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The PlasmaKristall-4 experiment on the International Space Station allows for the study of the 3-dimensional interaction between plasma and dust particles. Previous simulations of the PK-4 environment have discovered fast moving ionization waves in the dc discharge[1]. These ionization waves vary the plasma parameters by up to an order of magnitude, which may affect the mechanisms responsible for the self-organization of chains seen in the PK-4 experiment. Here, we adapt a molecular dynamics simulation to employ temporally varying plasma conditions in order to investigate the effect on the dust charging and electrostatic potential. In order to describe the differences between the average of the plasma conditions and the time-varying plasma condition, we present a model to reproduce the potential that takes into account the negative potential from the dust grain and the positive potential from the ion wake., Comment: The following article has been submitted to Physics of Plasmas

Large language models (LLMs) have revolutionized how we interact with technology, but their personalization to individual user preferences remains a significant challenge, particularly in on-device applications. Traditional methods often depend heavily on labeled datasets and can be resource-intensive. To address these issues, we present Adaptive Self-Supervised Learning Strategies (ASLS), which utilizes self-supervised learning techniques to personalize LLMs dynamically. The framework comprises a user profiling layer for collecting interaction data and a neural adaptation layer for real-time model fine-tuning. This innovative approach enables continuous learning from user feedback, allowing the model to generate responses that align closely with user-specific contexts. The adaptive mechanisms of ASLS minimize computational demands and enhance personalization efficiency. Experimental results across various user scenarios illustrate the superior performance of ASLS in boosting user engagement and satisfaction, highlighting its potential to redefine LLMs as highly responsive and context-aware systems on-device., Comment: First ASLS

We present a computational framework for obtaining multidimensional phase-space solutions of systems of non-linear coupled differential equations, using high-order implicit Runge-Kutta Physics- Informed Neural Networks (IRK-PINNs) schemes. Building upon foundational work originally solving differential equations for fields depending on coordinates [J. Comput. Phys. 378, 686 (2019)], we adapt the scheme to a context where the coordinates are treated as functions. This modification enables us to efficiently solve equations of motion for a particle in an external field. Our scheme is particularly useful for explicitly time-independent and periodic fields. We apply this approach to successfully solve the equations of motion for a mass particle placed in a central force field and a charged particle in a periodic electric field., Comment: 10 pages, 4 figures, published in the International Conference on Scientific Computing and Machine Learning, see http://scml.jp

In this paper, we explore accelerating Hamiltonian ground state energy calculation on NISQ devices. We suggest using search-based methods together with machine learning to accelerate quantum algorithms, exemplified in the Quantum Eigensolver use case. We trained two small models on classically mined data from systems with up to 16 qubits, using XGBoost's Python regressor. We evaluated our preliminary approach on 20-, 24- and 28-qubit systems by optimising the Eigensolver's hyperparameters. These models predict hyperparameter values, leading to a 0.13\%-0.15\% reduction in error when tested on 28-qubit systems. However, due to inconclusive results with 20- and 24-qubit systems, we suggest further examination of the training data based on Hamiltonian characteristics. In future work, we plan to train machine learning models to optimise other aspects or subroutines of quantum algorithm execution beyond its hyperparameters.

The in-vivo identification of the kidney stone types during an ureteroscopy would be a major medical advance in urology, as it could reduce the time of the tedious renal calculi extraction process, while diminishing infection risks. Furthermore, such an automated procedure would make possible to prescribe anti-recurrence treatments immediately. Nowadays, only few experienced urologists are able to recognize the kidney stone types in the images of the videos displayed on a screen during the endoscopy. Thus, several deep learning (DL) models have recently been proposed to automatically recognize the kidney stone types using ureteroscopic images. However, these DL models are of black box nature whicl limits their applicability in clinical settings. This contribution proposes a case-based reasoning DL model which uses prototypical parts (PPs) and generates local and global descriptors. The PPs encode for each class (i.e., kidney stone type) visual feature information (hue, saturation, intensity and textures) similar to that used by biologists. The PPs are optimally generated due a new loss function used during the model training. Moreover, the local and global descriptors of PPs allow to explain the decisions ("what" information, "where in the images") in an understandable way for biologists and urologists. The proposed DL model has been tested on a database including images of the six most widespread kidney stone types. The overall average classification accuracy was 90.37. When comparing this results with that of the eight other DL models of the kidney stone state-of-the-art, it can be seen that the valuable gain in explanability was not reached at the expense of accuracy which was even slightly increased with respect to that (88.2) of the best method of the literature. These promising and interpretable results also encourage urologists to put their trust in AI-based solutions., Comment: Paper submitted to Artificial Intelligence in Medicine. (AIIM), Elsevier

We report the first measurement of the azimuthal anisotropy of J$/\psi$ at forward rapidity ($1.2<|\eta|<2.2$) in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV at the Relativistic Heavy Ion Collider. The data were collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The second Fourier coefficient ($v_2$) of the azimuthal distribution of $J/\psi$ is determined as a function of the transverse momentum ($p_T$) using the event-plane method. The measurements were performed for several selections of collision centrality: 0\%--50\%, 10\%--60\%, and 10\%-40\%. We find that in all cases the values of $v_2(p_T)$, which quantify the elliptic flow of J$/\psi$, are consistent with zero. The results are consistent with measurements at midrapidity, indicating no significant elliptic flow of the J$/\psi$ within the quark-gluon-plasma medium at collision energies of $\sqrt{s_{_{NN}}}=200$ GeV., Comment: 369 authors from 72 institutions, 12 pages, 7 figures, 5 tables. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

