Gravitational Duals from Equations of State

Behavioral malware detection has been shown to be an effective method for detecting malware running on computing hosts. Machine learning (ML) models are often used for this task, which use representative behavioral data from a device to make a classification as to whether an observation is malware or not. Although these models can perform well, machine learning models in security are often trained on imbalanced training datasets that yield poor real-world efficacy, as they favor the overrepresented class. Thus, we need a way to augment the underrepresented class. Some common data augmentation techniques include SMOTE, data resampling/upsampling, or using generative algorithms. In this work, we explore using generative algorithms for this task, and show how those results compare to results obtained using SMOTE and upsampling. Specifically, we feed the less-represented class of data into a Generative Adversarial Network (GAN) to create enough realistic synthetic data to balance the dataset. In this work, we show how using a GAN to balance a dataset that favors benign data helps a shallow Neural Network achieve a higher Area Under the Receiver Operating Characteristic Curve (AUC) and a lower False Positive Rate (FPR).

Behavioral malware detection is an effective way to detect ever-changing malware. Often, kernel-level system calls are collected on device and then processed and fed to machine learning models. In this work, we show that using simple natural language processing (NLP) techniques on system calls, such as a bag-of-n-grams model, coupled with shallow machine learning classifiers, are not as useful for stealthier malware. In contrast, training a Word2Vec-like model, which we call sys2vec, on the system call traces and feeding the resulting embeddings to a language model classifier provides consistently better results. We evaluate and compare the two classifiers using Area Under the Receiver Operating Characteristic Curve (AUC) and the True Positive Rate (TPR) at an acceptable False Positive Rate (FPR). We then discuss how this work can be further expanded in the language model space going forward.

European Physical Journal C

This paper presents a study of Z→ llγ decays with the ATLAS detector at the Large Hadron Collider. The analysis uses a proton–proton data sample corresponding to an integrated luminosity of 20.2 fb− 1 collected at a centre-ofmass energy

arXiv preprint arXiv:2403.15742

We propose a possible quantum signature of the early Universe that could lead to observational imprints of the quantum nature of the inflationary period. Graviton production in the presence of an inflaton scalar field results in entangled states in polarization. This is because of a non-trivial effect due to the derivatives on two scalar fluctuations and it provides a fingerprint that depends on the polarization of the graviton that Alice and/or Bob measured in their patch. At horizon crossing, interactions between the gravitons and inflatons perform the required Bell experiments leading to a definitive measure. We hint how this signature could be measure in the high-order correlation function of galaxies, in particular on the halo bias and the intrinsic alignment.

Monthly Notices of the Royal Astronomical Society

In this paper, we introduce a novel data augmentation methodology based on Conditional Progressive Generative Adversarial Networks (CPGAN) to generate diverse black hole (BH) images, accounting for variations in spin and electron temperature prescriptions. These generated images are valuable resources for training deep learning algorithms to accurately estimate black hole parameters from observational data. Our model can generate BH images for any spin value within the range of [−1, 1], given an electron temperature distribution. To validate the effectiveness of our approach, we employ a convolutional neural network to predict the BH spin using both the GRMHD images and the images generated by our proposed model. Our results demonstrate a significant performance improvement when training is conducted with the augmented data set while testing is performed using GRMHD simulated data …

Journal of Cosmology and Astroparticle Physics

The local supercluster acts as a gravity deflection source for cosmic background neutrinos. This deflection by gravity changes the neutrino helicity and therefore has important consequences for ground based tritium capture experiments aimed at determining if the neutrino is Dirac or Majorana. Here we explore the deflection effect of the local supercluster using two simulations from the DEMNUni suite characterised by very different mass resolutions, as they are both filled with 2048 3 dark matter particles (and an equal number of massive neutrino particles) but have comoving volumes of (2 h-1 Gpc) 3 and (500 h-1 Mpc) 3, respectively. We reaffirm our previous results and show that the lightest neutrinos are ultra-relativistic enough to suffer little deflection by gravity and at the same time not relativistic enough to achieve the same capture rate for Dirac and Majorana cases. This means that the capture rate in Ptolemy …

Neural networks are universal approximators and are studied for their use in solving differential equations. However, a major criticism is the lack of error bounds for obtained solutions. This paper proposes a technique to rigorously evaluate the error bound of Physics-Informed Neural Networks (PINNs) on most linear ordinary differential equations (ODEs), certain nonlinear ODEs, and first-order linear partial differential equations (PDEs). The error bound is based purely on equation structure and residual information and does not depend on assumptions of how well the networks are trained. We propose algorithms that bound the error efficiently. Some proposed algorithms provide tighter bounds than others at the cost of longer run time.

