make AI systems more reliable and predictable in real-world applications.
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Multitask Learning with Stochastic Interpolants
Researchers have developed a new mathematical framework for modeling how complex systems evolve over time, which could improve machine learning models and better simulate real-world processes like fluid dynamics.
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Domain-Independent Dynamic Programming with Constraint Propagation
Researchers developed a new algorithm that can efficiently solve complex combinatorial problems across many different domains, potentially enabling better planning and optimization in a variety of real-world applications.
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MALLES: A Multi-agent LLMs-based Economic Sandbox with Consumer Preference Alignment
Researchers developed a simulated economic system using AI agents to better understand how consumer preferences shape real-world markets.
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Cost-benefit analysis of deceased donor organ transplantation: an economic evaluation
Organ transplants can greatly improve patients' lives but are also costly. A new analysis aims to weigh these benefits and costs to better understand the economic value of transplantation programs.
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Rigorous Error Certification for Neural PDE Solvers: From Empirical Residuals to Solution Guarantees
Researchers developed a new way to certify the accuracy of neural network-based PDE solvers, helping improve the reliability of these models in applications like fluid dynamics and weather forecasting.
preprint
Moments in the CFT Landscape
Researchers developed a new mathematical technique to study the broad landscape of possible fundamental physics theories. This could help uncover deep insights about the nature of reality.
preprint
The effects of bar strength and kinematics on galaxy evolution II: The global and local impacts of slow-strong bars
Slow and strong galactic bars can significantly influence galaxy evolution, either accelerating or contributing to processes that cause galaxies to stop forming new stars.
preprint
A Continuous-Variable Quantum Fourier Layer: Applications to Filtering and PDE Solving
Researchers developed a new quantum computing method that can help solve complex math problems and filter data more efficiently. This could lead to faster and more accurate modeling of real-world systems like weather forecasting and traffic flows.
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On the Equivalence of Random Network Distillation, Deep Ensembles, and Bayesian Inference
New algorithm advances could make AI systems more transparent about their uncertainty, potentially improving the safety and reliability of AI in real-world applications.
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