We present the first forward-rapidity measurements of elliptic anisotropy of open-heavy-flavor muons at the BNL Relativistic Heavy Ion Collider. The measurements are based on data samples of Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The measurements are performed in the pseudorapidity range $1.2<|\eta|<2$ and cover transverse momenta $1

Recently, Gheibi, Jorgensen, and Takahashi introduced a new homological invariant called quasi-projective dimension, which is a generalization of projective dimension. They proved that the depth formula holds for two finitely gene\-rated Tor-independent modules over a Noetherian local ring when one of the modules has finite quasi-projective dimension. In this paper, we extend this result by pro\-ving that for finitely generated modules \( M \) and \( N \) over a Noetherian local ring \( R \) with \( \operatorname{qpd}_R M < \infty \), the equality \( \operatorname{depth} N = \operatorname{depth}(\operatorname{Tor}_q^R(M,N)) + \operatorname{qpd}_R M - q \) holds, where \( q := \sup\{ i \geq 0 : \operatorname{Tor}_i^R(M,N) \neq 0 \} \), provided that \( q < \infty \), and either \( q = 0 \) or \( \operatorname{depth}(\operatorname{Tor}_q^R(M,N)) \leq 1 \). This outcome generalizes a celebrated theorem by Auslander and allows us to derive new consequences and applications, for instance, we recover an important theorem of Araya and Yoshino., Comment: 10 pages, comments and suggestions are welcome. In this new version, Corollary 4.4 has been added, which recover a theorem of Araya and Yoshino

Measuring Efficiency in neural network system development is an open research problem. This paper presents an experimental framework to measure the training efficiency of a neural architecture. To demonstrate our approach, we analyze the training efficiency of Convolutional Neural Networks and Bayesian equivalents on the MNIST and CIFAR-10 tasks. Our results show that training efficiency decays as training progresses and varies across different stopping criteria for a given neural model and learning task. We also find a non-linear relationship between training stopping criteria, training Efficiency, model size, and training Efficiency. Furthermore, we illustrate the potential confounding effects of overtraining on measuring the training efficiency of a neural architecture. Regarding relative training efficiency across different architectures, our results indicate that CNNs are more efficient than BCNNs on both datasets. More generally, as a learning task becomes more complex, the relative difference in training efficiency between different architectures becomes more pronounced.

We present an open source Python library for simulating overlapping (i.e., blended) images of galaxies and performing self-consistent comparisons of detection and deblending algorithms based on a suite of metrics. The package, named Blending Toolkit (BTK), serves as a modular, flexible, easy-to-install, and simple-to-use interface for exploring and analyzing systematic effects related to blended galaxies in cosmological surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST). BTK has three main components: (1) a set of modules that perform fast image simulations of blended galaxies, using the open source image simulation package GalSim; (2) a module that standardizes the inputs and outputs of existing deblending algorithms; (3) a library of deblending metrics commonly defined in the galaxy deblending literature. In combination, these modules allow researchers to explore the impacts of galaxy blending in cosmological surveys. Additionally, BTK provides researchers who are developing a new deblending algorithm a framework to evaluate algorithm performance and make principled comparisons with existing deblenders. BTK includes a suite of tutorials and comprehensive documentation. The source code is publicly available on GitHub at https://github.com/LSSTDESC/BlendingToolKit., Comment: 15 pages, 9 figures, 2 tables

We examine 4 years of Kepler 30-min data, and 5 Sectors of TESS 2-min data for the dM3 star KIC-8507979/TIC-272272592. This rapidly rotating (P=1.2 day) star has previously been identified as flare active, with a possible long-term decline in its flare output. Such slow changes in surface magnetic activity are potential indicators of Solar-like activity cycles, which can yield important information about the structure of the stellar dynamo. We find that while TIC-272272592 shows evidence for both short and long timescale variations in its flare activity, it is unlikely physically motivated. Only a handful of stars have been subjected to such long baseline point-in-time flare studies, and we urge caution in comparing results between telescopes due to differences in bandpass, signal to noise, and cadence. In this work, we develop an approach to measure variations in the flare frequency distributions over time, which is quantified as a function of the observing baseline. For TIC-272272592, we find a $2.7\sigma$ detection of a Sector which has a flare deficit, therefore indicating the short term variation could be a result of sampling statistics. This quantifiable approach to describing flare rate variation is a powerful new method for measuring the months-to-years changes in surface magnetic activity, and provides important constraints on activity cycles and dynamo models for low mass stars., Comment: Accepted to AAS Journals, 12 pages, 9 Figures and 1 Table

