Study analyzes climate-tech investments across 14 sectors.
problem Accelerating climate-tech innovation in nascent value chains.
method Analysis of 4,172 firms and 12,929 investments over 15 years.
result Only 15% of firms develop end products, 59% support them, and 26% develop services.
In this short survey article we collect the current state of the art in the nascent field of \textit{quantum enhancements}, a type of knot invariant defined by collecting values of quantum invariants of knots with colorings by various algebraic objects over the set of such colorings. This class of invariants includes c…
New method maps global value chains at product level from trade data.
problem Lack of detailed product-level value chain information in existing datasets.
method Machine learning and trade theory applied to international trade data.
result Approximate product-level value chain information inferred from trade patterns.
FS-GCLSTM predicts stock returns by leveraging value-chain relationships.
problem Traditional time series models fail to capture complex interdependencies in modern markets.
method FS-GCLSTM integrates value-chain networks and graph convolutions to predict stock returns.
result FS-GCLSTM consistently delivers superior portfolio performance compared to traditional models.
ProGO optimizes non-convex functions without gradients, outperforming existing methods.
problem Challenges in global optimization, especially with non-convex functions and limited gradient information.
method Probabilistic approach using multidimensional integration and latent slice sampler.
result ProGO converges to global optima efficiently and outperforms existing methods.
The paper introduces a novel method for stable off-policy learning using value function chaining.
problem Stability issues in off-policy reinforcement learning.
method The approach involves learning on-policy first, then chaining off-policy value estimates.
result The method guarantees convergence and can approximate off-policy TD solutions.
Our work sheds new light on the role of oil prices in shaping the world economy by investigating flows of goods and services through global value chains between 1960 and 2011, by means of Markov Chain and network analysis. We show that over that time period the international division of labor and trade patterns are tig…
GenAI offers financial benefits but requires risk management.
problem Managing risks in financial applications of AI.
method Balancing AI's potential with risk control strategies.
result Proper risk management is essential for AI growth in finance.
In this note we introduce certain invariants of real Lefschetz fibrations. We call these invariants {\em real Lefschetz chains}. We prove that if the fiber genus is greater than 1, then the real Lefschetz chains are complete invariants of real Lefschetz fibrations with only real critical values. If however the fiber ge…
TD learning reduces prediction error in Markov chain problems.
problem Estimating value functions in Markov chains with temporal inconsistency.
method Temporal difference learning minimizes temporal inconsistency between successive estimates.
result TD learning can significantly reduce mean-squared error in value estimates.
The paper calculates the value of information in high-dimensional decision making.
problem Determining the value of acquiring new information in high-dimensional decision problems.
method Using tools from sub-Gaussian processes and generic chaining for asymptotic analysis.
result Asymptotic results on the expected value of information as dimensionality increases.
Study shows cryptocurrency market impact on DeFi returns stronger than other drivers.
problem Understanding drivers of DeFi returns and their relative importance.
method Investigated four drivers: cryptocurrency market exposure, network effect, investor attention, and valuation ratio. Designed a new market index, DeFiX.
result Cryptocurrency market impact on DeFi returns is stronger than other drivers and provides superior explanatory power.
Matrix Chernoff bound for Markov chains applied to co-occurrence matrices.
problem Analyzing the behavior of co-occurrence statistics in sequential data.
method Proved a matrix Chernoff-type bound for sums of matrix-valued random variables sampled via a regular Markov chain.
result Achieved exponentially fast convergence rate and sample complexity analysis for co-occurrence matrices.
FluxLayer solves cross-chain liquidity fragmentation for better MEV capture.
problem Cross-chain fragmented liquidity and MEV optimization.
method Three-layer framework integrating settlement, intent, and leverage mechanisms.
result FluxLayer enhances cross-chain MEV by capturing more arbitrage opportunities.
