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Martin Gonzalez

Publications and source records attributed to Martin Gonzalez.

17 recordsLinked to original sources
arXiv2026

Doping dependence of local moments in infinite layer nickelates

The infinite layer nickelates are notable for their lack of long-range antiferromagnetic ordering, in contrast to the parent compounds of the superconducting cuprates. Instead, the nickelates show evidence of short-range glassy behavior in both the undoped and optimally-doped regimes, implying that local electronic moments exist independent of superconductivity. However, the systematic doping-dependent magnetic behavior is not yet fully resolved, and characterizing it could uncover the relationship between local moments and the superconducting dome. In this work, we use muon spin rotation ($\mu$SR) on a (La,Sr)NiO$_2$ doping series from the undoped parent compound, through the superconducting dome, to the over-doped normal state (Sr substitution 0% $\leq$ x $\leq$ 25%) to probe the magnetic ground state and the temperature-dependent static and dynamic behavior. We find that local moments experience spin freezing into a glassy state at temperatures on the order of a few tens of kelvin regardless of the doping level. We also observe a subtle destabilization of the glassy state with increased hole doping. These observations suggest that magnetism and superconductivity are largely decoupled phenomena with indirect interactions described in a multi-orbital framework.

cond-mat.supr-con↗
arXiv2025

Data Curation Matters: Model Collapse and Spurious Shift Performance Prediction from Training on Uncurated Text Embeddings

Training models on uncurated Text Embeddings (TEs) derived from raw tabular data can lead to a severe failure mode known as model collapse, where predictions converge to a single class regardless of input. By comparing models trained with identical hyper-parameter configurations on both raw tabular data and their TE-derived counterparts, we find that collapse is a consistent failure mode in the latter setting. We introduce a set of metrics that capture the extent of model collapse, offering a new perspective on TE quality as a proxy for data curation. Our results reveal that TE alone does not effectively function as a curation layer - and that their quality significantly influences downstream learning. More insidiously, we observe that the presence of model collapse can yield artificially inflated and spurious Accuracy-on-the-Line correlation. These findings highlight the need for more nuanced curation and evaluation of embedding-based representations, particularly in out-of-distribution settings.

cs.LG↗
arXiv2025

Disorder-induced suppression of superconductivity in infinite-layer nickelates

The pairing symmetry of superconducting infinite-layer nickelates is a fundamental yet experimentally challenging question. We employ high-energy electron irradiation to induce disorder in superconducting Nd$_{0.825}$Sr$_{0.175}$NiO$_2$ thin films and examine the impact of pair-breaking defects on superconductivity and elucidate the nature of the superconducting gap. Our measurements reveal a complete suppression of superconductivity with increasing disorder, suggesting an unconventional, sign-changing order parameter.

cond-mat.supr-con↗
arXiv2025

Spin-glass state in nickelate superconductors

Magneto-optical measurements in La${}_{0.8}$Sr${}_{0.2}$NiO${}_2$ and Nd${}_{0.825}$Sr${}_{0.175}$NiO${}_2$ reveal an intriguing new facet of infinite-layer nickelate superconductors: the onset of spin-glass behavior at a temperature far exceeding the superconducting critical temperature $T_c$. This discovery sharply contrasts with copper oxide superconductors, where magnetism and superconductivity remain largely exclusive. Moreover, the magnitude and onset temperature of the polar Kerr effect in Nd${}_{0.825}$Sr${}_{0.175}$NiO${}_2$ fabricated on SrTiO${}_3$ and (LaAlO${}_3$)${}_{0.3}$(Sr${}_2$TaAlO${}_6$)${}_{0.7}$ substrates differ dramatically, while $T_c$ does not.

cond-mat.str-el↗
arXiv2024

Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion Models

Automatic speech recognition (ASR) systems are known to be vulnerable to adversarial attacks. This paper addresses detection and defence against targeted white-box attacks on speech signals for ASR systems. While existing work has utilised diffusion models (DMs) to purify adversarial examples, achieving state-of-the-art results in keyword spotting tasks, their effectiveness for more complex tasks such as sentence-level ASR remains unexplored. Additionally, the impact of the number of forward diffusion steps on performance is not well understood. In this paper, we systematically investigate the use of DMs for defending against adversarial attacks on sentences and examine the effect of varying forward diffusion steps. Through comprehensive experiments on the Mozilla Common Voice dataset, we demonstrate that two forward diffusion steps can completely defend against adversarial attacks on sentences. Moreover, we introduce a novel, training-free approach for detecting adversarial attacks by leveraging a pre-trained DM. Our experimental results show that this method can detect adversarial attacks with high accuracy.