The $J/\psi$ and $\psi(2S)$ charmonium states, composed of $c\bar{c}$ quark pairs and known since the 1970s, are widely believed to serve as ideal probes to test quantum chromodynamics in high-energy hadronic interactions. However, there is not yet a complete understanding of the charmonium-production mechanism. Recent measurements of $J/\psi$ production as a function of event charged-particle multiplicity at the collision energies of both the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) show enhanced $J/\psi$ production yields with increasing multiplicity. One potential explanation for this type of dependence is multiparton interactions (MPI). We carry out the first measurements of self-normalized $J/\psi$ yields and the $\psi(2S)$ to $J/\psi$ ratio at both forward and backward rapidities as a function of self-normalized charged-particle multiplicity in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV. In addition, detailed {\sc pythia} studies tuned to RHIC energies were performed to investigate the MPI impacts. We find that the PHENIX data at RHIC are consistent with recent LHC measurements and can only be described by {\sc pythia} calculations that include MPI effects. The forward and backward $\psi(2S)$ to $J/\psi$ ratio, which serves as a unique and powerful approach to study final-state effects on charmonium production, is found to be less dependent on the charged-particle multiplicity., Comment: 301 authors from 69 institutions, 8 pages, 3 figures. v1 is version submitted to Physical Review D Letters. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Connected Autonomous Vehicles have great potential to improve automobile safety and traffic flow, especially in cooperative applications where perception data is shared between vehicles. However, this cooperation must be secured from malicious intent and unintentional errors that could cause accidents. Previous works typically address singular security or reliability issues for cooperative driving in specific scenarios rather than the set of errors together. In this paper, we propose CONClave, a tightly coupled authentication, consensus, and trust scoring mechanism that provides comprehensive security and reliability for cooperative perception in autonomous vehicles. CONClave benefits from the pipelined nature of the steps such that faults can be detected significantly faster and with less compute. Overall, CONClave shows huge promise in preventing security flaws, detecting even relatively minor sensing faults, and increasing the robustness and accuracy of cooperative perception in CAVs while adding minimal overhead., Comment: 6 pages, 6 figures, Design Automation Conference June 2024

Luminous accretion disks around black holes are expected to have densities of $\sim 10^{15-22}\,$cm$^{-3}$, which are high enough such that plasma physics effects become important. Many of these effects have been traditionally neglected in the calculation of atomic parameters, and therefore from photoionization models, and ultimately also from X-ray reflection models. In this paper, we describe updates to the atomic rates used by the XSTAR code, which is in turn part of the XILLVER disk reflection model. We discuss the effect of adding necessary high density corrections into the XILLVER code. Specifically, we find that the change of recombination rates play an important role, dominating the differences between model versions. With synthetic spectra, we show that even in a highly ionized state, high density slabs can produce strong iron ($\sim$6.5-9$\,$keV) and oxygen ($\sim0.6-0.8\,$keV) resonance features. The significant iron emission could address the problem of the supersolar iron abundances found in some sources., Comment: 21 pages, 13 figures. Accepted by ApJ

The aim of this paper is to study the existence of eigenvalues in the gap of the essential spectrum of the one-dimensional Dirac operator in the presence of a bounded potential. We employ a generalized variational principle to prove existence of such eigenvalues, estimate how many eigenvalues there are, and give upper and lower bounds for them.

The jet cross-section and jet-substructure observables in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV were measured by the PHENIX Collaboration at the Relativistic Heavy Ion Collider (RHIC). Jets are reconstructed from charged-particle tracks and electromagnetic-calorimeter clusters using the anti-$k_{t}$ algorithm with a jet radius $R=0.3$ for jets with transverse momentum within $8.0

Let $G$ be a connected graph on $n$ vertices and $1 \le k \le n-1$ an integer. The $k$-token graph of $G$ is the graph $F_k(G)$ whose vertices are all the $k$-subsets of vertices of $G$, two of which are adjacent whenever their symmetric difference is an edge of $G$. Every automorphism of $G$ induces an automorphism of $F_k(G)$ in a natural way. Suppose that $S:=\{x,y\}$ is a cut set of $G$, such that $x$ and $y$ have the same neighbours in $G\setminus \{x,y\}$. In this paper we show that there exist a large number of automorphisms of $F_k(G)$ defined by $S$ that are not induced by automorphisms of $G$. We also describe the group produced by all such $2$-cuts of $G$.

We focus on strongly connected, strong for short, digraphs since in this setting distance is defined for every pair of vertices. Distance ideals generalize the spectrum and Smith normal form of several distance matrices associated with strong digraphs. We introduce the concept of pattern which allow us to characterize the family $\Gamma_1$ of digraphs with only one trivial distance ideal over ${\mathbb Z}$. This result generalizes an analogous result for undirected graphs that states that connected graphs with one trivial ideal over $\mathbb{Z}$ consists of either complete graphs or complete bipartite graphs. It turns out that the strong digraphs in $\Gamma_1$ consists in the circuit with 3 vertices and a family $\Lambda$ of strong digraphs that contains complete graphs and complete bipartite graphs, regarded as digraphs. We also compute all distance ideals of some strong digraphs in the family $\Lambda$. Then, we explore circuits, which turn out to be an infinite family of minimal forbidden digraphs, as induced subdigraphs, for the strong digraphs in $\Gamma_1$. This suggests that a characterization of $\Gamma_1$ in terms of forbidden induced subdigraphs is harder than using patterns., Comment: 18 pages, 10 figures

Many ligands commonly used to prepare nanoparticle catalysts with precise nanoscale features contain nitrogen (e.g., oleylamine); here, we found that the use of nitrogen-containing ligands during the synthesis of metal oxide nanoparticle catalysts substantially impacted product analysis during photocatalytic studies. We confirmed these experimental results via hybrid Density Functional Theory computations of the materials' electronic properties to evaluate their viability as photocatalysts for nitrogen reduction. This nitrogen ligand contamination, and subsequent interference in photocatalytic studies, is avoidable through the careful design of synthetic pathways that exclude nitrogen-containing constituents. This result highlights the urgent need for careful evaluation of catalyst synthesis protocols, as contamination by nitrogen-containing ligands may go unnoticed since the presence of nitrogen is often not detected or probed., Comment: 13 pages, 3 figures

