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The discovery of novel quantum materials within ternary phase spaces containing antagonistic pair such as Fe with Bi, Pb, In, and Ag, presents significant challenges yet holds great potential. In this work, we investigate the stabilization of these immiscible pairs through the integration of Cerium (Ce), an abundant rare-earth and cost-effective element. By employing a machine learning (ML)-guided framework, particularly crystal graph convolutional neural networks (CGCNN), combined with first-principles calculations, we efficiently explore the composition/structure space and predict 9 stable and 37 metastable Ce-Fe-X (X=Bi, Pb, In and Ag) ternary compounds. Our findings include the identification of multiple new stable and metastable phases, which are evaluated for their structural and energetic properties. These discoveries not only contribute to the advancement of quantum materials but also offer viable alternatives to critical rare earth elements, underscoring the importance of Ce-based intermetallic compounds in technological applications.

Density functional theory (DFT), including its extensions designed to treat strongly correlated localized electron systems such as DFT+$U$ and DFT+dynamical mean field theory, has proven exceedingly useful in studying the magnetic properties of solids. However, materials with rare earths ($R$) have remained a notable exception. The most vital rare-earth magnetic properties, such as magnetocrystalline anisotropy (MA), have been notoriously elusive due to the ubiquitous self-interaction error present in nearly all available DFT flavors. In this work, we show explicitly how the orbital dependence of self-interaction error may contradict Hund's rules and plague MA calculations, and how analyzing DFT metastable states that respect Hund's rules can alleviate the problem. We systematically investigate and discuss several rare-earth-containing families, $R$Co$_5$, $R_2$Fe$_{14}$B, $R$Fe$_{12}$, and $R$Mn$_6$Sn$_6$, to benchmark the MA calculations in DFT+$U$. For all compounds we investigated, we found that our methodology reproduces the magnetic easy-axis, easy-plane, and non-trivial easy-cone anisotropies in full agreement with low-temperature experimental measurements. Besides the fully-numerical ab initio approach, we further illustrate an efficient semi-analytical perturbation method that treats the crystal field as a perturbation in the limit of large spin-orbit coupling. This approach evaluates the rare-earth anisotropy by assessing the dependence of crystal-field energy on spin-quantization axis rotation using $4f$ crystal-field levels obtained from non-spin-orbit calculations. Our analytical method provides a quantitative microscopic understanding of the factors that control MA and can be used for predicting new high-MA materials., Comment: 11 pages, 4 figures

Magnetic anisotropy is a key feature of rare earth materials from permanent magnets to heavy fermions. We explore the complex interplay of Kondo physics and anisotropy in a minimal impurity model using numerical renormalization group. While anisotropy suppresses Kondo physics, Kondo physics enhances the anisotropy. Importantly, we find distinct renormalization of the magnetic anisotropy measured via dynamical spin response (inelastic neutron scattering) versus electronic excitations in the impurity spectral function (resonant inelastic x-rays and scanning tunneling spectroscopy). The two measurement types have different responses and dependences on the temperature and Kondo scales., Comment: 6 pages, 4 figures, plus supplementary material included

techniques can be considered as an attractive alternative to the often-cumbersome traditional manufacturing routes. With the use of high-power lasers, localized hot zones that are necessary for glass making can be obtained rapidly. For instance, Laser-Powder-Deposition enables rapid fabrication of short, high gain fibers used in e.g., distributed feedback fiber lasers (DFFL). DFFLs offer sought after performance suitable for a broad range of applications in modern photonics i.e., superior stability and narrower, single-frequency linewidth compared to conventional fiber lasers. Tunable, narrow laser sources with output in eye-safe spectrum are desired for sensing, signal multiplexing, LIDAR systems, quantum applications etc. In this work we present DFFL obtained using Laser-Powder-Deposition made Er-doped silica fiber. Milliwatt level, narrow line lasing (< 704 kHz, equipment limited) was obtained using a phaseshifted grating written in 16 mm long fiber. The backward slope efficiency was as high as 24% when pumping at 976 nm. The results presented in this work showcase new possibilities in fiber fabrication that were unlocked through laser-assisted additive manufacturing. This fiber laser sets the stage for the future of rapid fabrication of advanced fiber devices through unconventional manufacturing routes.

Food insecurity, defined as the lack of physical or economic access to safe, nutritious and sufficient food, remains one of the main challenges of the 2030 Agenda for Sustainable Development. Food insecurity is a complex phenomenon, resulting from the interplay of environmental, socio-demographic, and political events. Previous work has investigated the nexus between climate change, conflict, migration and food security at the household level, however these relations are still largely unexplored at national scales. In this context, during the Complexity72h workshop, held at the Universidad Carlos III de Madrid in June 2024, we explored the co-evolution of international migration flows and food insecurity at the national scale, accounting for remittances, as well as for changes in the economic, conflict, and climate situation. To this aim, we gathered data from several publicly available sources (Food and Agriculture Organization, World Bank, and UN Department of Economic and Social Affairs) and analyzed the association between food insecurity and migration, migration and remittances, and remittances and food insecurity. We then propose a framework linking together these associations to model the co-evolution of food insecurity and international migrations.

