arXiv research

The paper introduces fixed-point centralities for networks and graphons.

As relational datasets modeled as graphs keep increasing in size and their data-acquisition is permeated by uncertainty, graph-based analysis techniques can become computationally and conceptually challenging. In particular, node centrality measures rely on the assumption that the graph is perfectly known -- a premise …

Paper introduces input perturbation for privacy in machine learning models.

Centralized exchanges influence staking behavior and decentralization in Proof of Stake blockchain ecosystems.

I show that the solution of a standard clearing model commonly used in contagion analyses for financial systems can be expressed as a specific form of a generalized Katz centrality measure under conditions that correspond to a system-wide shock. This result provides a formal explanation for earlier empirical results wh…

It was observed by Tod and later by Dunajski and Tod that the Boyer-Finley (BF) and the dispersionless Kadomtsev-Petviashvili (dKP) equations possess solutions whose level surfaces are central quadrics in the space of independent variables (the so-called central quadric ansatz). It was demonstrated that generic solutio…

Central bank strategy to maintain currency exchange rate within limits.

Dataset for rainfall modeling in central Europe from 1981-2011.

Unified model for network risks, including bilateral and central clearing, with practical applications.

Study optimal futures trading strategies for assets with multiscale central tendency price model.

Model estimates foreign exchange reserve compositions of undisclosed central banks.

ARA combines aggregated RAPPOR and Tf-Idf estimation for centralized DP analysis.

Unified framework deciphers global central bank communications.

The paper derives the QGS equations using stochastic central extensions.

Network metrics form a fundamental part of the network analysis toolbox. Used to quantitatively measure different aspects of the network, these metrics can give insights into the underlying network structure and function. In this work, we connect network metrics to modern probabilistic machine learning. We focus on the…

Study finds significant price declines and capital reallocation from centralized to decentralized exchanges after FTX collapse.

We formulate and analyze a multi-agent model for the evolution of individual and systemic risk in which the local agents interact with each other through a central agent who, in turn, is influenced by the mean field of the local agents. The central agent is stabilized by a bistable potential, the only stabilizing force…

We consider a stochastic game between a trader and a central bank in a target zone market with a lower currency peg. This currency peg is maintained by the central bank through the generation of permanent price impact, thereby aggregating an ever increasing risky position in foreign reserves. We describe this situation…

The study classifies policy announcements' impact on stock market volatility.

Access to sufficient annotated data is a common challenge in training deep neural networks on medical images. As annotating data is expensive and time-consuming, it is difficult for an individual medical center to reach large enough sample sizes to build their own, personalized models. As an alternative, data from all …

A hypersurface $M$ in $\mathbb{R}^n$, $n \geq 4$, has central ovaloid property if $M$ intersects some hyperplane transversally along an ovaloid and every such ovaloid on $M$ has central symmetry. We show that a complete, connected, smooth hypersurface with central ovaloid property must either be a cylinder over a centr…

We provide a systematic study of the problem of finding the source of a rumor in a network. We model rumor spreading in a network with a variant of the popular SIR model and then construct an estimator for the rumor source. This estimator is based upon a novel topological quantity which we term \textbf{rumor centrality…

Decentralized learning achieves centralized performance via Gibbs measures.

Central banks play a key role in promoting sustainable finance.

The purpose of this paper is to show how central extensions of (possibly infinite-dimensional) Lie algebras integrate to central extensions of étale Lie 2-groups. In finite dimensions, central extensions of Lie algebras integrate to central extensions of Lie groups, a fact which is due to the vanishing of π_2 for each …

Federated survival analysis outperforms local and centralized training, with RSF offering the best balance of discrimination, calibration, and robustness.

Constructs examples of centrally harmonic spaces and shows they are not generically harmonic.

This paper identifies and bounds ICE central moments using PO marginal central moments.

Study shows volume density in central harmonic spaces can vary arbitrarily.

We determine the universal central extension of the Lie algebra of hamiltonian vector fields, thereby classifying its central extensions. Furthermore, we classify the central extensions of the Lie algebra of symplectic vector fields, of the Poisson Lie algebra, and of its compactly supported version.

Central bank optimizes bailout cash injection to limit defaults.

Study Drinfeld centralizers and Rouquier complexes in homotopy categories.

Criterion for lifting smooth contact maps between Carnot groups to central extensions.

In this paper, we propose a data collaboration analysis method for distributed datasets. The proposed method is a centralized machine learning while training datasets and models remain distributed over some institutions. Recently, data became large and distributed with decreasing costs of data collection. If we can cen…

Node centrality is one of the most important and widely used concepts in the study of complex networks. Here, we extend the paradigm of node centrality in financial and economic networks to consider the changes of node "importance" produced not only by the variation of the topology of the system but also as a consequen…

Modeling tech transfer to explain convergence in Central and Eastern Europe.

FUSE neural centrality framework improves data point measurement in high dimensions.

FSL-BDP models time-to-default without centralizing data, improving privacy mechanisms in federated settings.

Vertex centrality measures are a multi-purpose analysis tool, commonly used in many application environments to retrieve information and unveil knowledge from the graphs and network structural properties. However, the algorithms of such metrics are expensive in terms of computational resources when running real-time ap…

We prove: If a complete connected smooth surface M in euclidean 3-space has general position, intersects some plane along a clean figure-8 (a loop with total curvature zero) and all compact intersections with planes have central symmetry, then M is a (geometric) cylinder over some central figure-8. On the way, we estab…

Federated learning has become increasingly important for modern machine learning, especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the central server-based architecture or centralized architecture. However, in many social network scenarios, centralized federated learning is not…

Model shows how centralization occurs in cryptocurrency mining.

We show existence of centrally symmetric maps on surfaces all of whose faces are quadrangles and pentagons for each orientable genus $g \geq 0$. We also show existence of centrally symmetric maps on surfaces all of whose faces are hexagons for each orientable genus $g = 2k-1$, $k\in \mathbb{N}$. We enumerate centrally …

Paper proves CLTs for Q-learning with asynchronous updates.

We construct a sequence of commuting central affine curve flows on $R^n\backslash 0$ invariant under the action of $SL(n,R)$ and prove the following results: (a) The central affine curvatures of a solution of the j-th central affine curve flow is a solution of the j-th flow of Gelfand-Dickey (GD$_n$) hierarchy on the s…

Model shows PoS networks can be captured by external finance, leading to centralization.

A new model detects complex network communities using node attributes.

The paper validates a centrality measure for financial networks during financial distress.