An environment interacting with a quantum system can enhance transport through the suppression of quantum effects responsible for localization. In this paper, we study the interplay between bulk dephasing and a linear potential in a boundary-driven tight-binding chain. A linear potential induces Wannier-Stark localization in the absence of noise, while dephasing induces diffusive transport in the absence of a tilt. We derive an approximate expression for the steady-state current as a function of both dephasing and tilt which closely matches the exact solution for a wide range of parameters. From it, we find that the maximum current occurs for a dephasing rate equal to the period of Bloch oscillations in the Wannier-Stark localized system. We also find that the current displays a maximum as a function of the system size, provided that the total potential tilt across the chain remains constant. Our results can be verified in current experimental platforms and represents a step forward in analytical studies of environment-assisted transport., Comment: Submitted to Frontiers in Physics. 20 pages, 7 figures

We present the first principles construction of the low-energy effective action for bosonic self-organized quantum quasicrystals. Our generalized elasticity approach retains the appropriate number of phase- and corresponding conjugate density- degrees-of-freedom required for a proper description of the Goldstone modes. For the dodecagonal and decagonal quasicrystal structures we obtain collective longitudinal and transversal excitations with an isotropic speed of sound. Meanwhile, for the octagonal structure, the coupling between phononic and phasonic degrees of freedom leads in turn to hybridization of the latter with the condensate sound mode, producing collective excitations with a longitudinal and transversal component, and an anisotropic speed of sound. Finally, we discuss the fate of each excitation mode at the low and high density phase transitions limiting the quantum quasicrystal phase., Comment: 4 pages main document, 8 pages supplemental material, 5 figures

We present a dynamic hedging scheme for S&P 500 options, where rebalancing decisions are enhanced by integrating information about the implied volatility surface dynamics. The optimal hedging strategy is obtained through a deep policy gradient-type reinforcement learning algorithm, with a novel hybrid neural network architecture improving the training performance. The favorable inclusion of forward-looking information embedded in the volatility surface allows our procedure to outperform several conventional benchmarks such as practitioner and smiled-implied delta hedging procedures, both in simulation and backtesting experiments.

We explore a system composed of scalar bosons and SU($3$) fermions in one dimension. Considering only local intra- and interspecies interactions, the system is described by the Bose-Fermi-Hubbard Hamiltonian, which is studied using the density matrix renormalization group method. In addition to the common gapless and mixed Mott insulator phases, we identify unknown gapped states, in which bosons couple with some or all flavors of fermions. We unveil different flavor-selective states characterized by itinerant fermions that coexist with an insulator state where the bosons tie with only one- or two-flavor fermions. The states reported here could be implemented in cold-atoms experiments., Comment: 10 pages, 7 figures, comments are welcome

The recent work of Horikawa and Nakagawa (2024) claims that under a complete market admitting statistical arbitrage, the difference between the hedging position provided by deep hedging and that of the replicating portfolio is a statistical arbitrage. This raises concerns as it entails that deep hedging can include a speculative component aimed simply at exploiting the structure of the risk measure guiding the hedging optimisation problem. We test whether such finding remains true in a GARCH-based market model. We observe that the difference between deep hedging and delta hedging can be a statistical arbitrage if the risk measure considered does not put sufficient relative weight on adverse outcomes. Nevertheless, a suitable choice of risk measure can prevent the deep hedging agent from including a speculative overlay within its hedging strategy., Comment: 14 pages, 1 figure

An existing challenge in power systems is the implementation of optimal demand management through dynamic pricing. This paper encompasses the design, analysis and implementation of a novel on-line pricing scheme based on coalitional game theory. The setting consists of a network with multiple energy retailers competing to attract consumers by announcing a price in a hierarchical leader-follower structure. The process of coalition formation under such a pricing scheme can be viewed as a game for which we show that a Stackelberg equilibrium exists, \ie given a price, consumers will respond by conforming to a reciprocal power consumption quantity. We propose a coalition formation algorithm and perform a game-theoretic stability analysis on the resulting coalitions. We integrate the pricing setting with a resistive micro-grid dynamic model. In this context we analyse the behaviour of the integrated system, bridging the gap between market and physical layers of the problem. Simulations provide a comparison of profits generated by the proposed scheme against a more traditional single retailer scheme, while simultaneously showing convergence towards a steady-state equilibrium. Additionally, we shed light into the system's physical response when subject to our proposed pricing scheme.

Continuous-variable projective measurements can not select individual measurement results as in the discrete case; instead, the possible outcomes are bounded by the selectivity interval of the measurement; then, it is say that continuous-variable measurement devices are insufficiently selective. By utilizing this concept we show that the probability and fidelity of teleportation in a two-mode cluster state can be handled by the localization of the selectivity interval of the measurement apparatus. Besides, we provide a mathematical expression describing the probability distribution of the measurement outcomes in the two-mode cluster, which is a fundamental solution of the heat equation. In addition, we show that the fidelity of teleportation in the two mode cluster is given by the quotient between the squared solution of a non-homogeneous heat equation and the solution of the conventional heat equation. Furthermore, we extend our approach to a configuration involving successive clusters with intermediate corrections between each teleportation step. To exemplify our proposal, we consider the specific case of a squeezed-coherent state as the quantum state under teleportation., Comment: 26 pages, 8 figures