eess.AS↗
arXiv2024

Electronically Amplified Electron-Phonon Interaction and Metal-Insulator Transition in Perovskite Nickelates

The relative role of electron-electron and electron-lattice interactions in driving the metal-insulator transition in perovskite nickelates opens a rare window into the non-trivial interplay of the two important degrees of freedom in solids. The most promising solution is to extract the electronic and lattice contributions during the phase transition by performing high-resolution spectroscopy measurements. Here, we present a three-dimensional electronic structure study of Nd1-xSrxNiO3 (x = 0 and 0.175) thin films with unprecedented accuracy, in which the low energy fermiology has a quantitative agreement with model simulations and first-principles calculations. Two characteristic phonons, the octahedral rotational and breathing modes, are illustrated to be coupled with the electron dynamics in the metallic phase, showing a kink structure along the band dispersion, as well as a hump feature in the energy spectrum. Entering the insulating state, the electron-phonon interaction is amplified by strong electron correlations, transforming the mobile large polarons at high temperatures to localized small polarons in the ground state. Moreover, the analysis of quasiparticle residue enables us to establish a transport-spectroscopy correspondence in Nd1-xSrxNiO3 thin films. Our findings demonstrate the essential role of electron-lattice interaction enhanced by the electronic correlation to stabilize the insulating phase in the perovskite nickelates.

cond-mat.str-el↗
arXiv2024

A finite-sample generalization bound for stable LPV systems

One of the main theoretical challenges in learning dynamical systems from data is providing upper bounds on the generalization error, that is, the difference between the expected prediction error and the empirical prediction error measured on some finite sample. In machine learning, a popular class of such bounds are the so-called Probably Approximately Correct (PAC) bounds. In this paper, we derive a PAC bound for stable continuous-time linear parameter-varying (LPV) systems. Our bound depends on the H2 norm of the chosen class of the LPV systems, but does not depend on the time interval for which the signals are considered.

cs.LG↗
arXiv2023

SEEDS: Exponential SDE Solvers for Fast High-Quality Sampling from Diffusion Models

A potent class of generative models known as Diffusion Probabilistic Models (DPMs) has become prominent. A forward diffusion process adds gradually noise to data, while a model learns to gradually denoise. Sampling from pre-trained DPMs is obtained by solving differential equations (DE) defined by the learnt model, a process which has shown to be prohibitively slow. Numerous efforts on speeding-up this process have consisted on crafting powerful ODE solvers. Despite being quick, such solvers do not usually reach the optimal quality achieved by available slow SDE solvers. Our goal is to propose SDE solvers that reach optimal quality without requiring several hundreds or thousands of NFEs to achieve that goal. We propose Stochastic Explicit Exponential Derivative-free Solvers (SEEDS), improving and generalizing Exponential Integrator approaches to the stochastic case on several frameworks. After carefully analyzing the formulation of exact solutions of diffusion SDEs, we craft SEEDS to analytically compute the linear part of such solutions. Inspired by the Exponential Time-Differencing method, SEEDS use a novel treatment of the stochastic components of solutions, enabling the analytical computation of their variance, and contains high-order terms allowing to reach optimal quality sampling $\sim3$-$5\times$ faster than previous SDE methods. We validate our approach on several image generation benchmarks, showing that SEEDS outperform or are competitive with previous SDE solvers. Contrary to the latter, SEEDS are derivative and training free, and we fully prove strong convergence guarantees for them.

cs.LG↗
arXiv2023

Software-defined Optoacoustic Tomography

In this work we present the first application of software-defined optoelectronics (SDO) for bidimensional optoacoustic tomography (OAT). The SDO concept refers to optoelectronic systems where the functionality associated with the conditioning and processing of optical and electrical signals are digitally implemented and controlled by software. This paradigm takes advantage of the flexibility of software-defined hardware platforms to develop adaptive instrumentation systems. We implement an OAT system based on a heterodyne interferometer in a Mach-Zehnder configuration and a commercial software-defined radio platform (SDR). Here the SDR serves as a function generator and oscilloscope at the same time providing perfect carrier synchronization between its transmitter and receiver in a coherent baseband modulator scheme. Therefore, the carrier synchronization enables us to have a much better phase recovery. We study the performance of the OAT SDO system by means of different bidimensional phantoms and the analysis of the reconstructed images.