The ATLAS detector is installed in its experimental cavern at Point 1 of the CERN Large Hadron Collider. During Run 2 of the LHC, a luminosity of was routinely achieved at the start of fills, twice the design luminosity. For Run 3, accelerator improvements, notably luminosity levelling, allow sustained running at an instantaneous luminosity of , with an average of up to 60 interactions per bunch crossing. The ATLAS detector has been upgraded to recover Run 1 single-lepton trigger thresholds while operating comfortably under Run 3 sustained pileup conditions. A fourth pixel layer 3.3 cm from the beam axis was added before Run 2 to improve vertex reconstruction and -tagging performance. New Liquid Argon Calorimeter digital trigger electronics, with corresponding upgrades to the Trigger and Data Acquisition system, take advantage of a factor of 10 finer granularity to improve triggering on electrons, photons, taus, and hadronic signatures through increased pileup rejection. The inner muon endcap wheels were replaced by New Small Wheels with Micromegas and small-strip Thin Gap Chamber detectors, providing both precision tracking and Level-1 Muon trigger functionality. Tile Calorimeter scintillation counters were added to improve electron energy resolution and background rejection. Upgrades to Minimum Bias Trigger Scintillators and Forward Detectors improve luminosity monitoring and enable total proton-proton cross section, diffractive physics, and heavy ion measurements. These upgrades are all compatible with operation in the much harsher environment anticipated after the High-Luminosity …

We present a generalizable methodology to perform "one-shot" transfer learning on systems of linear ordinary and partial differential equations using physics informed neural networks (PINNs). PINNS have attracted researchers as an avenue through which both data and studied physical constraints can be leveraged in learning solutions to differential equations. Despite their benefits, PINNs are currently limited by the computational costs needed to train such networks on different but related tasks. Transfer learning addresses this drawback. In this work, we present a generalizable methodology to perform "one-shot" transfer learning on linear systems of equations. First, we describe a process to train PINNs on equations with varying conditions across multiple "heads". Second, we show how this multi-headed training process can be used to yield a latent space representation of a particular differential equation form. Third, we derive closed-form formulas, which represent generalized network weights that minimize the loss function. Finally, we demonstrate how the learned latent representation and derived network weights can be utilized to instantaneously transfer learn solutions to equations, demonstrating the ability to quickly solve many systems of equations in a variety of environments.

Journal of Cosmology and Astroparticle Physics

We present a proof-of-principle determination of the Hubble parameter H (z) from photometric data, obtaining a determination at an effective redshift of z= 0.75 (0.65< z< 0.85) of H (0.75)= 105.0±7.9 (stat)±7.3 (sys) km s-1 Mpc-1, with 7.5% statistical and 7% systematic (10% with statistical and systematics combined in quadrature) accuracy. This is obtained in a cosmology model-independent fashion, but assuming a linear age-redshift relation in the relevant redshift range, as such, it can be used to constrain arbitrary cosmologies as long as H (z) can be considered slowly varying over redshift. In particular, we have applied a neural network, trained on a well-studied spectroscopic sample of 140 objects, to the COSMOS2015 survey to construct a set of 19 thousand near-passively evolving galaxies and build an age-redshift relation. The Hubble parameter is given by the derivative of the red envelope of the age-redshift …

Physical Review D

The field of machine learning has drawn increasing interest from various other fields due to the success of its methods at solving a plethora of different problems. An application of these has been to train artificial neural networks to solve differential equations without the need of a numerical solver. This particular application offers an alternative to conventional numerical methods, with advantages such as lower memory required to store solutions, parallelization, and, in some cases, a lower overall computational cost than its numerical counterparts. In this work, we train artificial neural networks to represent a bundle of solutions of the differential equations that govern the background dynamics of the Universe for four different models. The models we have chosen are Λ CDM, the Chevallier-Polarski-Linder parametric dark energy model, a quintessence model with an exponential potential, and the Hu-Sawicki f (R …

The European Physical Journal C

The ICARUS collaboration employed the 760-ton T600 detector in a successful 3-year physics run at the underground LNGS laboratory, performing a sensitive search for LSND-like anomalous appearance in the CERN Neutrino to Gran Sasso beam, which contributed to the constraints on the allowed neutrino oscillation parameters to a narrow region around 1 eV. After a significant overhaul at CERN, the T600 detector has been installed at Fermilab. In 2020 the cryogenic commissioning began with detector cool down, liquid argon filling and recirculation. ICARUS then started its operations collecting the first neutrino events from the booster neutrino beam (BNB) and the Neutrinos at the Main Injector (NuMI) beam off-axis, which were used to test the ICARUS event selection, reconstruction and analysis algorithms. ICARUS successfully completed its commissioning phase in June 2022. The first goal of the …

We use a two-step procedure to train Bayesian neural networks that provide uncertainties over the solutions to differential equation (DE) systems provided by Physics-Informed Neural Networks (PINNs). We take advantage of available error bounds over PINNs to formulate a heteroscedastic variance that improves the uncertainty estimation. Furthermore, we solve forward problems and utilize the uncertainties obtained to improve parameter estimation in inverse problems in the fields of cosmology and fermentation.