This manuscript revisits the phenomenological emergent dark energy model (PEDE) by confronting it with recent cosmological data from early and late times. In particular we analyze PEDE model by using the baryon acoustic oscillation (BAO) measurements coming from both Dark Energy Spectroscopy Instrument (DESI) data release 1 and Sloan Digital Sky Survey (SDSS). Additionally, the measurements from cosmic chronometers, supernovae type Ia (Pantheon+), quasars, hydrogen II galaxies and cosmic background radiation distance priors are considered. By performing a Bayesian analysis based on Monte Carlo Markov Chain, we find consistent results on the constraints when SDSS and DESI are considered. However, we find higher values on the Hubble constant than Supernova $H_0$ for the Equation of State (SH0ES) does although it is still in agreement, within $1\sigma$ confidence level, when BAO measurements are added. Furthermore, we estimate the age of the Universe younger $\sim3\%$ than the one predicted by the standard cosmology. Additionally, we report values of $q_0 = -0.771^{+0.007}_{-0.007}$, $z_T = 0.764^{+0.011}_{-0.011}$ for the deceleration parameter today and the deceleration-acceleration transition redshift, respectively. However, PEDE cosmology is disfavoured by the combined samples., Comment: Version accepted in Physics of the Dark Universe

The PHENIX experiment measured the centrality dependence of two-pion Bose-Einstein correlation functions in $\sqrt{s_{_{NN}}}=200$~GeV Au$+$Au collisions at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory. The data are well represented by L\'evy-stable source distributions. The extracted source parameters are the correlation-strength parameter $\lambda$, the L\'evy index of stability $\alpha$, and the L\'evy-scale parameter $R$ as a function of transverse mass $m_T$ and centrality. The $\lambda(m_T)$ parameter is constant at larger values of $m_T$, but decreases as $m_T$ decreases. The L\'evy scale parameter $R(m_T)$ decreases with $m_T$ and exhibits proportionality to the length scale of the nuclear overlap region. The L\'evy exponent $\alpha(m_T)$ is independent of $m_T$ within uncertainties in each investigated centrality bin, but shows a clear centrality dependence. At all centralities, the L\'evy exponent $\alpha$ is significantly different from that of Gaussian ($\alpha=2$) or Cauchy ($\alpha=1$) source distributions. Comparisons to the predictions of Monte-Carlo simulations of resonance-decay chains show that in all but the most peripheral centrality class (50%-60%), the obtained results are inconsistent with the measurements, unless a significant reduction of the in-medium mass of the $\eta'$ meson is included. In each centrality class, the best value of the in-medium $\eta'$ mass is compared to the mass of the $\eta$ meson, as well as to several theoretical predictions that consider restoration of $U_A(1)$ symmetry in hot hadronic matter., Comment: 401 authors from 75 institutions, 20 pages, 15 figures, 2 tables. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

We explore the interaction between a three-level atom and a single-mode quantized cavity, known as the three-level ladder-type Jaynes-Cummings model. By employing the exact solution of the Schr\"odinger equation, we investigate how the initial conditions of the atom influence the occupation probabilities of the atomic energy levels, average photon number, and the nonclassicality of light, assessed through the Mandel $\mathcal{Q} (t)$ parameter and the Wigner function. Our findings are rigorously validated through comprehensive numerical simulations, ensuring robust and consistent outcomes.

The $^{246}$Cm(n,$\gamma$) and $^{248}$Cm(n,$\gamma$) cross-sections have been measured at the Experimental Area 2 (EAR2) of the n_TOF facility at CERN with three C$_6$D$_6$ detectors. This measurement is part of a collective effort to improve the capture cross-section data for Minor Actinides (MAs), which are required to estimate the production and transmutation rates of these isotopes in light water reactors and innovative reactor systems. In particular, the neutron capture in $^{246}$Cm and $^{248}$Cm open the path for the formation of other Cm isotopes and heavier elements such as Bk and Cf and the knowledge of (n,$\gamma$) cross-sections of these Cm isotopes plays an important role in the transport, transmutation and storage of the spent nuclear fuel. The reactions $^{246}$Cm(n,$\gamma$) and $^{248}$Cm(n,$\gamma$) have been the two first capture measurements analyzed at n_TOF EAR2. Until this experiment and two recent measurements performed at J-PARC, there was only one set of data of the capture cross-sections of $^{246}$Cm and $^{248}$Cm, that was obtained in 1969 in an underground nuclear explosion experiment. In the measurement at n_TOF a total of 13 resonances of $^{246}$Cm between 4 and 400 eV and 5 of $^{248}$Cm between 7 and 100 eV have been identified and fitted. The radiative kernels obtained for $^{246}$Cm are compatible with JENDL-5, but some of them are not with JENDL-4, which has been adopted by JEFF-3.3 and ENDF/B-VIII.0. The radiative kernels obtained for the first three $^{248}$Cm resonances are compatible with JENDL-5, however, the other two are not compatible with any other evaluation and are 20% and 60% larger than JENDL-5.

Nonlinear optical materials of atomic thickness--such as non-centrosymmetric 2H transition metal dichalcogenide monolayers--have a second order nonlinear susceptibility ($\chi^{(2)}$) whose intensity can be tuned by strain. However, whether $\chi^{(2)}$ is enhanced or reduced by tensile strain is a subject of conflicting reports. Here, we grow high-quality MoSe$_2$ monolayers under controlled biaxial strain created by two different substrates, and study their linear and non-linear optical responses with a combination of experimental and theoretical approaches. A 15-fold overall enhancement in second harmonic generation (SHG) intensity is observed on MoSe$_2$ monolayers grown on SiO$_2$ when compared to its value when on a Si$_3$N$_4$ substrate. A seven-fold enhancement was ascertained to substrate interference, and a factor of two to the enhancement of $\chi^{(2)}$ arising from biaxial strain: substrate interference and strain are independent handles to engineer the SHG strength of non-centrosymmetric 2D materials.

