arXiv subjects

Georgios Papadopoulos

Publications and source records attributed to Georgios Papadopoulos.

15 recordsLinked to original sources

arXiv2026

Brane flows

Based on effective D-brane actions, we present a generalisation of the Ricci flow that includes the flow of a theory with a $n$-form field strength for $n\geq 0$. This is a generalisation of both the Ricci flows and the generalised Ricci flows. Following Perelman, we show that flows that keep a suitable field-dependent volume fixed are monotonic. We also show that all steady brane flow solitons are gradient solitons and use this to demonstrate that on some occasions this implies the existence of a Killing vector field that leaves all the other fields invariant. Particular cases of gradient solitons are NS5 and D5 branes, and the volume which is kept fixed in these cases is the T-duality invariant volume (NS5 brane) or its S-dual (D5 brane). We also generalise the above analysis to gravitational actions coupled to form gauge potentials that also exhibit a Chern-Simons type term. We find an alteration is required in the adaptation of Perelman's modification to this case, which yields a new functional that also exhibits a Chern-Simons term. Under suitable assumptions, we proceed to prove the monotonicity of the flow and that all steady flow solitons are gradient solitons. We also explore the consequences of the last statement on the geometry of solitons.

hep-th↗

arXiv2026

Heterotic horizons and AdS$_3$ backgrounds that preserve 6 supersymmetries

We prove, under suitable global assumptions, that the only heterotic horizons with closed 3-form field strength that preserve strictly 6 supersymmetries have spatial horizon section diffeomorphic to either $SU(3)$ or $S^2\times S^3\times SO(3)$, up to identifications with the action of a discrete group. Under similar assumptions, which include the compactness of the transverse space, we demonstrate that there are no heterotic AdS$_3$ solutions that preserve 6 supersymmetries. The proof is based on a topological argument. We also re-examine the conditions required for the existence of such backgrounds that preserve 4 supersymmetries focusing on those that admit an additional $\oplus^2\mathfrak{u}(1)$ symmetry. We provide some additional explanation for the existence of solutions and point out the similarities that these conditions have with those that have recently emerged in the classification of compact strong 6-dimensional Calabi-Yau manifolds with torsion.

hep-th↗

arXiv2025

On the rigidity of special and exceptional geometries with torsion a closed $3$-form

Under some suitable assumptions Riemannian manifolds $(M, g, H)$ that admit a connection $\hat\nabla$ with torsion a 3-form $H$, which is both closed $d H=0$ and $\hat\nabla$-covariantly constant, are locally isometric to a product $N\times G$, where $G$ is a semisimple group and $N$ is a Riemannian manifold with $H\vert_N=0$. If $M$ is simply connected and complete, then by the de Rham theorem $M=N\times G$ globally. We use this to simplify the proof of similar results for strong CYT and HKT manifolds that obey the above hypotheses and extend them to strong $G_2$ and $\mathrm{Spin}(7)$ manifolds with torsion. As an application, we describe the geometry of all complete and simply connected $G_2$ and $\mathrm{Spin}(7)$ manifolds that satisfy the above conditions. Compact, strong, 8-dimensional HKT manifolds, which are not hyper-K\"ahler, admit an either $\oplus^4 \mathfrak{u}(1)$ or a $\mathfrak{u}(1)\oplus \mathfrak{su}(2)$ locally free action, otherwise, they are group manifolds. We find that if these Lie algebra actions can be integrated to an appropriate free action of $T^4$ or $S(U(1)\times U(2))$ Lie groups that preserves the span of three complex structures, then these HKT manifolds are either locally isometric and tri-holomorphic to $\mathbb{R}\times S^3\times B^4$ or diffeomorphic to $SU(3)$, where $B^4= \mathbb{R}\times S^3$, $\mathbb{R}^4$ or $K_3$.

math.DG↗

arXiv2025

Sigma model renormalisation group flows, singularities and some remarks on cosmology

