arXiv subjects

Ioannis Konstantinidis

Publications and source records attributed to Ioannis Konstantinidis.

3 recordsLinked to original sources

arXiv2026

The Decentralisation Paradox in Digital Identity: Centralising Decentralisation with Digital Wallets?

Digital identity is shifting from service- and network-centric approaches toward user-centric ones that promise users increased control over their data. Despite their decentralised design, such approaches often reintroduce centralised components in different forms. This paper conceptualises this tension as the decentralisation paradox and shows that user-centric architectures tend to redistribute rather than eliminate centralisation. Drawing on Critical Systems Thinking (CST), digital identity is framed as a "wicked problem" that spans the technical, legal, social and ethical dimensions. It introduces the Digital Identity Tetrahedron as a multidimensional framework for analysing decentralisation in digital identity ecosystems. Understanding these interdependencies is essential for designing reliable architectures and ensuring that the next generation of digital identity surpasses superficial decentralisation.

cs.CR↗

arXiv2026

Detecting the 3D Ising model phase transition with a ground-state-trained autoencoder

We develop a one-class, deep-learning framework to detect the phase transition and recover critical behavior of the 3D Ising model. A 3D convolutional neural network autoencoder (CAE) is trained on ground-state configurations only, without prior knowledge of the critical temperature, the Hamiltonian, or the order parameter. After training, the model is applied to Monte Carlo configurations across a wide temperature range and different lattice sizes. The mean-square reconstruction error is shown to be sensitive to the transition. Finite-size scaling of the peak location for the reconstruction error susceptibility yields the critical temperature $T_c=4.5128(58)$ and the correlation-length critical exponent $ν=0.63(27)$, consistent with results from the literature. Our results show that a one-class CAE, trained on zero-temperature configurations only, can recover nontrivial critical behavior of the 3D Ising model.

cond-mat.stat-mech↗

arXiv2025

Deep learning of phase transitions with minimal examples

Over the past several years, there have been many studies demonstrating the ability of deep neural networks to identify phase transitions in many physical systems, notably in classical statistical physics systems. One often finds that the prediction of deep learning methods trained on many ensembles below and above the critical temperature $T_{\rm c}$ behaves similarly to an order parameter, and this analogy has been successfully used to locate $T_{\rm c}$ and estimate universal critical exponents. In this work, we pay particular attention to the ability of a convolutional neural network to capture these critical parameters for the 2-$d$ Ising model when the network is trained on configurations at $T=0$ and $T=\infty$ only. We directly compare its output to the same network trained at multiple temperatures below and above $T_{\rm c}$ to gain understanding of how this extreme restriction of training data can impact a neural network's ability to classify phases. We find that the network trained on two temperatures is still able to identify $T_{\rm c}$ and $ν$, while the extraction of $γ$ becomes more challenging.

cond-mat.stat-mech↗