This study investigates the application of machine learning predictors for min-entropy estimation in Random Number Generators (RNGs), a key component in cryptographic applications where accurate entropy assessment is essential for cybersecurity. Our research indicates that these predictors, and indeed any predictor that leverages sequence correlations, primarily estimate average min-entropy, a metric not extensively studied in this context. We explore the relationship between average min-entropy and the traditional min-entropy, focusing on their dependence on the number of target bits being predicted. Utilizing data from Generalized Binary Autoregressive Models, a subset of Markov processes, we demonstrate that machine learning models (including a hybrid of convolutional and recurrent Long Short-Term Memory layers and the transformer-based GPT-2 model) outperform traditional NIST SP 800-90B predictors in certain scenarios. Our findings underscore the importance of considering the number of target bits in min-entropy assessment for RNGs and highlight the potential of machine learning approaches in enhancing entropy estimation techniques for improved cryptographic security.

We propose several hierarchical graphs that represent the semantic relations between physical theories, their fundamental constants and units of measurement. We begin with an alternative representation of Zelmanov's cube of fundamental constants as a concept lattice. We then propose the inclusion of a new fundamental constant: Milgrom's critical acceleration and discuss the implications of such analysis. We then look for the same fundamental constants in a graph that relates magnitudes and units of measurement in the International System of Units. This exercise shows the potential of visualizing hierarchical networks as a tool to better comprehend the interrelations and dependencies of physical magnitudes, units and theories. New regimes of application may be deduced, as well as an interesting reflection on our ontologies and corresponding theoretical objects., Comment: 12 pages, 9 figures, Accepted for publication in the journal Symmetry

Nonlinear photomagnetization is a process by which an oscillating electric field can induce a static magnetization. We show that all 32 crystallographic point groups admit spin polarization to second order using circularly polarized electric fields (as in the usual spin orientation) but only 29 points groups admit spin polarization to second order using linearly polarized electric fields. The excluded point groups are the highly symmetric $m$-3$m$, -$43m$ and 432. To illustrate our results, we compute the second order electric spin susceptibility of Te, Se, SnS$_2$, GaAs, InSb and Si using density functional theory which corresponds to nonmagnetic materials with and without inversion symmetry. We show that our nonlinear photomagnetization can reach magnetic moments of 0.39 $\mu_B/\text{atom}$ comparable to those of naturally occurring ferromagnets., Comment: 4+ pages, 1 figure, supplementary information

Knowledge management (KM) involves collecting, organizing, storing, and disseminating information to improve decision-making, innovation, and performance. Implementing KM at scale has become essential for organizations to effectively leverage vast accessible data. This paper is a compilation of concepts that emerged from KM workshops hosted by EMBL-EBI, attended by SMEs and industry. We provide guidance on envisioning, executing, evaluating, and evolving knowledge management platforms. We emphasize essential considerations such as setting knowledge domain boundaries and measuring success, as well as the importance of making knowledge accessible for downstream applications and non-computational users and highlights necessary personal and organizational skills for success. We stress the importance of collaboration and the need for convergence on shared principles and commitment to provide or seek resources to advance KM. The community is invited to join the journey of KM and contribute to the advancement of the field by applying and improving on the guidelines described., Comment: 18 pages, 1 figure

For uncountable downwards closed subtrees $U$ and $W$ of an $\omega_1$-tree $T$, we say that $U$ and $W$ are strongly almost disjoint if their intersection is a finite union of countable chains. The tree $T$ is strongly non-saturated if there exists a strongly almost disjoint family of $\omega_2$-many uncountable downwards closed subtrees of $T$. In this article we construct a c.c.c. forcing which adds a Suslin tree together with a family of $\omega_2$-many strongly almost disjoint automorphisms of it (and thus the square of the Suslin tree is strongly non-saturated). To achieve this goal, we introduce a new idea called $\rho$-separation, which is an adaptation to the finite context of the notion of separation which was recently introduced by Stejskalov\'{a} and the first author for the purpose of adding automorphisms of a tree with a forcing with countable conditions.

High-momentum two-particle correlations are a useful tool for studying jet-quenching effects in the quark-gluon plasma. Angular correlations between neutral-pion triggers and charged hadrons with transverse momenta in the range 4--12~GeV/$c$ and 0.5--7~GeV/$c$, respectively, have been measured by the PHENIX experiment in 2014 for Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$~GeV. Suppression is observed in the yield of high-momentum jet fragments opposite the trigger particle, which indicates jet suppression stemming from in-medium partonic energy loss, while enhancement is observed for low-momentum particles. The ratio and differences between the yield in Au$+$Au collisions and $p$$+$$p$ collisions, $I_{AA}$ and $\Delta_{AA}$, as a function of the trigger-hadron azimuthal separation, $\Delta\phi$, are measured for the first time at the Relativistic Heavy Ion Collider. These results better quantify how the yield of low-$p_T$ associated hadrons is enhanced at wide angle, which is crucial for studying energy loss as well as medium-response effects., Comment: 535 authors from 84 institutions, 12 pages, 8 figures. v2 is version accepted for publication in Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Inverse and bilevel optimization problems play a central role in both theory and applications. These two classes are known to be closely related due to the pioneering work of Dempe and Lohse (2006), and thus have often been discussed together ever since. In this paper, we consider inverse problems for multi-unit assignment valuations. Multi-unit assignment valuations form a subclass of strong-substitutes valuations that can be represented by edge-weighted complete bipartite graphs. These valuations play a key role in auction theory as the strong substitutes condition implies the existence of a Walrasian equilibrium. A recent line of research concentrated on the problem of deciding whether a bivariate valuation function is an assignment valuation or not. In this paper, we consider an \emph{inverse} variant of the problem: we are given a bivariate function $g$, and our goal is to find a bivariate multi-unit assignment valuation function $f$ that is as close to $g$ as possible. The difference between $f$ and $g$ can be measured either in $\ell_1$- or $\ell_\infty$-norm. Using tools from discrete convex analysis, we show that the problem is strongly NP-hard. On the other hand, we derive linear programming formulations that solve relaxed versions of the problem.