We investigate the properties of the renormalisation group (RG) flow of two-dimensional sigma models with a generic metric coupling by utilising known results for the Ricci flow. We point out that on many occasions the RG flow develops singularities, due to strong coupling behaviour, before it reaches a UV or an IR fixed point. We illustrate our analysis with several examples. We give particular emphasis to type I singularities, where the length of the curvature of the sigma model target space grows at most as $|t-T|^{-1}$ as the flow parameter $t$ approaches the singularity at $T$. For these, the geometry near the singularity is described in terms of a shrinking Ricci soliton that exhibits a cosmological constant even though the original RG flow does not. Assuming that the spacetime satisfies an RG flow equation, we use the Ricci solitons to introduce a cosmological constant in a string theory setting. This can allow for different cosmological constants at different regions of spacetime. In particular, we point out how the de-Sitter space is a solution of the theory. We also raise the question on whether the techniques used to prove the geometrisation conjecture can be applied to prove the homogeneity and isotropy of the universe at large scales.

hep-th↗

arXiv2025

A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks

The loss landscape of Variational Quantum Neural Networks (VQNNs) is characterized by local minima that grow exponentially with increasing qubits. Because of this, it is more challenging to recover information from model gradients during training compared to classical Neural Networks (NNs). In this paper we present a numerical scheme that successfully reconstructs input training, real-world, practical data from trainable VQNNs' gradients. Our scheme is based on gradient inversion that works by combining gradients estimation with the finite difference method and adaptive low-pass filtering. The scheme is further optimized with Kalman filter to obtain efficient convergence. Our experiments show that our algorithm can invert even batch-trained data, given the VQNN model is sufficiently over-parameterized.

cs.LG↗

arXiv2025

Geometry of the moduli space of Hermitian-Einstein connections on manifolds with a dilaton

We demonstrate that the moduli space of Hermitian-Einstein connections $\text{M}^*_{HE}(M^{2n})$ of vector bundles over compact non-Gauduchon Hermitian manifolds $(M^{2n}, g, \omega)$ that exhibit a dilaton field $\Phi$ admit a strong K\"ahler with torsion structure provided a certain condition is imposed on their Lee form $\theta$ and the dilaton. We find that the geometries that satisfy this condition include those that solve the string field equations or equivalently the gradient flow soliton type of equations. In addition, we demonstrate that if the underlying manifold $(M^{2n}, g, \omega)$ admits a holomorphic and Killing vector field $X$ that leaves $\Phi$ also invariant, then the moduli spaces $\text{M}^*_{HE}(M^{2n})$ admits an induced holomorphic and Killing vector field $\alpha_X$. Furthermore, if $X$ is covariantly constant with respect to the compatible connection $\hat\nabla$ with torsion a 3-form on $(M^{2n}, g, \omega)$, then $\alpha_X$ is also covariantly constant with respect to the compatible connection $\hat D$ with torsion a 3-form on $\text{M}^*_{HE}(M^{2n})$ provided that $K^\flat\wedge X^\flat$ is a $(1,1)$-form with $K^\flat=\theta+2d\Phi$ and $\Phi$ is invariant under both $X$ and $IX$, where $I$ is the complex structure of $M^{2n}$.

math.DG↗

arXiv2025

Geometry and symmetries of Hermitian-Einstein and instanton connection moduli spaces

We investigate the geometry of the moduli spaces $\mathscr{M}_{\HE}^*(M^{2n})$ of Hermitian-Einstein irreducible connections on a vector bundle $E$ over a K\"ahler with torsion (KT) manifold $M^{2n}$ that admits holomorphic and $\h\nabla$-covariantly constant vector fields, where $\h\nabla$ is the connection with skew-symmetric torsion $H$. We demonstrate that such vector fields induce an action on $\mathscr{M}_{\HE}^*(M^{2n})$ that leaves both the metric and complex structure invariant. Moreover, if an additional condition is satisfied, the induced vector fields are covariantly constant with respect to the connection with skew-symmetric torsion $\h{\mathcal{ D}}$ on $\mathscr{M}_{\HE}^*(M^{2n})$. We demonstrate that in the presence of such vector fields, the geometry of $\mathscr{M}_{\HE}^*(M^{2n})$ can be modelled on that of holomorphic toric principal bundles with base space KT manifolds and give some examples. We also extend our analysis to the moduli spaces $\mathscr{M}_{\asd}^*(M^{4})$ of instanton connections on vector bundles over KT, bi-KT (generalised K\"ahler) and hyper-K\"ahler with torsion (HKT) manifolds $M^4$. We find that the geometry of $\mathscr{M}_{\asd}^*(S^3\times S^1)$ can be modelled on that of principal bundles with fibre $S^3\times S^1$ over Quaternionic K\"ahler manifolds with torsion (QKT). In addition motivated by applications to AdS/CFT, we explore the (superconformal) symmetry algebras of two-dimensional sigma models with target spaces such moduli spaces.

