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Modern random access mechanisms combine packet repetitions with multi-user detection mechanisms at the receiver to maximize the throughput and reliability in massive Internet of Things (IoT) scenarios. However, optimizing the access policy, which selects the number of repetitions, is a complicated problem, and failing to do so can lead to an inefficient use of resources and, potentially, to an increased congestion. In this paper, we follow a game-theoretic approach for optimizing the access policies of selfish users in modern random access mechanisms. Our goal is to find adequate values for the rewards given after a success to achieve a Nash equilibrium (NE) that optimizes the throughput of the system while considering the cost of transmission. Our results show that a mixed strategy, where repetitions are selected according to the irregular repetition slotted ALOHA (IRSA) protocol, attains a NE that maximizes the throughput in the special case with two users. In this scenario, our method increases the throughput by 30% when compared to framed ALOHA. Furthermore, we present three methods to attain a NE with near-optimal throughput for general modern random access scenarios, which exceed the throughput of framed ALOHA by up to 34%., Comment: Submitted for publication to IEEE WCNC 2025

The description of thermodynamic phase transitions in terms of percolation transitions of suitably defined clusters has a long tradition and boasts a number of important successes, the most prominent ones being in ferromagnetic lattice models. Spin glasses and other frustrated systems are not among them as the clusters of aligned spins usually considered in this context start to percolate in the disordered phase and hence fail to indicate the onset of ordering. In this mini-review we provide an overview of the state of the art in this field, including recent advances, and outline the main open questions in the area., Comment: 9 pages, 2 figures, to appear in Front. Phys

In July 2023, New York City became the first jurisdiction globally to mandate bias audits for commercial algorithmic systems, specifically for automated employment decisions systems (AEDTs) used in hiring and promotion. Local Law 144 (LL 144) requires AEDTs to be independently audited annually for race and gender bias, and the audit report must be publicly posted. Additionally, employers are obligated to post a transparency notice with the job listing. In this study, 155 student investigators recorded 391 employers' compliance with LL 144 and the user experience for prospective job applicants. Among these employers, 18 posted audit reports and 13 posted transparency notices. These rates could potentially be explained by a significant limitation in the accountability mechanisms enacted by LL 144. Since the law grants employers substantial discretion over whether their system is in scope of the law, a null result cannot be said to indicate non-compliance, a condition we call ``null compliance." Employer discretion may also explain our finding that nearly all audits reported an impact factor over 0.8, a rule of thumb often used in employment discrimination cases. We also find that the benefit of LL 144 to ordinary job seekers is limited due to shortcomings in accessibility and usability. Our findings offer important lessons for policy-makers as they consider regulating algorithmic systems, particularly the degree of discretion to grant to regulated parties and the limitations of relying on transparency and end-user accountability.

The Griffiths phase in systems with quenched disorder occurs below the ordering transition of the pure system down to the ordering transition of the actual disordered system. While it does not exhibit long-range order, large fluctuations in the disorder degrees of freedom result in exponentially rare, long-range ordered states and hence the occurrence of broad distributions in response functions. Inside the Griffiths phase of the two-dimensional bond-diluted Ising model the distribution of the magnetic susceptibility is expected to have such a broad, exponential tail. A large-deviations Monte Carlo algorithm is used to sample this distribution and the exponential tail is extracted over a wide range of the support down to very small probabilities of the order of $10^{-300}$. We study the behavior of the susceptibility distribution across the full phase diagram, from the paramagnetic state through the Griffiths phase to the ferromagnetically ordered system and down to the zero-temperature point. We extract the rate function of large-deviation theory as well as its finite-size scaling behavior and we reveal interesting differences and similarities between the cases. A connection between the fraction of ferromagnetic bonds in a given disorder sample and the size of the magnetic susceptibility is demonstrated numerically., Comment: 15 pages, 17 figures, RevTeX4.2

Time-resolved photoemission orbital tomography (tr-POT) offers unique possibilities for tracing molecular electron dynamics. The recorded pump-induced changes of the angle-resolved photoemission intensities allow to characterize unoccupied molecular states in momentum space and to deduce the incoherent temporal evolution of their population. Here, we show for the example of CuPc/Cu(001)-2O that the method also gives access to the coherent regime and that different excitation pathways can be disentangled by a careful analysis of the time-dependent change of the photoemission momentum pattern. In particular, we demonstrate by varying photon energy and polarization of the pump light, how the incoherent temporal evolution of the LUMO distribution can be distinguished from coherent contributions of the projected HOMO. Moreover, we report the selective excitation of molecules with a specific orientation at normal incidence by aligning the electric field of the pump light along the molecular axis.