The present work concerns the detailed construction of a holographic model for a type-II s-wave superconductor defined on a 5-dimensional anisotropic rotating black hole. We examine the role of rotation and anisotropy on the properties of the superconductor model focusing on the condensate and the AC conductivity, for which we obtain closed formulas, using both analytical and numerical methods. The results reveal that the rotation is responsible for the appearance of a peak and for introducing an exponentially vanishing behavior in the high-frequency limit of the real component of the AC conductivity. Such a behavior aligns with that observed in high-temperature superconductor models and experiments, where the peak and vanishing behavior result from quasiparticle damping, suggesting a plausible relation between the rotation of a black hole and quasiparticle damping effects in a superconducting material. In addition, we provide a detailed construction of the vortex lattice presented in arXiv:2208.05988 and study its behavior as a function of an external uniform magnetic field. Once again, it is shown that the vortex lattice can be continuously deformed along with a change in the vortex population by virtue of the magnetic field, providing a promising avenue for holographically modeling the vortex lattice deformations observed in experimental superconducting materials. As an observed experimental effect, we describe both the vortex lattice deformation and the vortex population increment under the action of an external magnetic field in the LiFeAs type-II superconductor. These effects supplement those previously found for the FeSe type-II superconductor studied in arXiv:2208.05988., Comment: 25 pages, 15 figures

Weakly interacting massive particles (WIMPs) may interact with a virtual pion that is exchanged between nucleons. This interaction channel is important to consider in models where the spin-independent isoscalar channel is suppressed. Using data from the first science run of the LUX-ZEPLIN dark matter experiment, containing 60 live days of data in a 5.5~tonne fiducial mass of liquid xenon, we report the results on a search for WIMP-pion interactions. We observe no significant excess and set an upper limit of $1.5\times10^{-46}$~cm$^2$ at a 90\% confidence level for a WIMP mass of 33~GeV/c$^2$ for this interaction.

The existence of steady-state multiplicity or multistationarity in the Earth System raises the possibility that the Earth may reach a "tipping point" and rapidly transition to a warmer steady-state from which recovery may be practically impossible. In detailed Earth models that require extensive computation time, it is difficult to make an a priori prediction of the existence of steady-state multiplicity. In this study, we demonstrate Chemical Reaction Network Theory (CRNT) analysis of a simple heuristic box model of the Earth System carbon cycle with the human intervention of direct air capture. The analysis reveals necessary conditions for the combination of system parameters where steady-state multiplicity may exist. With this method, other negative emissions technologies (NET) may be screened in a relatively simple manner to aid in the priority setting by policymakers., Comment: arXiv admin note: text overlap with arXiv:2209.12143

The Data Acquisition System (DAQ) for the LUX-ZEPLIN (LZ) dark matter detector is described. The signals from 745 PMTs, distributed across three subsystems, are sampled with 100-MHz 32-channel digitizers (DDC-32s). A basic waveform analysis is carried out on the on-board Field Programmable Gate Arrays (FPGAs) to extract information about the observed scintillation and electroluminescence signals. This information is used to determine if the digitized waveforms should be preserved for offline analysis. The system is designed around the Kintex-7 FPGA. In addition to digitizing the PMT signals and providing basic event selection in real time, the flexibility provided by the use of FPGAs allows us to monitor the performance of the detector and the DAQ in parallel to normal data acquisition. The hardware and software/firmware of this FPGA-based Architecture for Data acquisition and Realtime monitoring (FADR) are discussed and performance measurements are described., Comment: 18 pages, 24 figures

This work proves pointwise convergence of the truncated Fourier double integral of non-Lebesgue integrable bounded variation functions. This leads to the Dirichlet-Jordan theorem proof for non-Lebesgue integrable functions, which has not been sufficiently studied. Note that recent contributions regarding this subject consider Lebesgue integrable functions, [F. Moricz, 2015], [B. Ghodadra-V. Fuulop, 2016].

In the quest for better energy storage solutions, the role of designing effective electrodes is crucial. Previous research has shown that using materials like single-side fluorinated graphene can improve the stability of ion insertion in few-layer graphene (FLG), which is vital as we move beyond lithium-ion batteries. Alternatives such as sodium and potassium, which are more abundant on Earth, appear promising, but thorough studies on how these ions insert into electrodes in stages are still needed. Our research focuses on the initial three alkali (Li, Na, K) and alkaline (Be, Mg, Ca) earth metals. Using Density Functional Theory (DFT) with advanced calculations, we've investigated how these ions interact with modified graphene at various stages of insertion. This method provides more precise electrical data and has helped us understand the complex interactions involved. Specifically, we found a new site for ion insertion that is energetically favorable. We also explored how modifying the graphene surface affects ions of different sizes and charges and examined how the number of graphene layers influences these interactions. Our discoveries are crucial for developing new materials that could replace lithium-ion batteries and provide a foundation for adjusting electrical properties in battery design through ion staging and surface modifications.

