Measurement of the Electron-Neutrino Charged-Current Cross Sections on ${}^{127}$I with the COHERENT NaI$\nu$E detector

Using an 185-kg NaI[Tl] array, COHERENT has measured the inclusiveelectron-neutrino charged-current cross section on ${}^{127}$I with piondecay-at-rest neutrinos produced by the Spallation Neutron Source at Oak RidgeNational Laboratory. Iodine is one the heaviest targets for which low-energy($\leq$ 50 MeV) inelastic neutrino-nucleus processes have been measured, andthis is the first measurement of its inclusive cross section. After a five-yeardetector exposure, COHERENT reports a flux-averaged cross section for electronneutrinos of $9.2^{+2.1}_{-1.8} \times 10^{-40}$ cm$^2$. This corresponds to avalue that is $\sim$41% lower than predicted using the MARLEY event generatorwith a measured Gamow-Teller strength distribution. In addition, the observedvisible spectrum from charged-current scattering on $^{127}$I has been measuredbetween 10 and 55 MeV, and the exclusive zero-neutron and one-or-more-neutronemission cross sections are measured to be $5.2^{+3.4}_{-3.1} \times 10^{-40}$and $2.2^{+3.5}_{-2.2} \times 10^{-40}$ cm$^2$, respectively.

First-principles electron-phonon interactions and electronic transport in large-angle twisted bilayer graphene

Data-driven compression of electron-phonon interactions

First-principles calculations of electron interactions in materials have seenrapid progress in recent years, with electron-phonon (e-ph) interactions beinga prime example. However, these techniques use large matrices encoding theinteractions on dense momentum grids, which reduces computational efficiencyand obscures interpretability. For e-ph interactions, existing interpolationtechniques leverage locality in real space, but the high dimensionality of thedata remains a bottleneck to balance cost and accuracy. Here we show anefficient way to compress e-ph interactions based on singular valuedecomposition (SVD), a widely used matrix / image compression technique.Leveraging (un)constrained SVD methods, we accurately predict materialproperties related to e-ph interactions - including charge mobility, spinrelaxation times, band renormalization, and superconducting criticaltemperature - while using only a small fraction (1-2%) of the interaction data.These findings unveil the hidden low-dimensional nature of e-ph interactions.Furthermore, they accelerate state-of-the-art first-principles e-phcalculations by about two orders of magnitudes without sacrificing accuracy.Our Pareto-optimal parametrization of e-ph interactions can be readilygeneralized to electron-electron and electron-defect interactions, as well asto other couplings, advancing quantitative studies of condensed matter.

Nanometer-Scale Acoustic Wave Packets Generated by Stochastic Core-Level Photoionization Events

From Fordism to Tourism Genoa, Turin, and Milan

Prelims

Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection

Fraudulent transactions and how to detect them remain a significant problem for financial institutions around the world. The need for advanced fraud detection systems to safeguard assets and maintain customer trust is paramount for financial institutions, but some factors make the development of effective and efficient fraud detection systems a challenge. One of such factors is the fact that fraudulent transactions are rare and that many transaction datasets are imbalanced; that is, there are fewer significant samples of fraudulent transactions than legitimate ones. This data imbalance can affect the performance or reliability of the fraud detection model. Moreover, due to the data privacy laws that all financial institutions are subject to follow, sharing customer data to facilitate a higher-performing centralized model is impossible. Furthermore, the fraud detection technique should be transparent so that it does not affect the user experience. Hence, this research introduces a novel approach using Federated Learning (FL) and Explainable AI (XAI) to address these challenges. FL enables financial institutions to collaboratively train a model to detect fraudulent transactions without directly sharing customer data, thereby preserving data privacy and confidentiality. Meanwhile, the integration of XAI ensures that the predictions made by the model can be understood and interpreted by human experts, adding a layer of transparency and trust to the system. Experimental results, based on realistic transaction datasets, reveal that the FL-based fraud detection system consistently demonstrates high performance metrics. This study grounds FL’s potential as an effective and privacy-preserving tool in the fight against fraud.

A review and analysis of computational approaches in diagnosing, monitoring, controlling, and developing treatments and vaccines against the Zika virus

Predicting Data Exfiltration using Supervised Machine Learning based on Tactics Mapping from Threat Reports and Event Logs

A Gaussian Process Regression Method to Nowcast Cloud-to-Ground Lightning from Remote Sensing and Numerical Weather Modeling Data

The climate change of the last decades is causing an increase in the frequency of extreme event occurrences. Numerous studies confirm the link between extreme meteorological events and lightning activity. The possibility of having short-term predictions of the intensity of lightning phenomena would allow the near-real time monitoring of the evolution of these events. Such knowledge would help in defining operational strategies to mitigate effects on the population and the infrastructures. In this context, this paper proposes a multidisciplinary approach aiming at developing a regression algorithm to nowcast the density of cloud-to-ground lightning strokes one hour in advance. The algorithm is developed on the basis of a joint combination of remote sensing data, numerical weather prediction model outcomes, and lightning observations. The possible dependence between the meteorological data and the density of lightning is estimated using Gaussian Process Regression models. Separate models are created for positive and negative strokes and for strokes occurrences over the land or sea. The experimental results indicate that the model quite accurately estimates low numbers of strokes whereas larger numbers of strokes are estimated with higher errors. Nevertheless, their presence is correctly detected. This suggests the potential of the method as a processing tool to support the management of weather-related hazards.