Let M be a closed connected manifold, f be a Morse map from M to a circle, v be a gradient-like vector field satisfying the transversality condition. The Novikov construction associates to these data a chain complex C∗=C∗(f,v). There is a chain homotopy equivalence between C∗ and completed simplicial cha…
With globalization, countries are more connected than before by trading flows, which currently amount to at least 36 trillion dollars. Interestingly, approximately 30-60 percent of global exports consist of intermediate products. Therefore, the trade flow network of a particular product with high added values can be re…
We compute the chains associated to the left-invariant CR structures on the three-sphere. These structures are characterized by a single real modulus a. For the standard structure a=1, the chains are well-known and are closed curves. We show that for almost all other values of the modulus a either two or three ty…
The paper finds a surprising positive correlation between upstreamness and downstreamness in global value chains.
problem The puzzling positive correlation between upstreamness and downstreamness in industries and countries.
method Analysis of a simple model of random Input/Output tables and experiments on empirical data.
result Upstreamness and downstreamness of the same industrial sector/country are positively correlated with a slope close to +1.
A privacy-preserving framework detects faults in circular economy processes.
problem Lack of shared data across company borders due to privacy concerns.
method Federated Principal Component Analysis (PCA) and Secure Multiparty Computation.
result The proposed FedMSPC framework outperforms standard PCA in fault detection.
Method reconstructs hidden Markov chains from insurance data.
problem Recovering hidden Markov chains from incomplete insurance data.
method Neural architecture to explicitly provide transition probabilities.
result Neural model successfully validates decompression of insurance information.
Enhances machine learning performance predictions with transparency.
problem Providing accurate and practical performance guarantees for machine learning.
method Natural extension of conformal prediction framework.
result Valid and well-calibrated predictive statements about future performance.
The paper analyzes stability of random matrix products with Markovian noise.
problem Analyzing stability of random matrix products with Markovian noise.
method Using a super-Lyapunov drift condition and controlled growth of matrix-valued functions, the paper provides an exponential stability result for the p-th moment of random matrix product.
result Finite-time p-th moment bounds for linear stochastic approximation and TD learning algorithms.
Paper develops a risk scoring framework for tokenized RWA markets.
problem Tokenized assets may not reflect true risk due to illiquidity and concentration.
method Develops a risk scoring framework based on observable indicators.
result Assets with limited transfer activity and concentrated ownership have high empirical risk.
Despite incredible recent advances in machine learning, building machine learning applications remains prohibitively time-consuming and expensive for all but the best-trained, best-funded engineering organizations. This expense comes not from a need for new and improved statistical models but instead from a lack of sys…
A geometric version of the Poincaré Lemma is established for the topological vector space of differential chains. In particular, every differential k-cycle with compact support in a contractible open subset U of a smooth n-manifold M is the boundary of a differential (k+1) -chain with compact support in U. Applications…
The last few years have seen an explosion of academic and popular interest in algorithmic fairness. Despite this interest and the volume and velocity of work that has been produced recently, the fundamental science of fairness in machine learning is still in a nascent state. In March 2018, we convened a group of expert…
Develops CLTs for Markov chain transition probabilities and policies.
problem Estimating transition probabilities and policies in controlled Markov chains.
method Non-parametric estimator for transition matrices; CLTs for value, Q-, and advantage functions; goodness-of-fit tests.
result Asymptotic normality of estimators under specific logging policies.
While the use of volatilities is pervasive throughout finance, our ability to determine the instantaneous volatility of stocks is nascent. Here, we present a method for measuring the temporal behavior of stocks, and show that stock prices for 24 DJIA stocks follow a stochastic process that describes an efficiently pric…
The paper extends game theory using Hodge theory on graphs.
problem Generalizing Shapley's value allocation formula for cooperative games on graphs.
method Connecting stochastic path integrals to Hodge-theoretic Poisson's equations on graphs.
result The value allocation operator is the solution to Poisson's equation in combinatorial Hodge theory.
Model credit ratings using economic states with Markov chains.
problem Credit rating migration influenced by economic state changes.
method Developed a Markov chain model for credit ratings conditional on economic states.
result Derived asymptotic behavior of the rating process using Markov theory.
The fragmentation of production across countries has become an important feature of the globalization in recent decades and is often conceptualized by the term, global value chains (GVCs). When empirically investigating the GVCs, previous studies are mainly interested in knowing how global the GVCs are rather than how …
A new method calculates intrinsic effective sample size for manifold-valued data.
problem Challenges in choosing effective sample size for manifold-valued data.
method Proposes an intrinsic effective sample size based on kernel discrepancy.
result Establishes an exact finite-sample risk interpretation and consistency of the estimator.