hep-th↗

arXiv2025

Private, Auditable, and Distributed Ledger for Financial Institutes

Distributed ledger technology offers several advantages for banking and finance industry, including efficient transaction processing and cross-party transaction reconciliation. The key challenges for adoption of this technology in financial institutes are (a) the building of a privacy-preserving ledger, (b) supporting auditing and regulatory requirements, and (c) flexibility to adapt to complex use-cases with multiple digital assets and actors. This paper proposes a framework for a private, audit-able, and distributed ledger (PADL) that adapts easily to fundamental use-cases within financial institutes. PADL employs widely-used cryptography schemes combined with zero-knowledge proofs to propose a transaction scheme for a `table' like ledger. It enables fast confidential peer-to-peer multi-asset transactions, and transaction graph anonymity, in a no-trust setup, but with customized privacy. We prove that integrity and anonymity of PADL is secured against a strong threat model. Furthermore, we showcase three fundamental real-life use-cases, namely, an assets exchange ledger, a settlement ledger, and a bond market ledger. Based on these use-cases we show that PADL supports smooth-lined inter-assets auditing while preserving privacy of the participants. For example, we show how a bank can be audited for its liquidity or credit risk without violation of privacy of itself or any other party, or how can PADL ensures honest coupon rate payment in bond market without sharing investors values. Finally, our evaluation shows PADL's advantage in performance against previous relevant schemes.

cs.CR↗

arXiv2024

Scale and Conformal Invariance in Heterotic $\sigma$-Models

We demonstrate that all perturbative scale invariant heterotic sigma models with a compact target space $M^D$ are conformally invariant. The proof, presented in detail for up to and including two loops, utilises a geometric analogue of the $c$-theorem based on a generalisation of the Perelman's results on geometric flows. Then, we present examples of scale invariant heterotic sigma models with target spaces that exhibit special geometry, which is characterised by the holonomy of the connection with torsion a 3-form, and explore the additional conditions that are necessary for such sigma models to be conformally invariant. For this, we find that the geometry of the target spaces is further restricted to be either conformally balanced or the a priori holonomy of the connection with torsion reduces. We identify the pattern of holonomy reduction in the cases that the holonomy is $SU(n)$ $(D=2n)$, $Sp(k)$ $D=4k)$, $G_2$ $(D=7)$ and $\mathrm{Spin}(7)$ $(D=8)$. We also investigate the properties of these geometries and present some examples.

hep-th↗

arXiv2024

Scale and Conformal Invariance in 2d Sigma Models, with an Application to N=4 Supersymmetry

By adapting previously known arguments concerning Ricci flow and the c-theorem, we give a direct proof that in a two-dimensional sigma-model with compact target space, scale invariance implies conformal invariance in perturbation theory. This argument, which applies to a general sigma-model constructed with a target space metric and B-field, is in accord with a more general proof in the literature that applies to arbitrary two-dimensional quantum field theories. Models with extended supersymmetry and a B-field are known to provide interesting test cases for the relation between scale invariance and conformal invariance in sigma-model perturbation theory. We give examples showing that in such models, the obstructions to conformal invariance suggested by general arguments can actually occur in models with target spaces that are not compact or complete. Thus compactness of the target space, or at least a suitable condition of completeness, is necessary as well as sufficient to ensure that scale invariance implies conformal invariance in models of this type.

hep-th↗

arXiv2024

Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders

Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a non-destructive, label-free manner. Many applications entail the unmixing of signals from mixtures of molecular species to identify the individual components present and their proportions, yet conventional methods for chemometrics often struggle with complex mixture scenarios encountered in practice. Here, we develop hyperspectral unmixing algorithms based on autoencoder neural networks, and we systematically validate them using both synthetic and experimental benchmark datasets created in-house. Our results demonstrate that unmixing autoencoders provide improved accuracy, robustness and efficiency compared to standard unmixing methods. We also showcase the applicability of autoencoders to complex biological settings by showing improved biochemical characterization of volumetric Raman imaging data from a monocytic cell.