Reinforcement learning (RL) has gained popularity in the realm of recommender systems due to its ability to optimize long-term rewards and guide users in discovering relevant content. However, the successful implementation of RL in recommender systems is challenging because of several factors, including the limited availability of online data for training on-policy methods. This scarcity requires expensive human interaction for online model training. Furthermore, the development of effective evaluation frameworks that accurately reflect the quality of models remains a fundamental challenge in recommender systems. To address these challenges, we propose a comprehensive framework for synthetic environments that simulate human behavior by harnessing the capabilities of large language models (LLMs). We complement our framework with in-depth ablation studies and demonstrate its effectiveness with experiments on movie and book recommendations. Using LLMs as synthetic users, this work introduces a modular and novel framework to train RL-based recommender systems. The software, including the RL environment, is publicly available on GitHub.

Symbolic Regression (SR) is a widely studied field of research that aims to infer symbolic expressions from data. A popular approach for SR is the Sparse Identification of Nonlinear Dynamical Systems (\sindy) framework, which uses sparse regression to identify governing equations from data. This study introduces an enhanced method, Nested SINDy, that aims to increase the expressivity of the SINDy approach thanks to a nested structure. Indeed, traditional symbolic regression and system identification methods often fail with complex systems that cannot be easily described analytically. Nested SINDy builds on the SINDy framework by introducing additional layers before and after the core SINDy layer. This allows the method to identify symbolic representations for a wider range of systems, including those with compositions and products of functions. We demonstrate the ability of the Nested SINDy approach to accurately find symbolic expressions for simple systems, such as basic trigonometric functions, and sparse (false but accurate) analytical representations for more complex systems. Our results highlight Nested SINDy's potential as a tool for symbolic regression, surpassing the traditional SINDy approach in terms of expressivity. However, we also note the challenges in the optimization process for Nested SINDy and suggest future research directions, including the designing of a more robust methodology for the optimization process. This study proves that Nested SINDy can effectively discover symbolic representations of dynamical systems from data, offering new opportunities for understanding complex systems through data-driven methods.

In this study, we delve into Federated Reinforcement Learning (FedRL) in the context of value-based agents operating across diverse Markov Decision Processes (MDPs). Existing FedRL methods typically aggregate agents' learning by averaging the value functions across them to improve their performance. However, this aggregation strategy is suboptimal in heterogeneous environments where agents converge to diverse optimal value functions. To address this problem, we introduce the Convergence-AwarE SAmpling with scReening (CAESAR) aggregation scheme designed to enhance the learning of individual agents across varied MDPs. CAESAR is an aggregation strategy used by the server that combines convergence-aware sampling with a screening mechanism. By exploiting the fact that agents learning in identical MDPs are converging to the same optimal value function, CAESAR enables the selective assimilation of knowledge from more proficient counterparts, thereby significantly enhancing the overall learning efficiency. We empirically validate our hypothesis and demonstrate the effectiveness of CAESAR in enhancing the learning efficiency of agents, using both a custom-built GridWorld environment and the classical FrozenLake-v1 task, each presenting varying levels of environmental heterogeneity.

Mining software repositories is a useful technique for researchers and practitioners to see what software developers actually do when developing software. Tools like Boa provide users with the ability to easily mine these open-source software repositories at a very large scale, with datasets containing hundreds of thousands of projects. The trade-off is that users must use the provided infrastructure, query language, runtime, and datasets and this might not fit all analysis needs. In this work, we present Boidae: a family of Boa installations controlled and customized by users. Boidae uses automation tools such as Ansible and Docker to facilitate the deployment of a customized Boa installation. In particular, Boidae allows the creation of custom datasets generated from any set of Git repositories, with helper scripts to aid in finding and cloning repositories from GitHub and SourceForge. In this paper, we briefly describe the architecture of Boidae and how researchers can utilize the infrastructure to generate custom datasets. Boidae's scripts and all infrastructure it builds upon are open-sourced. A video demonstration of Boidae's installation and extension is available at https://go.unl.edu/boidae.

