Unified framework connects physical laws and machine learning.
problem Combining physical laws and machine learning for scientific applications.
method Universal Differential Equations (UDEs) as a unifying framework.
result Wide variety of applications can be efficiently handled through UDE formalism.
A new method combines SciML and UQ with physical constraints.
problem Uncertainty quantification in scientific machine learning tasks.
method Physics-constrained polynomial chaos expansion.
result Effective uncertainty quantification and SciML integration.
New approach connects UQ in SciML to viscous HJ PDEs for efficient uncertainty quantification.
problem Challenges in interpretability and expensive training procedures in UQ for SciML.
method Established connection between Bayesian inference and viscous HJ PDEs, developed Riccati-based methodology.
result Efficiently updates model predictions without retraining or data access, suitable for real-time inferences.
New framework trains large SciML models solving PDEs in reasonable time.
problem Training large SciML models solving PDEs is challenging and time-consuming.
method Data parallel distributed deep learning framework with optimized methods.
result Neural PDE solvers can be viably trained for practical applications.
This thesis advances algorithms and software for QMC, GP, and sciML.
problem Efficient high-dimensional integration, interpolation, and PDE modeling.
method Developed new algorithms and software for QMC, GP, and sciML.
result Efficient and accurate methods for high-dimensional problems.
Thanks to the recent availability of comprehensive and detailed online databases of startup companies, it has become possible to more directly investigate startup ecosystems i.e. startup populations in specific regions. In this paper, we analyze the emergence of 20+ such ecosystems in Europe and the USA, with a specifi…
MDNs offer a data-efficient alternative to diffusion and flow models for multimodal scientific learning.
problem Capturing multimodal conditional uncertainty in scientific inverse problems.
method Mixture Density Networks (MDNs) as explicit parametric density estimators.
result MDNs achieve superior generalization, interpretability, and sample efficiency in scientific tasks.
LightAutoML automates ML for a large financial services company.
problem Building high-quality ML models for a complex financial ecosystem.
method Developed an AutoML system tailored to a large European financial services company's unique requirements.
result LightAutoML outperformed experienced data scientists and other open-source solutions.
TempBalance boosts model performance with low data.
problem Low-data training and fine-tuning in model alignment.
method Inspired by HT-SR theory, TempBalance balances training quality across layers.
result TempBalance improves model performance as data decreases.
Enhances neural operators with physics knowledge for more accurate simulations.
problem Improving accuracy and generalization of neural operators for physical systems.
method Jointly learns from original PDEs and simplified forms, incorporating fundamental physics.
result Significant improvement in nRMSE across various PDE problems.
Defines data science as a natural ecosystem with challenges and missions.
problem Challenges and missions in data science due to 5D complexities and data life cycle phases.
method Systemic and data-centric view of data science as a fusion of data universe and its challenges, formalizing a general-purpose architecture.
result Essential data science as a natural ecosystem integrating specific disciplines and high-impact applications.
FLUXtrapolation benchmarks machine learning for extrapolating ecosystem fluxes under distribution shifts.
problem Machine learning challenges in extrapolating ecosystem fluxes under distribution shifts.
method Defined temporal, spatial, and temperature-based extrapolation scenarios; evaluated performance across domains, temporal aggregations, and tail errors.
result Baselines perform similarly under median hourly RMSE but differ under tail-focused and multi-scale evaluations.
Polynomial Chaos Expansion improves operator learning for PDEs.
problem Approximating mappings between infinite-dimensional functional spaces.
method Polynomial Chaos Expansion (PCE) for operator learning.
result PCE achieves strong performance in operator learning and uncertainty quantification.
Debt-financed collateral in DeFi increases stability risks.
problem Financial stability risks in DeFi ecosystems due to debt-financed collateral.
method Categorization and classification algorithm to measure debt-financed collateral.
result Wide-spread use of stablecoins as debt-financed collateral increases financial stability risks.
Enhances uncertainty modeling in random PDEs using PINNs and generative models.
problem Uncertainty in complex systems modeled by random PDEs.
method Combines Physics-Informed Neural Networks (PINNs) with generative modeling techniques.
result Systematic control of uncertainty with maintained predictive accuracy.