cs.LG↗

arXiv2023

A Benchmark Generative Probabilistic Model for Weak Supervised Learning

Finding relevant and high-quality datasets to train machine learning models is a major bottleneck for practitioners. Furthermore, to address ambitious real-world use-cases there is usually the requirement that the data come labelled with high-quality annotations that can facilitate the training of a supervised model. Manually labelling data with high-quality labels is generally a time-consuming and challenging task and often this turns out to be the bottleneck in a machine learning project. Weak Supervised Learning (WSL) approaches have been developed to alleviate the annotation burden by offering an automatic way of assigning approximate labels (pseudo-labels) to unlabelled data based on heuristics, distant supervision and knowledge bases. We apply probabilistic generative latent variable models (PLVMs), trained on heuristic labelling representations of the original dataset, as an accurate, fast and cost-effective way to generate pseudo-labels. We show that the PLVMs achieve state-of-the-art performance across four datasets. For example, they achieve 22% points higher F1 score than Snorkel in the class-imbalanced Spouse dataset. PLVMs are plug-and-playable and are a drop-in replacement to existing WSL frameworks (e.g. Snorkel) or they can be used as benchmark models for more complicated algorithms, giving practitioners a compelling accuracy boost.

cs.LG↗

arXiv2021

A Comprehensive Survey on Nanophotonics Neural Networks

In the last years, materializations of neuromorphic circuits based on nanophotonic arrangements have been proposed, which contain complete optical circuits, laser, photodetectors, photonic crystals, optical fibers, flat waveguides, and other passive optical elements of nanostructured materials, which eliminate the time of simultaneous processing of big groups of data, taking advantage of the quantum perspective and thus highly increasing the potentials of contemporary intelligent computational systems. This article is an effort to record and study the research that has been conducted concerning the methods of development and materi-alization of neuromorphic circuits of Neural Networks of nanophotonic arrangements. In particular, an investigative study of the methods of developing nanophotonic neuromorphic processors, their originality in neuronic architectural structure, their training methods and their optimization has been realized along with the study of special issues such as optical activation functions and cost functions.

cs.ET↗

arXiv2017

Web Robot Detection in Academic Publishing

Recent industry reports assure the rise of web robots which comprise more than half of the total web traffic. They not only threaten the security, privacy and efficiency of the web but they also distort analytics and metrics, doubting the veracity of the information being promoted. In the academic publishing domain, this can cause articles to be faulty presented as prominent and influential. In this paper, we present our approach on detecting web robots in academic publishing websites. We use different supervised learning algorithms with a variety of characteristics deriving from both the log files of the server and the content served by the website. Our approach relies on the assumption that human users will be interested in specific domains or articles, while web robots crawl a web library incoherently. We experiment with features adopted in previous studies with the addition of novel semantic characteristics which derive after performing a semantic analysis using the Latent Dirichlet Allocation (LDA) algorithm. Our real-world case study shows promising results, pinpointing the significance of semantic features in the web robot detection problem.

cs.AI↗

arXiv2014

Analysis of Asymptotically Optimal Sampling-based Motion Planning Algorithms for Lipschitz Continuous Dynamical Systems

Over the last 20 years significant effort has been dedicated to the development of sampling-based motion planning algorithms such as the Rapidly-exploring Random Trees (RRT) and its asymptotically optimal version (e.g. RRT*). However, asymptotic optimality for RRT* only holds for linear and fully actuated systems or for a small number of non-linear systems (e.g. Dubin's car) for which a steering function is available. The purpose of this paper is to show that asymptotically optimal motion planning for dynamical systems with differential constraints can be achieved without the use of a steering function. We develop a novel analysis on sampling-based planning algorithms that sample the control space. This analysis demonstrated that asymptotically optimal path planning for any Lipschitz continuous dynamical system can be achieved by sampling the control space directly. We also determine theoretical bounds on the convergence rates for this class of algorithms. As the number of iterations increases, the trajectory generated by these algorithms, approaches the optimal control trajectory, with probability one. Simulation results are promising.

cs.RO↗