Renewable fuels of non-biological origin (RFNBOs) are needed to decarbonize hard-to-electrify sectors that rely on liquid or gaseous fuels, such as long-haul shipping. The EU's Delegated Act on RFNBOs defines renewable hydrogen by considering rules on additionality as well as temporal and geographical correlation of the electricity used. For a Danish case study, we examine the impact on the capacity expansion problem of an energy hub producing renewable hydrogen, e-methanol, and e-ammonia using a mixed-integer linear problem formulation. We analyze the investments in production capacity, storage assets, and Power Purchase Agreement (PPA) volume under different fuel price assumptions for 2030. We find that e-methanol (combined with limited storage to secure hydrogen supply to the synthesizer) provides the best business case with a PPA volume based on the maximum allowed electrolyzer size., Comment: Accepted version

The accurate calculation and uncertainty quantification of the characteristics of spent nuclear fuel (SNF) play a crucial role in ensuring the safety, efficiency, and sustainability of nuclear energy production, waste management, and nuclear safeguards. State of the art physics-based models, while reliable, are computationally intensive and time-consuming. This paper presents a surrogate modeling approach using neural networks (NN) to predict a number of SNF characteristics with reduced computational costs compared to physics-based models. An NN is trained using data generated from CASMO5 lattice calculations. The trained NN accurately predicts decay heat and nuclide concentrations of SNF, as a function of key input parameters, such as enrichment, burnup, cooling time between cycles, mean boron concentration and fuel temperature. The model is validated against physics-based decay heat simulations and measurements of different uranium oxide fuel assemblies from two different pressurized water reactors. In addition, the NN is used to perform sensitivity analysis and uncertainty quantification. The results are in very good alignment to CASMO5, while the computational costs (taking into account the costs of generating training samples) are reduced by a factor of 10 or more. Our findings demonstrate the feasibility of using NNs as surrogate models for fast characterization of SNF, providing a promising avenue for improving computational efficiency in assessing nuclear fuel behavior and associated risks.

We extend our analysis of bound states in $\mathcal{N}=1$ supersymmetric Yang-Mills theory by the consideration of baryonic operators, which are composed of three gluino fields. The corresponding states are similar to the baryons in QCD, but due to the difference between gluino and quark fields, their properties and the fermion line contractions involved in their correlation functions are different from QCD. In this work, we first explain the derivation of these operators and the contractions needed in numerical calculations of their correlators. In contrast to QCD the correlators contain a spectacle piece, which requires methods for all-to-all propagators. We provide a first estimate of the two-point function and the mass of the lightest baryonic state in $\mathcal{N}=1$ supersymmetric Yang-Mills theory.

Suitable cluster definitions have allowed researchers to describe many ordering transitions in spin systems as geometric phenomena related to percolation. For spin glasses and some other systems with quenched disorder, however, such a connection has not been fully established, and the numerical evidence remains incomplete. Here we use Monte Carlo simulations to study the percolation properties of several classes of clusters occurring in the Edwards-Anderson Ising spin-glass model in two dimensions. The Fortuin-Kasteleyn-Coniglio-Klein clusters originally defined for the ferromagnetic problem do percolate at a temperature that remains non-zero in the thermodynamic limit. On the Nishimori line, this location is accurately predicted by an argument due to Yamaguchi. More relevant for the spin-glass transition are clusters defined on the basis of the overlap of several replicas. We show that various such cluster types have percolation thresholds that shift to lower temperature by increasing the system size, in agreement with the zero-temperature spin-glass transition in two dimensions. The overlap is linked to the difference in density of the two largest clusters, thus supporting a picture where the spin-glass transition corresponds to an emergent density difference of the two largest clusters inside the percolating phase., Comment: 16 pages, 18 figures, 1 table, accepted version

We review our efforts in investigating gauge theories with fermions in the adjoint representation of the gauge group by means of numerical simulations. These theories have applications in possible extensions of the Standard Model of particle physics, being a core part of supersymmetric gauge theories. They also play an important role in uncovering fundamental properties of strongly interacting theories due to distinct features, such as a substantially different phase diagram., Comment: 17 pages, 3 figures, contribution to special issue " Numerical Studies of Strongly Coupled Gauge Theories (SCGTs) in the Search of New Physics" (Universe 2022)