Over the last 23 years, the U.S. Securities and Exchange Commission has required over 34,000 companies to file over 165,000 annual reports. These reports, the so-called "Form 10-Ks," contain a characterization of a company's financial performance and its risks, including the regulatory environment in which a company op…
Stablecoins are reshaping global monetary systems, offering hybrid structures with public and private monies.
problem The evolution of stablecoins from crypto innovation to a global monetary component.
method Econometric analysis and hybrid system design modeling.
result Stablecoins maintain strong peg stability, and a hybrid system design ensures financial resilience.
SFC aims to protect the Amazon with a digital currency and smart contracts.
problem Protecting the Amazon's ecosystem and ensuring resource credibility.
method Blockchain, digital contracts, smart contracts with oracles.
result Ensures credibility and security for financial resources invested in Amazon projects.
Newsroom in online ecosystem is difficult to untangle. With prevalence of social media, interactions between journalists and individuals become visible, but lack of understanding to inner processing of information feedback loop in public sphere leave most journalists baffled. Can we provide an organized view to charact…
The paper analyzes security issues in blockchain ecosystems with multiple SSPs and proposes two models for better stake management.
problem Security issues in blockchain ecosystems with multiple SSPs and stake fragmentation.
method Formalized the Multiple SSP Problem and analyzed two architectures: Model M and Model S through convex optimization and game-theoretic lens. result Model S achieves tighter security guarantees through single validator sets and aggregated slashing logic. The green area of economy is the key of healthy living. It is necessary to convene economic and ecologic framework to establish a market attentive to drastic reduction of emissions damaging our climate and landscapes in rural areas, to the protection of biological diversity of the planet, to stop producing nuclear wast…
Study quantifies systemic risk in DeFi using network analysis.
problem Systemic risk in decentralized finance (DeFi) ecosystem.
method Network-based fragility analysis of TVL dynamics.
result Developed CFI and RCS to quantify structural fragility and risk contribution.
FST.ai 2.0 improves Taekwondo decision-making with AI, reducing review time and increasing trust.
problem Fair, transparent, and explainable decision-making in Taekwondo.
method Pose-based action recognition, epistemic uncertainty modeling, interactive dashboards.
result 85% reduction in decision review time, 93% referee trust in AI-assisted decisions.
The AIBC is an Artificial Intelligence and blockchain technology based large-scale decentralized ecosystem that allows system-wide low-cost sharing of computing and storage resources. The AIBC consists of four layers: a fundamental layer, a resource layer, an application layer, and an ecosystem layer. The AIBC implemen…
We introduce Microsoft Machine Learning for Apache Spark (MMLSpark), an ecosystem of enhancements that expand the Apache Spark distributed computing library to tackle problems in Deep Learning, Micro-Service Orchestration, Gradient Boosting, Model Interpretability, and other areas of modern computation. Furthermore, we…
Study examines European banks' digital transformation strategies.
problem Lack of a common framework for open banking innovation in banking sector.
method Qualitative analysis of partnerships and API development.
result European banks are diversifying and boosting customer relationship management.
Study examines machine learning competitions' impact on AI development.
problem Fostering innovation and skill development in AI.
method Analysis of major competition platforms, workflows, and participant demographics.
result MLCs promote collaboration, reproducibility, and continuous innovation in AI.
Enhances VC startup success predictions using graph augmented time series models.
problem Challenges in predicting startup success due to limited financial data and subjective forecasts.
method Integrates inter-company relationships into time series analysis using GraphRAG.
result Significantly outperforms previous models in startup success predictions.
The reproducibility of scientific research has become a point of critical concern. We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community. We discuss the range of open data sharing re…
Study maps research streams in biodiversity finance, identifies key areas.
problem Biodiversity loss and need for finance to reverse trends.
method Quantitative bibliometric analysis of 189,456 references.
result Identifies eight primary research streams in biodiversity finance.
The blockchain technology promises to transform finance, money and even governments. However, analyses of blockchain applicability and robustness typically focus on isolated systems whose actors contribute mainly by running the consensus algorithm. Here, we highlight the importance of considering trustless platforms wi…
PINNs can learn trivial solutions; new approach improves performance.
problem PINNs fail to learn non-trivial solutions in data-scarce settings.
method Developed an alternative sampling approach and new penalty term.
result PINNs can be improved to learn non-trivial solutions with up to 80% fewer collocation points.