eess.SP↗
arXiv2023

Predicting Elite NBA Lineups Using Individual Player Order Statistics

NBA team managers and owners try to acquire high-performing players. An important consideration in these decisions is how well the new players will perform in combination with their teammates. Our objective is to identify elite five-person lineups, which we define as those having a positive plus-minus per minute (PMM). Using individual player order statistics, our model can identify an elite lineup even if the five players in the lineup have never played together, which can inform player acquisition decisions, salary negotiations, and real-time coaching decisions. We combine seven classification tools into a unanimous consent classifier (all-or-nothing classifier, or ANC) in which a lineup is predicted to be elite only if all seven classifiers predict it to be elite. In this way, we achieve high positive predictive value (i.e., precision), the likelihood that a lineup classified as elite will indeed have a positive PMM. We train and test the model on individual player and lineup data from the 2017-18 season and use the model to predict the performance of lineups drawn from all 30 NBA teams' 2018-19 regular season rosters. Although the ANC is conservative and misses some high-performing lineups, it achieves high precision and recommends positionally balanced lineups.

stat.AP↗
arXiv2022

Noisy Learning for Neural ODEs Acts as a Robustness Locus Widening

We investigate the problems and challenges of evaluating the robustness of Differential Equation-based (DE) networks against synthetic distribution shifts. We propose a novel and simple accuracy metric which can be used to evaluate intrinsic robustness and to validate dataset corruption simulators. We also propose methodology recommendations, destined for evaluating the many faces of neural DEs' robustness and for comparing them with their discrete counterparts rigorously. We then use this criteria to evaluate a cheap data augmentation technique as a reliable way for demonstrating the natural robustness of neural ODEs against simulated image corruptions across multiple datasets.

cs.LG↗
arXiv2022

Realization Theory Of Recurrent Neural ODEs Using Polynomial System Embeddings

In this paper we show that neural ODE analogs of recurrent (ODE-RNN) and Long Short-Term Memory (ODE-LSTM) networks can be algorithmically embeddeded into the class of polynomial systems. This embedding preserves input-output behavior and can suitably be extended to other neural DE architectures. We then use realization theory of polynomial systems to provide necessary conditions for an input-output map to be realizable by an ODE-LSTM and sufficient conditions for minimality of such systems. These results represent the first steps towards realization theory of recurrent neural ODE architectures, which is is expected be useful for model reduction and learning algorithm analysis of recurrent neural ODEs.

math.OC↗
arXiv2021

On the universal ellipsitomic KZB connection

We construct a twisted version of the genus one universal Knizhnik-Zamolodchikov-Bernard (KZB) connection introduced by Calaque-Enriquez-Etingof, that we call the ellipsitomic KZB connection. This is a flat connection on a principal bundle over the moduli space of $Γ$-structured elliptic curves with marked points, where $Γ=\mathbb{Z}/M\mathbb{Z}\times\mathbb{Z}/N\mathbb{Z}$, and $M,N\geq1$ are two integers. It restricts to a flat connection on $Γ$-twisted configuration spaces of points on elliptic curves, which can be used to construct a filtered-formality isomorphism for some interesting subgroups of the pure braid group on the torus. We show that the universal ellipsitomic KZB connection realizes as the usual KZB connection associated with elliptic dynamical $r$-matrices with spectral parameter, and finally, also produces representations of cyclotomic Cherednik algebras.

math.QA↗
arXiv2020

Surface Drinfeld Torsors I : Higher Genus Associators

We develop a higher genus version of Drinfeld associators by means of operad theory. We start by introducing a framed version of rational associators and Grothendieck-Teichmüller groups and show that their definition is independent of the framing data. Next, we define a framed version of the universal KZ connection and we use it to show that over the complex numbers, the rational framed Drinfeld torsor is not empty. Next, we concentrate on the higher genus version of this story. We define an operad module of framed parenthesized higher genus braidings in prounipotent groupoids and we define its chord diagram counterpart. We then use these operadic modules to operadicly define higher genus associators and Grothendieck-Teichmüller groups, which again do not depend on the framing data. Finally, we compare our results in the genus $1$ case with those appearing in the litterature.

math.QA↗
arXiv2020

Ellipsitomic Associators

We develop a notion of ellipsitomic associators by means of operad theory. We take this opportunity to review the operadic point-of-view on Drinfeld associators and to provide such an operadic approach for elliptic associators too. We then show that ellipsitomic associators do exist, using the monodromy of the universal ellipsitomic KZB connection, that we introduced in a previous work. We finally relate the KZB ellipsitomic associator to certain Eisenstein series associated with congruence subgroups of $SL_2(\mathbb{Z})$, and to twisted elliptic multiple zeta values.

math.QA↗
arXiv2020

A moperadic approach to cyclotomic associators

This is a companion paper to "Ellipsitomic associators". We provide a (m)operadic description of Enriquez's torsor of cyclotomic associators, as well as of its associated cyclotomic Grothendieck-Teichm\"uller groups.

math.QA↗