In Federated Reinforcement Learning (FRL), agents aim to collaboratively learn a common task, while each agent is acting in its local environment without exchanging raw trajectories. Existing approaches for FRL either (a) do not provide any fault-tolerance guarantees (against misbehaving agents), or (b) rely on a trusted central agent (a single point of failure) for aggregating updates. We provide the first decentralized Byzantine fault-tolerant FRL method. Towards this end, we first propose a new centralized Byzantine fault-tolerant policy gradient (PG) algorithm that improves over existing methods by relying only on assumptions standard for non-fault-tolerant PG. Then, as our main contribution, we show how a combination of robust aggregation and Byzantine-resilient agreement methods can be leveraged in order to eliminate the need for a trusted central entity. Since our results represent the first sample complexity analysis for Byzantine fault-tolerant decentralized federated non-convex optimization, our technical contributions may be of independent interest. Finally, we corroborate our theoretical results experimentally for common RL environments, demonstrating the speed-up of decentralized federations w.r.t. the number of participating agents and resilience against various Byzantine attacks., Comment: Accepted at AAMAS'24

CePd3S4 exhibits interplay between ferromagnetism (FM), quadrupolar order, and the Kondo effect at low temperatures with a FM transition temperature that is much higher than the value expected from the de Gennes scaling of the heavier RPd3S4 compounds. In this work, we investigated the electrical transport and magnetic properties of CePd3S4 under pressure up through 12 GPa so as to better understand the interplay between electronic and magnetic phases in this material. Our findings show that the low pressure FM state is suddenly replaced by a new magnetically ordered phase that is most likely antiferromagnetic that spans from ~ 7 GPa to ~ 11 GPa. Whereas this could be described as an example of avoided quantum criticality, given that clear changes in resistance and Hall data are detected near 6.3 GPa for all temperatures below 300 K, it is also possible that the change in ground state is a response to a pressure induced change in structure. The lack of any discernible change in the pressure dependence of the room temperature unit cell parameter/volume across this whole pressure range suggests that this change in structure is either more subtle than could be detected by our measurements (i.e. development of weak, new wave vector) or the transition is electronic (such as a Lifshitz transition)., Comment: 16 pages, 16 figures, 74 references

The clarion call for causal reduction in the study of capital markets is intensifying. However, in self-referencing and open systems such as capital markets, the idea of unidirectional causation (if applicable) may be limiting at best, and unstable or fallacious at worst. In this work, we critically assess the use of scientific deduction and causal inference within the study of empirical finance and financial econometrics. We then demonstrate the idea of competing causal chains using a toy model adapted from ecological predator/prey relationships. From this, we develop the alternative view that the study of empirical finance, and the risks contained therein, may be better appreciated once we admit that our current arsenal of quantitative finance tools may be limited to ex post causal inference under popular assumptions. Where these assumptions are challenged, for example in a recognizable reflexive context, the prescription of unidirectional causation proves deeply problematic., Comment: 9 pages, 2 figures, streamlined discuss and additional references

We report on elastic and inelastic neutron scattering from single-grain isotopically-enriched samples to elucidate the local magnetic correlations between Tb$^{3+}$ moments in quasicrystalline i-Tb-Cd. The inelastic neutron scattering measurements of the CEF excitations demonstrated that the Tb$^{3+}$ moments are directed primarily along the local five-fold axes of the Tsai-type cluster as was found for the TbCd6 approximant phase. Based on the inelastic measurements, we consider of a simple Ising-type model for the moment configurations on a single Tb$^{3+}$ icosahedron and enumerate the lowest energy moment configurations. We then calculate the diffuse scattering from these configurations and compare with the experimental magnetic diffuse scattering measurements to identify the most likely single cluster moment configurations and find reasonable agreement between the broad features observed in our scattering simulations. We further consider the role of higher-order (longer range) intercluster correlations for the magnetic scattering.

In recent in vitro experiments on co-culture between breast tumour spheroids and activated immune cells, it was observed that the introduction of the stress hormone cortisol resulted in a decreased immune cell infiltration into the spheroids. Moreover, the presence of cortisol deregulated the normal levels of the pro- and anti-inflammatory cytokines IFN-{\gamma} and IL-10. We present an individual-based model to explore the interaction dynamics between tumour and immune cells under psychological stress conditions. With our model, we explore the processes underlying the emergence of different levels of immune infiltration, with particular focus on the biological mechanisms regulated by IFN-{\gamma} and IL-10. The set-up of numerical simulations is defined to mimic the scenarios considered in the experimental study. Similarly to the experimental quantitative analysis, we compute a score that quantifies the level of immune cell infiltration into the tumour. The results of numerical simulations indicate that the motility of immune cells, their capability to infiltrate through tumour cells, their growth rate and the interplay between these cell parameters can affect the level of immune cell infiltration in different ways. Ultimately, numerical simulations of this model support a deeper understanding of the impact of biological stress-induced mechanisms on immune infiltration.