Journal of Cosmology and Astroparticle Physics

Evidence for almost spatial flatness of the Universe has been provided from several observational probes, including the Cosmic Microwave Background (CMB) and Baryon Acoustic Oscillations (BAO) from galaxy clustering data. However, other than inflation, and in this case only in the limit of infinite time, there is no strong a priori motivation for a spatially flat Universe. Using the renormalization group (RG) technique in curved spacetime, we present in this work a theoretical motivation for spatial flatness. Starting from a general spacetime, the first step of the RG, coarse-graining, gives a Friedmann-Lemaître-Robertson-Walker (FLRW) metric with a set of parameters. Then, we study the rescaling properties of the curvature parameter, and find that zero spatial curvature of the FLRW metric is singled out as the unique scale-free, non-singular background for cosmological perturbations.

INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS

There is a wave of interest in using physics-informed neural networks for solving differential equations. Most of the existing methods are based on feed-forward networks, while recurrent neural networks solvers have not been extensively explored. We introduce a reservoir computing (RC) architecture, an echo-state recurrent neural network capable of discovering approximate solutions that satisfy ordinary differential equations (ODEs). We suggest an approach to calculate time derivatives of recurrent neural network outputs without using back-propagation. The internal weights of an RC are fixed, while only a linear output layer is trained, yielding efficient training. However, RC performance strongly depends on finding the optimal hyper-parameters, which is a computationally expensive process. We use Bayesian optimization to discover optimal sets in a high-dimensional hyper-parameter space efficiently and …

Journal of High Energy Physics

Erratum to: Holographic bubbles with Jecco: expanding, collapsing and critical Erratum to: Holographic bubbles with Jecco: expanding, collapsing and critical Full Text JHEP 0 3 ( 2 0 2 3 ) 2 2 5 Published for SISSA by Springer Received: March 21, 2023 Accepted: March 23, 2023 Published: March 28, 2023 Erratum: Holographic bubbles with Jecco: expanding, collapsing and critical Yago Bea,a,b Jorge Casalderrey-Solana,b Thanasis Giannakopoulos,c Aron Jansen,b David Mateos,b,d Mikel Sanchez-Garitaonandiab and Miguel Zilhãoe,c aDepartment of Physics and Helsinki Institute of Physics, University of Helsinki, PL 64, FI-00014 Helsinki, Finland bDepartament de Física Quàntica i Astrofísica and Institut de Ciències del Cosmos (ICC), Universitat de Barcelona, Martí i Franquès 1, ES-08028, Barcelona, Spain cCentro de Astrofísica e Gravitação — CENTRA, Departamento de Física, Instituto Superior Técnico — …

arXiv preprint arXiv:2308.06404

We conducted empirical experiments to assess the transferability of a light curve transformer to datasets with different cadences and magnitude distributions using various positional encodings (PEs). We proposed a new approach to incorporate the temporal information directly to the output of the last attention layer. Our results indicated that using trainable PEs lead to significant improvements in the transformer performances and training times. Our proposed PE on attention can be trained faster than the traditional non-trainable PE transformer while achieving competitive results when transfered to other datasets.

arXiv preprint arXiv:2403.14763

Holography relates gravitational theories in five dimensions to four-dimensional quantum field theories in flat space. Under this map, the equation of state of the field theory is encoded in the black hole solutions of the gravitational theory. Solving the five-dimensional Einstein's equations to determine the equation of state is an algorithmic, direct problem. Determining the gravitational theory that gives rise to a prescribed equation of state is a much more challenging, inverse problem. We present a novel approach to solve this problem based on physics-informed neural networks. The resulting algorithm is not only data-driven but also informed by the physics of the Einstein's equations. We successfully apply it to theories with crossovers, first- and second-order phase transitions.

arXiv preprint arXiv:2403.14763

Holography relates gravitational theories in five dimensions to four-dimensional quantum field theories in flat space. Under this map, the equation of state of the field theory is encoded in the black hole solutions of the gravitational theory. Solving the five-dimensional Einstein's equations to determine the equation of state is an algorithmic, direct problem. Determining the gravitational theory that gives rise to a prescribed equation of state is a much more challenging, inverse problem. We present a novel approach to solve this problem based on physics-informed neural networks. The resulting algorithm is not only data-driven but also informed by the physics of the Einstein's equations. We successfully apply it to theories with crossovers, first- and second-order phase transitions.

arXiv preprint arXiv:2403.14763

Holography relates gravitational theories in five dimensions to four-dimensional quantum field theories in flat space. Under this map, the equation of state of the field theory is encoded in the black hole solutions of the gravitational theory. Solving the five-dimensional Einstein's equations to determine the equation of state is an algorithmic, direct problem. Determining the gravitational theory that gives rise to a prescribed equation of state is a much more challenging, inverse problem. We present a novel approach to solve this problem based on physics-informed neural networks. The resulting algorithm is not only data-driven but also informed by the physics of the Einstein's equations. We successfully apply it to theories with crossovers, first- and second-order phase transitions.