Paper introduces a method to assess liquidity risk in meme tokens using entity-linked address analysis.
problem High market volatility and vulnerability to manipulation in meme tokens.
method Multi-dimensional approach integrating fund flow analysis, behavioral similarity, and anomalous transaction detection.
result Significant disparities between apparent and actual liquidity in meme token markets.
This study compares CeFi and DeFi, finding some DeFi assets are not truly decentralized.
problem Understanding the differences and boundaries between CeFi and DeFi.
method Systematic analysis covering legal, economic, security, privacy, and market manipulation aspects.
result Certain DeFi assets may not be truly decentralized and pose security risks.
Kernelmethods library simplifies kernel-based ML in Python.
problem Lack of a well-defined Python library for kernel-based ML.
method Develops a Python library that supports various kernel types and customizations.
result Enables efficient, modular, and interoperable kernel-based operations.
The paper analyzes risks and revenue dynamics of a liquid restaking protocol in decentralized finance.
problem Interconnected risks and revenue dynamics of a liquid restaking protocol in decentralized finance.
method Empirical analysis using OLS regression, Granger-causality, and random forest feature importance tests.
result Revenue is primarily driven by value locked in the ecosystem, yield of liquid restaking token, and multi-blockchain expansion.
Study creates open-access wildfire dataset for Russia.
problem Data scarcity for comprehensive Eurasian wildfire research.
method Machine learning for exploratory data analysis and predictive modeling.
result Identified key environmental factors influencing fire behavior.
Centralized exchanges influence staking behavior and decentralization in Proof of Stake blockchain ecosystems.
problem How do centralized exchanges affect staking behavior and decentralization in Proof of Stake blockchain ecosystems?
method Formulate a continuous-time mean field model of miners as validators and traders in a centralized market.
result Centralized trading activities enhance staking participation and promote decentralization through market incentives.
Coding collaborations link crypto returns, revealing systemic transparency.
problem Cryptocurrencies' market behavior overlooked due to isolated code approach.
method Analyzed 4% of developers contributing to multiple cryptocurrencies.
result First coding event linking two cryptocurrencies synchronizes their returns.
In cloud computing environments with many virtual machines, containers, and other systems, an epidemic of malware can be highly threatening to business processes. In this vision paper, we introduce a hierarchical approach to performing malware detection and analysis using several recent advances in machine learning on …
Blockchain helps secure payments between AI agents.
problem Ensuring secure payments between untrusted AI agents.
method Systematized four-stage lifecycle for A2A payments on blockchain.
result Challenges remain in weak intent binding, misuse, and limited accountability.
Using the formalism of Lyapunov potential function it is shown that the stability principles for biomass in the ecosystem and for employment in economics are mathematically similar. The ecosystem is found to have a stable and an unstable stationary state with high (forest) and low (grasslands) biomass, respectively. In…
We present a model of an economy inspired by individual based model approaches in evolutionary ecology. We demonstrate that evolutionary dynamics in a space of companies interconnected through a correlated interaction matrix produces time dependencies of the total size of the economy total number of companies, companie…
mlr3torch simplifies deep learning in R for tabular and image data.
problem Simplifying deep learning workflows in R for various data types.
method Built on torch, integrates mlr3 ecosystem, supports custom architectures.
result Extensible framework for tabular and image data, including preprocessing and model definition.
Paper tackles DOCTR-L with SciPhy RL, solving neural PDEs from data.
problem High-dimensional optimal control with stochastic policies.
method Soft HJB equation, Neural PDEs, Physics-Informed Neural Networks.
result Reduces DOCTR-L to solving neural PDEs from data.
Study identifies NFT whales driving the market with consistent high returns.
problem Lack of financial analysis of NFT trading ecosystem.
method Longitudinal study of 3.8M NFT transactions, classifying traders into whales, dolphins, and minnows.
result Top 0.1% of NFT traders (whales) drive the market with consistent, high returns.
This paper evaluates investment risks in LATAM AI startups using DCF method.
problem Unique challenges and risks faced by LATAM tech startups.
method Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) metrics; Discounted Cash Flow (DCF) method.
result Developed a ranking of emerging powers in Latin America for tech startup investment.
Cryptocurrencies evolve through survival of the fittest, modeled with evolutionary finance.
problem Understanding the dynamics of cryptocurrency markets.
method Evolutionary finance concepts applied to toy models of cryptocurrency data.
result Survival of the fittest in cryptofinance is explained through scaling laws.