In application as hyperthermia and nanowarming, power dissipation arises when the time-dependent magnetization $M(t)$ of an out-of-equilibrium system of nanoparticles lags behind the applied field $H(t)$. The key parameter governing this process is the relaxation time $\tau$ of the system, which induces a phase shift $\phi_n$ between $H(t)$ and every nth harmonic component of $M(t)$. In this work, we present an expression for $M(t)$ in terms of $\tau$ and the equilibrium magnetization, valid for any magnetic system exhibiting odd equilibrium response. From this calculation, we obtain a method for determining the effective $\tau$ of a MNPs sample directly from the experimental measurement of $M(t)$. Additionally, we demonstrate that the power dissipation (SAR: Specific Absorption Rate) of any magnetic sample under a sinusoidal field can be obtained from the first harmonic component of $M(t)$. As an illustrative application, we explore the variation of $\tau$ for magnetic MNPs in aqueous suspension during the melting process of the matrix. In this case, the change in $\tau$ can be understood as a result of the reorientation of the MNPs in the direction of the applied field as the matrix becomes liquid.

Understanding the insulin signaling cascade provides insights on the underlying mechanisms of biological phenomena such as insulin resistance, diabetes, Alzheimer's disease, and cancer. For this reason, previous studies utilized chemical reaction network theory to perform comparative analyses of reaction networks of insulin signaling in healthy (INSMS: INSulin Metabolic Signaling) and diabetic cells (INRES: INsulin RESistance). This study extends these analyses using various methods which give further insights regarding insulin signaling. Using embedded networks, we discuss evidence of the presence of a structural "bifurcation" in the signaling process between INSMS and INRES. Concordance profiles of INSMS and INRES show that both have a high propensity to remain monostationary. Moreover, the concordance properties allow us to present heuristic evidence that INRES has a higher level of stability beyond its monostationarity. Finally, we discuss a new way of analyzing reaction networks through network translation. This method gives rise to three new insights: (i) each stoichiometric class of INSMS and INRES contains a unique positive equilibrium; (ii) any positive equilibrium of INSMS is exponentially stable and is a global attractor in its stoichiometric class; and (iii) any positive equilibrium of INRES is locally asymptotically stable. These results open up opportunities for collaboration with experimental biologists to understand insulin signaling better., Comment: 18 pages, 0 figure

Diluted magnetic semiconductors (DMS) have attracted significant attention for their potential in spintronic applications. Particularly, magnetically-doped GaN is highly attractive due to its high relevance for the CMOS industry and the possibility of developing advanced spintronic devices which are fully compatible with the current industrial procedures. Despite this interest, there remains a need to investigate the spintronic parameters that characterize interfaces within these systems. Here, we perform spin Hall magnetoresistance (SMR) measurements to evaluate the spin transfer at a Pt/(Ga,Mn)N interface. We determine the transparency of the interface through the estimation of the real part of the spin mixing conductance finding $G_r = 2.6\times 10^{14} \, \Omega^{-1} m^{-2}$, comparable to state-of-the-art yttrium iron garnet (YIG)/Pt interfaces. Moreover, the magnetic ordering probed by SMR above the (Ga,Mn)N Curie temperature TC provides a broader temperature range for the efficient generation and detection of spin currents, relaxing the conditions for this material to be applied in new spintronic devices., Comment: 19 pages, 3 main figures, 2 supplementary figures

While civilized users employ social media to stay informed and discuss daily occurrences, haters perceive these platforms as fertile ground for attacking groups and individuals. The prevailing approach to counter this phenomenon involves detecting such attacks by identifying toxic language. Effective platform measures aim to report haters and block their network access. In this context, employing hate speech detection methods aids in identifying these attacks amidst vast volumes of text, which are impossible for humans to analyze manually. In our study, we expand upon the usual hate speech detection methods, typically based on text classifiers, to develop a Named Entity Recognition (NER) System for Identity Groups. To achieve this, we created a dataset that allows extending a conventional NER to recognize identity groups. Consequently, our tool not only detects whether a sentence contains an attack but also tags the sentence tokens corresponding to the mentioned group. Results indicate that the model performs competitively in identifying groups with an average f1-score of 0.75, outperforming in identifying ethnicity attack spans with an f1-score of 0.80 compared to other identity groups. Moreover, the tool shows an outstanding generalization capability to minority classes concerning sexual orientation and gender, achieving an f1-score of 0.77 and 0.72, respectively. We tested the utility of our tool in a case study on social media, annotating and comparing comments from Facebook related to news mentioning identity groups. The case study reveals differences in the types of attacks recorded, effectively detecting named entities related to the categories of the analyzed news articles. Entities are accurately tagged within their categories, with a negligible error rate for inter-category tagging.

Understanding the radiogenic neutron production rate through the (${\alpha}$, n) reaction is essential in many fields of physics like dark matter searches, neutrino studies, nuclear astrophysics and medical physics. This white paper provides a review of the current landscape of (${\alpha}$, n) yields, neutron spectra and correlated ${\gamma}$-rays calculations, and describes the existing tools and the available cross-sections. The uncertainties that contribute to (${\alpha}$, n) yield calculations are also discussed with plans for a program to improve the accuracy of these estimates. Novel ideas to measure (${\alpha}$, n) cross-sections for a variety of materials of interest are presented. The goal of this study is to reduce the uncertainty in the expected sensitivity of next-generation physics experiments in the keV--MeV regime., Comment: 37 pages, 8 figures