New algorithm improves volatility forecasting using Pairwise Markov Chains.
problem Inability to effectively predict volatility due to feature problem and non-stationarity.
method Introduced a new algorithm for prediction with Pairwise Markov Chains (PMC), extending its capabilities.
result Enhanced performance of volatility forecasting models compared to GARCH(1,1) and feedforward neural models.
P3LS preserves privacy while integrating data across companies.
problem Privacy concerns in cross-organizational data exchange and integration.
method Privacy-preserving federated learning technique using SVD-based PLS and random masks.
result Improves prediction performance on process-related indicators.
We use noncommutative localization to construct a chain complex which counts the critical points of a circle-valued Morse function on a manifold, generalizing the Novikov complex. As a consequence we obtain new topological lower bounds on the minimum number of critical points of a circle-valued Morse function within a …
Deep neural networks optimize inventory decisions in complex supply chains.
problem Optimizing inventory decisions in stochastic multi-echelon supply chains.
method Pairwise modeling and DNN agents for order-up-to levels.
result The method performs better than alternate methods in general supply chain networks.
The study analyzes how cross-chain interoperability affects decentralized lending protocols' performance.
problem Understudied cross-chain elements in DeFi lending risk management.
method Panel regression fixed effects and OLS models applied to empirical analysis.
result Cross-chain activity impacts protocol performance, with bridge volume being a critical driver.
This paper develops numerical methods for finding optimal dividend pay-out and reinsurance policies. A generalized singular control formulation of surplus and discounted payoff function are introduced, where the surplus is modeled by a regime-switching process subject to both regular and singular controls. To approxima…
Study analyzes fees linked to VIX index in annuity contracts.
problem Impact of VIX-linked fees on annuity contract surrender strategies.
method Two-layer continuous-time Markov chain approximation for fund value process.
result Optimal surrender strategy is more robust to VIX-linked fees.
Unified framework for complex financial networks using lattice theory.
problem Complex financial networks with multiple currencies and dependencies.
method Recast classical financial clearing model into lattice liability networks.
result Lattice-valued clearing sections form a complete lattice, enabling tractable analysis.
Develops a new risk measure for Markov chains' asymptotic behavior.
problem Lack of risk measures for asymptotic regimes of Markov chains.
method Simulation-based approach using large deviations theory, density estimation, and stochastic approximation.
result Developed Asymptotic CVaR (ACVaR) for Markov chains.
The Riemannian barycentre is one of the most widely used statistical descriptors for probability distributions on Riemannian manifolds. At present, existing algorithms are able to compute the Riemannian barycentre of a probability distribution, only if i.i.d. samples of this distribution are readily available. However,…
Review of sigma models on flag manifolds, linking to spin chains and integrable theories.
problem Understanding phase transitions and anomalies in spin chains and sigma models.
method Analyzing topological angles, discrete 't Hooft anomalies, and integrable models.
result Gapless phases in certain spin chains can be explained by discrete anomalies in continuum theories.
Neuromorphic Computing is a nascent research field in which models and devices are designed to process information by emulating biological neural systems. Thanks to their superior energy efficiency, analog neuromorphic systems are highly promising for embedded, wearable, and implantable systems. However, optimizing neu…
Method estimates number of clusters in Block Markov Chain trajectories.
problem Challenges in choosing number of clusters for sequential data.
method Spectral embedding and density-based clustering.
result Asymptotically consistent method for estimating clusters.
The paper shows supply chain features improve cyber risk prediction.
problem Predicting cyber risk from supply chain attributes.
method Machine learning, external supply chain features, AUC improvement.
result Supply chain network features improve AUC by 2.3%.
The study analyzes and benchmarks graph conformal prediction methods.
problem Uncertainty quantification in graph node classification.
method Analysis and scaling of existing graph conformal prediction methods.
result Justified recommendations for future graph conformal prediction research.
The study examines convergence of stochastic processes on large graphs and adjacency matrices.
problem Analyzing convergence of stochastic processes on large graphs and adjacency matrices.
method Introduced new metrics on the space of measure-valued graphons and used them to show convergence of random trajectories to deterministic curves.
result The Metropolis chain converges to a deterministic gradient flow curve on the space of graphons under certain conditions.